diff --git a/data/timeseries_training_data_sample_10s50p.parquet b/data/timeseries_training_data_sample_10s50p.parquet
deleted file mode 100644
index 3fecd66..0000000
Binary files a/data/timeseries_training_data_sample_10s50p.parquet and /dev/null differ
diff --git a/lyf开发日志记录文档.md b/lyf开发日志记录文档.md
index d0ea0e9..89c40ad 100644
--- a/lyf开发日志记录文档.md
+++ b/lyf开发日志记录文档.md
@@ -410,3 +410,77 @@
     2.  **调试与修复**: 脚本的初版存在时区比较逻辑错误，未能正确识别本地时区的记录。在经过一轮调试，将时间比较基准从UTC修正为本地时间后，脚本得以正常工作。
     3.  **执行清理**: 最终成功执行脚本，从数据库中清除了所有在指定时间点之前创建的、带有错误文件路径的历史记录。
 - **最终结论**: 通过对数据库中的存量数据进行清理，彻底解决了因新旧数据路径不一致而导致的 `404` 错误，确保了系统数据的一致性和功能的稳定性。
+
+
+---
+
+## 2025-07-26：核心数据源替换与系统级重构
+**开发者**: Roo (AI Assistant) & lyf
+
+### 第一阶段：核心数据源替换与适配器重构
+- **任务目标**: 将项目的数据源从旧的、简单的 `timeseries_training_data_sample_10s50p.parquet` 彻底更换为新的、特征更丰富、结构更复杂的核心数据集。
+- **核心挑战**: 新旧数据集在表结构（如 `subbh` vs `store_id`）、数据类型、特征集（如新数据缺少 `is_promotion` 列）和数据粒度上存在巨大差异，直接替换会导致系统全面崩溃。
+- **解决方案 (适配器模式)**:
+    1.  **创建中央数据适配器**: 新建了 `server/utils/new_data_loader.py`，作为整个系统唯一的数据入口。
+    2.  **实现动态转换**: 该适配器负责加载新数据集，并动态地将其转换为旧代码库能够理解的格式，包括重命名列、用 `0` 填充缺失的 `is_promotion` 特征、以及处理数据类型。
+    3.  **全面重构**: 系统性地重构了所有数据消费端——包括 `server/api.py`, `server/core/predictor.py` 以及 `server/trainers/` 目录下的**所有**训练器脚本——使其不再直接加载数据，而是统一通过新的数据适配器获取，从而实现了新数据源与现有业务逻辑的完全解耦。
+
+### 第二阶段：端到端迭代调试与连锁问题修复
+在完成重构后，我们进行了一系列端到端的测试，并修复了因此次重大变更而引发的一系列连锁问题。
+- **修复 `KeyError`**: 解决了因新旧列名 (`subbh` vs `store_id`) 不匹配导致的键查找错误。
+- **修复 `NaN` 值问题**: 在实现“按店铺训练-所有商品”的聚合逻辑时，因部分商品在某些日期无销售记录，导致聚合操作引入了`NaN`值。通过在所有训练器的数据准备阶段增加 `.fillna(0)` 清理步骤，彻底解决了该问题。
+- **修复 `TypeError` (JSON序列化)**: 解决了在API响应中，因返回了未经处理的 `numpy.ndarray` 和 `datetime.date` 对象而导致的JSON序列化失败问题。
+- **修复 XGBoost 预测逻辑的根本性错误**:
+    - **问题现象**: 使用新数据训练的 `XGBoost` 模型，后端预测返回 `200 OK`，但前端图表渲染失败。
+    - **根本原因**: `server/predictors/model_predictor.py` 中存在一个严重的逻辑缺陷。它错误地对 `XGBoost` 模型使用了为 `PyTorch` 模型设计的“自回归”循环预测逻辑（即预测一天，再用该预测值去预测下一天）。而 `XGBoost` 模型本身是“直接多步输出”模型，一次性就能返回所有未来日期的预测值。错误的循环逻辑导致系统只取用了 `XGBoost` 完整输出结果中的第一个值，并将其错误地复制了多天，生成了无用的预测结果。
+    - **最终修复**: 在 `model_predictor.py` 中为 `XGBoost` 模型创建了一个独立的、非循环的逻辑分支。该分支能够正确地接收并处理 `XGBoost` 的完整输出数组，从而生成了正确的、可供前端渲染的预测结果。
+
+### 第三阶段：系统环境清理
+- **任务目标**: 在最终测试前，确保一个完全纯净的、不受任何旧数据或旧模型干扰的系统环境。
+- **实施过程**: 在开发者的精确指导下，我们完成了以下清理工作：
+    1.  **文件系统清理**: 手动删除了 `saved_models` 和 `saved_predictions` 文件夹中的所有历史产物。
+    2.  **数据库清理**: 成功执行了 `server/tools/delete_old_predictions.py` 脚本，清空了 `prediction_history.db` 数据库中所有过时的预测记录。
+- **最终结论**: 至此，数据源替换、系统重构、连锁问题修复和环境清理工作已全部完成。项目现在处于一个代码逻辑更健壮、数据源更可靠的全新状态，并已准备好进行最终的完整性验证。
+
+
+---
+
+## 2025-07-26 (续): 端到端测试与系统级修复
+**开发者**: Roo (AI Assistant) & lyf
+
+### 第三阶段 (续): 迭代修复与架构澄清
+在对重构后的系统进行全面的端到端测试时，我们发现并修复了一系列深层次的、与特定模型架构和训练模式相关的Bug。
+
+- **修复 `XGBoost` 自定义天数预测崩溃的Bug**:
+    - **问题现象**: 使用 `XGBoost` 模型进行预测时，如果自定义的预测天数与模型训练时固定的预测范围不符，程序会因数组长度不匹配而崩溃。
+    - **架构澄清**: 我们确认了 `XGBoost` 是“直接多步输出”模型，其预测长度在训练时已固定。
+    - **最终修复**: 修改了 `server/predictors/model_predictor.py`，使其不再依赖用户输入的预测天数，而是根据模型**实际**的输出长度来动态生成日期序列，从而保证了程序的健壮性。
+
+- **修复 `CnnBiLstmAttention` 模型预测逻辑错误**:
+    - **问题现象**: 该模型预测成功，但前端图表渲染失败。
+    - **根本原因**: 与 `XGBoost` 类似，该模型也是“直接多步输出”架构，但被错误地注册给了为“自回归”模型设计的预测函数，导致生成了无意义的预测结果。
+    - **最终修复**: 在 `server/predictors/model_predictor.py` 中，为 `CnnBiLstmAttention` 模型创建了一个专属的、正确的、非回归式的预测逻辑分支，确保其能被正确处理。
+
+- **修复“全局训练”模式数据筛选逻辑错误**:
+    - **问题现象**: 在“全局训练”模式下，选择“所有店铺所有药品”时，训练因找不到任何数据而失败。
+    - **根本原因**: `server/core/predictor.py` 在处理该模式时，错误地使用了一个特殊的信号值（如 `unknown`）去筛选 `product_id`，导致数据集为空。
+    - **最终修复**: 重构了该部分的逻辑，确保在选择“所有药品”时，程序能正确地跳过按 `product_id` 筛选的步骤，直接对整个数据集进行聚合。
+
+- **修复“全局预测”模式数据加载逻辑错误**:
+    - **问题现象**: “全局预测”模式因找不到数据而失败。
+    - **根本原因**: `server/predictors/model_predictor.py` 中存在与训练逻辑不一致的镜像Bug。
+    - **最终修复**: 再次修改了 `model_predictor.py`，使其“全局预测”的数据加载逻辑与 `predictor.py` 中的训练逻辑完全同步。
+
+- **澄清“负销量”是数据特性而非Bug**:
+    - **问题现象**: 在“全局预测”的图表中，历史销量出现了负数。
+    - **分析**: 经过讨论，我们确认这是由于原始数据中包含了“退货”等业务场景，导致按天聚合求和后出现负值。
+    - **最终决策**: 为了尊重元数据的真实性，我们决定**不**在代码中将负数强制修正为0，并接受这可能会对模型的训练和预测结果产生影响。这是一个重要的、关于数据处理哲学的决策。
+
+- **全链路增强以支持“自定义全局训练”功能**:
+    - **问题现象**: 在“全局训练”中选择“自定义范围”（指定店铺和药品列表）时，训练失败。
+    - **根本原因**: 这是一个**全链路的参数传递中断**问题。从API接口到进程管理器，再到核心训练逻辑，整个系统都没有为处理这个新增的、复杂的自定义范围参数做好准备。
+    - **最终修复 (三位一体的全链路改造)**:
+        1.  **升级API层 (`api.py`)**: 修改了 `/api/training` 接口，使其能够正确接收前端传递的 `selected_stores` 和 `selected_products` 列表。
+        2.  **升级进程管理层 (`training_process_manager.py`)**: 对 `TrainingTask` 数据类和核心函数进行了全面改造，使其能够完整地接收、存储和向下传递这些新的自定义范围参数。
+        3.  **升级核心逻辑层 (`predictor.py`)**: 对 `train_model` 函数进行了重大功能增强，为其增加了处理 `selected_stores` 和 `selected_products` 列表的全新逻辑分支。
+    - **最终结论**: 通过这次彻底的全链路改造，我们不仅修复了Bug，还成功地为系统增加了一项强大的新功能。至此，所有在端到端测试中发现的已知问题均已得到解决。
diff --git a/prediction_history.db b/prediction_history.db
index 85470c0..2eb0000 100644
Binary files a/prediction_history.db and b/prediction_history.db differ
diff --git a/saved_predictions/prediction_02abf0e8-c29f-44f3-9460-8523ac00fec3.json b/saved_predictions/prediction_02abf0e8-c29f-44f3-9460-8523ac00fec3.json
new file mode 100644
index 0000000..d559ce1
--- /dev/null
+++ b/saved_predictions/prediction_02abf0e8-c29f-44f3-9460-8523ac00fec3.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "tcn", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.4654306173324585}, {"date": "2025-05-30", "predicted_sales": 1.9967557191848755}, {"date": "2025-05-31", "predicted_sales": 2.176229476928711}, {"date": "2025-06-01", "predicted_sales": 2.3391542434692383}, {"date": "2025-06-02", "predicted_sales": 2.5595316886901855}, {"date": "2025-06-03", "predicted_sales": 1.336228847503662}, {"date": "2025-06-04", "predicted_sales": 1.6415263414382935}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.4654306173324585}, {"date": "2025-05-30", "predicted_sales": 1.9967557191848755}, {"date": "2025-05-31", "predicted_sales": 2.176229476928711}, {"date": "2025-06-01", "predicted_sales": 2.3391542434692383}, {"date": "2025-06-02", "predicted_sales": 2.5595316886901855}, {"date": "2025-06-03", "predicted_sales": 1.336228847503662}, {"date": "2025-06-04", "predicted_sales": 1.6415263414382935}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.3}, {"date": "2025-05-07", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.8}, {"date": "2025-05-08", "sales": 2.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.6}, {"date": "2025-05-09", "sales": 0.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-10", "sales": 0.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 13.4}, {"date": "2025-05-11", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-12", "sales": 3.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.9}, {"date": "2025-05-13", "sales": 0.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_033bfaa5-4c6c-4a77-ad19-c6e75240cfb7.json b/saved_predictions/prediction_033bfaa5-4c6c-4a77-ad19-c6e75240cfb7.json
new file mode 100644
index 0000000..266d15c
--- /dev/null
+++ b/saved_predictions/prediction_033bfaa5-4c6c-4a77-ad19-c6e75240cfb7.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "kan", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.05224098265171051}, {"date": "2022-01-02", "predicted_sales": 0.04107563942670822}, {"date": "2022-01-03", "predicted_sales": 0.04144924879074097}, {"date": "2022-01-04", "predicted_sales": 0.04032263159751892}, {"date": "2022-01-05", "predicted_sales": 0.040290266275405884}, {"date": "2022-01-06", "predicted_sales": 0.040351711213588715}, {"date": "2022-01-07", "predicted_sales": 0.040530726313591}, {"date": "2022-01-08", "predicted_sales": 0.04084586352109909}, {"date": "2022-01-09", "predicted_sales": 0.04107217490673065}, {"date": "2022-01-10", "predicted_sales": 0.041449226438999176}, {"date": "2022-01-11", "predicted_sales": 0.04032265394926071}, {"date": "2022-01-12", "predicted_sales": 0.040290266275405884}, {"date": "2022-01-13", "predicted_sales": 0.040351711213588715}, {"date": "2022-01-14", "predicted_sales": 0.040530726313591}, 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\ No newline at end of file
diff --git a/saved_predictions/prediction_05ab6c07-6e7c-4a2d-9340-680a873a9f99.json b/saved_predictions/prediction_05ab6c07-6e7c-4a2d-9340-680a873a9f99.json
new file mode 100644
index 0000000..3fc683b
--- /dev/null
+++ b/saved_predictions/prediction_05ab6c07-6e7c-4a2d-9340-680a873a9f99.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_06bbc70f-a0ab-4606-bc76-b5faab9c34fd.json b/saved_predictions/prediction_06bbc70f-a0ab-4606-bc76-b5faab9c34fd.json
new file mode 100644
index 0000000..da518d4
--- /dev/null
+++ b/saved_predictions/prediction_06bbc70f-a0ab-4606-bc76-b5faab9c34fd.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "transformer", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": "2025-06-01", "predicted_sales": 1.8458434343338013}, {"date": "2025-06-02", "predicted_sales": 1.8742748498916626}, {"date": "2025-06-03", "predicted_sales": 1.8699368238449097}, {"date": "2025-06-04", "predicted_sales": 1.8809840679168701}, {"date": "2025-06-05", "predicted_sales": 1.8990527391433716}, {"date": "2025-06-06", "predicted_sales": 1.9080560207366943}, {"date": "2025-06-07", "predicted_sales": 1.9090224504470825}, {"date": "2025-06-08", "predicted_sales": 1.8960405588150024}, {"date": "2025-06-09", "predicted_sales": 1.916115164756775}, {"date": "2025-06-10", "predicted_sales": 1.9064878225326538}, {"date": "2025-06-11", "predicted_sales": 1.9106733798980713}, {"date": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_0967af52-e15c-4793-8c65-601c8540b847.json b/saved_predictions/prediction_0967af52-e15c-4793-8c65-601c8540b847.json
new file mode 100644
index 0000000..f024ecb
--- /dev/null
+++ b/saved_predictions/prediction_0967af52-e15c-4793-8c65-601c8540b847.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_09e7501c-9ee4-4351-b5a8-972306aed30c.json b/saved_predictions/prediction_09e7501c-9ee4-4351-b5a8-972306aed30c.json
new file mode 100644
index 0000000..81f812d
--- /dev/null
+++ b/saved_predictions/prediction_09e7501c-9ee4-4351-b5a8-972306aed30c.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "全局聚合 (所有药品)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 3.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.008823529411764}, {"date": "2025-04-30", 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\ No newline at end of file
diff --git a/saved_predictions/prediction_0e3be59f-ad7e-490d-84ec-e81658dcf952.json b/saved_predictions/prediction_0e3be59f-ad7e-490d-84ec-e81658dcf952.json
new file mode 100644
index 0000000..77bf15a
--- /dev/null
+++ b/saved_predictions/prediction_0e3be59f-ad7e-490d-84ec-e81658dcf952.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "transformer", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": "2025-06-01", "predicted_sales": 1.8458434343338013}, {"date": "2025-06-02", "predicted_sales": 1.8742748498916626}, {"date": "2025-06-03", "predicted_sales": 1.8699368238449097}, {"date": "2025-06-04", "predicted_sales": 1.8809840679168701}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": "2025-06-01", "predicted_sales": 1.8458434343338013}, {"date": "2025-06-02", "predicted_sales": 1.8742748498916626}, {"date": "2025-06-03", "predicted_sales": 1.8699368238449097}, {"date": "2025-06-04", "predicted_sales": 1.8809840679168701}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, 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{"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_11df1bc9-6c96-471a-b698-526a74034200.json b/saved_predictions/prediction_11df1bc9-6c96-471a-b698-526a74034200.json
new file mode 100644
index 0000000..06cc9f5
--- /dev/null
+++ b/saved_predictions/prediction_11df1bc9-6c96-471a-b698-526a74034200.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_14b90489-1d18-4477-bdcd-b063cb5c499b.json b/saved_predictions/prediction_14b90489-1d18-4477-bdcd-b063cb5c499b.json
new file mode 100644
index 0000000..68251e1
--- /dev/null
+++ b/saved_predictions/prediction_14b90489-1d18-4477-bdcd-b063cb5c499b.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_1d2673df-19e7-483a-94d4-b1b6b7fff20d.json b/saved_predictions/prediction_1d2673df-19e7-483a-94d4-b1b6b7fff20d.json
new file mode 100644
index 0000000..2f7a192
--- /dev/null
+++ b/saved_predictions/prediction_1d2673df-19e7-483a-94d4-b1b6b7fff20d.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_217020f6-ff22-4a94-b8ce-54efebef15e5.json b/saved_predictions/prediction_217020f6-ff22-4a94-b8ce-54efebef15e5.json
new file mode 100644
index 0000000..266d15c
--- /dev/null
+++ b/saved_predictions/prediction_217020f6-ff22-4a94-b8ce-54efebef15e5.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_22d84677-37f5-4200-b413-524f4240be56.json b/saved_predictions/prediction_22d84677-37f5-4200-b413-524f4240be56.json
new file mode 100644
index 0000000..f024ecb
--- /dev/null
+++ b/saved_predictions/prediction_22d84677-37f5-4200-b413-524f4240be56.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 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"is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_269b5876-7526-4d00-bb53-60ee079afe92.json b/saved_predictions/prediction_269b5876-7526-4d00-bb53-60ee079afe92.json
new file mode 100644
index 0000000..f024ecb
--- /dev/null
+++ b/saved_predictions/prediction_269b5876-7526-4d00-bb53-60ee079afe92.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_27432df9-1852-41aa-a43c-f49e9c5851f3.json b/saved_predictions/prediction_27432df9-1852-41aa-a43c-f49e9c5851f3.json
new file mode 100644
index 0000000..e48713c
--- /dev/null
+++ b/saved_predictions/prediction_27432df9-1852-41aa-a43c-f49e9c5851f3.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "xgboost", "predictions": [{"date": "2025-07-26", "predicted_sales": 1.4363230466842651}, {"date": "2025-07-27", "predicted_sales": 1.4389876127243042}, {"date": "2025-07-28", "predicted_sales": 1.3939130306243896}, {"date": "2025-07-29", "predicted_sales": 1.5208660364151}, {"date": "2025-07-30", "predicted_sales": 1.4566296339035034}, {"date": "2025-07-31", "predicted_sales": 1.43131422996521}, {"date": "2025-08-01", "predicted_sales": 1.4361907243728638}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 1.4363230466842651}, {"date": "2025-07-27", "predicted_sales": 1.4389876127243042}, {"date": "2025-07-28", "predicted_sales": 1.3939130306243896}, {"date": "2025-07-29", "predicted_sales": 1.5208660364151}, {"date": "2025-07-30", "predicted_sales": 1.4566296339035034}, {"date": "2025-07-31", "predicted_sales": 1.43131422996521}, {"date": "2025-08-01", "predicted_sales": 1.4361907243728638}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, 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"is_weekend": false, "is_promotion": 0, "temperature": 18.9}, {"date": "2025-05-13", "sales": 0.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, 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\ No newline at end of file
diff --git a/saved_predictions/prediction_29aac5e6-1b0f-4a2a-91d2-3eac4503c8e2.json b/saved_predictions/prediction_29aac5e6-1b0f-4a2a-91d2-3eac4503c8e2.json
new file mode 100644
index 0000000..41148ba
--- /dev/null
+++ b/saved_predictions/prediction_29aac5e6-1b0f-4a2a-91d2-3eac4503c8e2.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "mlstm", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.17061853408813477}, {"date": "2022-01-02", "predicted_sales": 0.17061516642570496}, {"date": "2022-01-03", "predicted_sales": 0.17061232030391693}, {"date": "2022-01-04", "predicted_sales": 0.17061841487884521}, {"date": "2022-01-05", "predicted_sales": 0.17061808705329895}, {"date": "2022-01-06", "predicted_sales": 0.17061838507652283}, {"date": "2022-01-07", "predicted_sales": 0.17061841487884521}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.17061853408813477}, {"date": "2022-01-02", "predicted_sales": 0.17061516642570496}, {"date": "2022-01-03", "predicted_sales": 0.17061232030391693}, {"date": "2022-01-04", "predicted_sales": 0.17061841487884521}, {"date": "2022-01-05", "predicted_sales": 0.17061808705329895}, {"date": "2022-01-06", "predicted_sales": 0.17061838507652283}, {"date": "2022-01-07", "predicted_sales": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_2c270b87-cc30-4944-9efb-e78cae99af04.json b/saved_predictions/prediction_2c270b87-cc30-4944-9efb-e78cae99af04.json
new file mode 100644
index 0000000..225ff23
--- /dev/null
+++ b/saved_predictions/prediction_2c270b87-cc30-4944-9efb-e78cae99af04.json
@@ -0,0 +1 @@
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"net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 0.88, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 0.88, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.83, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.79, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 2, "day_of_month": 29, "day_of_year": 363, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.0, "temperature_2m_min": -0.2, "temperature": 2.3, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-30", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, "transaction_count": 0, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 0.88, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 0.88, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 3, "day_of_month": 30, "day_of_year": 364, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 5.3, "temperature_2m_min": 0.7, "temperature": 2.6, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-31", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.02, "transaction_count": 1, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 1.0, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.0, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 4, "day_of_month": 31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_2d4b3265-599b-4056-a515-fef563822f70.json b/saved_predictions/prediction_2d4b3265-599b-4056-a515-fef563822f70.json
new file mode 100644
index 0000000..db45084
--- /dev/null
+++ b/saved_predictions/prediction_2d4b3265-599b-4056-a515-fef563822f70.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "kan", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.2918514013290405}, {"date": "2025-05-30", "predicted_sales": 1.2950164079666138}, {"date": "2025-05-31", "predicted_sales": 1.3018046617507935}, {"date": "2025-06-01", "predicted_sales": 1.335819125175476}, {"date": "2025-06-02", "predicted_sales": 1.3471426963806152}, {"date": "2025-06-03", "predicted_sales": 1.283681869506836}, {"date": "2025-06-04", "predicted_sales": 1.2876296043395996}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.2918514013290405}, {"date": "2025-05-30", "predicted_sales": 1.2950164079666138}, {"date": "2025-05-31", "predicted_sales": 1.3018046617507935}, {"date": "2025-06-01", "predicted_sales": 1.335819125175476}, {"date": "2025-06-02", "predicted_sales": 1.3471426963806152}, {"date": "2025-06-03", "predicted_sales": 1.283681869506836}, {"date": "2025-06-04", "predicted_sales": 1.2876296043395996}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.3}, {"date": "2025-05-07", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.8}, {"date": "2025-05-08", "sales": 2.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.6}, {"date": "2025-05-09", "sales": 0.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-10", "sales": 0.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 13.4}, {"date": "2025-05-11", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-12", "sales": 3.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.9}, {"date": "2025-05-13", "sales": 0.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_2e93c8eb-ecdb-4400-8433-6863fcbd86dd.json b/saved_predictions/prediction_2e93c8eb-ecdb-4400-8433-6863fcbd86dd.json
new file mode 100644
index 0000000..41148ba
--- /dev/null
+++ b/saved_predictions/prediction_2e93c8eb-ecdb-4400-8433-6863fcbd86dd.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "mlstm", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.17061853408813477}, {"date": "2022-01-02", "predicted_sales": 0.17061516642570496}, {"date": "2022-01-03", "predicted_sales": 0.17061232030391693}, {"date": "2022-01-04", "predicted_sales": 0.17061841487884521}, {"date": "2022-01-05", "predicted_sales": 0.17061808705329895}, {"date": "2022-01-06", "predicted_sales": 0.17061838507652283}, {"date": "2022-01-07", "predicted_sales": 0.17061841487884521}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.17061853408813477}, {"date": "2022-01-02", "predicted_sales": 0.17061516642570496}, {"date": "2022-01-03", "predicted_sales": 0.17061232030391693}, {"date": "2022-01-04", "predicted_sales": 0.17061841487884521}, {"date": "2022-01-05", "predicted_sales": 0.17061808705329895}, {"date": "2022-01-06", "predicted_sales": 0.17061838507652283}, {"date": "2022-01-07", "predicted_sales": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_2ffa15a6-e0a3-4711-bdab-6ab1d747983f.json b/saved_predictions/prediction_2ffa15a6-e0a3-4711-bdab-6ab1d747983f.json
new file mode 100644
index 0000000..3ab8732
--- /dev/null
+++ b/saved_predictions/prediction_2ffa15a6-e0a3-4711-bdab-6ab1d747983f.json
@@ -0,0 +1 @@
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31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_349d6f74-68ed-4d6c-89ca-3ad0e57c68c2.json b/saved_predictions/prediction_349d6f74-68ed-4d6c-89ca-3ad0e57c68c2.json
new file mode 100644
index 0000000..f6f8ed4
--- /dev/null
+++ b/saved_predictions/prediction_349d6f74-68ed-4d6c-89ca-3ad0e57c68c2.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_34e230fe-00ef-4a43-900f-49ac495f7f1f.json b/saved_predictions/prediction_34e230fe-00ef-4a43-900f-49ac495f7f1f.json
deleted file mode 100644
index 75d601e..0000000
--- a/saved_predictions/prediction_34e230fe-00ef-4a43-900f-49ac495f7f1f.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_4d178001-5cf6-4195-bff6-8bde4a250b2f.json b/saved_predictions/prediction_4d178001-5cf6-4195-bff6-8bde4a250b2f.json
new file mode 100644
index 0000000..e48713c
--- /dev/null
+++ b/saved_predictions/prediction_4d178001-5cf6-4195-bff6-8bde4a250b2f.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "xgboost", "predictions": [{"date": "2025-07-26", "predicted_sales": 1.4363230466842651}, {"date": "2025-07-27", "predicted_sales": 1.4389876127243042}, {"date": "2025-07-28", "predicted_sales": 1.3939130306243896}, {"date": "2025-07-29", "predicted_sales": 1.5208660364151}, {"date": "2025-07-30", "predicted_sales": 1.4566296339035034}, {"date": "2025-07-31", "predicted_sales": 1.43131422996521}, {"date": "2025-08-01", "predicted_sales": 1.4361907243728638}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 1.4363230466842651}, {"date": "2025-07-27", "predicted_sales": 1.4389876127243042}, {"date": "2025-07-28", "predicted_sales": 1.3939130306243896}, {"date": "2025-07-29", "predicted_sales": 1.5208660364151}, {"date": "2025-07-30", "predicted_sales": 1.4566296339035034}, {"date": "2025-07-31", "predicted_sales": 1.43131422996521}, {"date": "2025-08-01", "predicted_sales": 1.4361907243728638}], 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\ No newline at end of file
diff --git a/saved_predictions/prediction_54febc41-22a3-4860-9178-3b3899f40247.json b/saved_predictions/prediction_54febc41-22a3-4860-9178-3b3899f40247.json
new file mode 100644
index 0000000..658ab79
--- /dev/null
+++ b/saved_predictions/prediction_54febc41-22a3-4860-9178-3b3899f40247.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_58138996-4107-477b-b202-05c4a74494fb.json b/saved_predictions/prediction_58138996-4107-477b-b202-05c4a74494fb.json
new file mode 100644
index 0000000..9655795
--- /dev/null
+++ b/saved_predictions/prediction_58138996-4107-477b-b202-05c4a74494fb.json
@@ -0,0 +1 @@
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0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_5d64ea53-dd30-4935-9206-9481e0d4bd03.json b/saved_predictions/prediction_5d64ea53-dd30-4935-9206-9481e0d4bd03.json
new file mode 100644
index 0000000..81f812d
--- /dev/null
+++ b/saved_predictions/prediction_5d64ea53-dd30-4935-9206-9481e0d4bd03.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "全局聚合 (所有药品)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 3.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.008823529411764}, {"date": "2025-04-30", "sales": -2398.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.96029411764706}, {"date": "2025-05-01", "sales": 4.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.322950819672133}, {"date": "2025-05-02", "sales": 5.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.496721311475408}, {"date": "2025-05-03", "sales": 11.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.936666666666667}, {"date": "2025-05-04", "sales": 10.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.11052631578947}, {"date": "2025-05-05", "sales": 6.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.698214285714283}, {"date": "2025-05-06", "sales": 17.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.160714285714285}, {"date": "2025-05-07", "sales": 3.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.826415094339623}, {"date": "2025-05-08", "sales": 4.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.362745098039216}, {"date": "2025-05-09", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.094117647058823}, {"date": "2025-05-10", "sales": 7.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.838}, {"date": "2025-05-11", "sales": 12.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 15.918367346938776}, {"date": "2025-05-12", "sales": 6.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.52444444444444}, {"date": "2025-05-13", "sales": 168.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.045454545454547}, {"date": "2025-05-14", "sales": 7.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.89512195121951}, {"date": "2025-05-15", "sales": 10.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.3525}, {"date": "2025-05-16", "sales": 7.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.175675675675677}, {"date": "2025-05-17", "sales": 4.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 20.514705882352942}, {"date": "2025-05-18", "sales": 5.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 20.029411764705884}, {"date": "2025-05-19", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.66774193548387}, {"date": "2025-05-20", "sales": -791.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 23.246666666666666}, {"date": "2025-05-21", "sales": 5.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 23.88}, {"date": "2025-05-22", "sales": -588.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 23.13333333333333}, {"date": "2025-05-23", "sales": -1194.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.221052631578946}, {"date": "2025-05-24", "sales": 9.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 18.253333333333334}, {"date": "2025-05-25", "sales": 8.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 15.183333333333335}, {"date": "2025-05-26", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.616666666666664}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 3.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.7}], "analysis": null}
\ No newline at end of file
diff --git a/saved_predictions/prediction_5ffa9e00-ca1a-423b-89b2-b265e39d06b3.json b/saved_predictions/prediction_5ffa9e00-ca1a-423b-89b2-b265e39d06b3.json
new file mode 100644
index 0000000..992f268
--- /dev/null
+++ b/saved_predictions/prediction_5ffa9e00-ca1a-423b-89b2-b265e39d06b3.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "tcn", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.2753969728946686}, {"date": "2022-01-02", "predicted_sales": 0.11470969021320343}, {"date": "2022-01-03", "predicted_sales": 0.13205347955226898}, {"date": "2022-01-04", "predicted_sales": 0.15956580638885498}, {"date": "2022-01-05", "predicted_sales": 0.1634170413017273}, {"date": "2022-01-06", "predicted_sales": 0.2438911497592926}, {"date": "2022-01-07", "predicted_sales": 0.2631840705871582}, {"date": "2022-01-08", "predicted_sales": 0.21079100668430328}, {"date": "2022-01-09", "predicted_sales": 0.1272052526473999}, {"date": "2022-01-10", "predicted_sales": 0.11031797528266907}, {"date": "2022-01-11", "predicted_sales": 0.12503358721733093}, {"date": "2022-01-12", "predicted_sales": 0.18376275897026062}, {"date": "2022-01-13", "predicted_sales": 0.2275746762752533}, {"date": "2022-01-14", "predicted_sales": 0.23402179777622223}, {"date": "2022-01-15", "predicted_sales": 0.22899922728538513}, {"date": "2022-01-16", "predicted_sales": 0.14285317063331604}, {"date": "2022-01-17", "predicted_sales": 0.11126618087291718}, {"date": "2022-01-18", "predicted_sales": 0.1276262402534485}, {"date": "2022-01-19", "predicted_sales": 0.18343165516853333}, {"date": "2022-01-20", "predicted_sales": 0.209133580327034}, {"date": "2022-01-21", "predicted_sales": 0.23268824815750122}, {"date": "2022-01-22", "predicted_sales": 0.2349555641412735}, {"date": "2022-01-23", "predicted_sales": 0.14741972088813782}, {"date": "2022-01-24", "predicted_sales": 0.11235162615776062}, {"date": "2022-01-25", "predicted_sales": 0.12731915712356567}, {"date": "2022-01-26", "predicted_sales": 0.1801455020904541}, {"date": "2022-01-27", "predicted_sales": 0.20854568481445312}, {"date": "2022-01-28", "predicted_sales": 0.23221680521965027}, {"date": "2022-01-29", "predicted_sales": 0.23538252711296082}, {"date": "2022-01-30", "predicted_sales": 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{"date": "2022-01-06", "predicted_sales": 0.2438911497592926}, {"date": "2022-01-07", "predicted_sales": 0.2631840705871582}, {"date": "2022-01-08", "predicted_sales": 0.21079100668430328}, {"date": "2022-01-09", "predicted_sales": 0.1272052526473999}, {"date": "2022-01-10", "predicted_sales": 0.11031797528266907}, {"date": "2022-01-11", "predicted_sales": 0.12503358721733093}, {"date": "2022-01-12", "predicted_sales": 0.18376275897026062}, {"date": "2022-01-13", "predicted_sales": 0.2275746762752533}, {"date": "2022-01-14", "predicted_sales": 0.23402179777622223}, {"date": "2022-01-15", "predicted_sales": 0.22899922728538513}, {"date": "2022-01-16", "predicted_sales": 0.14285317063331604}, {"date": "2022-01-17", "predicted_sales": 0.11126618087291718}, {"date": "2022-01-18", "predicted_sales": 0.1276262402534485}, {"date": "2022-01-19", "predicted_sales": 0.18343165516853333}, {"date": "2022-01-20", "predicted_sales": 0.209133580327034}, {"date": "2022-01-21", "predicted_sales": 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diff --git a/saved_predictions/prediction_628f25d4-6920-402a-815d-157a97a5b6f7.json b/saved_predictions/prediction_628f25d4-6920-402a-815d-157a97a5b6f7.json
new file mode 100644
index 0000000..658ab79
--- /dev/null
+++ b/saved_predictions/prediction_628f25d4-6920-402a-815d-157a97a5b6f7.json
@@ -0,0 +1 @@
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diff --git a/saved_predictions/prediction_6bdb2673-3093-4a64-9930-bebddd30dbeb.json b/saved_predictions/prediction_6bdb2673-3093-4a64-9930-bebddd30dbeb.json
new file mode 100644
index 0000000..24d36e3
--- /dev/null
+++ b/saved_predictions/prediction_6bdb2673-3093-4a64-9930-bebddd30dbeb.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_797971f5-1876-4204-a26c-1ffe85829d13.json b/saved_predictions/prediction_797971f5-1876-4204-a26c-1ffe85829d13.json
new file mode 100644
index 0000000..d786028
--- /dev/null
+++ b/saved_predictions/prediction_797971f5-1876-4204-a26c-1ffe85829d13.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_7dfe5975-3461-4b8e-8f45-bafee22def49.json b/saved_predictions/prediction_7dfe5975-3461-4b8e-8f45-bafee22def49.json
new file mode 100644
index 0000000..28de5f5
--- /dev/null
+++ b/saved_predictions/prediction_7dfe5975-3461-4b8e-8f45-bafee22def49.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_805d8056-d8c9-47a5-b64e-9ff39f7f9dcf.json b/saved_predictions/prediction_805d8056-d8c9-47a5-b64e-9ff39f7f9dcf.json
new file mode 100644
index 0000000..b08ed02
--- /dev/null
+++ b/saved_predictions/prediction_805d8056-d8c9-47a5-b64e-9ff39f7f9dcf.json
@@ -0,0 +1 @@
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diff --git a/saved_predictions/prediction_8ade87a6-2342-4ba1-b754-430555d2bdfb.json b/saved_predictions/prediction_8ade87a6-2342-4ba1-b754-430555d2bdfb.json
new file mode 100644
index 0000000..f6f8ed4
--- /dev/null
+++ b/saved_predictions/prediction_8ade87a6-2342-4ba1-b754-430555d2bdfb.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_8c06ca6b-7f86-4638-ac28-aa9a1b593a3c.json b/saved_predictions/prediction_8c06ca6b-7f86-4638-ac28-aa9a1b593a3c.json
new file mode 100644
index 0000000..bfe1cfd
--- /dev/null
+++ b/saved_predictions/prediction_8c06ca6b-7f86-4638-ac28-aa9a1b593a3c.json
@@ -0,0 +1 @@
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0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 0.88, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 0.88, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.83, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.79, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 2, "day_of_month": 29, "day_of_year": 363, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.0, "temperature_2m_min": -0.2, "temperature": 2.3, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-30", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, "transaction_count": 0, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 0.88, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 0.88, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 3, "day_of_month": 30, "day_of_year": 364, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 5.3, "temperature_2m_min": 0.7, "temperature": 2.6, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-31", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.02, "transaction_count": 1, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 1.0, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.0, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 4, "day_of_month": 31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.004030156015817608, "trend_type": "平稳", "r_squared": 0.04556838611827181, "p_value": 0.6458063674760298, "volatility": 0.5530129075050354, "volatility_level": "高"}, "statistics": {"mean": 0.06827862560749054, "median": 0.07853572070598602, "min": 0.01834644004702568, "max": 0.11897968500852585, "std": 0.0377589613199234, "q1": 0.03216321021318436, "q3": 0.0988810658454895}, "day_over_day": [-80.36012268066406, 548.5165405273438, -63.20726776123047, 138.36843872070312, -24.73663902282715, -73.83287811279297], "influencing_factors": {"product_id": "11020059", "model_type": "xgboost", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "xgboost模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.07个单位，最高日销量为0.12个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_8db26191-f577-46bd-bf19-bfcf6e559806.json b/saved_predictions/prediction_8db26191-f577-46bd-bf19-bfcf6e559806.json
new file mode 100644
index 0000000..d559ce1
--- /dev/null
+++ b/saved_predictions/prediction_8db26191-f577-46bd-bf19-bfcf6e559806.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "tcn", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.4654306173324585}, {"date": "2025-05-30", "predicted_sales": 1.9967557191848755}, {"date": "2025-05-31", "predicted_sales": 2.176229476928711}, {"date": "2025-06-01", "predicted_sales": 2.3391542434692383}, {"date": "2025-06-02", "predicted_sales": 2.5595316886901855}, {"date": "2025-06-03", "predicted_sales": 1.336228847503662}, {"date": "2025-06-04", "predicted_sales": 1.6415263414382935}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.4654306173324585}, {"date": "2025-05-30", "predicted_sales": 1.9967557191848755}, {"date": "2025-05-31", "predicted_sales": 2.176229476928711}, {"date": "2025-06-01", "predicted_sales": 2.3391542434692383}, {"date": "2025-06-02", "predicted_sales": 2.5595316886901855}, {"date": "2025-06-03", "predicted_sales": 1.336228847503662}, {"date": "2025-06-04", "predicted_sales": 1.6415263414382935}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.3}, {"date": "2025-05-07", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.8}, {"date": "2025-05-08", "sales": 2.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.6}, {"date": "2025-05-09", "sales": 0.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-10", "sales": 0.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 13.4}, {"date": "2025-05-11", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-12", "sales": 3.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.9}, {"date": "2025-05-13", "sales": 0.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_8dfa0f31-853b-4570-a792-d4cb78bb62e1.json b/saved_predictions/prediction_8dfa0f31-853b-4570-a792-d4cb78bb62e1.json
new file mode 100644
index 0000000..a49f98f
--- /dev/null
+++ b/saved_predictions/prediction_8dfa0f31-853b-4570-a792-d4cb78bb62e1.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "全局聚合 (所有药品)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-03-01", "predicted_sales": 0.0}, {"date": "2025-03-02", "predicted_sales": 0.0}, {"date": "2025-03-03", "predicted_sales": 0.0}, {"date": "2025-03-04", "predicted_sales": 0.0}, {"date": "2025-03-05", "predicted_sales": 0.0}, {"date": "2025-03-06", "predicted_sales": 0.0}, {"date": "2025-03-07", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-03-01", "predicted_sales": 0.0}, {"date": "2025-03-02", "predicted_sales": 0.0}, {"date": "2025-03-03", "predicted_sales": 0.0}, {"date": "2025-03-04", "predicted_sales": 0.0}, {"date": "2025-03-05", "predicted_sales": 0.0}, {"date": "2025-03-06", "predicted_sales": 0.0}, {"date": "2025-03-07", "predicted_sales": 0.0}], "history_data": [{"date": "2025-01-30", "sales": 2.0, "weekday": 3, "month": 1, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 9.004545454545454}, {"date": "2025-01-31", 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\ No newline at end of file
diff --git a/saved_predictions/prediction_8ecdd606-5afa-41b1-bc23-67f4c9a02393.json b/saved_predictions/prediction_8ecdd606-5afa-41b1-bc23-67f4c9a02393.json
new file mode 100644
index 0000000..6ac15b9
--- /dev/null
+++ b/saved_predictions/prediction_8ecdd606-5afa-41b1-bc23-67f4c9a02393.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_92721511-d531-459f-b64d-409a4a96254e.json b/saved_predictions/prediction_92721511-d531-459f-b64d-409a4a96254e.json
new file mode 100644
index 0000000..81f812d
--- /dev/null
+++ b/saved_predictions/prediction_92721511-d531-459f-b64d-409a4a96254e.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_94988ddc-94e6-4ead-acdc-af6b4985e67e.json b/saved_predictions/prediction_94988ddc-94e6-4ead-acdc-af6b4985e67e.json
new file mode 100644
index 0000000..fe964cf
--- /dev/null
+++ b/saved_predictions/prediction_94988ddc-94e6-4ead-acdc-af6b4985e67e.json
@@ -0,0 +1 @@
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"is_weekend": false, "weekday": 3, "day_of_month": 30, "day_of_year": 364, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 5.3, "temperature_2m_min": 0.7, "temperature": 2.6, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-31", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.02, "transaction_count": 1, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 1.0, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.0, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 4, "day_of_month": 31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.004030156015817608, "trend_type": "平稳", "r_squared": 0.04556838611827181, "p_value": 0.6458063674760298, "volatility": 0.5530129075050354, "volatility_level": "高"}, "statistics": {"mean": 0.06827862560749054, "median": 0.07853572070598602, "min": 0.01834644004702568, "max": 0.11897968500852585, "std": 0.0377589613199234, "q1": 0.03216321021318436, "q3": 0.0988810658454895}, "day_over_day": [-80.36012268066406, 548.5165405273438, -63.20726776123047, 138.36843872070312, -24.73663902282715, -73.83287811279297], "influencing_factors": {"product_id": "11020059", "model_type": "xgboost", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "xgboost模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.07个单位，最高日销量为0.12个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_95df18a4-4d66-40d8-8426-e3f8f32a66a5.json b/saved_predictions/prediction_95df18a4-4d66-40d8-8426-e3f8f32a66a5.json
new file mode 100644
index 0000000..4b80267
--- /dev/null
+++ b/saved_predictions/prediction_95df18a4-4d66-40d8-8426-e3f8f32a66a5.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "transformer", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.2825997769832611}, {"date": "2022-01-02", "predicted_sales": 0.2631796598434448}, {"date": "2022-01-03", "predicted_sales": 0.26221904158592224}, {"date": "2022-01-04", "predicted_sales": 0.2872700095176697}, {"date": "2022-01-05", "predicted_sales": 0.28371042013168335}, {"date": "2022-01-06", "predicted_sales": 0.282085657119751}, {"date": "2022-01-07", "predicted_sales": 0.28061574697494507}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.2825997769832611}, {"date": "2022-01-02", "predicted_sales": 0.2631796598434448}, {"date": "2022-01-03", "predicted_sales": 0.26221904158592224}, {"date": "2022-01-04", "predicted_sales": 0.2872700095176697}, {"date": "2022-01-05", "predicted_sales": 0.28371042013168335}, {"date": "2022-01-06", "predicted_sales": 0.282085657119751}, {"date": "2022-01-07", "predicted_sales": 0.28061574697494507}], "history_data": [{"store_id": "01010108", "product_id": "11020059", "date": "2021-12-02", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, "transaction_count": 0, "sales_quantity_rolling_mean_7d": 0.5, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 0.5, "sales_quantity_rolling_sum_7d": 1.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 1.0, "sales_quantity_rolling_mean_15d": 0.5, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 0.5, "sales_quantity_rolling_sum_15d": 1.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 1.0, "sales_quantity_rolling_mean_30d": 0.71, "return_quantity_rolling_mean_30d": -0.14, "net_sales_quantity_rolling_mean_30d": 0.57, "sales_quantity_rolling_sum_30d": 5.0, "return_quantity_rolling_sum_30d": -1.0, "net_sales_quantity_rolling_sum_30d": 4.0, "sales_quantity_rolling_mean_90d": 0.77, "return_quantity_rolling_mean_90d": -0.09, "net_sales_quantity_rolling_mean_90d": 0.68, "sales_quantity_rolling_sum_90d": 17.0, "return_quantity_rolling_sum_90d": -2.0, "net_sales_quantity_rolling_sum_90d": 15.0, "is_weekend": false, "weekday": 3, "day_of_month": 2, "day_of_year": 336, "week_of_month": 1, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, 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"DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 9.9, "temperature_2m_min": 4.9, "temperature": 7.4, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-04", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, "transaction_count": 0, "sales_quantity_rolling_mean_7d": 0.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 0.0, "sales_quantity_rolling_sum_7d": 0.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 0.0, "sales_quantity_rolling_mean_15d": 0.5, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 0.5, 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\ No newline at end of file
diff --git a/saved_predictions/prediction_95e509c5-8c69-47e8-84b7-ccfa9143c949.json b/saved_predictions/prediction_95e509c5-8c69-47e8-84b7-ccfa9143c949.json
new file mode 100644
index 0000000..3fc683b
--- /dev/null
+++ b/saved_predictions/prediction_95e509c5-8c69-47e8-84b7-ccfa9143c949.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_9ada52fa-3d61-43d7-a343-30361956aabc.json b/saved_predictions/prediction_9ada52fa-3d61-43d7-a343-30361956aabc.json
new file mode 100644
index 0000000..225ff23
--- /dev/null
+++ b/saved_predictions/prediction_9ada52fa-3d61-43d7-a343-30361956aabc.json
@@ -0,0 +1 @@
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{"store_id": "01010108", "product_id": "11020059", "date": "2021-12-31", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.02, "transaction_count": 1, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 1.0, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.0, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 4, "day_of_month": 31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_9b9394a2-7d38-4880-8a89-10ca1777091b.json b/saved_predictions/prediction_9b9394a2-7d38-4880-8a89-10ca1777091b.json
new file mode 100644
index 0000000..77bf15a
--- /dev/null
+++ b/saved_predictions/prediction_9b9394a2-7d38-4880-8a89-10ca1777091b.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "transformer", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": "2025-06-01", "predicted_sales": 1.8458434343338013}, {"date": "2025-06-02", "predicted_sales": 1.8742748498916626}, {"date": "2025-06-03", "predicted_sales": 1.8699368238449097}, {"date": "2025-06-04", "predicted_sales": 1.8809840679168701}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": "2025-06-01", "predicted_sales": 1.8458434343338013}, {"date": "2025-06-02", "predicted_sales": 1.8742748498916626}, {"date": "2025-06-03", "predicted_sales": 1.8699368238449097}, {"date": "2025-06-04", "predicted_sales": 1.8809840679168701}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.3}, {"date": "2025-05-07", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.8}, {"date": "2025-05-08", "sales": 2.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.6}, {"date": "2025-05-09", "sales": 0.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-10", "sales": 0.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 13.4}, {"date": "2025-05-11", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-12", "sales": 3.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.9}, {"date": "2025-05-13", "sales": 0.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_9e5303d0-d6a6-4d6f-8049-d05ff041f62d.json b/saved_predictions/prediction_9e5303d0-d6a6-4d6f-8049-d05ff041f62d.json
new file mode 100644
index 0000000..28de5f5
--- /dev/null
+++ b/saved_predictions/prediction_9e5303d0-d6a6-4d6f-8049-d05ff041f62d.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "kan", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.2918514013290405}, {"date": "2025-05-30", "predicted_sales": 1.2950164079666138}, {"date": "2025-05-31", "predicted_sales": 1.3018046617507935}, {"date": "2025-06-01", "predicted_sales": 1.335819125175476}, {"date": "2025-06-02", "predicted_sales": 1.3471426963806152}, {"date": "2025-06-03", "predicted_sales": 1.283681869506836}, {"date": "2025-06-04", "predicted_sales": 1.2876296043395996}, {"date": "2025-06-05", "predicted_sales": 1.2919962406158447}, {"date": "2025-06-06", "predicted_sales": 1.2987719774246216}, {"date": "2025-06-07", "predicted_sales": 1.3057448863983154}, {"date": "2025-06-08", "predicted_sales": 1.337744951248169}, {"date": "2025-06-09", "predicted_sales": 1.3471529483795166}, {"date": "2025-06-10", "predicted_sales": 1.283681869506836}, {"date": "2025-06-11", "predicted_sales": 1.2876296043395996}, {"date": "2025-06-12", "predicted_sales": 1.2919962406158447}, {"date": "2025-06-13", "predicted_sales": 1.2987719774246216}, {"date": "2025-06-14", "predicted_sales": 1.3057448863983154}, {"date": "2025-06-15", "predicted_sales": 1.337744951248169}, {"date": "2025-06-16", "predicted_sales": 1.3471529483795166}, {"date": "2025-06-17", "predicted_sales": 1.283681869506836}, {"date": "2025-06-18", "predicted_sales": 1.2876296043395996}, {"date": "2025-06-19", "predicted_sales": 1.2919962406158447}, {"date": "2025-06-20", "predicted_sales": 1.2987719774246216}, {"date": "2025-06-21", "predicted_sales": 1.3057448863983154}, {"date": "2025-06-22", "predicted_sales": 1.337744951248169}, {"date": "2025-06-23", "predicted_sales": 1.3471529483795166}, {"date": "2025-06-24", "predicted_sales": 1.283681869506836}, {"date": "2025-06-25", "predicted_sales": 1.2876296043395996}, {"date": "2025-06-26", "predicted_sales": 1.2919962406158447}, {"date": "2025-06-27", "predicted_sales": 1.2987719774246216}, {"date": "2025-06-28", "predicted_sales": 1.3057448863983154}, {"date": "2025-06-29", "predicted_sales": 1.337744951248169}, {"date": "2025-06-30", "predicted_sales": 1.3471529483795166}, {"date": "2025-07-01", "predicted_sales": 1.283681869506836}, {"date": "2025-07-02", "predicted_sales": 1.2912344932556152}, {"date": "2025-07-03", "predicted_sales": 1.2959400415420532}, {"date": "2025-07-04", "predicted_sales": 1.3028221130371094}, {"date": "2025-07-05", "predicted_sales": 1.3100336790084839}, {"date": "2025-07-06", "predicted_sales": 1.3404955863952637}, {"date": "2025-07-07", "predicted_sales": 1.3492450714111328}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.2918514013290405}, {"date": "2025-05-30", "predicted_sales": 1.2950164079666138}, {"date": "2025-05-31", "predicted_sales": 1.3018046617507935}, {"date": "2025-06-01", "predicted_sales": 1.335819125175476}, {"date": "2025-06-02", "predicted_sales": 1.3471426963806152}, {"date": "2025-06-03", "predicted_sales": 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"predicted_sales": 1.2919962406158447}, {"date": "2025-06-20", "predicted_sales": 1.2987719774246216}, {"date": "2025-06-21", "predicted_sales": 1.3057448863983154}, {"date": "2025-06-22", "predicted_sales": 1.337744951248169}, {"date": "2025-06-23", "predicted_sales": 1.3471529483795166}, {"date": "2025-06-24", "predicted_sales": 1.283681869506836}, {"date": "2025-06-25", "predicted_sales": 1.2876296043395996}, {"date": "2025-06-26", "predicted_sales": 1.2919962406158447}, {"date": "2025-06-27", "predicted_sales": 1.2987719774246216}, {"date": "2025-06-28", "predicted_sales": 1.3057448863983154}, {"date": "2025-06-29", "predicted_sales": 1.337744951248169}, {"date": "2025-06-30", "predicted_sales": 1.3471529483795166}, {"date": "2025-07-01", "predicted_sales": 1.283681869506836}, {"date": "2025-07-02", "predicted_sales": 1.2912344932556152}, {"date": "2025-07-03", "predicted_sales": 1.2959400415420532}, {"date": "2025-07-04", "predicted_sales": 1.3028221130371094}, {"date": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_a05ea211-e2fa-4c2c-9dfc-706217bcc6d3.json b/saved_predictions/prediction_a05ea211-e2fa-4c2c-9dfc-706217bcc6d3.json
new file mode 100644
index 0000000..db45084
--- /dev/null
+++ b/saved_predictions/prediction_a05ea211-e2fa-4c2c-9dfc-706217bcc6d3.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_a78568dd-e9d9-4136-bfd1-c457c986a272.json b/saved_predictions/prediction_a78568dd-e9d9-4136-bfd1-c457c986a272.json
new file mode 100644
index 0000000..dfc042b
--- /dev/null
+++ b/saved_predictions/prediction_a78568dd-e9d9-4136-bfd1-c457c986a272.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_a7ed287a-5bba-4f2e-9d22-0252a3cfbfb1.json b/saved_predictions/prediction_a7ed287a-5bba-4f2e-9d22-0252a3cfbfb1.json
new file mode 100644
index 0000000..2e36406
--- /dev/null
+++ b/saved_predictions/prediction_a7ed287a-5bba-4f2e-9d22-0252a3cfbfb1.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "mlstm", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, 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{"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_a8f2ed67-ec50-44b7-a024-67ed6d9f5243.json b/saved_predictions/prediction_a8f2ed67-ec50-44b7-a024-67ed6d9f5243.json
new file mode 100644
index 0000000..f024ecb
--- /dev/null
+++ b/saved_predictions/prediction_a8f2ed67-ec50-44b7-a024-67ed6d9f5243.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, 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"is_promotion": 0, "temperature": 20.9}, {"date": "2025-05-14", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-05-15", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-16", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, 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\ No newline at end of file
diff --git a/saved_predictions/prediction_a9a36b5b-7daa-43eb-b65d-d06dea5f9dac.json b/saved_predictions/prediction_a9a36b5b-7daa-43eb-b65d-d06dea5f9dac.json
new file mode 100644
index 0000000..7c249c1
--- /dev/null
+++ b/saved_predictions/prediction_a9a36b5b-7daa-43eb-b65d-d06dea5f9dac.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-19", "sales": 4.0, "weekday": 5, "month": 4, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-04-20", "sales": 3.0, "weekday": 6, "month": 4, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 20.1}, {"date": "2025-04-21", "sales": 2.0, "weekday": 0, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 23.2}, {"date": "2025-04-22", "sales": 0.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.0}, {"date": "2025-04-23", "sales": 2.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.6}, {"date": "2025-04-24", "sales": 1.0, "weekday": 3, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.399999999999999}, {"date": "2025-04-25", "sales": 3.0, "weekday": 4, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.0}, {"date": "2025-04-26", "sales": 2.0, "weekday": 5, "month": 4, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 13.7}, {"date": "2025-04-27", "sales": 4.0, "weekday": 6, "month": 4, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-04-28", "sales": 2.0, "weekday": 0, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.3}, {"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.3}, {"date": "2025-05-07", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.8}, {"date": "2025-05-08", "sales": 2.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.6}, {"date": "2025-05-09", "sales": 0.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-10", "sales": 0.0, "weekday": 5, "month": 5, 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"weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_b00ec055-7dae-4ef6-a8b5-cfcccfb56baa.json b/saved_predictions/prediction_b00ec055-7dae-4ef6-a8b5-cfcccfb56baa.json
deleted file mode 100644
index d2e0f22..0000000
--- a/saved_predictions/prediction_b00ec055-7dae-4ef6-a8b5-cfcccfb56baa.json
+++ /dev/null
@@ -1 +0,0 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_b2b505d5-1217-4cf6-98db-ed127b5b61e7.json b/saved_predictions/prediction_b2b505d5-1217-4cf6-98db-ed127b5b61e7.json
new file mode 100644
index 0000000..3ab8732
--- /dev/null
+++ b/saved_predictions/prediction_b2b505d5-1217-4cf6-98db-ed127b5b61e7.json
@@ -0,0 +1 @@
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31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_b37dbac5-dc53-4528-9182-1bdf8d8bb836.json b/saved_predictions/prediction_b37dbac5-dc53-4528-9182-1bdf8d8bb836.json
deleted file mode 100644
index 5981783..0000000
--- a/saved_predictions/prediction_b37dbac5-dc53-4528-9182-1bdf8d8bb836.json
+++ /dev/null
@@ -1 +0,0 @@
-{"product_id": "17020190", "product_name": "左炔诺孕酮片", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-25", "predicted_sales": 3.459650993347168}, {"date": "2025-07-26", "predicted_sales": 3.489595890045166}, {"date": "2025-07-27", "predicted_sales": 3.516561508178711}, {"date": "2025-07-28", "predicted_sales": 3.53338623046875}, {"date": "2025-07-29", "predicted_sales": 3.4927194118499756}, {"date": "2025-07-30", "predicted_sales": 3.460852861404419}, {"date": "2025-07-31", "predicted_sales": 3.4948110580444336}], "prediction_data": [{"date": "2025-07-25", "predicted_sales": 3.459650993347168}, {"date": "2025-07-26", "predicted_sales": 3.489595890045166}, {"date": "2025-07-27", "predicted_sales": 3.516561508178711}, {"date": "2025-07-28", "predicted_sales": 3.53338623046875}, {"date": "2025-07-29", "predicted_sales": 3.4927194118499756}, {"date": "2025-07-30", "predicted_sales": 3.460852861404419}, {"date": "2025-07-31", "predicted_sales": 3.4948110580444336}], "history_data": [{"date": "2025-06-25", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 2, "month": 6, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-06-26", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 3, "month": 6, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-06-27", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 4, "month": 6, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-06-28", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 5, "month": 6, "is_holiday": false, "is_weekend": true, "is_promotion": false, 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"temperature": 20.0}, {"date": "2025-07-03", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 3, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-04", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 4, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-05", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 5, "month": 7, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-06", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 6, "month": 7, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-07", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 0, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-08", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 1, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-09", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 2, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-10", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 3, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-11", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 4, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-12", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 5, "month": 7, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-13", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 6, "month": 7, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-14", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 0, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-15", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 1, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-16", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 2, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-17", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 3, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-18", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 4, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-19", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 5, "month": 7, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-20", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 6, "month": 7, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-21", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 0, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-22", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 1, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-23", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 2, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-24", "sales": 0.0, "product_id": "17020190", "product_name": "左炔诺孕酮片", "store_id": "GLOBAL", "store_name": "全部店铺-SUM", "weekday": 3, "month": 7, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}], "analysis": {"trend": {"slope": 0.0008625728743416922, "trend_type": "平稳", "r_squared": 0.004794615347496793, "p_value": 0.8827307616676634, "volatility": 0.007133638657102353, "volatility_level": "低"}, "statistics": {"mean": 3.492511136191232, "median": 3.4927194118499756, "min": 3.459650993347168, "max": 3.53338623046875, "std": 0.024914312451494232, "q1": 3.4752243757247925, "q3": 3.5056862831115723}, "day_over_day": [0.8655467489518861, 0.77274329129256, 0.4784424288017895, -1.1509304663073707, -0.9123707543595094, 0.9812089100556087], "influencing_factors": {"product_id": "17020190", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品17020190的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性低，表明销量相对稳定，预测可信度较高。\n预测期内平均日销量为3.49个单位，最高日销量为3.53个单位，最低日销量为3.46个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_bd11c5d4-49d0-4f72-af4b-2885ad3ba385.json b/saved_predictions/prediction_bd11c5d4-49d0-4f72-af4b-2885ad3ba385.json
deleted file mode 100644
index 4e9ac2a..0000000
--- a/saved_predictions/prediction_bd11c5d4-49d0-4f72-af4b-2885ad3ba385.json
+++ /dev/null
@@ -1,525 +0,0 @@
-{
-    "product_id": "17021449",
-    "product_name": "布洛芬混悬液(美林)",
-    "model_type": "cnn_bilstm_attention",
-    "predictions": [
-        {
-            "date": "2025-07-25",
-            "predicted_sales": 0.8147072196006775
-        },
-        {
-            "date": "2025-07-26",
-            "predicted_sales": 0.8167740106582642
-        },
-        {
-            "date": "2025-07-27",
-            "predicted_sales": 0.8197348117828369
-        },
-        {
-            "date": "2025-07-28",
-            "predicted_sales": 0.8219858407974243
-        },
-        {
-            "date": "2025-07-29",
-            "predicted_sales": 0.8112776875495911
-        },
-        {
-            "date": "2025-07-30",
-            "predicted_sales": 0.8004958629608154
-        },
-        {
-            "date": "2025-07-31",
-            "predicted_sales": 0.8058184385299683
-        }
-    ],
-    "prediction_data": [
-        {
-            "date": "2025-07-25",
-            "predicted_sales": 0.8147072196006775
-        },
-        {
-            "date": "2025-07-26",
-            "predicted_sales": 0.8167740106582642
-        },
-        {
-            "date": "2025-07-27",
-            "predicted_sales": 0.8197348117828369
-        },
-        {
-            "date": "2025-07-28",
-            "predicted_sales": 0.8219858407974243
-        },
-        {
-            "date": "2025-07-29",
-            "predicted_sales": 0.8112776875495911
-        },
-        {
-            "date": "2025-07-30",
-            "predicted_sales": 0.8004958629608154
-        },
-        {
-            "date": "2025-07-31",
-            "predicted_sales": 0.8058184385299683
-        }
-    ],
-    "history_data": [
-        {
-            "date": "2025-06-25",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 2,
-            "month": 6,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-06-26",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 3,
-            "month": 6,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-06-27",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 4,
-            "month": 6,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-06-28",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 5,
-            "month": 6,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-06-29",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 6,
-            "month": 6,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-06-30",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 0,
-            "month": 6,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-01",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 1,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-02",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 2,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-03",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 3,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-04",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 4,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-05",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 5,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-06",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 6,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-07",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 0,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-08",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 1,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-09",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 2,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-10",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 3,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-11",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 4,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-12",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 5,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-13",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 6,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-14",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 0,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-15",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 1,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-16",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 2,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-17",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 3,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-18",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 4,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
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-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-19",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 5,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-20",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 6,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": true,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-21",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 0,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-22",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 1,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-23",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 2,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        },
-        {
-            "date": "2025-07-24",
-            "sales": 0.0,
-            "product_id": "17021449",
-            "product_name": "布洛芬混悬液(美林)",
-            "store_id": "GLOBAL",
-            "store_name": "全部店铺-SUM",
-            "weekday": 3,
-            "month": 7,
-            "is_holiday": false,
-            "is_weekend": false,
-            "is_promotion": false,
-            "temperature": 20.0
-        }
-    ],
-    "analysis": {
-        "trend": {
-            "slope": -0.0024171343871525353,
-            "trend_type": "平稳",
-            "r_squared": 0.4619268323887481,
-            "p_value": 0.0930247330579927,
-            "volatility": 0.008749220910445412,
-            "volatility_level": "低"
-        },
-        "statistics": {
-            "mean": 0.8129705531256539,
-            "median": 0.8147072196006775,
-            "min": 0.8004958629608154,
-            "max": 0.8219858407974243,
-            "std": 0.007112858962983344,
-            "q1": 0.8085480630397797,
-            "q3": 0.8182544112205505
-        },
-        "day_over_day": [
-            0.25368512857903625,
-            0.36249942896524706,
-            0.2746045406674448,
-            -1.3027174820243943,
-            -1.328993112252526,
-            0.6649098159565847
-        ],
-        "influencing_factors": {
-            "product_id": "17021449",
-            "model_type": "cnn_bilstm_attention",
-            "feature_count": 7,
-            "important_features": [
-                "价格",
-                "周末",
-                "节假日"
-            ]
-        },
-        "explanation": "cnn_bilstm_attention模型对产品17021449的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性低，表明销量相对稳定，预测可信度较高。\n预测期内平均日销量为0.81个单位，最高日销量为0.82个单位，最低日销量为0.80个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"
-    }
-}
\ No newline at end of file
diff --git a/saved_predictions/prediction_be05fff9-6070-4f30-859d-7fec6216c6a2.json b/saved_predictions/prediction_be05fff9-6070-4f30-859d-7fec6216c6a2.json
new file mode 100644
index 0000000..7f3c5be
--- /dev/null
+++ b/saved_predictions/prediction_be05fff9-6070-4f30-859d-7fec6216c6a2.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "mlstm", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}, {"date": "2025-06-05", "predicted_sales": 1.1068590879440308}, {"date": "2025-06-06", "predicted_sales": 1.1068657636642456}, {"date": "2025-06-07", "predicted_sales": 1.106870174407959}, {"date": "2025-06-08", "predicted_sales": 1.1068938970565796}, {"date": "2025-06-09", "predicted_sales": 1.1069002151489258}, {"date": "2025-06-10", "predicted_sales": 1.1068769693374634}, {"date": "2025-06-11", "predicted_sales": 1.1068545579910278}, {"date": "2025-06-12", "predicted_sales": 1.1068599224090576}, {"date": "2025-06-13", "predicted_sales": 1.1068662405014038}, {"date": "2025-06-14", "predicted_sales": 1.1068724393844604}, {"date": "2025-06-15", "predicted_sales": 1.1068952083587646}, {"date": "2025-06-16", "predicted_sales": 1.1069015264511108}, {"date": "2025-06-17", "predicted_sales": 1.1068809032440186}, {"date": "2025-06-18", "predicted_sales": 1.1068599224090576}, {"date": "2025-06-19", "predicted_sales": 1.1068648099899292}, {"date": "2025-06-20", "predicted_sales": 1.1068711280822754}, {"date": "2025-06-21", "predicted_sales": 1.1068778038024902}, {"date": "2025-06-22", "predicted_sales": 1.1069002151489258}, {"date": "2025-06-23", "predicted_sales": 1.1069073677062988}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}, {"date": "2025-06-05", "predicted_sales": 1.1068590879440308}, {"date": "2025-06-06", "predicted_sales": 1.1068657636642456}, {"date": "2025-06-07", "predicted_sales": 1.106870174407959}, {"date": "2025-06-08", "predicted_sales": 1.1068938970565796}, {"date": "2025-06-09", "predicted_sales": 1.1069002151489258}, {"date": "2025-06-10", "predicted_sales": 1.1068769693374634}, {"date": "2025-06-11", "predicted_sales": 1.1068545579910278}, {"date": "2025-06-12", "predicted_sales": 1.1068599224090576}, {"date": "2025-06-13", "predicted_sales": 1.1068662405014038}, {"date": "2025-06-14", "predicted_sales": 1.1068724393844604}, {"date": "2025-06-15", "predicted_sales": 1.1068952083587646}, {"date": "2025-06-16", "predicted_sales": 1.1069015264511108}, {"date": "2025-06-17", "predicted_sales": 1.1068809032440186}, {"date": "2025-06-18", "predicted_sales": 1.1068599224090576}, {"date": "2025-06-19", "predicted_sales": 1.1068648099899292}, {"date": "2025-06-20", "predicted_sales": 1.1068711280822754}, {"date": "2025-06-21", "predicted_sales": 1.1068778038024902}, {"date": "2025-06-22", "predicted_sales": 1.1069002151489258}, {"date": "2025-06-23", "predicted_sales": 1.1069073677062988}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-06", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.3}, {"date": "2025-05-07", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.8}, {"date": "2025-05-08", "sales": 2.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 20.6}, {"date": "2025-05-09", "sales": 0.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, 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"is_weekend": false, "is_promotion": 0, "temperature": 19.4}, {"date": "2025-05-17", "sales": 2.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.3}, {"date": "2025-05-18", "sales": 0.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 19.6}, {"date": "2025-05-19", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 21.9}, {"date": "2025-05-20", "sales": 4.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.9}, {"date": "2025-05-21", "sales": 1.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.8}, {"date": "2025-05-22", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 22.899999999999995}, {"date": "2025-05-23", "sales": 2.0, "weekday": 4, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 19.0}, {"date": "2025-05-24", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-25", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 14.9}, {"date": "2025-05-26", "sales": 0.0, "weekday": 0, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.7}, {"date": "2025-05-27", "sales": 1.0, "weekday": 1, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.1}, {"date": "2025-05-28", "sales": 2.0, "weekday": 2, "month": 5, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 16.4}], "analysis": null, "store_name": "店铺 01010323"}
\ No newline at end of file
diff --git a/saved_predictions/prediction_cfdc8490-1276-4f9c-90ac-7740b8b0f78b.json b/saved_predictions/prediction_cfdc8490-1276-4f9c-90ac-7740b8b0f78b.json
new file mode 100644
index 0000000..6ac15b9
--- /dev/null
+++ b/saved_predictions/prediction_cfdc8490-1276-4f9c-90ac-7740b8b0f78b.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "mlstm", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}, {"date": "2025-06-05", "predicted_sales": 1.1068590879440308}, {"date": "2025-06-06", "predicted_sales": 1.1068657636642456}, {"date": "2025-06-07", "predicted_sales": 1.106870174407959}, {"date": "2025-06-08", "predicted_sales": 1.1068938970565796}, {"date": "2025-06-09", "predicted_sales": 1.1069002151489258}, {"date": "2025-06-10", "predicted_sales": 1.1068769693374634}, {"date": "2025-06-11", "predicted_sales": 1.1068545579910278}, {"date": "2025-06-12", "predicted_sales": 1.1068599224090576}, {"date": "2025-06-13", "predicted_sales": 1.1068662405014038}, {"date": "2025-06-14", "predicted_sales": 1.1068724393844604}, {"date": "2025-06-15", "predicted_sales": 1.1068952083587646}, {"date": "2025-06-16", "predicted_sales": 1.1069015264511108}, {"date": "2025-06-17", "predicted_sales": 1.1068809032440186}, {"date": "2025-06-18", "predicted_sales": 1.1068599224090576}, {"date": "2025-06-19", "predicted_sales": 1.1068648099899292}, {"date": "2025-06-20", "predicted_sales": 1.1068711280822754}, {"date": "2025-06-21", "predicted_sales": 1.1068778038024902}, {"date": "2025-06-22", "predicted_sales": 1.1069002151489258}, {"date": "2025-06-23", "predicted_sales": 1.1069073677062988}, {"date": "2025-06-24", "predicted_sales": 1.1068859100341797}, {"date": "2025-06-25", "predicted_sales": 1.1068639755249023}, {"date": "2025-06-26", "predicted_sales": 1.1068683862686157}, {"date": "2025-06-27", "predicted_sales": 1.1068737506866455}, {"date": "2025-06-28", "predicted_sales": 1.1068809032440186}, {"date": "2025-06-29", "predicted_sales": 1.1069010496139526}, {"date": "2025-06-30", "predicted_sales": 1.1069082021713257}, {"date": "2025-07-01", "predicted_sales": 1.1068849563598633}, {"date": "2025-07-02", "predicted_sales": 1.1068648099899292}, {"date": "2025-07-03", "predicted_sales": 1.106868863105774}, {"date": "2025-07-04", "predicted_sales": 1.106874704360962}, {"date": "2025-07-05", "predicted_sales": 1.1068813800811768}, {"date": "2025-07-06", "predicted_sales": 1.106902003288269}, {"date": "2025-07-07", "predicted_sales": 1.1069082021713257}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", 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\ No newline at end of file
diff --git a/saved_predictions/prediction_d7eec498-5270-465e-af42-da606f74b431.json b/saved_predictions/prediction_d7eec498-5270-465e-af42-da606f74b431.json
new file mode 100644
index 0000000..992f268
--- /dev/null
+++ b/saved_predictions/prediction_d7eec498-5270-465e-af42-da606f74b431.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_d8b533ed-6005-4fef-a204-82c942b42b50.json b/saved_predictions/prediction_d8b533ed-6005-4fef-a204-82c942b42b50.json
new file mode 100644
index 0000000..225ff23
--- /dev/null
+++ b/saved_predictions/prediction_d8b533ed-6005-4fef-a204-82c942b42b50.json
@@ -0,0 +1 @@
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-0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 4, "day_of_month": 31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_daee98ce-5c41-4a20-8de9-bf05089d2d28.json b/saved_predictions/prediction_daee98ce-5c41-4a20-8de9-bf05089d2d28.json
new file mode 100644
index 0000000..2e36406
--- /dev/null
+++ b/saved_predictions/prediction_daee98ce-5c41-4a20-8de9-bf05089d2d28.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "mlstm", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 1.1068295240402222}, {"date": "2025-05-31", "predicted_sales": 1.1068456172943115}, {"date": "2025-06-01", "predicted_sales": 1.1068729162216187}, {"date": "2025-06-02", "predicted_sales": 1.1068742275238037}, {"date": "2025-06-03", "predicted_sales": 1.1068698167800903}, {"date": "2025-06-04", "predicted_sales": 1.1068545579910278}], "history_data": [{"date": "2025-04-29", "sales": 2.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 17.1}, {"date": "2025-04-30", "sales": 0.0, "weekday": 2, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 15.4}, {"date": "2025-05-01", "sales": 1.0, "weekday": 3, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 20.5}, {"date": "2025-05-02", "sales": 1.0, "weekday": 4, "month": 5, "is_holiday": true, "is_weekend": false, "is_promotion": 0, "temperature": 21.1}, {"date": "2025-05-03", "sales": 1.0, "weekday": 5, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 21.0}, {"date": "2025-05-04", "sales": 1.0, "weekday": 6, "month": 5, "is_holiday": true, "is_weekend": true, "is_promotion": 0, "temperature": 18.6}, {"date": "2025-05-05", "sales": 2.0, "weekday": 0, "month": 5, "is_holiday": true, 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\ No newline at end of file
diff --git a/saved_predictions/prediction_df2df2af-978d-403d-9a3a-2f70c9f37ee0.json b/saved_predictions/prediction_df2df2af-978d-403d-9a3a-2f70c9f37ee0.json
new file mode 100644
index 0000000..4b80267
--- /dev/null
+++ b/saved_predictions/prediction_df2df2af-978d-403d-9a3a-2f70c9f37ee0.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_e3fedc7c-bfec-4f34-8e78-80d546d0434e.json b/saved_predictions/prediction_e3fedc7c-bfec-4f34-8e78-80d546d0434e.json
new file mode 100644
index 0000000..7c249c1
--- /dev/null
+++ b/saved_predictions/prediction_e3fedc7c-bfec-4f34-8e78-80d546d0434e.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_e7283efe-bc39-4986-9861-0ad95880bbf4.json b/saved_predictions/prediction_e7283efe-bc39-4986-9861-0ad95880bbf4.json
new file mode 100644
index 0000000..2f7a192
--- /dev/null
+++ b/saved_predictions/prediction_e7283efe-bc39-4986-9861-0ad95880bbf4.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_e891f548-cb3a-4231-89aa-459fb6401c7b.json b/saved_predictions/prediction_e891f548-cb3a-4231-89aa-459fb6401c7b.json
new file mode 100644
index 0000000..fe964cf
--- /dev/null
+++ b/saved_predictions/prediction_e891f548-cb3a-4231-89aa-459fb6401c7b.json
@@ -0,0 +1 @@
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{"store_id": "01010108", "product_id": "11020059", "date": "2021-12-31", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.02, "transaction_count": 1, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 1.0, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.0, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.83, "sales_quantity_rolling_sum_90d": 20.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 19.0, "is_weekend": false, "weekday": 4, "day_of_month": 31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.004030156015817608, "trend_type": "平稳", "r_squared": 0.04556838611827181, "p_value": 0.6458063674760298, "volatility": 0.5530129075050354, "volatility_level": "高"}, "statistics": {"mean": 0.06827862560749054, "median": 0.07853572070598602, "min": 0.01834644004702568, "max": 0.11897968500852585, "std": 0.0377589613199234, "q1": 0.03216321021318436, "q3": 0.0988810658454895}, "day_over_day": [-80.36012268066406, 548.5165405273438, -63.20726776123047, 138.36843872070312, -24.73663902282715, -73.83287811279297], "influencing_factors": {"product_id": "11020059", "model_type": "xgboost", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "xgboost模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.07个单位，最高日销量为0.12个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}}
\ No newline at end of file
diff --git a/saved_predictions/prediction_efcca40c-f1d6-40a5-a3c5-e33d5154fd5d.json b/saved_predictions/prediction_efcca40c-f1d6-40a5-a3c5-e33d5154fd5d.json
new file mode 100644
index 0000000..7c249c1
--- /dev/null
+++ b/saved_predictions/prediction_efcca40c-f1d6-40a5-a3c5-e33d5154fd5d.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-19", "sales": 4.0, "weekday": 5, "month": 4, "is_holiday": false, "is_weekend": true, "is_promotion": 0, "temperature": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_f257c2fc-236d-4375-89a1-d14ed35ce17a.json b/saved_predictions/prediction_f257c2fc-236d-4375-89a1-d14ed35ce17a.json
new file mode 100644
index 0000000..81f812d
--- /dev/null
+++ b/saved_predictions/prediction_f257c2fc-236d-4375-89a1-d14ed35ce17a.json
@@ -0,0 +1 @@
+{"product_id": null, "product_name": "全局聚合 (所有药品)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "history_data": [{"date": "2025-04-29", "sales": 3.0, "weekday": 1, "month": 4, "is_holiday": false, "is_weekend": false, "is_promotion": 0, "temperature": 18.008823529411764}, {"date": "2025-04-30", 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\ No newline at end of file
diff --git a/saved_predictions/prediction_fbaa629f-91dc-4262-b829-fc0fb741cef2.json b/saved_predictions/prediction_fbaa629f-91dc-4262-b829-fc0fb741cef2.json
new file mode 100644
index 0000000..dfc042b
--- /dev/null
+++ b/saved_predictions/prediction_fbaa629f-91dc-4262-b829-fc0fb741cef2.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "transformer", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.2825997769832611}, {"date": "2022-01-02", "predicted_sales": 0.2631796598434448}, {"date": "2022-01-03", "predicted_sales": 0.26221904158592224}, {"date": "2022-01-04", "predicted_sales": 0.2872700095176697}, {"date": "2022-01-05", "predicted_sales": 0.28371042013168335}, {"date": "2022-01-06", "predicted_sales": 0.282085657119751}, {"date": "2022-01-07", "predicted_sales": 0.28061574697494507}, {"date": "2022-01-08", "predicted_sales": 0.2804362177848816}, {"date": "2022-01-09", "predicted_sales": 0.2631623446941376}, {"date": "2022-01-10", "predicted_sales": 0.2617184817790985}, {"date": "2022-01-11", "predicted_sales": 0.28814640641212463}, {"date": "2022-01-12", "predicted_sales": 0.2859044373035431}, {"date": "2022-01-13", "predicted_sales": 0.2838728725910187}, {"date": "2022-01-14", "predicted_sales": 0.2820758521556854}, 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"net_sales_quantity_rolling_sum_15d": 4.0, "sales_quantity_rolling_mean_30d": 0.86, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 0.86, "sales_quantity_rolling_sum_30d": 6.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 6.0, "sales_quantity_rolling_mean_90d": 0.83, "return_quantity_rolling_mean_90d": -0.04, "net_sales_quantity_rolling_mean_90d": 0.78, "sales_quantity_rolling_sum_90d": 19.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 18.0, "is_weekend": false, "weekday": 0, "day_of_month": 27, "day_of_year": 361, "week_of_month": 4, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": 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"day_of_month": 29, "day_of_year": 363, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.0, "temperature_2m_min": -0.2, "temperature": 2.3, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-30", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, "transaction_count": 0, "sales_quantity_rolling_mean_7d": 1.0, "return_quantity_rolling_mean_7d": 0.0, "net_sales_quantity_rolling_mean_7d": 1.0, "sales_quantity_rolling_sum_7d": 3.0, "return_quantity_rolling_sum_7d": 0.0, "net_sales_quantity_rolling_sum_7d": 3.0, "sales_quantity_rolling_mean_15d": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "sales_quantity_rolling_sum_15d": 5.0, "return_quantity_rolling_sum_15d": 0.0, "net_sales_quantity_rolling_sum_15d": 5.0, "sales_quantity_rolling_mean_30d": 0.88, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 0.88, "sales_quantity_rolling_sum_30d": 7.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 7.0, "sales_quantity_rolling_mean_90d": 0.87, "return_quantity_rolling_mean_90d": -0.04, 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106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": null}
\ No newline at end of file
diff --git a/server/api.py b/server/api.py
index fa76b31..91f45d5 100644
--- a/server/api.py
+++ b/server/api.py
@@ -63,9 +63,11 @@ from core.config import (
 )
 
 # 导入多店铺数据工具
-from utils.multi_store_data_utils import (
-    get_available_stores, get_available_products, get_sales_statistics
-)
+# from utils.multi_store_data_utils import (
+#     get_available_stores, get_available_products, get_sales_statistics
+# )
+# 以上旧模块将被新的统一数据加载器替代
+from utils.new_data_loader import load_new_data
 
 # 导入数据库初始化工具
 from init_multi_store_db import get_db_connection
@@ -202,6 +204,9 @@ class CustomJSONEncoder(json.JSONEncoder):
         # 处理日期时间类型
         elif isinstance(obj, datetime):
             return obj.isoformat()
+        # 新增：处理date对象
+        elif isinstance(obj, pd.Timestamp) or hasattr(obj, 'isoformat'):
+            return obj.isoformat()
         return super(CustomJSONEncoder, self).default(obj)
 
 # Helper function to convert numpy types to native python types for JSON serialization
@@ -515,11 +520,21 @@ def swagger_ui():
     }
 })
 def get_products():
+    """获取所有产品列表 (已重构为使用新数据源)"""
     try:
-        from utils.multi_store_data_utils import get_available_products
-        products = get_available_products()
+        df = load_new_data()
+        # 从新数据中提取唯一的产品ID
+        products_df = df[['product_id']].drop_duplicates().sort_values('product_id')
+        
+        # 由于新数据没有product_name，我们创建一个兼容的格式
+        products = [
+            {'product_id': pid, 'product_name': f'产品 {pid}'}
+            for pid in products_df['product_id']
+        ]
+        
         return jsonify({"status": "success", "data": products})
     except Exception as e:
+        logger.error(f"获取产品列表失败: {traceback.format_exc()}")
         return jsonify({"status": "error", "message": str(e)}), 500
 
 @app.route('/api/products/<product_id>', methods=['GET'])
@@ -570,25 +585,32 @@ def get_products():
     }
 })
 def get_product(product_id):
+    """获取单个产品详情 (已重构为使用新数据源)"""
     try:
-        from utils.multi_store_data_utils import load_multi_store_data
-        df = load_multi_store_data(product_id=product_id)
+        df = load_new_data()
+        product_df = df[df['product_id'] == product_id]
         
-        if df.empty:
+        if product_df.empty:
             return jsonify({"status": "error", "message": "产品不存在"}), 404
         
+        # 从新数据中提取信息
+        product_name = f"产品 {product_id}" # 备用名称
+        if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+            product_name = product_df['product_name'].iloc[0]
+
         product_info = {
             "product_id": product_id,
-            "product_name": df['product_name'].iloc[0],
-            "data_points": len(df),
+            "product_name": product_name,
+            "data_points": len(product_df),
             "date_range": {
-                "start": df['date'].min().strftime('%Y-%m-%d'),
-                "end": df['date'].max().strftime('%Y-%m-%d')
+                "start": product_df['date'].min().strftime('%Y-%m-%d'),
+                "end": product_df['date'].max().strftime('%Y-%m-%d')
             }
         }
         
         return jsonify({"status": "success", "data": product_info})
     except Exception as e:
+        logger.error(f"获取产品详情失败: {traceback.format_exc()}")
         return jsonify({"status": "error", "message": str(e)}), 500
 
 @app.route('/api/products/<product_id>/sales', methods=['GET'])
@@ -644,29 +666,29 @@ def get_product(product_id):
     }
 })
 def get_product_sales(product_id):
+    """获取产品销售数据 (已重构为使用新数据源)"""
     try:
         start_date = request.args.get('start_date')
         end_date = request.args.get('end_date')
         
-        from utils.multi_store_data_utils import load_multi_store_data
-        df = load_multi_store_data(
-            product_id=product_id,
-            start_date=start_date,
-            end_date=end_date
-        )
+        df = load_new_data()
+        df_product = df[df['product_id'] == product_id]
+
+        if start_date:
+            df_product = df_product[df_product['date'] >= pd.to_datetime(start_date)]
+        if end_date:
+            df_product = df_product[df_product['date'] <= pd.to_datetime(end_date)]
+
+        if df_product.empty:
+            return jsonify({"status": "error", "message": "产品不存在或在指定日期范围内无数据"}), 404
         
-        if df.empty:
-            return jsonify({"status": "error", "message": "产品不存在或无数据"}), 404
+        df_product = df_product.sort_values('date')
+        df_product['date'] = df_product['date'].dt.strftime('%Y-%m-%d')
         
-        # 确保数据按日期排序
-        df = df.sort_values('date')
-        
-        # 转换日期为字符串以便JSON序列化
-        df['date'] = df['date'].dt.strftime('%Y-%m-%d')
-        
-        sales_data = df.to_dict('records')
+        sales_data = df_product.to_dict('records')
         return jsonify({"status": "success", "data": sales_data})
     except Exception as e:
+        logger.error(f"获取产品销售数据失败: {traceback.format_exc()}")
         return jsonify({"status": "error", "message": str(e)}), 500
 
 @app.route('/api/data/upload', methods=['POST'])
@@ -897,12 +919,16 @@ def start_training():
     
     # 使用新的训练进程管理器提交任务
     try:
+        # 升级：将自定义范围参数也传递给训练任务
         task_id = training_manager.submit_task(
             product_id=product_id or "unknown",
             model_type=model_type,
             training_mode=training_mode,
             store_id=store_id,
-            epochs=epochs
+            epochs=epochs,
+            selected_stores=data.get('selected_stores'),
+            selected_products=data.get('selected_products'),
+            aggregation_method=aggregation_method
         )
         
         logger.info(f"🚀 训练任务已提交到进程管理器: {task_id[:8]}")
@@ -1500,7 +1526,9 @@ def predict():
             traceback.print_exc()
             # 不应阻止向用户返回结果，因此只打印警告
 
-        return jsonify(response_data)
+        # 在返回前，使用我们的辅助函数对整个响应进行一次深度清洗
+        cleaned_response_data = convert_numpy_types_for_json(response_data)
+        return jsonify(cleaned_response_data)
     except Exception as e:
         print(f"预测失败: {str(e)}")
         import traceback
@@ -2550,32 +2578,24 @@ def get_latest_model_id(model_type, product_id):
 
 # 获取产品名称的辅助函数
 def get_product_name(product_id):
-    """根据产品ID获取产品名称"""
+    """根据产品ID获取产品名称 (已重构)"""
     try:
-        # 从Excel文件中查找产品名称
-        from utils.multi_store_data_utils import load_multi_store_data
-        df = load_multi_store_data()
-        product_df = df[df['product_id'] == product_id]
-        if not product_df.empty:
-            return product_df['product_name'].iloc[0]
-        
-        return None
+        # 注意：新数据源中没有 'product_name'。此函数现在返回一个占位符。
+        # 在未来的迭代中，可能需要关联一个产品信息表。
+        return f"产品 {product_id}"
     except Exception as e:
-        print(f"获取产品名称失败: {str(e)}")
-        return None
-# 获取店铺名称的辅助函数
+        logger.warning(f"获取产品名称时出现问题: {e}")
+        return product_id
+
 def get_store_name(store_id):
-    """根据店铺ID获取店铺名称"""
+    """根据店铺ID获取店铺名称 (已重构)"""
     try:
-        from utils.multi_store_data_utils import get_available_stores
-        stores = get_available_stores()
-        for store in stores:
-            if store['store_id'] == store_id:
-                return store['store_name']
-        return None
+        # 注意：新数据源中没有 'store_name'。此函数现在返回一个占位符。
+        # 在未来的迭代中，可能需要关联一个店铺信息表。
+        return f"店铺 {store_id}"
     except Exception as e:
-        print(f"获取店铺名称失败: {str(e)}")
-        return None
+        logger.warning(f"获取店铺名称时出现问题: {e}")
+        return store_id
 
 
 # run_prediction 函数已被移除，因为其逻辑已完全整合到 /api/prediction 路由处理函数中
@@ -3837,23 +3857,38 @@ def update_train_task_with_websocket():
 
 @app.route('/api/stores', methods=['GET'])
 def get_stores():
-    """
-    获取所有店铺列表
-    """
+    """获取所有店铺列表 (已重构为使用新数据源并填充信息)"""
     try:
-        from utils.multi_store_data_utils import get_available_stores
-        stores = get_available_stores()
+        df = load_new_data()
         
+        # 从新数据中提取唯一的店铺信息
+        # 修正：只选择数据文件中实际存在的列
+        # 根据之前的分析，新数据有 'district' 列，但没有 'city' 和 'province'
+        stores_df = df[['store_id', 'district']].drop_duplicates('store_id')
+        
+        stores_data = []
+        for _, row in stores_df.iterrows():
+            # 构建位置信息
+            location = row['district'] if pd.notna(row['district']) else "Unknown Location"
+            
+            stores_data.append({
+                "store_id": row['store_id'],
+                "store_name": f"店铺 {row['store_id']}", # 使用ID作为临时名称
+                "location": location,
+                "type": "标准药店", # 填充默认值
+                "size": "120㎡", # 填充默认值
+                "opening_date": "2023-01-01", # 填充默认值
+                "status": "营业中" # 填充默认值
+            })
+            
         return jsonify({
             "status": "success",
-            "data": stores,
-            "count": len(stores)
+            "data": stores_data,
+            "count": len(stores_data)
         })
     except Exception as e:
-        return jsonify({
-            "status": "error",
-            "message": f"获取店铺列表失败: {str(e)}"
-        }), 500
+        logger.error(f"获取店铺列表失败: {traceback.format_exc()}")
+        return jsonify({"status": "error", "message": f"获取店铺列表失败: {str(e)}"}), 500
 
 @app.route('/api/stores/<store_id>', methods=['GET'])
 def get_store(store_id):
@@ -4043,11 +4078,20 @@ def delete_store(store_id):
 
 @app.route('/api/stores/<store_id>/products', methods=['GET'])
 def get_store_products(store_id):
-    """
-    获取店铺的产品列表
-    """
+    """获取店铺的产品列表 (已重构为使用新数据源)"""
     try:
-        products = get_available_products(store_id=store_id)
+        df = load_new_data()
+        store_df = df[df['store_id'] == store_id]
+        
+        if store_df.empty:
+            return jsonify({"status": "success", "data": [], "count": 0})
+            
+        products_df = store_df[['product_id']].drop_duplicates().sort_values('product_id')
+        
+        products = [
+            {'product_id': pid, 'product_name': f'产品 {pid}'}
+            for pid in products_df['product_id']
+        ]
         
         return jsonify({
             "status": "success",
@@ -4055,10 +4099,8 @@ def get_store_products(store_id):
             "count": len(products)
         })
     except Exception as e:
-        return jsonify({
-            "status": "error",
-            "message": f"获取店铺产品列表失败: {str(e)}"
-        }), 500
+        logger.error(f"获取店铺产品列表失败: {traceback.format_exc()}")
+        return jsonify({"status": "error", "message": f"获取店铺产品列表失败: {str(e)}"}), 500
 
 @app.route('/api/stores/<store_id>/statistics', methods=['GET'])
 def get_store_statistics(store_id):
diff --git a/server/core/predictor.py b/server/core/predictor.py
index 6d232a0..fdb3ef9 100644
--- a/server/core/predictor.py
+++ b/server/core/predictor.py
@@ -20,11 +20,13 @@ from datetime import datetime
 # 上述导入已不再需要，因为我们现在通过模型注册表动态获取训练器
 from predictors.model_predictor import load_model_and_predict
 from utils.data_utils import prepare_data, prepare_sequences
-from utils.multi_store_data_utils import (
-    load_multi_store_data, 
-    get_store_product_sales_data,
-    aggregate_multi_store_data
-)
+# from utils.multi_store_data_utils import (
+#     load_multi_store_data,
+#     get_store_product_sales_data,
+#     aggregate_multi_store_data
+# )
+# 以上旧模块已被新的统一数据加载器替代
+from utils.new_data_loader import load_new_data
 from analysis.metrics import evaluate_model
 from core.config import DEVICE, DEFAULT_MODEL_DIR, DEFAULT_DATA_PATH
 
@@ -53,18 +55,16 @@ class PharmacyPredictor:
             
         print(f"使用设备: {self.device}")
         
-        # 尝试加载多店铺数据
-        try:
-            self.data = load_multi_store_data(data_path)
-            print(f"已加载多店铺数据，来源: {data_path}")
-        except Exception as e:
-            print(f"加载数据失败: {e}")
-            self.data = None
+        # 重构：不再预加载整个数据集到内存
+        # self.data 将在需要时动态加载
+        self.data = None
+        print("PharmacyPredictor 已初始化，将在需要时动态加载数据。")
 
     def train_model(self, product_id, model_type='transformer', epochs=100, batch_size=32,
                 learning_rate=0.001, sequence_length=30, forecast_horizon=7,
                 hidden_size=64, num_layers=2, dropout=0.1, use_optimized=False,
                 store_id=None, training_mode='product', aggregation_method='sum',
+                selected_stores: list = None, selected_products: list = None, # 新增参数以支持自定义范围
                 socketio=None, task_id=None, version=None, continue_training=False,
                 progress_callback=None):
         """
@@ -104,76 +104,78 @@ class PharmacyPredictor:
                 except Exception as e:
                     print(f"进度回调失败: {e}", flush=True)
         
-        if self.data is None:
-            log_message("没有可用的数据，请先加载或生成数据", 'error')
+        # --- 数据加载与筛选重构 ---
+        # 统一使用新的数据加载器，替换掉所有旧的、分散的加载逻辑
+        log_message("正在使用新的统一数据加载器...", 'info')
+        try:
+            full_df = load_new_data()
+        except Exception as e:
+            log_message(f"使用新数据加载器失败: {e}", 'error')
             return None
 
-        # 根据训练模式准备数据
         if training_mode == 'product':
-            # 按产品训练：使用所有店铺的该产品数据
-            product_data = self.data[self.data['product_id'] == product_id].copy()
+            product_data = full_df[full_df['product_id'] == product_id].copy()
             if product_data.empty:
                 log_message(f"找不到产品 {product_id} 的数据", 'error')
                 return None
-            log_message(f"按产品训练模式: 产品 {product_id}, 数据量: {len(product_data)}")
-            
+            log_message(f"按产品训练模式: 产品 {product_id}, 数据量: {len(product_data)}", 'info')
+
         elif training_mode == 'store':
-            # 按店铺训练
             if not store_id:
                 log_message("店铺训练模式需要指定 store_id", 'error')
                 return None
             
-            # 如果product_id是'unknown'，则表示为店铺所有商品训练一个聚合模型
-            if product_id == 'unknown':
-                try:
-                    # 使用新的聚合函数，按店铺聚合
-                    product_data = aggregate_multi_store_data(
-                        store_id=store_id,
-                        aggregation_method=aggregation_method,
-                        file_path=self.data_path
-                    )
-                    log_message(f"按店铺聚合训练: 店铺 {store_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
-                    # 将product_id设置为'store_{store_id}'，与API查找逻辑保持一致
-                    product_id = f"store_{store_id}"
-                except Exception as e:
-                    log_message(f"聚合店铺 {store_id} 数据失败: {e}", 'error')
-                    return None
-            else:
+            # 筛选出该店铺的所有数据
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+
+            # 判断是为单个产品训练还是为整个店铺聚合训练
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
                 # 为店铺的单个特定产品训练
-                try:
-                    product_data = get_store_product_sales_data(
-                        store_id=store_id,
-                        product_id=product_id,
-                        file_path=self.data_path
-                    )
-                    log_message(f"按店铺-产品训练: 店铺 {store_id}, 产品 {product_id}, 数据量: {len(product_data)}")
-                except Exception as e:
-                    log_message(f"获取店铺产品数据失败: {e}", 'error')
-                    return None
-                
+                product_data = store_df[store_df['product_id'] == product_id].copy()
+                log_message(f"按店铺-产品训练: 店铺 {store_id}, 产品 {product_id}, 数据量: {len(product_data)}", 'info')
+            else:
+                # 为整个店铺聚合训练
+                log_message(f"按店铺聚合训练: 店铺 {store_id} (所有药品)", 'info')
+                product_data = store_df.groupby('date').agg({
+                    'sales': 'sum',
+                    'weekday': 'first', 'month': 'first', 'is_holiday': 'first',
+                    'is_weekend': 'first', 'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                log_message(f"聚合后数据量: {len(product_data)}", 'info')
+
+                # 数据清洗：使用0填充聚合后可能产生的NaN值
+                product_data.fillna(0, inplace=True)
+                log_message("已对聚合数据进行NaN值填充处理（使用0填充）", 'info')
+
         elif training_mode == 'global':
-            # 全局训练：聚合所有店铺的产品数据
-            try:
-                # 如果product_id是'unknown'，则表示为全局所有商品训练一个聚合模型
-                if product_id == 'unknown':
-                    product_data = aggregate_multi_store_data(
-                        product_id=None,  # 传递None以触发真正的全局聚合
-                        aggregation_method=aggregation_method,
-                        file_path=self.data_path
-                    )
-                    log_message(f"全局训练模式: 所有产品, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
-                    # 将product_id设置为一个有意义的标识符
-                    product_id = 'all_products'
-                else:
-                    product_data = aggregate_multi_store_data(
-                        product_id=product_id,
-                        aggregation_method=aggregation_method,
-                        file_path=self.data_path
-                    )
-                    log_message(f"全局训练模式: 产品 {product_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
-            except Exception as e:
-                log_message(f"聚合全局数据失败: {e}", 'error')
+            # 升级：支持“自定义范围”的全局训练
+            if selected_stores and selected_products:
+                log_message(f"全局训练模式: 自定义范围 -> 店铺 {selected_stores}, 产品 {selected_products}", 'info')
+                # 根据选择的店铺和产品列表进行精确筛选
+                product_data = full_df[
+                    full_df['store_id'].isin(selected_stores) &
+                    full_df['product_id'].isin(selected_products)
+                ].copy()
+            # 回退到旧的全局训练逻辑
+            elif product_id and product_id not in ['unknown', 'all_products']:
+                log_message(f"全局训练模式: 聚合单个产品 '{product_id}' 的跨店数据...", 'info')
+                product_data = full_df[full_df['product_id'] == product_id].copy()
+            else:
+                log_message("全局训练模式: 聚合所有店铺、所有药品的数据...", 'info')
+                product_data = full_df.copy()
+
+            if product_data.empty:
+                log_message(f"在指定的全局范围或自定义范围内找不到任何数据。", 'error')
                 return None
+
+            # 对筛选出的数据按天进行聚合
+            product_data = product_data.groupby('date').agg({
+                'sales': aggregation_method,
+                'weekday': 'first', 'month': 'first', 'is_holiday': 'first',
+                'is_weekend': 'first', 'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            log_message(f"全局训练聚合完成，聚合方法: {aggregation_method}, 数据量: {len(product_data)}", 'info')
+            
         else:
             log_message(f"不支持的训练模式: {training_mode}", 'error')
             return None
@@ -183,8 +185,16 @@ class PharmacyPredictor:
             # 店铺模型的标识符只应基于店铺ID
             model_identifier = f"store_{store_id}"
         elif training_mode == 'global':
-            # 全局模型的标识符不应依赖于单个product_id
-            model_identifier = f"global_{aggregation_method}"
+            # 升级：为自定义范围的全局模型创建唯一标识符
+            if selected_stores and selected_products:
+                # 创建一个基于内容哈希的稳定标识符
+                import hashlib
+                s_stores = "_".join(sorted(selected_stores))
+                s_products = "_".join(sorted(selected_products))
+                hash_input = f"{s_stores}_{s_products}_{aggregation_method}"
+                model_identifier = f"global_custom_{hashlib.md5(hash_input.encode()).hexdigest()[:8]}"
+            else:
+                model_identifier = f"global_{aggregation_method}"
         else: # product mode
             model_identifier = product_id
         
diff --git a/server/predictors/model_predictor.py b/server/predictors/model_predictor.py
index 2463543..e87170b 100644
--- a/server/predictors/model_predictor.py
+++ b/server/predictors/model_predictor.py
@@ -23,7 +23,7 @@ import xgboost as xgb
 
 from analysis.trend_analysis import analyze_prediction_result
 from utils.visualization import plot_prediction_results
-from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
+from utils.new_data_loader import load_new_data
 from core.config import DEVICE, get_model_file_path, DEFAULT_DATA_PATH
 from models.model_registry import get_predictor, register_predictor
 
@@ -49,35 +49,75 @@ def default_pytorch_predictor(model, checkpoint, product_df, future_days, start_
 
     history_for_chart_df = product_df[product_df['date'] < start_date_dt].tail(history_lookback_days)
 
-    all_predictions = []
-    current_sequence_df = prediction_input_df.copy()
+    # --- 重构预测逻辑以正确处理XGBoost ---
+    if isinstance(model, xgb.Booster):
+        # --- XGBoost 预测路径 (非回归式，一次性预测) ---
+        X_current_scaled = scaler_X.transform(prediction_input_df[features].values)
+        X_input_reshaped = X_current_scaled.reshape(1, -1)
+        d_input = xgb.DMatrix(X_input_reshaped)
+        
+        # 一次性获取所有未来天数的预测
+        y_pred_scaled = model.predict(d_input, iteration_range=(0, model.best_iteration))
+        
+        # 反归一化整个序列
+        y_pred_unscaled = scaler_y.inverse_transform(y_pred_scaled.reshape(1, -1)).flatten()
+        y_pred_unscaled = np.maximum(0, y_pred_unscaled) # 确保销量不为负
 
-    for _ in range(future_days):
-        X_current_scaled = scaler_X.transform(current_sequence_df[features].values)
-        # **核心改进**: 智能判断模型类型并调用相应的预测方法
-        if isinstance(model, xgb.Booster):
-            # XGBoost 模型预测路径
-            X_input_reshaped = X_current_scaled.reshape(1, -1)
-            d_input = xgb.DMatrix(X_input_reshaped)
-            # **关键修复**: 使用 best_iteration 进行预测，以匹配早停策略
-            y_pred_scaled = model.predict(d_input, iteration_range=(0, model.best_iteration))
-            next_step_pred_scaled = y_pred_scaled.reshape(1, -1)
-        else:
-            # 默认 PyTorch 模型预测路径
+        # 生成未来日期序列
+        # 修正: 未来日期的数量必须与模型实际输出的预测点数量一致
+        # 而不是遵循用户输入的 future_days，因为XGBoost模型输出的长度是固定的。
+        future_dates = pd.date_range(start=start_date_dt, periods=len(y_pred_unscaled))
+        
+        # 直接构建结果DataFrame
+        predictions_df = pd.DataFrame({
+            'date': future_dates,
+            'predicted_sales': y_pred_unscaled
+        })
+
+    elif isinstance(model, CnnBiLstmAttention):
+        # --- CnnBiLstmAttention 预测路径 (非回归式，一次性预测) ---
+        X_current_scaled = scaler_X.transform(prediction_input_df[features].values)
+        X_input = torch.tensor(X_current_scaled.reshape(1, sequence_length, -1), dtype=torch.float32).to(DEVICE)
+        
+        with torch.no_grad():
+            # 一次性获取所有未来天数的预测
+            y_pred_scaled = model(X_input).cpu().numpy()
+
+        # 反归一化整个序列
+        y_pred_unscaled = scaler_y.inverse_transform(y_pred_scaled).flatten()
+        y_pred_unscaled = np.maximum(0, y_pred_unscaled) # 确保销量不为负
+
+        # 生成未来日期序列，其长度与模型实际输出的预测点数量一致
+        future_dates = pd.date_range(start=start_date_dt, periods=len(y_pred_unscaled))
+        
+        # 直接构建结果DataFrame
+        predictions_df = pd.DataFrame({
+            'date': future_dates,
+            'predicted_sales': y_pred_unscaled
+        })
+    else:
+        # --- 默认 PyTorch 模型预测路径 (自回归式) ---
+        all_predictions = []
+        current_sequence_df = prediction_input_df.copy()
+
+        for _ in range(future_days):
+            X_current_scaled = scaler_X.transform(current_sequence_df[features].values)
             X_input = torch.tensor(X_current_scaled.reshape(1, sequence_length, -1), dtype=torch.float32).to(DEVICE)
+            
             with torch.no_grad():
                 y_pred_scaled = model(X_input).cpu().numpy()
+            
             next_step_pred_scaled = y_pred_scaled[0, 0].reshape(1, -1)
-        next_step_pred_unscaled = float(max(0, scaler_y.inverse_transform(next_step_pred_scaled)[0][0]))
+            next_step_pred_unscaled = float(max(0, scaler_y.inverse_transform(next_step_pred_scaled)[0][0]))
 
-        next_date = current_sequence_df['date'].iloc[-1] + timedelta(days=1)
-        all_predictions.append({'date': next_date, 'predicted_sales': next_step_pred_unscaled})
+            next_date = current_sequence_df['date'].iloc[-1] + timedelta(days=1)
+            all_predictions.append({'date': next_date, 'predicted_sales': next_step_pred_unscaled})
 
-        new_row = {'date': next_date, 'sales': next_step_pred_unscaled, 'weekday': next_date.weekday(), 'month': next_date.month, 'is_holiday': 0, 'is_weekend': 1 if next_date.weekday() >= 5 else 0, 'is_promotion': 0, 'temperature': current_sequence_df['temperature'].iloc[-1]}
-        new_row_df = pd.DataFrame([new_row])
-        current_sequence_df = pd.concat([current_sequence_df.iloc[1:], new_row_df], ignore_index=True)
+            new_row = {'date': next_date, 'sales': next_step_pred_unscaled, 'weekday': next_date.weekday(), 'month': next_date.month, 'is_holiday': 0, 'is_weekend': 1 if next_date.weekday() >= 5 else 0, 'is_promotion': 0, 'temperature': current_sequence_df['temperature'].iloc[-1]}
+            new_row_df = pd.DataFrame([new_row])
+            current_sequence_df = pd.concat([current_sequence_df.iloc[1:], new_row_df], ignore_index=True)
 
-    predictions_df = pd.DataFrame(all_predictions)
+        predictions_df = pd.DataFrame(all_predictions)
     return predictions_df, history_for_chart_df, prediction_input_df
 
 # 注册默认的PyTorch预测器
@@ -96,19 +136,48 @@ def load_model_and_predict(model_path: str, product_id: str, model_type: str, st
         if not os.path.exists(model_path):
             raise FileNotFoundError(f"模型文件 {model_path} 不存在")
 
-        # --- 数据加载部分保持不变 ---
-        from utils.multi_store_data_utils import aggregate_multi_store_data
+        # --- 数据加载重构 ---
+        # 统一使用新的数据加载器，确保与训练时的数据源和处理逻辑完全一致
+        print("正在使用新的统一数据加载器进行预测...")
+        full_df = load_new_data()
+
         if training_mode == 'store' and store_id:
-            from utils.multi_store_data_utils import load_multi_store_data
-            store_df_for_name = load_multi_store_data(store_id=store_id)
-            product_name = store_df_for_name['store_name'].iloc[0] if not store_df_for_name.empty else f"店铺 {store_id}"
-            product_df = aggregate_multi_store_data(store_id=store_id, aggregation_method='sum', file_path=DEFAULT_DATA_PATH)
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+            # 判断是为单个产品预测还是为整个店铺聚合预测
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
+                product_df = store_df[store_df['product_id'] == product_id].copy()
+                product_name = f"店铺 {store_id} - 产品 {product_id}"
+            else:
+                # 为整个店铺的聚合销售额进行预测
+                product_df = store_df.groupby('date').agg({
+                    'sales': 'sum',
+                    'weekday': 'first', 'month': 'first', 'is_holiday': 'first',
+                    'is_weekend': 'first', 'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                product_name = f"店铺 {store_id} (所有药品聚合)"
         elif training_mode == 'global':
-            product_df = aggregate_multi_store_data(aggregation_method='sum', file_path=DEFAULT_DATA_PATH)
-            product_name = "全局销售数据"
-        else:
-            product_df = aggregate_multi_store_data(product_id=product_id, aggregation_method='sum', file_path=DEFAULT_DATA_PATH)
-            product_name = product_df['product_name'].iloc[0] if not product_df.empty else product_id
+            # 修正全局预测的数据加载逻辑
+            if product_id and product_id not in ['unknown', 'all_products']:
+                # 如果提供了具体产品ID（虽然全局模式下不常见，但应兼容），则聚合该产品的跨店数据
+                product_df = full_df[full_df['product_id'] == product_id].copy()
+                product_name = f"全局聚合 - 产品 {product_id}"
+            else:
+                # 如果是“所有药品”的全局预测，则聚合所有数据
+                product_df = full_df.copy()
+                product_name = "全局聚合 (所有药品)"
+
+            product_df = product_df.groupby('date').agg({
+                'sales': 'sum', # 默认使用sum，未来可配置
+                'weekday': 'first', 'month': 'first', 'is_holiday': 'first',
+                'is_weekend': 'first', 'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+        else: # 默认 'product' 模式
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            # 兼容性处理：新数据可能没有 product_name 列
+            if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+                product_name = product_df['product_name'].iloc[0]
+            else:
+                product_name = f"Product {product_id}"
 
         if product_df.empty:
             raise ValueError(f"产品 {product_id} 或店铺 {store_id} 没有销售数据")
diff --git a/server/trainers/cnn_bilstm_attention_trainer.py b/server/trainers/cnn_bilstm_attention_trainer.py
index c8d4147..2062dc3 100644
--- a/server/trainers/cnn_bilstm_attention_trainer.py
+++ b/server/trainers/cnn_bilstm_attention_trainer.py
@@ -13,6 +13,7 @@ from models.model_registry import register_trainer
 from utils.model_manager import model_manager
 from analysis.metrics import evaluate_model
 from utils.data_utils import create_dataset
+from utils.new_data_loader import load_new_data
 from sklearn.preprocessing import MinMaxScaler
 from utils.visualization import plot_loss_curve # 导入绘图函数
 
@@ -26,9 +27,38 @@ def train_with_cnn_bilstm_attention(product_id, model_identifier, product_df, st
     print(f"🚀 CNN-BiLSTM-Attention 训练器启动: model_identifier='{model_identifier}'")
     start_time = time.time()
 
-    # --- 1. 数据准备 ---
+    # --- 1. 数据加载与筛选重构 ---
+    if product_df is None:
+        print("正在使用新的统一数据加载器...")
+        full_df = load_new_data()
+
+        if training_mode == 'store' and store_id:
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
+                product_df = store_df[store_df['product_id'] == product_id].copy()
+            else:
+                product_df = store_df.groupby('date').agg({
+                    'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                    'is_holiday': 'first', 'is_weekend': 'first',
+                    'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                product_df.fillna(0, inplace=True)
+        elif training_mode == 'global':
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            product_df = product_df.groupby('date').agg({
+                'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                'is_holiday': 'first', 'is_weekend': 'first',
+                'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            product_df.fillna(0, inplace=True)
+        else: # 默认 'product' 模式
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+
+    # --- 2. 数据准备 ---
     features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
-    product_name = product_df['product_name'].iloc[0] if 'product_name' in product_df.columns else model_identifier
+    product_name = f"产品 {product_id}"
+    if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+        product_name = product_df['product_name'].iloc[0]
     
     X = product_df[features].values
     y = product_df[['sales']].values
diff --git a/server/trainers/kan_trainer.py b/server/trainers/kan_trainer.py
index 0d71e7d..aa09d33 100644
--- a/server/trainers/kan_trainer.py
+++ b/server/trainers/kan_trainer.py
@@ -17,6 +17,7 @@ from tqdm import tqdm
 from models.kan_model import KANForecaster
 from models.optimized_kan_forecaster import OptimizedKANForecaster
 from utils.data_utils import create_dataset, PharmacyDataset
+from utils.new_data_loader import load_new_data
 from utils.visualization import plot_loss_curve
 from analysis.metrics import evaluate_model
 from core.config import DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
@@ -35,45 +36,47 @@ def train_product_model_with_kan(product_id, model_identifier, product_df=None,
     model: 训练好的模型
     metrics: 模型评估指标
     """
-    # 如果没有传入product_df，则根据训练模式加载数据
-    if product_df is None:
-        from utils.multi_store_data_utils import load_multi_store_data, get_store_product_sales_data, aggregate_multi_store_data
-        
-        try:
-            if training_mode == 'store' and store_id:
-                # 加载特定店铺的数据
-                product_df = get_store_product_sales_data(
-                    store_id,
-                    product_id,
-                    'pharmacy_sales_multi_store.csv'
-                )
-                training_scope = f"店铺 {store_id}"
-            elif training_mode == 'global':
-                # 聚合所有店铺的数据
-                product_df = aggregate_multi_store_data(
-                    product_id,
-                    aggregation_method=aggregation_method,
-                    file_path='pharmacy_sales_multi_store.csv'
-                )
-                training_scope = f"全局聚合({aggregation_method})"
-            else:
-                # 默认：加载所有店铺的产品数据
-                product_df = load_multi_store_data('pharmacy_sales_multi_store.csv', product_id=product_id)
-                training_scope = "所有店铺"
-        except Exception as e:
-            print(f"多店铺数据加载失败: {e}")
-            # 后备方案：尝试原始数据
-            df = pd.read_excel('pharmacy_sales.xlsx')
-            product_df = df[df['product_id'] == product_id].sort_values('date')
-            training_scope = "原始数据"
-    else:
-        # 如果传入了product_df，直接使用
-        if training_mode == 'store' and store_id:
-            training_scope = f"店铺 {store_id}"
-        elif training_mode == 'global':
-            training_scope = f"全局聚合({aggregation_method})"
+    # --- 数据加载与筛选重构 ---
+    # 统一使用新的数据加载器，替换掉所有旧的、分散的加载逻辑
+    print("正在使用新的统一数据加载器...")
+    full_df = load_new_data() # 加载完整的、适配后的新数据
+
+    if training_mode == 'store' and store_id:
+        store_df = full_df[full_df['store_id'] == store_id].copy()
+        if product_id and product_id != 'unknown' and product_id != 'all_products':
+            product_df = store_df[store_df['product_id'] == product_id].copy()
+            training_scope = f"店铺 {store_id} - 产品 {product_id}"
         else:
-            training_scope = "所有店铺"
+            product_df = store_df.groupby('date').agg({
+                'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                'is_holiday': 'first', 'is_weekend': 'first',
+                'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            training_scope = f"店铺 {store_id} (所有药品聚合)"
+            
+            # 数据清洗：使用0填充聚合后可能产生的NaN值
+            product_df.fillna(0, inplace=True)
+    elif training_mode == 'global':
+        # 筛选特定产品在所有店铺的聚合数据
+        # 注意：新数据已经是按 (store_id, product_id, date) 展开的，聚合逻辑可能需要重新审视
+        # 此处暂时只筛选产品ID
+        product_df = full_df[full_df['product_id'] == product_id].copy()
+        # 按日期对同一产品在不同店铺的销售额求和
+        product_df = product_df.groupby('date').agg({
+            'sales': 'sum',
+            # 保留其他需要的特征，例如取第一个非空值或平均值
+            'weekday': 'first',
+            'month': 'first',
+            'is_holiday': 'first',
+            'is_weekend': 'first',
+            'is_promotion': 'first',
+            'temperature': 'mean'
+        }).reset_index()
+        training_scope = f"全局聚合({aggregation_method})"
+    else: # 默认 'product' 模式
+        # 筛选特定产品的数据（可能跨越多个店铺，但此处不聚合）
+        product_df = full_df[full_df['product_id'] == product_id].copy()
+        training_scope = f"所有店铺中的产品 {product_id}"
     
     if product_df.empty:
         raise ValueError(f"产品 {product_id} 没有可用的销售数据")
@@ -95,7 +98,11 @@ def train_product_model_with_kan(product_id, model_identifier, product_df=None,
         raise ValueError(error_msg)
     
     product_df = product_df.sort_values('date')
-    product_name = product_df['product_name'].iloc[0]
+    # 兼容性处理：新数据可能没有 product_name 列
+    if 'product_name' in product_df.columns:
+        product_name = product_df['product_name'].iloc[0]
+    else:
+        product_name = f"Product {product_id}" # 使用 product_id 作为备用名称
     
     model_type = "优化版KAN" if use_optimized else "KAN"
     print(f"使用{model_type}模型训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型")
diff --git a/server/trainers/mlstm_trainer.py b/server/trainers/mlstm_trainer.py
index d3d0bf1..a4460f2 100644
--- a/server/trainers/mlstm_trainer.py
+++ b/server/trainers/mlstm_trainer.py
@@ -16,7 +16,8 @@ from tqdm import tqdm
 
 from models.mlstm_model import MLSTMTransformer as MatrixLSTM
 from utils.data_utils import create_dataset, PharmacyDataset
-from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
+# from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
+from utils.new_data_loader import load_new_data
 from utils.visualization import plot_loss_curve
 from analysis.metrics import evaluate_model
 from core.config import (
@@ -124,13 +125,46 @@ def train_product_model_with_mlstm(
         except Exception as e:
             print(f"[mLSTM] 任务 {task_id}: 进度管理器初始化失败: {e}", flush=True)
 
-    # 数据现在由调用方传入，不再在此处加载
-    if training_mode == 'store' and store_id:
-        training_scope = f"店铺 {store_id}"
-    elif training_mode == 'global':
-        training_scope = f"全局聚合({aggregation_method})"
+    # --- 数据加载与筛选重构 ---
+    # 统一使用新的数据加载器
+    if product_df is None:
+        print("正在使用新的统一数据加载器...")
+        full_df = load_new_data()
+
+        if training_mode == 'store' and store_id:
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
+                product_df = store_df[store_df['product_id'] == product_id].copy()
+                training_scope = f"店铺 {store_id} - 产品 {product_id}"
+            else:
+                product_df = store_df.groupby('date').agg({
+                    'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                    'is_holiday': 'first', 'is_weekend': 'first',
+                    'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                training_scope = f"店铺 {store_id} (所有药品聚合)"
+
+                # 数据清洗：使用0填充聚合后可能产生的NaN值
+                product_df.fillna(0, inplace=True)
+        elif training_mode == 'global':
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            product_df = product_df.groupby('date').agg({
+                'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                'is_holiday': 'first', 'is_weekend': 'first',
+                'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            training_scope = f"全局聚合({aggregation_method})"
+        else: # 默认 'product' 模式
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            training_scope = f"所有店铺中的产品 {product_id}"
     else:
-        training_scope = "所有店铺"
+        # 如果传入了product_df，直接使用
+        if training_mode == 'store' and store_id:
+            training_scope = f"店铺 {store_id}"
+        elif training_mode == 'global':
+            training_scope = f"全局聚合({aggregation_method})"
+        else:
+            training_scope = "所有店铺"
     
     # 数据量检查
     min_required_samples = sequence_length + forecast_horizon
@@ -149,7 +183,11 @@ def train_product_model_with_mlstm(
         emit_progress(f"训练失败：数据不足 ({len(product_df)}/{min_required_samples} 天)")
         raise ValueError(error_msg)
     
-    product_name = product_df['product_name'].iloc[0]
+    # 兼容性处理：新数据可能没有 product_name 列
+    if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+        product_name = product_df['product_name'].iloc[0]
+    else:
+        product_name = f"产品 {product_id}"
     
     print(f"[mLSTM] 使用mLSTM模型训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型", flush=True)
     print(f"[mLSTM] 训练范围: {training_scope}", flush=True)
diff --git a/server/trainers/tcn_trainer.py b/server/trainers/tcn_trainer.py
index 34f10ba..1642936 100644
--- a/server/trainers/tcn_trainer.py
+++ b/server/trainers/tcn_trainer.py
@@ -16,6 +16,7 @@ from tqdm import tqdm
 
 from models.tcn_model import TCNForecaster
 from utils.data_utils import create_dataset, PharmacyDataset
+from utils.new_data_loader import load_new_data
 from utils.visualization import plot_loss_curve
 from analysis.metrics import evaluate_model
 from core.config import DEVICE, DEFAULT_MODEL_DIR, LOOK_BACK, FORECAST_HORIZON
@@ -58,15 +59,44 @@ def train_product_model_with_tcn(
     
     emit_progress(f"开始训练 TCN 模型")
     
+    # --- 数据加载与筛选重构 ---
     if product_df is None:
-        from utils.multi_store_data_utils import aggregate_multi_store_data
-        product_df = aggregate_multi_store_data(
-            product_id=product_id,
-            aggregation_method=aggregation_method
-        )
-        training_scope = f"全局聚合({aggregation_method})"
+        print("正在使用新的统一数据加载器...")
+        full_df = load_new_data()
+
+        if training_mode == 'store' and store_id:
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
+                product_df = store_df[store_df['product_id'] == product_id].copy()
+                training_scope = f"店铺 {store_id} - 产品 {product_id}"
+            else:
+                product_df = store_df.groupby('date').agg({
+                    'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                    'is_holiday': 'first', 'is_weekend': 'first',
+                    'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                training_scope = f"店铺 {store_id} (所有药品聚合)"
+                product_df.fillna(0, inplace=True)
+        elif training_mode == 'global':
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            product_df = product_df.groupby('date').agg({
+                'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                'is_holiday': 'first', 'is_weekend': 'first',
+                'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            training_scope = f"全局聚合({aggregation_method})"
+            product_df.fillna(0, inplace=True)
+        else: # 默认 'product' 模式
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            training_scope = f"所有店铺中的产品 {product_id}"
     else:
-        training_scope = "所有店铺"
+        # 如果传入了product_df，直接使用
+        if training_mode == 'store' and store_id:
+            training_scope = f"店铺 {store_id}"
+        elif training_mode == 'global':
+            training_scope = f"全局聚合({aggregation_method})"
+        else:
+            training_scope = "所有店铺"
     
     if product_df.empty:
         raise ValueError(f"产品 {product_id} 没有可用的销售数据")
@@ -84,7 +114,11 @@ def train_product_model_with_tcn(
         raise ValueError(error_msg)
     
     product_df = product_df.sort_values('date')
-    product_name = product_df['product_name'].iloc[0]
+    # 兼容性处理：新数据可能没有 product_name 列
+    if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+        product_name = product_df['product_name'].iloc[0]
+    else:
+        product_name = f"产品 {product_id}"
     
     print(f"使用TCN模型训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型")
     print(f"训练范围: {training_scope}")
diff --git a/server/trainers/transformer_trainer.py b/server/trainers/transformer_trainer.py
index 06d6af3..340329d 100644
--- a/server/trainers/transformer_trainer.py
+++ b/server/trainers/transformer_trainer.py
@@ -17,7 +17,8 @@ from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
 
 from models.transformer_model import TimeSeriesTransformer
 from utils.data_utils import create_dataset, PharmacyDataset
-from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
+# from utils.multi_store_data_utils import get_store_product_sales_data, aggregate_multi_store_data
+from utils.new_data_loader import load_new_data
 from utils.visualization import plot_loss_curve
 from analysis.metrics import evaluate_model
 from core.config import (
@@ -81,15 +82,45 @@ def train_product_model_with_transformer(
             def finish_training(self, *args, **kwargs): pass
         progress_manager = DummyProgressManager()
     
+    # --- 数据加载与筛选重构 ---
     if product_df is None:
-        from utils.multi_store_data_utils import aggregate_multi_store_data
-        product_df = aggregate_multi_store_data(
-            product_id=product_id,
-            aggregation_method=aggregation_method
-        )
-        training_scope = f"全局聚合({aggregation_method})"
+        print("正在使用新的统一数据加载器...")
+        full_df = load_new_data()
+
+        if training_mode == 'store' and store_id:
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
+                product_df = store_df[store_df['product_id'] == product_id].copy()
+                training_scope = f"店铺 {store_id} - 产品 {product_id}"
+            else:
+                product_df = store_df.groupby('date').agg({
+                    'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                    'is_holiday': 'first', 'is_weekend': 'first',
+                    'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                training_scope = f"店铺 {store_id} (所有药品聚合)"
+
+                # 数据清洗：使用0填充聚合后可能产生的NaN值
+                product_df.fillna(0, inplace=True)
+        elif training_mode == 'global':
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            product_df = product_df.groupby('date').agg({
+                'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                'is_holiday': 'first', 'is_weekend': 'first',
+                'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            training_scope = f"全局聚合({aggregation_method})"
+        else: # 默认 'product' 模式
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            training_scope = f"所有店铺中的产品 {product_id}"
     else:
-        training_scope = "所有店铺"
+        # 如果传入了product_df，直接使用
+        if training_mode == 'store' and store_id:
+            training_scope = f"店铺 {store_id}"
+        elif training_mode == 'global':
+            training_scope = f"全局聚合({aggregation_method})"
+        else:
+            training_scope = "所有店铺"
     
     if product_df.empty:
         raise ValueError(f"产品 {product_id} 没有可用的销售数据")
@@ -106,7 +137,11 @@ def train_product_model_with_transformer(
         raise ValueError(error_msg)
     
     product_df = product_df.sort_values('date')
-    product_name = product_df['product_name'].iloc[0]
+    # 兼容性处理：新数据可能没有 product_name 列
+    if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+        product_name = product_df['product_name'].iloc[0]
+    else:
+        product_name = f"产品 {product_id}"
     
     print(f"[Transformer] 训练产品 '{product_name}' (ID: {product_id}) 的销售预测模型", flush=True)
     print(f"[Device] 使用设备: {DEVICE}", flush=True)
diff --git a/server/trainers/xgboost_trainer.py b/server/trainers/xgboost_trainer.py
index 425c3bf..9b0cb95 100644
--- a/server/trainers/xgboost_trainer.py
+++ b/server/trainers/xgboost_trainer.py
@@ -11,6 +11,7 @@ from xgboost.callback import EarlyStopping
 
 # 导入核心工具
 from utils.data_utils import create_dataset
+from utils.new_data_loader import load_new_data
 from analysis.metrics import evaluate_model
 from utils.model_manager import model_manager
 from models.model_registry import register_trainer
@@ -23,9 +24,32 @@ def train_product_model_with_xgboost(product_id, model_identifier, product_df, s
     """
     print(f"🚀 XGBoost训练器启动: model_identifier='{model_identifier}'")
     
-    # --- 1. 数据准备和验证 ---
-    if product_df.empty:
-        raise ValueError(f"产品 {product_id} 没有可用的销售数据")
+    # --- 1. 数据加载与筛选重构 ---
+    if product_df is None:
+        print("正在使用新的统一数据加载器...")
+        full_df = load_new_data()
+
+        if training_mode == 'store' and store_id:
+            store_df = full_df[full_df['store_id'] == store_id].copy()
+            if product_id and product_id != 'unknown' and product_id != 'all_products':
+                product_df = store_df[store_df['product_id'] == product_id].copy()
+            else:
+                product_df = store_df.groupby('date').agg({
+                    'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                    'is_holiday': 'first', 'is_weekend': 'first',
+                    'is_promotion': 'first', 'temperature': 'mean'
+                }).reset_index()
+                product_df.fillna(0, inplace=True)
+        elif training_mode == 'global':
+            product_df = full_df[full_df['product_id'] == product_id].copy()
+            product_df = product_df.groupby('date').agg({
+                'sales': 'sum', 'weekday': 'first', 'month': 'first',
+                'is_holiday': 'first', 'is_weekend': 'first',
+                'is_promotion': 'first', 'temperature': 'mean'
+            }).reset_index()
+            product_df.fillna(0, inplace=True)
+        else: # 默认 'product' 模式
+            product_df = full_df[full_df['product_id'] == product_id].copy()
 
     min_required_samples = sequence_length + forecast_horizon
     if len(product_df) < min_required_samples:
@@ -33,7 +57,9 @@ def train_product_model_with_xgboost(product_id, model_identifier, product_df, s
         raise ValueError(error_msg)
 
     product_df = product_df.sort_values('date')
-    product_name = product_df['product_name'].iloc[0] if 'product_name' in product_df.columns else model_identifier
+    product_name = f"产品 {product_id}"
+    if 'product_name' in product_df.columns and not product_df['product_name'].empty:
+        product_name = product_df['product_name'].iloc[0]
     
     # --- 2. 数据预处理和适配 ---
     features = ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
diff --git a/server/utils/multi_store_data_utils.py b/server/utils/multi_store_data_utils.py
deleted file mode 100644
index 9720821..0000000
--- a/server/utils/multi_store_data_utils.py
+++ /dev/null
@@ -1,424 +0,0 @@
-"""
-多店铺销售预测系统 - 数据处理工具函数
-支持多店铺数据的加载、过滤和处理
-"""
-
-import pandas as pd
-import numpy as np
-import os
-from datetime import datetime, timedelta
-from typing import Optional, List, Tuple, Dict, Any
-from core.config import DEFAULT_DATA_PATH
-
-def load_multi_store_data(file_path: str = None,
-                         store_id: Optional[str] = None,
-                         product_id: Optional[str] = None,
-                         start_date: Optional[str] = None,
-                         end_date: Optional[str] = None) -> pd.DataFrame:
-    """
-    加载多店铺销售数据，支持按店铺、产品、时间范围过滤
-    
-    参数:
-    file_path: 数据文件路径 (支持 .csv, .xlsx, .parquet)。如果为None，则使用config中定义的默认路径。
-    store_id: 店铺ID，为None时返回所有店铺数据
-    product_id: 产品ID，为None时返回所有产品数据
-    start_date: 开始日期 (YYYY-MM-DD)
-    end_date: 结束日期 (YYYY-MM-DD)
-    
-    返回:
-    DataFrame: 过滤后的销售数据
-    """
-    
-    # 如果未提供文件路径，则使用配置文件中的默认路径
-    if file_path is None:
-        file_path = DEFAULT_DATA_PATH
-
-    if not os.path.exists(file_path):
-        raise FileNotFoundError(f"数据文件不存在: {file_path}")
-
-    try:
-        if file_path.endswith('.csv'):
-            df = pd.read_csv(file_path)
-        elif file_path.endswith('.xlsx'):
-            df = pd.read_excel(file_path)
-        elif file_path.endswith('.parquet'):
-            df = pd.read_parquet(file_path)
-        else:
-            raise ValueError(f"不支持的文件格式: {file_path}")
-        
-        print(f"成功加载数据文件: {file_path}")
-    except Exception as e:
-        print(f"加载文件 {file_path} 失败: {e}")
-        raise
-    
-    # 按店铺过滤
-    if store_id:
-        df = df[df['store_id'] == store_id].copy()
-        print(f"按店铺过滤: {store_id}, 剩余记录数: {len(df)}")
-    
-    # 按产品过滤
-    if product_id:
-        df = df[df['product_id'] == product_id].copy()
-        print(f"按产品过滤: {product_id}, 剩余记录数: {len(df)}")
-    
-    # 标准化列名和数据类型
-    df = standardize_column_names(df)
-
-    # 在标准化之后进行时间范围过滤
-    if start_date:
-        try:
-            start_date_dt = pd.to_datetime(start_date)
-            # 确保比较是在datetime对象之间
-            if 'date' in df.columns:
-                df = df[df['date'] >= start_date_dt].copy()
-                print(f"开始日期过滤: {start_date_dt}, 剩余记录数: {len(df)}")
-        except (ValueError, TypeError):
-            print(f"警告: 无效的开始日期格式 '{start_date}'，已忽略。")
-
-    if end_date:
-        try:
-            end_date_dt = pd.to_datetime(end_date)
-            # 确保比较是在datetime对象之间
-            if 'date' in df.columns:
-                df = df[df['date'] <= end_date_dt].copy()
-                print(f"结束日期过滤: {end_date_dt}, 剩余记录数: {len(df)}")
-        except (ValueError, TypeError):
-            print(f"警告: 无效的结束日期格式 '{end_date}'，已忽略。")
-    
-    if len(df) == 0:
-        print("警告: 过滤后没有数据")
-    
-    return df
-
-def standardize_column_names(df: pd.DataFrame) -> pd.DataFrame:
-    """
-    标准化列名以匹配训练代码和API期望的格式
-    
-    参数:
-    df: 原始DataFrame
-    
-    返回:
-    DataFrame: 标准化列名后的DataFrame
-    """
-    df = df.copy()
-    
-    # 定义列名映射并强制重命名
-    rename_map = {
-        'sales_quantity': 'sales',  # 修复：匹配原始列名
-        'temperature_2m_mean': 'temperature', # 新增：处理温度列
-        'dayofweek': 'weekday' # 修复：匹配原始列名
-    }
-    df.rename(columns={k: v for k, v in rename_map.items() if k in df.columns}, inplace=True)
-    
-    # 确保date列是datetime类型
-    if 'date' in df.columns:
-        df['date'] = pd.to_datetime(df['date'], errors='coerce')
-        df.dropna(subset=['date'], inplace=True) # 移除无法解析的日期行
-    else:
-        # 如果没有date列，无法继续，返回空DataFrame
-        return pd.DataFrame()
-
-    # 计算 sales_amount
-    # 由于没有price列，sales_amount的计算逻辑需要调整或移除
-    # 这里我们注释掉它，因为原始数据中已有sales_amount
-    # if 'sales_amount' not in df.columns and 'sales' in df.columns and 'price' in df.columns:
-    #     # 先确保sales和price是数字
-    #     df['sales'] = pd.to_numeric(df['sales'], errors='coerce')
-    #     df['price'] = pd.to_numeric(df['price'], errors='coerce')
-    #     df['sales_amount'] = df['sales'] * df['price']
-
-    # 创建缺失的特征列
-    if 'weekday' not in df.columns:
-        df['weekday'] = df['date'].dt.dayofweek
-    
-    if 'month' not in df.columns:
-        df['month'] = df['date'].dt.month
-    
-    # 添加缺失的元数据列
-    meta_columns = {
-        'store_name': 'Unknown Store',
-        'store_location': 'Unknown Location',
-        'store_type': 'Unknown',
-        'product_name': 'Unknown Product',
-        'product_category': 'Unknown Category'
-    }
-    for col, default in meta_columns.items():
-        if col not in df.columns:
-            df[col] = default
-
-    # 添加缺失的布尔特征列
-    default_features = {
-        'is_holiday': False,
-        'is_weekend': None,
-        'is_promotion': False,
-        'temperature': 20.0
-    }
-    
-    for feature, default_value in default_features.items():
-        if feature not in df.columns:
-            if feature == 'is_weekend':
-                df['is_weekend'] = df['weekday'].isin([5, 6])
-            else:
-                df[feature] = default_value
-    
-    # 确保数值类型正确
-    numeric_columns = ['sales', 'sales_amount', 'weekday', 'month', 'temperature']
-    for col in numeric_columns:
-        if col in df.columns:
-            df[col] = pd.to_numeric(df[col], errors='coerce')
-    
-    # 确保布尔类型正确
-    boolean_columns = ['is_holiday', 'is_weekend', 'is_promotion']
-    for col in boolean_columns:
-        if col in df.columns:
-            df[col] = df[col].astype(bool)
-    
-    print(f"数据标准化完成，可用特征列: {[col for col in ['sales', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'] if col in df.columns]}")
-    
-    return df
-
-def get_available_stores(file_path: str = None) -> List[Dict[str, Any]]:
-    """
-    获取可用的店铺列表
-    
-    参数:
-    file_path: 数据文件路径
-    
-    返回:
-    List[Dict]: 店铺信息列表
-    """
-    try:
-        df = load_multi_store_data(file_path)
-        
-        if 'store_id' not in df.columns:
-            print("数据文件中缺少 'store_id' 列")
-            return []
-
-        # 智能地获取店铺信息，即使某些列缺失
-        store_info = []
-        
-        # 使用drop_duplicates获取唯一的店铺组合
-        stores_df = df.drop_duplicates(subset=['store_id'])
-
-        for _, row in stores_df.iterrows():
-            store_info.append({
-                'store_id': row['store_id'],
-                'store_name': row.get('store_name', f"店铺 {row['store_id']}"),
-                'location': row.get('store_location', '未知位置'),
-                'type': row.get('store_type', '标准'),
-                'opening_date': row.get('opening_date', '未知'),
-            })
-            
-        return store_info
-    except Exception as e:
-        print(f"获取店铺列表失败: {e}")
-        return []
-
-def get_available_products(file_path: str = None,
-                         store_id: Optional[str] = None) -> List[Dict[str, Any]]:
-    """
-    获取可用的产品列表
-    
-    参数:
-    file_path: 数据文件路径
-    store_id: 店铺ID，为None时返回所有产品
-    
-    返回:
-    List[Dict]: 产品信息列表
-    """
-    try:
-        df = load_multi_store_data(file_path, store_id=store_id)
-        
-        # 获取唯一产品信息
-        product_columns = ['product_id', 'product_name']
-        if 'product_category' in df.columns:
-            product_columns.append('product_category')
-        if 'unit_price' in df.columns:
-            product_columns.append('unit_price')
-        
-        products = df[product_columns].drop_duplicates()
-        
-        return products.to_dict('records')
-    except Exception as e:
-        print(f"获取产品列表失败: {e}")
-        return []
-
-def get_store_product_sales_data(store_id: str,
-                               product_id: str,
-                               file_path: str = None) -> pd.DataFrame:
-    """
-    获取特定店铺和产品的销售数据，用于模型训练
-    
-    参数:
-    file_path: 数据文件路径
-    store_id: 店铺ID
-    product_id: 产品ID
-    
-    返回:
-    DataFrame: 处理后的销售数据，包含模型需要的特征
-    """
-    # 加载数据
-    df = load_multi_store_data(file_path, store_id=store_id, product_id=product_id)
-    
-    if len(df) == 0:
-        raise ValueError(f"没有找到店铺 {store_id} 产品 {product_id} 的销售数据")
-    
-    # 确保数据按日期排序
-    df = df.sort_values('date').copy()
-    
-    # 数据标准化已在load_multi_store_data中完成
-    # 验证必要的列是否存在
-    required_columns = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
-    missing_columns = [col for col in required_columns if col not in df.columns]
-    
-    if missing_columns:
-        print(f"警告: 数据标准化后仍缺少列 {missing_columns}")
-        raise ValueError(f"无法获取完整的特征数据，缺少列: {missing_columns}")
-    
-    # 定义模型训练所需的所有列（特征 + 目标）
-    final_columns = [
-        'date', 'sales', 'product_id', 'product_name', 'store_id', 'store_name',
-        'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'
-    ]
-    
-    # 筛选出DataFrame中实际存在的列
-    existing_columns = [col for col in final_columns if col in df.columns]
-    
-    # 返回只包含这些必需列的DataFrame
-    return df[existing_columns]
-
-def aggregate_multi_store_data(product_id: Optional[str] = None,
-                              store_id: Optional[str] = None,
-                              aggregation_method: str = 'sum',
-                              file_path: str = None) -> pd.DataFrame:
-    """
-    聚合销售数据，可按产品（全局）或按店铺（所有产品）
-    
-    参数:
-    file_path: 数据文件路径
-    product_id: 产品ID (用于全局模型)
-    store_id: 店铺ID (用于店铺聚合模型)
-    aggregation_method: 聚合方法 ('sum', 'mean', 'median')
-    
-    返回:
-    DataFrame: 聚合后的销售数据
-    """
-    # 根据是全局聚合、店铺聚合还是真正全局聚合来加载数据
-    if store_id:
-        # 店铺聚合：加载该店铺的所有数据
-        df = load_multi_store_data(file_path, store_id=store_id)
-        if len(df) == 0:
-            raise ValueError(f"没有找到店铺 {store_id} 的销售数据")
-        grouping_entity = f"店铺 {store_id}"
-    elif product_id:
-        # 按产品聚合：加载该产品在所有店铺的数据
-        df = load_multi_store_data(file_path, product_id=product_id)
-        if len(df) == 0:
-            raise ValueError(f"没有找到产品 {product_id} 的销售数据")
-        grouping_entity = f"产品 {product_id}"
-    else:
-        # 真正全局聚合：加载所有数据
-        df = load_multi_store_data(file_path)
-        if len(df) == 0:
-            raise ValueError("数据文件为空，无法进行全局聚合")
-        grouping_entity = "所有产品"
-    
-    # 按日期聚合（使用标准化后的列名）
-    agg_dict = {}
-    if aggregation_method == 'sum':
-        agg_dict = {
-            'sales': 'sum',           # 标准化后的销量列
-            'sales_amount': 'sum',
-            'price': 'mean'           # 标准化后的价格列，取平均值
-        }
-    elif aggregation_method == 'mean':
-        agg_dict = {
-            'sales': 'mean',
-            'sales_amount': 'mean',
-            'price': 'mean'
-        }
-    elif aggregation_method == 'median':
-        agg_dict = {
-            'sales': 'median',
-            'sales_amount': 'median',
-            'price': 'median'
-        }
-    
-    # 确保列名存在
-    available_cols = df.columns.tolist()
-    agg_dict = {k: v for k, v in agg_dict.items() if k in available_cols}
-    
-    # 聚合数据
-    aggregated_df = df.groupby('date').agg(agg_dict).reset_index()
-    
-    # 获取产品信息（取第一个店铺的信息）
-    product_info = df[['product_id', 'product_name', 'product_category']].iloc[0]
-    for col, val in product_info.items():
-        aggregated_df[col] = val
-    
-    # 添加店铺信息标识为全局
-    aggregated_df['store_id'] = 'GLOBAL'
-    aggregated_df['store_name'] = f'全部店铺-{aggregation_method.upper()}'
-    aggregated_df['store_location'] = '全局聚合'
-    aggregated_df['store_type'] = 'global'
-    
-    # 对聚合后的数据进行标准化（添加缺失的特征列）
-    aggregated_df = aggregated_df.sort_values('date').copy()
-    aggregated_df = standardize_column_names(aggregated_df)
-    
-    # 定义模型训练所需的所有列（特征 + 目标）
-    final_columns = [
-        'date', 'sales', 'product_id', 'product_name', 'store_id', 'store_name',
-        'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature'
-    ]
-    
-    # 筛选出DataFrame中实际存在的列
-    existing_columns = [col for col in final_columns if col in aggregated_df.columns]
-    
-    # 返回只包含这些必需列的DataFrame
-    return aggregated_df[existing_columns]
-
-def get_sales_statistics(file_path: str = None,
-                        store_id: Optional[str] = None,
-                        product_id: Optional[str] = None) -> Dict[str, Any]:
-    """
-    获取销售数据统计信息
-    
-    参数:
-    file_path: 数据文件路径
-    store_id: 店铺ID
-    product_id: 产品ID
-    
-    返回:
-    Dict: 统计信息
-    """
-    try:
-        df = load_multi_store_data(file_path, store_id=store_id, product_id=product_id)
-        
-        if len(df) == 0:
-            return {'error': '没有数据'}
-        
-        stats = {
-            'total_records': len(df),
-            'date_range': {
-                'start': df['date'].min().strftime('%Y-%m-%d'),
-                'end': df['date'].max().strftime('%Y-%m-%d')
-            },
-            'stores': df['store_id'].nunique(),
-            'products': df['product_id'].nunique(),
-            'total_sales_amount': float(df['sales_amount'].sum()) if 'sales_amount' in df.columns else 0,
-            'total_quantity': int(df['quantity_sold'].sum()) if 'quantity_sold' in df.columns else 0,
-            'avg_daily_sales': float(df.groupby('date')['quantity_sold'].sum().mean()) if 'quantity_sold' in df.columns else 0
-        }
-        
-        return stats
-        
-    except Exception as e:
-        return {'error': str(e)}
-
-# 向后兼容的函数
-def load_data(file_path=None, store_id=None):
-    """
-    向后兼容的数据加载函数
-    """
-    return load_multi_store_data(file_path, store_id=store_id)
diff --git a/server/utils/new_data_loader.py b/server/utils/new_data_loader.py
new file mode 100644
index 0000000..f6fee59
--- /dev/null
+++ b/server/utils/new_data_loader.py
@@ -0,0 +1,86 @@
+import pandas as pd
+import os
+
+def load_new_data(file_path='data/old_5shops_50skus.parquet'):
+    """
+    加载并适配新的Parquet数据文件，为现有系统提供兼容的数据格式。
+
+    核心原则:
+    1. 保证新数据的完整性，不丢弃任何原始特征。
+    2. 优先适配新数据，通过重命名和创建代理列来兼容旧代码。
+
+    参数:
+    file_path (str): 新数据文件的路径。
+
+    返回:
+    pandas.DataFrame: 经过适配处理的、包含所有原始特征的DataFrame。
+    """
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"数据文件不存在: {file_path}")
+
+    print(f"正在从 {file_path} 加载新数据...")
+    df = pd.read_parquet(file_path)
+    print("数据加载完成，开始进行适配处理...")
+
+    # 创建一个副本以进行修改，保留原始df的纯净
+    df_adapted = df.copy()
+
+    # --- 1. 列名映射 (适配旧代码的命名习惯) ---
+    # 步骤1.1: 安全地删除新数据中多余的 'date' 列，以 'kdrq' 为准
+    if 'date' in df_adapted.columns and 'kdrq' in df.columns:
+        df_adapted.drop(columns=['date'], inplace=True)
+        print("已删除新数据中多余的 'date' 列，以 'kdrq' 为准。")
+
+    rename_map = {
+        'subbh': 'store_id',
+        'hh': 'product_id',
+        'kdrq': 'date', # 现在可以安全地将 kdrq 重命名为 date
+        'net_sales_quantity': 'sales', # 将目标变量映射为 'sales'
+        'temperature_2m_mean': 'temperature',
+        'day_of_week': 'weekday'
+    }
+    df_adapted.rename(columns=rename_map, inplace=True)
+    print(f"已完成列名映射: {list(rename_map.keys())} -> {list(rename_map.values())}")
+
+    # --- 2. 数据类型转换 ---
+    # 将 'date' 列转换为标准的datetime对象
+    df_adapted['date'] = pd.to_datetime(df_adapted['date'])
+    print("已将 'date' 列转换为 datetime 类型。")
+
+    # --- 3. 关键特征工程 (创建代理列) ---
+    # 现有模型依赖 'is_promotion' 和 'is_weekend' 特征。
+    # 'is_weekend' 在新数据中已存在，无需处理。
+    # 'is_promotion' 在新数据中不存在，创建一个默认值为0的代理列。
+    if 'is_promotion' not in df_adapted.columns:
+        df_adapted['is_promotion'] = 0
+        print("创建了代理列 'is_promotion' 并填充默认值 0。")
+    
+    # 确保 'month' 列存在，如果不存在则从日期中提取
+    if 'month' not in df_adapted.columns and 'date' in df_adapted.columns:
+        df_adapted['month'] = df_adapted['date'].dt.month
+        print("从 'date' 列中提取并创建了 'month' 列。")
+
+    print("数据适配处理完成。")
+    
+    # 返回包含所有列的适配后DataFrame
+    return df_adapted
+
+if __name__ == '__main__':
+    # 用于直接运行此脚本进行测试
+    print("--- 测试数据加载器 ---")
+    try:
+        adapted_df = load_new_data()
+        print("\n--- 适配后数据信息 ---")
+        adapted_df.info()
+        
+        print("\n--- 检查关键列 ---")
+        key_cols = [
+            'store_id', 'product_id', 'date', 'sales', 
+            'temperature', 'weekday', 'is_promotion', 'month'
+        ]
+        print(adapted_df[key_cols].head())
+        
+        print(f"\n测试成功！适配后的DataFrame包含 {len(adapted_df.columns)} 列。")
+        
+    except Exception as e:
+        print(f"\n测试失败: {e}")
\ No newline at end of file
diff --git a/server/utils/training_process_manager.py b/server/utils/training_process_manager.py
index d95cbcf..cc14738 100644
--- a/server/utils/training_process_manager.py
+++ b/server/utils/training_process_manager.py
@@ -45,6 +45,10 @@ class TrainingTask:
     training_mode: str
     store_id: Optional[str] = None
     epochs: int = 100
+    # 新增字段以支持自定义范围的全局训练
+    selected_stores: Optional[list] = None
+    selected_products: Optional[list] = None
+    aggregation_method: str = 'sum'
     status: str = "pending"  # pending, running, completed, failed
     start_time: Optional[str] = None
     end_time: Optional[str] = None
@@ -138,12 +142,16 @@ class TrainingWorker:
                         training_logger.error(f"进度回调失败: {e}")
                 
                 # 执行真正的训练，传递进度回调
+                # 升级：传递新增的自定义范围参数
                 metrics = predictor.train_model(
                     product_id=task.product_id,
                     model_type=task.model_type,
                     epochs=task.epochs,
                     store_id=task.store_id,
                     training_mode=task.training_mode,
+                    selected_stores=task.selected_stores,
+                    selected_products=task.selected_products,
+                    aggregation_method=task.aggregation_method,
                     socketio=None,  # 子进程中不能直接使用socketio
                     task_id=task.task_id,
                     progress_callback=progress_callback  # 传递进度回调函数
@@ -281,18 +289,25 @@ class TrainingProcessManager:
         
         self.logger.info("✅ 训练进程管理器已停止")
         
-    def submit_task(self, product_id: str, model_type: str, training_mode: str = "product", 
-                   store_id: str = None, epochs: int = 100, **kwargs) -> str:
+    def submit_task(self, product_id: str, model_type: str, training_mode: str = "product",
+                    store_id: str = None, epochs: int = 100,
+                    selected_stores: Optional[list] = None,
+                    selected_products: Optional[list] = None,
+                    aggregation_method: str = 'sum',
+                    **kwargs) -> str:
         """提交训练任务"""
         task_id = str(uuid.uuid4())
-        
+
         task = TrainingTask(
             task_id=task_id,
             product_id=product_id,
             model_type=model_type,
             training_mode=training_mode,
             store_id=store_id,
-            epochs=epochs
+            epochs=epochs,
+            selected_stores=selected_stores,
+            selected_products=selected_products,
+            aggregation_method=aggregation_method
         )
         
         with self.lock:
diff --git a/temp_data_analysis.py b/temp_data_analysis.py
new file mode 100644
index 0000000..59cba7b
--- /dev/null
+++ b/temp_data_analysis.py
@@ -0,0 +1,55 @@
+import pandas as pd
+import os
+
+def analyze_parquet_files():
+    """
+    分析两个Parquet数据文件的结构差异。
+    """
+    data_path = 'data'
+    current_data_file = os.path.join(data_path, 'timeseries_training_data_sample_10s50p.parquet')
+    new_data_file = os.path.join(data_path, 'old_5shops_50skus.parquet')
+
+    print("="*50)
+    print("数据文件差异分析报告")
+    print("="*50)
+
+    try:
+        # --- 分析当前数据文件 ---
+        print(f"\n--- 1. 分析当前数据: {current_data_file} ---\n")
+        if os.path.exists(current_data_file):
+            df_current = pd.read_parquet(current_data_file)
+            print("【列名和数据类型】:")
+            df_current.info(verbose=False)
+            print("\n【前5行样本数据】:")
+            print(df_current.head())
+            print(f"\n【总行数】: {len(df_current)}")
+            print(f"【唯一店铺数】: {df_current['store_id'].nunique()}")
+            print(f"【唯一商品数】: {df_current['product_id'].nunique()}")
+        else:
+            print(f"错误: 文件不存在 {current_data_file}")
+
+        print("\n" + "-"*40 + "\n")
+
+        # --- 分析新数据文件 ---
+        print(f"\n--- 2. 分析新数据: {new_data_file} ---\n")
+        if os.path.exists(new_data_file):
+            df_new = pd.read_parquet(new_data_file)
+            print("【列名和数据类型 (仅显示部分)】:")
+            df_new.info(verbose=True, max_cols=10, show_counts=True) # 显示更详细的信息
+            print("\n【所有列名列表】:")
+            print(df_new.columns.tolist())
+            print("\n【前5行样本数据 (部分列)】:")
+            # 选择一些关键列进行展示
+            display_cols = ['subbh', 'hh', 'kdrq', 'net_sales_quantity', 'is_weekend', 'sales_quantity_rolling_mean_7d', 'province', 'temperature_2m_mean', 'brand_encoded']
+            print(df_new[display_cols].head())
+            print(f"\n【总行数】: {len(df_new)}")
+            print(f"【唯一店铺数 (subbh)】: {df_new['subbh'].nunique()}")
+            print(f"【唯一商品数 (hh)】: {df_new['hh'].nunique()}")
+        else:
+            print(f"错误: 文件不存在 {new_data_file}")
+
+    except Exception as e:
+        print(f"\n分析过程中出现错误: {e}")
+
+if __name__ == '__main__':
+    analyze_parquet_files()