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\ No newline at end of file
diff --git a/saved_predictions/prediction_022a844c-7fd4-4bef-9543-68edc07ecb68.json b/saved_predictions/prediction_022a844c-7fd4-4bef-9543-68edc07ecb68.json
new file mode 100644
index 0000000..4306742
--- /dev/null
+++ b/saved_predictions/prediction_022a844c-7fd4-4bef-9543-68edc07ecb68.json
@@ -0,0 +1 @@
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"net_sales_quantity_rolling_sum_90d": 8.0, "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": "2020-09-17", "last_sale_date": "2023-02-11", "lifecycle_days": 877, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "播州区", "零售大类代码_encoded": 1.0, "零售中类代码_encoded": 5.0, "零售小类代码_encoded": 26.0, "商品ABC分类_encoded": 4.0, "商品手册代码_encoded": 20.0, "产地_encoded": 4.0, "brand_encoded": 667.0, "approval_type_encoded": 0.0, "DISTRICT_LAT": 27.535735, "DISTRICT_LON": 106.829081, "adcode": "520304", "district_name": "播州区", "area_sq_km": 2482.4, "poi_residential_count": 100.0, "poi_school_count": 17.0, "poi_mall_count": 5.0, "poi_finance_count": 18.0, "poi_company_count": 81.0, "business_areas": ["人民西路"], "latitude": 27.546425, "longitude": 106.83497, "temperature_2m_max": 6.3, "temperature_2m_min": 0.2, "temperature": 3.0, "is_promotion": 0}, {"store_id": "01010149", "product_id": "11034889", "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": 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": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "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": 1.33, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.33, "sales_quantity_rolling_sum_30d": 4.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 4.0, "sales_quantity_rolling_mean_90d": 1.33, "return_quantity_rolling_mean_90d": 0.0, "net_sales_quantity_rolling_mean_90d": 1.33, "sales_quantity_rolling_sum_90d": 8.0, "return_quantity_rolling_sum_90d": 0.0, "net_sales_quantity_rolling_sum_90d": 8.0, "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": "2020-09-17", "last_sale_date": "2023-02-11", "lifecycle_days": 877, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "播州区", "零售大类代码_encoded": 1.0, "零售中类代码_encoded": 5.0, "零售小类代码_encoded": 26.0, "商品ABC分类_encoded": 4.0, "商品手册代码_encoded": 20.0, "产地_encoded": 4.0, "brand_encoded": 667.0, "approval_type_encoded": 0.0, "DISTRICT_LAT": 27.535735, "DISTRICT_LON": 106.829081, "adcode": "520304", "district_name": "播州区", "area_sq_km": 2482.4, "poi_residential_count": 100.0, "poi_school_count": 17.0, "poi_mall_count": 5.0, "poi_finance_count": 18.0, "poi_company_count": 81.0, "business_areas": ["人民西路"], "latitude": 27.546425, "longitude": 106.83497, "temperature_2m_max": 6.4, "temperature_2m_min": -0.2, "temperature": 2.9, "is_promotion": 0}, {"store_id": "01010149", "product_id": "11034889", "date": "2021-12-31", "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": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "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": 1.33, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.33, "sales_quantity_rolling_sum_30d": 4.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 4.0, "sales_quantity_rolling_mean_90d": 1.33, "return_quantity_rolling_mean_90d": 0.0, "net_sales_quantity_rolling_mean_90d": 1.33, "sales_quantity_rolling_sum_90d": 8.0, "return_quantity_rolling_sum_90d": 0.0, "net_sales_quantity_rolling_sum_90d": 8.0, "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": "2020-09-17", "last_sale_date": "2023-02-11", "lifecycle_days": 877, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "播州区", "零售大类代码_encoded": 1.0, "零售中类代码_encoded": 5.0, "零售小类代码_encoded": 26.0, "商品ABC分类_encoded": 4.0, "商品手册代码_encoded": 20.0, "产地_encoded": 4.0, "brand_encoded": 667.0, "approval_type_encoded": 0.0, "DISTRICT_LAT": 27.535735, "DISTRICT_LON": 106.829081, "adcode": "520304", "district_name": "播州区", "area_sq_km": 2482.4, "poi_residential_count": 100.0, "poi_school_count": 17.0, "poi_mall_count": 5.0, "poi_finance_count": 18.0, "poi_company_count": 81.0, "business_areas": ["人民西路"], "latitude": 27.546425, "longitude": 106.83497, "temperature_2m_max": 5.4, "temperature_2m_min": 2.4, "temperature": 3.6, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.002366740278634629, "trend_type": "平稳", "r_squared": 0.989681982009832, "p_value": 2.4014781968987333e-22, "volatility": 0.5479219618825161, "volatility_level": "高"}, "statistics": {"mean": 0.028801187872886658, "median": 0.02668028697371483, "min": 0.004798881709575653, "max": 0.05773380398750305, "std": 0.015780803363858988, "q1": 0.01442805863916874, "q3": 0.04195976071059704}, "day_over_day": [-4.471279794551484, -8.30140123000233, -5.9695385331589055, -4.92435622002401, -4.789685263261104, -5.0540024464551285, -4.815713103428161, -10.756502483691424, -11.405919219383227, -8.029866292025966, -5.686949467920498, -5.354907577351652, -6.729075507271606, -7.991560026857336, -13.226990679975831, -18.419553750526237, -11.893148772121544, -10.45125174676762, -10.248249816478888, -14.276822411637921, -17.123826097392662, -37.817714027808954], "influencing_factors": {"product_id": "11034889", "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11034889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0.03个单位，最高日销量为0.06个单位，最低日销量为0.00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_0b270059-cc62-4512-910a-e42554a14705.json b/saved_predictions/prediction_0b270059-cc62-4512-910a-e42554a14705.json
new file mode 100644
index 0000000..cd0a043
--- /dev/null
+++ b/saved_predictions/prediction_0b270059-cc62-4512-910a-e42554a14705.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "kan", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "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": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_0d18b861-3d92-4459-a249-366874171050.json b/saved_predictions/prediction_0d18b861-3d92-4459-a249-366874171050.json
new file mode 100644
index 0000000..9aac546
--- /dev/null
+++ b/saved_predictions/prediction_0d18b861-3d92-4459-a249-366874171050.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_2e57a0ae-d09f-4c55-9e8d-9f07c3ab2987.json b/saved_predictions/prediction_2e57a0ae-d09f-4c55-9e8d-9f07c3ab2987.json
new file mode 100644
index 0000000..dfc0d52
--- /dev/null
+++ b/saved_predictions/prediction_2e57a0ae-d09f-4c55-9e8d-9f07c3ab2987.json
@@ -0,0 +1 @@
+{"product_id": "12018307", "product_name": "Product 12018307", "model_type": "transformer", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.0}, {"date": "2022-01-02", "predicted_sales": 0.0}, {"date": "2022-01-03", "predicted_sales": 0.0}, {"date": "2022-01-04", "predicted_sales": 0.0}, {"date": "2022-01-05", "predicted_sales": 0.0}, {"date": "2022-01-06", "predicted_sales": 0.0}, {"date": "2022-01-07", "predicted_sales": 0.0}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.0}, {"date": "2022-01-02", "predicted_sales": 0.0}, {"date": "2022-01-03", "predicted_sales": 0.0}, {"date": "2022-01-04", "predicted_sales": 0.0}, {"date": "2022-01-05", "predicted_sales": 0.0}, {"date": "2022-01-06", "predicted_sales": 0.0}, {"date": "2022-01-07", "predicted_sales": 0.0}], "history_data": [{"store_id": "01010108", "product_id": "12018307", "date": "2021-12-02", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, 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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_4d8bd747-d3b7-4ebd-bd1a-066b8023cf66.json b/saved_predictions/prediction_4d8bd747-d3b7-4ebd-bd1a-066b8023cf66.json
new file mode 100644
index 0000000..396712a
--- /dev/null
+++ b/saved_predictions/prediction_4d8bd747-d3b7-4ebd-bd1a-066b8023cf66.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "kan", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "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": 
\ No newline at end of file
diff --git a/saved_predictions/prediction_66b2f258-bd18-4274-8116-29c79e777524.json b/saved_predictions/prediction_66b2f258-bd18-4274-8116-29c79e777524.json
new file mode 100644
index 0000000..396712a
--- /dev/null
+++ b/saved_predictions/prediction_66b2f258-bd18-4274-8116-29c79e777524.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "kan", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "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": 
\ No newline at end of file
diff --git a/saved_predictions/prediction_75863964-89ef-4540-811f-ce0a2cf43aba.json b/saved_predictions/prediction_75863964-89ef-4540-811f-ce0a2cf43aba.json
new file mode 100644
index 0000000..dfc0d52
--- /dev/null
+++ b/saved_predictions/prediction_75863964-89ef-4540-811f-ce0a2cf43aba.json
@@ -0,0 +1 @@
+{"product_id": "12018307", "product_name": "Product 12018307", "model_type": "transformer", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.0}, {"date": "2022-01-02", "predicted_sales": 0.0}, {"date": "2022-01-03", "predicted_sales": 0.0}, {"date": "2022-01-04", "predicted_sales": 0.0}, {"date": "2022-01-05", "predicted_sales": 0.0}, {"date": "2022-01-06", "predicted_sales": 0.0}, {"date": "2022-01-07", "predicted_sales": 0.0}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.0}, {"date": "2022-01-02", "predicted_sales": 0.0}, {"date": "2022-01-03", "predicted_sales": 0.0}, {"date": "2022-01-04", "predicted_sales": 0.0}, {"date": "2022-01-05", "predicted_sales": 0.0}, {"date": "2022-01-06", "predicted_sales": 0.0}, {"date": "2022-01-07", "predicted_sales": 0.0}], "history_data": [{"store_id": "01010108", "product_id": "12018307", "date": "2021-12-02", "sales_quantity": 0.0, "return_quantity": 0.0, "sales": 0.0, "gross_profit_total": 0.0, 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\ No newline at end of file
diff --git a/saved_predictions/prediction_79e8f86f-4760-447e-9aab-37dc823b3b65.json b/saved_predictions/prediction_79e8f86f-4760-447e-9aab-37dc823b3b65.json
new file mode 100644
index 0000000..b61d4e5
--- /dev/null
+++ b/saved_predictions/prediction_79e8f86f-4760-447e-9aab-37dc823b3b65.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/saved_predictions/prediction_89ef0d6b-f018-4f4c-8d86-2318d24b9cb0.json b/saved_predictions/prediction_89ef0d6b-f018-4f4c-8d86-2318d24b9cb0.json
new file mode 100644
index 0000000..c5bdc11
--- /dev/null
+++ b/saved_predictions/prediction_89ef0d6b-f018-4f4c-8d86-2318d24b9cb0.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": "2025-05-29", "predicted_sales": 0.0}, {"date": "2025-05-30", "predicted_sales": 0.0}, {"date": "2025-05-31", "predicted_sales": 0.0}, {"date": "2025-06-01", "predicted_sales": 0.0}, {"date": "2025-06-02", "predicted_sales": 0.0}, {"date": "2025-06-03", "predicted_sales": 0.0}, {"date": "2025-06-04", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 0.0}, {"date": "2025-05-30", "predicted_sales": 0.0}, {"date": "2025-05-31", "predicted_sales": 0.0}, {"date": "2025-06-01", "predicted_sales": 0.0}, {"date": "2025-06-02", "predicted_sales": 0.0}, {"date": "2025-06-03", "predicted_sales": 0.0}, {"date": "2025-06-04", "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", 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"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_91cbb6bc-52c7-4bbe-8abb-69c82eb5b565.json b/saved_predictions/prediction_91cbb6bc-52c7-4bbe-8abb-69c82eb5b565.json
new file mode 100644
index 0000000..9aac546
--- /dev/null
+++ b/saved_predictions/prediction_91cbb6bc-52c7-4bbe-8abb-69c82eb5b565.json
@@ -0,0 +1 @@
+{"product_id": "11055747", "product_name": "Product 11055747", "model_type": "mlstm", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.09991277754306793}, {"date": "2022-01-02", "predicted_sales": 0.09991367161273956}, {"date": "2022-01-03", "predicted_sales": 0.09991510212421417}, {"date": "2022-01-04", "predicted_sales": 0.09991492331027985}, {"date": "2022-01-05", "predicted_sales": 0.09991545975208282}, {"date": "2022-01-06", "predicted_sales": 0.09991492331027985}, {"date": "2022-01-07", "predicted_sales": 0.09991340339183807}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.09991277754306793}, {"date": "2022-01-02", "predicted_sales": 0.09991367161273956}, {"date": "2022-01-03", "predicted_sales": 0.09991510212421417}, {"date": "2022-01-04", "predicted_sales": 0.09991492331027985}, {"date": "2022-01-05", "predicted_sales": 0.09991545975208282}, {"date": "2022-01-06", "predicted_sales": 0.09991492331027985}, {"date": "2022-01-07", "predicted_sales": 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\ No newline at end of file
diff --git a/saved_predictions/prediction_a66b9aa3-ad16-4ad1-96d1-8ae1063fad69.json b/saved_predictions/prediction_a66b9aa3-ad16-4ad1-96d1-8ae1063fad69.json
new file mode 100644
index 0000000..cdd8699
--- /dev/null
+++ b/saved_predictions/prediction_a66b9aa3-ad16-4ad1-96d1-8ae1063fad69.json
@@ -0,0 +1 @@
+{"product_id": "11034889", "product_name": "Product 11034889", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.05773380398750305}, {"date": "2022-01-02", "predicted_sales": 0.05515236407518387}, {"date": "2022-01-03", "predicted_sales": 0.05057394504547119}, {"date": "2022-01-04", "predicted_sales": 0.04755491390824318}, {"date": "2022-01-05", "predicted_sales": 0.04521314054727554}, {"date": "2022-01-06", "predicted_sales": 0.043047573417425156}, {"date": "2022-01-07", "predicted_sales": 0.04087194800376892}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.05773380398750305}, {"date": "2022-01-02", "predicted_sales": 0.05515236407518387}, {"date": "2022-01-03", "predicted_sales": 0.05057394504547119}, {"date": "2022-01-04", "predicted_sales": 0.04755491390824318}, {"date": "2022-01-05", "predicted_sales": 0.04521314054727554}, {"date": "2022-01-06", "predicted_sales": 0.043047573417425156}, {"date": "2022-01-07", 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1.0, "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": 1.33, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.33, "sales_quantity_rolling_sum_30d": 4.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 4.0, "sales_quantity_rolling_mean_90d": 1.33, "return_quantity_rolling_mean_90d": 0.0, "net_sales_quantity_rolling_mean_90d": 1.33, "sales_quantity_rolling_sum_90d": 8.0, "return_quantity_rolling_sum_90d": 0.0, "net_sales_quantity_rolling_sum_90d": 8.0, "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": "2020-09-17", "last_sale_date": "2023-02-11", "lifecycle_days": 877, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "播州区", "零售大类代码_encoded": 1.0, "零售中类代码_encoded": 5.0, "零售小类代码_encoded": 26.0, "商品ABC分类_encoded": 4.0, "商品手册代码_encoded": 20.0, "产地_encoded": 4.0, "brand_encoded": 667.0, "approval_type_encoded": 0.0, "DISTRICT_LAT": 27.535735, "DISTRICT_LON": 106.829081, "adcode": "520304", "district_name": "播州区", "area_sq_km": 2482.4, "poi_residential_count": 100.0, "poi_school_count": 17.0, "poi_mall_count": 5.0, "poi_finance_count": 18.0, "poi_company_count": 81.0, "business_areas": ["人民西路"], "latitude": 27.546425, "longitude": 106.83497, "temperature_2m_max": 6.4, "temperature_2m_min": -0.2, "temperature": 2.9, "is_promotion": 0}, {"store_id": "01010149", "product_id": "11034889", "date": "2021-12-31", "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": 1.0, "return_quantity_rolling_mean_15d": 0.0, "net_sales_quantity_rolling_mean_15d": 1.0, "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": 1.33, "return_quantity_rolling_mean_30d": 0.0, "net_sales_quantity_rolling_mean_30d": 1.33, "sales_quantity_rolling_sum_30d": 4.0, "return_quantity_rolling_sum_30d": 0.0, "net_sales_quantity_rolling_sum_30d": 4.0, "sales_quantity_rolling_mean_90d": 1.33, "return_quantity_rolling_mean_90d": 0.0, "net_sales_quantity_rolling_mean_90d": 1.33, "sales_quantity_rolling_sum_90d": 8.0, "return_quantity_rolling_sum_90d": 0.0, "net_sales_quantity_rolling_sum_90d": 8.0, "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": "2020-09-17", "last_sale_date": "2023-02-11", "lifecycle_days": 877, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "播州区", "零售大类代码_encoded": 1.0, "零售中类代码_encoded": 5.0, "零售小类代码_encoded": 26.0, "商品ABC分类_encoded": 4.0, "商品手册代码_encoded": 20.0, "产地_encoded": 4.0, "brand_encoded": 667.0, "approval_type_encoded": 0.0, "DISTRICT_LAT": 27.535735, "DISTRICT_LON": 106.829081, "adcode": "520304", "district_name": "播州区", "area_sq_km": 2482.4, "poi_residential_count": 100.0, "poi_school_count": 17.0, "poi_mall_count": 5.0, "poi_finance_count": 18.0, "poi_company_count": 81.0, "business_areas": ["人民西路"], "latitude": 27.546425, "longitude": 106.83497, "temperature_2m_max": 5.4, "temperature_2m_min": 2.4, "temperature": 3.6, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.0028627126344612664, "trend_type": "平稳", "r_squared": 0.9832553981293237, "p_value": 1.2393125952743122e-05, "volatility": 0.11882425391638739, "volatility_level": "中"}, "statistics": {"mean": 0.048592526997838704, "median": 0.04755491390824318, "min": 0.04087194800376892, "max": 0.05773380398750305, "std": 0.005773970766430095, "q1": 0.04413035698235035, "q3": 0.05286315456032753}, "day_over_day": [-4.471279794551484, -8.30140123000233, -5.9695385331589055, -4.92435622002401, -4.789685263261104, -5.0540024464551285], "influencing_factors": {"product_id": "11034889", "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11034889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中，表明销量有一定波动，但整体可控。\n预测期内平均日销量为0.05个单位，最高日销量为0.06个单位，最低日销量为0.04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_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 @@
-{"product_id": null, "product_name": "一树贵阳金阳南路分店（福州街分店）", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-25", "predicted_sales": 13.282493591308594}, {"date": "2025-07-26", "predicted_sales": 13.293810844421387}, {"date": "2025-07-27", "predicted_sales": 13.289920806884766}, {"date": "2025-07-28", "predicted_sales": 13.431988716125488}, {"date": "2025-07-29", "predicted_sales": 13.326523780822754}, {"date": "2025-07-30", "predicted_sales": 12.930220603942871}, {"date": "2025-07-31", "predicted_sales": 13.056842803955078}], "prediction_data": [{"date": "2025-07-25", "predicted_sales": 13.282493591308594}, {"date": "2025-07-26", "predicted_sales": 13.293810844421387}, {"date": "2025-07-27", "predicted_sales": 13.289920806884766}, {"date": "2025-07-28", "predicted_sales": 13.431988716125488}, {"date": "2025-07-29", "predicted_sales": 13.326523780822754}, {"date": "2025-07-30", "predicted_sales": 12.930220603942871}, {"date": "2025-07-31", "predicted_sales": 13.056842803955078}], "history_data": [{"date": "2025-06-25", "sales": 0.0, "product_id": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": "17002608", "product_name": "云南白药创可贴(便携型)", "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": null, "store_name": "一树贵阳金阳南路分店（福州街分店）"}
\ 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, "temperature": 20.0}, {"date": "2025-06-29", "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": 6, "is_holiday": false, "is_weekend": true, "is_promotion": false, "temperature": 20.0}, {"date": "2025-06-30", "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": 6, "is_holiday": false, "is_weekend": false, "is_promotion": false, "temperature": 20.0}, {"date": "2025-07-01", "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": "2025-07-02", "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": "2025-07-03", "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,
-            "is_promotion": false,
-            "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_ed968d3b-8365-4ace-b14f-04debe8b8103.json b/saved_predictions/prediction_ed968d3b-8365-4ace-b14f-04debe8b8103.json
new file mode 100644
index 0000000..cd0a043
--- /dev/null
+++ b/saved_predictions/prediction_ed968d3b-8365-4ace-b14f-04debe8b8103.json
@@ -0,0 +1 @@
+{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "kan", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "prediction_data": [{"date": "2022-01-01", "predicted_sales": 0.12464457750320435}, {"date": "2022-01-02", "predicted_sales": 0.11735431849956512}, {"date": "2022-01-03", "predicted_sales": 0.11733274161815643}, {"date": "2022-01-04", "predicted_sales": 0.11753901839256287}, {"date": "2022-01-05", "predicted_sales": 0.11754211783409119}, {"date": "2022-01-06", "predicted_sales": 0.11753572523593903}, {"date": "2022-01-07", "predicted_sales": 0.11738376319408417}], "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, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 11.0, "temperature_2m_min": 2.4, "temperature": 6.4, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-03", "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": 4, "day_of_month": 3, "day_of_year": 337, "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, "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, "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": true, "weekday": 5, "day_of_month": 4, "day_of_year": 338, "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, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 13.3, "temperature_2m_min": 5.7, "temperature": 9.5, "is_promotion": 0}, {"store_id": "01010108", "product_id": "11020059", "date": "2021-12-05", "sales_quantity": 2.0, "return_quantity": 0.0, "sales": 2.0, "gross_profit_total": 1.88, "transaction_count": 2, "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, "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": 0.8, "return_quantity_rolling_mean_90d": -0.05, "net_sales_quantity_rolling_mean_90d": 0.75, "sales_quantity_rolling_sum_90d": 16.0, "return_quantity_rolling_sum_90d": -1.0, "net_sales_quantity_rolling_sum_90d": 15.0, "is_weekend": true, "weekday": 6, "day_of_month": 5, "day_of_year": 339, "week_of_month": 1, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": 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"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": null}
\ No newline at end of file
diff --git a/saved_predictions/prediction_f42cdd61-8f4e-47e7-a5e4-74e9689e949c.json b/saved_predictions/prediction_f42cdd61-8f4e-47e7-a5e4-74e9689e949c.json
new file mode 100644
index 0000000..c5bdc11
--- /dev/null
+++ b/saved_predictions/prediction_f42cdd61-8f4e-47e7-a5e4-74e9689e949c.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": "2025-05-29", "predicted_sales": 0.0}, {"date": "2025-05-30", "predicted_sales": 0.0}, {"date": "2025-05-31", "predicted_sales": 0.0}, {"date": "2025-06-01", "predicted_sales": 0.0}, {"date": "2025-06-02", "predicted_sales": 0.0}, {"date": "2025-06-03", "predicted_sales": 0.0}, {"date": "2025-06-04", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 0.0}, {"date": "2025-05-30", "predicted_sales": 0.0}, {"date": "2025-05-31", "predicted_sales": 0.0}, {"date": "2025-06-01", "predicted_sales": 0.0}, {"date": "2025-06-02", "predicted_sales": 0.0}, {"date": "2025-06-03", "predicted_sales": 0.0}, {"date": "2025-06-04", "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, "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/server/api.py b/server/api.py
index fa76b31..bc4b893 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'])
@@ -1500,7 +1522,9 @@ 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 +2574,24 @@ 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 +3853,38 @@ 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 +4074,20 @@ 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 +4095,8 @@ 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..c7d8241 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
 
@@ -53,13 +55,10 @@ 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,
@@ -104,76 +103,54 @@ 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')
+
         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')
-                return None
+            product_data = full_df[full_df['product_id'] == product_id].copy()
+            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"全局训练模式: 产品 {product_id}, 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
diff --git a/server/predictors/model_predictor.py b/server/predictors/model_predictor.py
index 2463543..2986b68 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
 
@@ -96,19 +96,40 @@ 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
+            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_name = f"全局聚合 - 产品 {product_id}"
+        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/kan_trainer.py b/server/trainers/kan_trainer.py
index 0d71e7d..77603b9 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 +36,44 @@ 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)"
+    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 +95,11 @@ 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..4222874 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 +125,43 @@ 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)"
+        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 +180,11 @@ 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/transformer_trainer.py b/server/trainers/transformer_trainer.py
index 06d6af3..6180fae 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 +82,42 @@ 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)"
+        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 +134,11 @@ 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/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/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()