From 393d5abf704424fd3d3a0aba281c2316d2492718 Mon Sep 17 00:00:00 2001 From: LYFxiaoan Date: Sat, 26 Jul 2025 15:15:44 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E5=96=84=E6=95=B4=E7=90=86=E6=95=B0?= =?UTF-8?q?=E6=8D=AE=E6=BA=90=E6=9B=BF=E6=8D=A2=E6=B5=81=E7=A8=8B=E5=92=8C?= =?UTF-8?q?=E9=A1=B9=E7=9B=AE=E5=B0=86=E6=8C=87=E5=8D=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- prediction_history.db | Bin 159744 -> 36864 bytes ..._02abf0e8-c29f-44f3-9460-8523ac00fec3.json | 1 - ..._033bfaa5-4c6c-4a77-ad19-c6e75240cfb7.json | 1 - ..._05ab6c07-6e7c-4a2d-9340-680a873a9f99.json | 1 - ..._06bbc70f-a0ab-4606-bc76-b5faab9c34fd.json | 1 - ..._0967af52-e15c-4793-8c65-601c8540b847.json | 1 - ..._09e7501c-9ee4-4351-b5a8-972306aed30c.json | 1 - ..._0e3be59f-ad7e-490d-84ec-e81658dcf952.json | 1 - ..._11df1bc9-6c96-471a-b698-526a74034200.json | 1 - ..._14b90489-1d18-4477-bdcd-b063cb5c499b.json | 1 - ..._1d2673df-19e7-483a-94d4-b1b6b7fff20d.json | 1 - 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31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_2d4b3265-599b-4056-a515-fef563822f70.json b/saved_predictions/prediction_2d4b3265-599b-4056-a515-fef563822f70.json deleted file mode 100644 index db45084..0000000 --- a/saved_predictions/prediction_2d4b3265-599b-4056-a515-fef563822f70.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "kan", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.2918514013290405}, {"date": "2025-05-30", "predicted_sales": 1.2950164079666138}, {"date": "2025-05-31", "predicted_sales": 1.3018046617507935}, {"date": "2025-06-01", "predicted_sales": 1.335819125175476}, {"date": "2025-06-02", "predicted_sales": 1.3471426963806152}, {"date": "2025-06-03", "predicted_sales": 1.283681869506836}, {"date": "2025-06-04", "predicted_sales": 1.2876296043395996}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.2918514013290405}, {"date": "2025-05-30", "predicted_sales": 1.2950164079666138}, {"date": "2025-05-31", "predicted_sales": 1.3018046617507935}, {"date": "2025-06-01", 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"old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_349d6f74-68ed-4d6c-89ca-3ad0e57c68c2.json b/saved_predictions/prediction_349d6f74-68ed-4d6c-89ca-3ad0e57c68c2.json deleted file mode 100644 index f6f8ed4..0000000 --- a/saved_predictions/prediction_349d6f74-68ed-4d6c-89ca-3ad0e57c68c2.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": 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0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_5d64ea53-dd30-4935-9206-9481e0d4bd03.json b/saved_predictions/prediction_5d64ea53-dd30-4935-9206-9481e0d4bd03.json deleted file mode 100644 index 81f812d..0000000 --- a/saved_predictions/prediction_5d64ea53-dd30-4935-9206-9481e0d4bd03.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": 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"old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.004030156015817608, "trend_type": "平稳", "r_squared": 0.04556838611827181, "p_value": 0.6458063674760298, "volatility": 0.5530129075050354, "volatility_level": "高"}, "statistics": {"mean": 0.06827862560749054, "median": 0.07853572070598602, "min": 0.01834644004702568, "max": 0.11897968500852585, "std": 0.0377589613199234, "q1": 0.03216321021318436, "q3": 0.0988810658454895}, "day_over_day": [-80.36012268066406, 548.5165405273438, -63.20726776123047, 138.36843872070312, -24.73663902282715, -73.83287811279297], "influencing_factors": {"product_id": "11020059", "model_type": "xgboost", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "xgboost模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.07个单位，最高日销量为0.12个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_8db26191-f577-46bd-bf19-bfcf6e559806.json b/saved_predictions/prediction_8db26191-f577-46bd-bf19-bfcf6e559806.json deleted file mode 100644 index d559ce1..0000000 --- a/saved_predictions/prediction_8db26191-f577-46bd-bf19-bfcf6e559806.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": null, "product_name": "店铺 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106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.004030156015817608, "trend_type": "平稳", "r_squared": 0.04556838611827181, "p_value": 0.6458063674760298, "volatility": 0.5530129075050354, "volatility_level": "高"}, "statistics": {"mean": 0.06827862560749054, "median": 0.07853572070598602, "min": 0.01834644004702568, "max": 0.11897968500852585, "std": 0.0377589613199234, "q1": 0.03216321021318436, "q3": 0.0988810658454895}, "day_over_day": [-80.36012268066406, 548.5165405273438, -63.20726776123047, 138.36843872070312, -24.73663902282715, -73.83287811279297], "influencing_factors": {"product_id": "11020059", "model_type": "xgboost", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "xgboost模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.07个单位，最高日销量为0.12个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_95df18a4-4d66-40d8-8426-e3f8f32a66a5.json b/saved_predictions/prediction_95df18a4-4d66-40d8-8426-e3f8f32a66a5.json deleted file mode 100644 index 4b80267..0000000 --- a/saved_predictions/prediction_95df18a4-4d66-40d8-8426-e3f8f32a66a5.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": "11020059", "product_name": "Product 11020059", "model_type": "transformer", "predictions": [{"date": "2022-01-01", "predicted_sales": 0.2825997769832611}, {"date": "2022-01-02", "predicted_sales": 0.2631796598434448}, {"date": "2022-01-03", "predicted_sales": 0.26221904158592224}, {"date": "2022-01-04", "predicted_sales": 0.2872700095176697}, {"date": "2022-01-05", "predicted_sales": 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31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_9b9394a2-7d38-4880-8a89-10ca1777091b.json b/saved_predictions/prediction_9b9394a2-7d38-4880-8a89-10ca1777091b.json deleted file mode 100644 index 77bf15a..0000000 --- a/saved_predictions/prediction_9b9394a2-7d38-4880-8a89-10ca1777091b.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "transformer", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": "2025-06-01", "predicted_sales": 1.8458434343338013}, {"date": "2025-06-02", "predicted_sales": 1.8742748498916626}, {"date": "2025-06-03", "predicted_sales": 1.8699368238449097}, {"date": "2025-06-04", "predicted_sales": 1.8809840679168701}], "prediction_data": [{"date": "2025-05-29", "predicted_sales": 1.8425170183181763}, {"date": "2025-05-30", "predicted_sales": 1.8479859828948975}, {"date": "2025-05-31", "predicted_sales": 1.8500258922576904}, {"date": 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"old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_be05fff9-6070-4f30-859d-7fec6216c6a2.json b/saved_predictions/prediction_be05fff9-6070-4f30-859d-7fec6216c6a2.json deleted file mode 100644 index 7f3c5be..0000000 --- a/saved_predictions/prediction_be05fff9-6070-4f30-859d-7fec6216c6a2.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": 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null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": 0.01256424986890384, "trend_type": "平稳", "r_squared": 0.379441207552355, "p_value": 0.1407941343594892, "volatility": 0.5225040912628174, "volatility_level": "高"}, "statistics": {"mean": 0.07807367295026779, "median": 0.07149868458509445, "min": 0.01840788871049881, "max": 0.14494162797927856, "std": 0.04079381376504898, "q1": 0.05095291510224342, "q3": 0.10488080978393555}, "day_over_day": [-78.86146545410156, 198.6934356689453, 30.037439346313477, -34.37267303466797, 208.89463806152344, -15.359390258789062], "influencing_factors": {"product_id": "11020059", "model_type": "cnn_bilstm_attention", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "cnn_bilstm_attention模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.08个单位，最高日销量为0.14个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_daee98ce-5c41-4a20-8de9-bf05089d2d28.json b/saved_predictions/prediction_daee98ce-5c41-4a20-8de9-bf05089d2d28.json deleted file mode 100644 index 2e36406..0000000 --- a/saved_predictions/prediction_daee98ce-5c41-4a20-8de9-bf05089d2d28.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "mlstm", "predictions": [{"date": "2025-05-29", "predicted_sales": 1.1068205833435059}, {"date": "2025-05-30", "predicted_sales": 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31, "day_of_year": 365, "week_of_month": 5, "month": 12, "quarter": 4, "is_holiday": false, "first_sale_date": "2019-01-21", "last_sale_date": "2024-04-13", "lifecycle_days": 1909, "sample_category": "old", "rolling_7d_valid": true, "rolling_15d_valid": true, "rolling_30d_valid": true, "rolling_90d_valid": true, "district": "清镇市", "零售大类代码_encoded": null, "零售中类代码_encoded": null, "零售小类代码_encoded": null, "商品ABC分类_encoded": null, "商品手册代码_encoded": null, "产地_encoded": null, "brand_encoded": null, "approval_type_encoded": null, "DISTRICT_LAT": 26.570435, "DISTRICT_LON": 106.468686, "adcode": "520181", "district_name": "清镇市", "area_sq_km": 1389.89, "poi_residential_count": 39.0, "poi_school_count": 6.0, "poi_mall_count": 0.0, "poi_finance_count": 4.0, "poi_company_count": 76.0, "business_areas": [], "latitude": 26.548646, "longitude": 106.460619, "temperature_2m_max": 6.4, "temperature_2m_min": 1.6, "temperature": 3.5, "is_promotion": 0}], "analysis": {"trend": {"slope": -0.004030156015817608, "trend_type": "平稳", "r_squared": 0.04556838611827181, "p_value": 0.6458063674760298, "volatility": 0.5530129075050354, "volatility_level": "高"}, "statistics": {"mean": 0.06827862560749054, "median": 0.07853572070598602, "min": 0.01834644004702568, "max": 0.11897968500852585, "std": 0.0377589613199234, "q1": 0.03216321021318436, "q3": 0.0988810658454895}, "day_over_day": [-80.36012268066406, 548.5165405273438, -63.20726776123047, 138.36843872070312, -24.73663902282715, -73.83287811279297], "influencing_factors": {"product_id": "11020059", "model_type": "xgboost", "feature_count": 7, "important_features": ["价格", "周末", "节假日"]}, "explanation": "xgboost模型对产品11020059的预测分析：\n预测显示销量整体呈平稳趋势，销量基本保持稳定。\n预测期内销量波动性高，表明销量可能受到多种因素的影响，预测的不确定性较高。\n预测期内平均日销量为0.07个单位，最高日销量为0.12个单位，最低日销量为0.02个单位。\n\n主要影响因素包括：价格, 周末, 节假日。"}} \ No newline at end of file diff --git a/saved_predictions/prediction_efcca40c-f1d6-40a5-a3c5-e33d5154fd5d.json b/saved_predictions/prediction_efcca40c-f1d6-40a5-a3c5-e33d5154fd5d.json deleted file mode 100644 index 7c249c1..0000000 --- a/saved_predictions/prediction_efcca40c-f1d6-40a5-a3c5-e33d5154fd5d.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": null, "product_name": "店铺 01010323 (所有药品聚合)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.2221669852733612}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", 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"store_name": "店铺 01010323"} \ No newline at end of file diff --git a/saved_predictions/prediction_f257c2fc-236d-4375-89a1-d14ed35ce17a.json b/saved_predictions/prediction_f257c2fc-236d-4375-89a1-d14ed35ce17a.json deleted file mode 100644 index 81f812d..0000000 --- a/saved_predictions/prediction_f257c2fc-236d-4375-89a1-d14ed35ce17a.json +++ /dev/null @@ -1 +0,0 @@ -{"product_id": null, "product_name": "全局聚合 (所有药品)", "model_type": "cnn_bilstm_attention", "predictions": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", "predicted_sales": 0.0}, {"date": "2025-07-30", "predicted_sales": 0.0}, {"date": "2025-07-31", "predicted_sales": 0.0}, {"date": "2025-08-01", "predicted_sales": 0.0}], "prediction_data": [{"date": "2025-07-26", "predicted_sales": 0.0}, {"date": "2025-07-27", "predicted_sales": 0.0}, {"date": "2025-07-28", "predicted_sales": 0.0}, {"date": "2025-07-29", 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1.1. 核心价值 -所有功能均通过Web界面操作： -1. **模型训练**: 用户可以选择某个**药品**、某个**店铺**或**全局**数据，然后选择一种机器学习算法（如Transformer、KAN等）进行训练。训练过程是**异步**的，并能通过WebSocket实时反馈进度。 -2. **销售预测**: 使用已经训练好的模型，对未来的销量进行预测。 -3. **结果可视化**: 将历史销量和预测销量在同一个图表中展示出来，方便用户直观地看到趋势。 -4. **模型与历史管理**: 提供对已训练模型和历史预测记录的查询、详情查看和删除功能。 +本系统是一个基于深度学习和机器学习模型的智能预测平台，旨在解决药品零售行业的核心痛点： -## 2. 技术栈 +* **精准库存管理**：通过对未来销量的精确预测，指导采购与备货，降低库存成本，避免缺货损失。 +* **智能化决策支持**：为营销活动、人员排班、资金规划等提供量化的、数据驱动的决策依据。 +* **多维度洞察**：支持从“单品”、“店铺”到“区域全局”等多个维度进行训练和预测，满足不同层级的业务分析需求。 -| 层面 | 本项目技术 | 说明 | -| :--- | :--- | :--- | -| **后端框架** | **Flask** | 轻量级的Python Web框架，用于提供API接口。 | -| **前端框架** | **Vue.js** | 用于构建用户交互界面的现代化JavaScript框架。 | -| **核心算法库** | **PyTorch** | 实现深度学习算法的核心库。 | -| **数据处理** | **Pandas** | Python中用于数据分析和处理的核心库。 | -| **数据库** | **SQLite** | 一个轻量级的本地文件数据库，用于记录模型元数据和预测历史。 | -| **实时通信** | **Flask-SocketIO** | 用于后端在训练时向前端实时推送日志和进度。 | -| **异步任务** | **multiprocessing** | Python标准库，用于将耗时的训练任务放到独立的子进程中执行，避免阻塞API服务。 | +### 1.2. 技术栈 -## 3. 系统架构与数据流 - -本项目是经典的前后端分离架构，其数据流和核心组件如下： - -``` -+-----------------------------------------------------------------+ -| 用户 (Browser - Vue.js) | -+-----------------------------------------------------------------+ - | (1. HTTP/WebSocket请求) -+-----------------------------------------------------------------+ -| 后端API层 (Backend API - Flask) | -| - api.py: 定义所有RESTful接口 (e.g., /api/training) | -| - 接收请求, 验证参数, 调用核心服务层 | -+-----------------------------------------------------------------+ - | (2. 调用核心服务) -+-----------------------------------------------------------------+ -| 核心服务与管理层 (Core Services) | -| - training_process_manager.py: 异步训练任务管理器 (关键) | -| - model_manager.py: 模型保存、加载、版本控制 (关键) | -| - model_registry.py: 算法与训练器的注册表 (关键) | -+-----------------------------------------------------------------+ - | (3. 执行具体任务) -+-----------------------------------------------------------------+ -| 算法实现与数据处理层 (Algorithm & Data) | -| - trainers/*.py: 具体的算法训练逻辑 (e.g., kan_trainer.py) | -| - predictors/model_predictor.py: 模型加载与预测逻辑 | -| - models/*.py: PyTorch模型定义 (e.g., kan_model.py) | -| - utils/data_utils.py: 数据预处理和转换工具 | -+-----------------------------------------------------------------+ - | (4. 读写物理文件) -+-----------------------------------------------------------------+ -| 物理存储层 (Storage) | -| - data/*.parquet: 原始时序数据 | -| - saved_models/*.pth: 训练好的模型文件 (权重、配置、缩放器) | -| - saved_predictions/*.json: 详细的预测结果文件 | -| - prediction_history.db: SQLite数据库 (存储元数据和文件路径索引) | -+-----------------------------------------------------------------+ -``` - -## 4. 核心工作流详解：“数据 -> 训练 -> 预测” - -#### **步骤一：数据准备** -- **原始数据**: 存储在 [`data/timeseries_training_data_sample_10s50p.parquet`](data/timeseries_training_data_sample_10s50p.parquet)。 -- **数据存储模式**: 本项目采用“数据库作索引，文件系统存内容”的设计模式。 - - **元数据 (索引)**: 存储在根目录的 `prediction_history.db` SQLite数据库中。它只记录轻量级的元信息（如模型版本、预测参数）和指向真实数据文件的**路径**。 - - **真实数据 (内容)**: - - **模型**: 训练好的模型（`.pth`文件）存放在 [`saved_models/`](saved_models/) 目录。 - - **预测结果**: 详细的预测数据（`.json`文件）存放在 [`saved_predictions/`](saved_predictions/) 目录。 - -#### **步骤二：模型训练 (异步)** -1. **API触发**: 前端调用 `POST /api/training` ([`server/api.py`](server/api.py:815))。 -2. **任务提交**: `api.py` 将训练请求提交给**训练进程管理器** (`training_process_manager`)，并立即返回一个任务ID，不阻塞主服务。 -3. **动态执行**: - * 管理器在**新的子进程**中运行任务。 - * 它通过**模型注册表** (`model_registry`) 找到 `model_type` 对应的训练器函数（例如，`kan` -> `kan_trainer.py` 中的函数）。 - * 训练器加载数据，进行归一化，然后执行PyTorch训练循环。 -4. **模型保存**: - * 训练完成后，调用**模型管理器** (`model_manager`) 的 `save_model` 方法。 - * 模型（权重、配置、缩放器）被打包保存在 [`saved_models/`](saved_models/) 目录下，命名如 `kan_product_P001_v1.pth`。 - * 模型的元数据（路径、版本、指标）被写入数据库的 `model_versions` 表。 - -#### **步骤三：模型预测 (同步)** -1. **API触发**: 前端调用 `POST /api/prediction` ([`server/api.py`](server/api.py:1273))。 -2. **模型定位**: `api.py` 调用**模型管理器** (`model_manager`)，根据参数从数据库中找到对应的模型文件路径。 -3. **加载与预测**: - * 核心逻辑在 [`server/predictors/model_predictor.py`](server/predictors/model_predictor.py) 的 `load_model_and_predict` 函数中。 - * 该函数加载 `.pth` 文件，并利用其中的 `config` 和 `state_dict` **精确重建模型实例**。 - * 执行**自回归预测**：预测一天，将结果作为下一天输入的一部分，循环往复。 -4. **结果保存**: - * 完整的预测结果（历史、预测、分析等）被保存为一个JSON文件到 [`saved_predictions/`](saved_predictions/) 目录。 - * 该次预测的元数据（包括JSON文件路径）被写入数据库的 `prediction_history` 表。 -5. **返回与渲染**: 完整的JSON结果被返回给前端，前端使用图表库进行可视化。 - -## 5. 如何添加一个新的算法？(开发者指南) - -假设你要添加一个名为 `NewNet` 的新算法。 - -**目标**: 让 `NewNet` 出现在前端的“模型类型”下拉框中，并能成功训练和预测。 - -1. **创建模型定义文件**: - * 在 `server/models/` 目录下，创建 `newnet_model.py`。 - * 在其中定义你的 `NewNet` 模型类，继承自 `torch.nn.Module`。 - -2. **创建训练器文件**: - * 在 `server/trainers/` 目录下，创建 `newnet_trainer.py`。 - * 复制一份现有训练器（如 `kan_trainer.py`）的内容作为模板。 - * **关键修改**: - * 导入你的 `NewNet` 模型。 - * 在训练函数中，实例化你的 `NewNet` 模型。 - * 在保存checkpoint时，确保 `config` 字典里包含了重建 `NewNet` 所需的所有超参数。 - * 在文件末尾，**注册你的训练器**: `register_trainer('newnet', your_training_function)`。 - -3. **创建预测器逻辑 (如果需要)**: - * 大多数情况，你可以复用默认的预测器。打开 [`server/predictors/model_predictor.py`](server/predictors/model_predictor.py)。 - * 在 `load_model_and_predict` 函数中，添加一个 `elif loaded_model_type == 'newnet':` 分支，确保它能根据 `config` 正确地创建 `NewNet` 模型实例。 - * 在文件末尾，**注册你的预测器**: `register_predictor('newnet', default_pytorch_predictor)`。如果你的模型有特殊的预测逻辑，可以自定义一个预测函数并注册它。 - -4. **更新前端界面**: - * 打开 `server/api.py` 中的 `get_model_types` 函数。 - * 在 `model_meta` 字典中添加 `'newnet'` 的元数据，包括名称、描述和标签类型。 - * **无需修改前端代码**，前端的下拉框会自动从这个API获取最新的模型列表。 - -完成以上步骤后，重启服务，你就可以在界面上选择并使用你的新算法了。这个插件式的设计大大简化了新算法的集成过程。 - -## 6. 系统维护与扩展 - -随着系统的持续运行，会不断产生模型文件、预测结果等历史产物。理解如何管理这些产物对于保持系统的健康至关重要。 - -### 6.1. 历史产物管理 (Artifacts Management) - -**问题**: -随着时间推移，`saved_models/` 和 `saved_predictions/` 目录下的文件会越来越多，导致项目体积变得臃肿。 - -**当前状态**: -系统目前依赖**手动清理**。您可以通过Web界面删除单个模型或单条预测历史，程序会自动删除对应的文件。 - -**推荐的解决方案**: -为了实现自动化管理，推荐创建一个独立的维护脚本，并实施**数据保留策略 (Data Retention Policy)**。 - -#### **自动化清理策略** - -1. **创建清理脚本**: - * 可以在 `server/tools/` 目录下创建一个新脚本，例如 `cleanup_artifacts.py`。 - -2. **定义保留规则**: - * **对于预测结果 (`.json` in `saved_predictions/`)**: - * **基于时间**: 只保留最近30天的记录。 - * **基于数量**: 只保留最新的1000条记录。 - * **对于模型 (`.pth` in `saved_models/`)**: - * **基于版本**: 只保留每个模型（同一种药品、同一种算法）最新的3个版本。 - * **保留最佳模型**: 始终保留性能最佳的 `best` 版本，不参与自动清理。 - -3. **执行脚本**: - * 脚本的逻辑是：连接数据库，查询出所有符合清理条件的记录，然后安全地删除硬盘上对应的文件，最后再删除数据库中的条目。 - * 这个脚本可以通过服务器的定时任务（如Linux的Cron Job或Windows的Task Scheduler）设置为每日自动执行。 - -#### **企业级方案：归档到冷存储** - -对于需要长期保留数据以备审计的场景，更专业的做法是将旧文件归档至廉价的云对象存储（如阿里云OSS, AWS S3等），而不是直接删除。数据库中仅更新文件路径指向云端地址即可。 +* **后端**: Python, Flask, Socket.IO, SQLAlchemy +* **前端**: Vue.js, Element Plus, Chart.js +* **AI框架**: PyTorch, XGBoost, Scikit-learn +* **核心模型**: Transformer, KAN, mLSTM, TCN, CNN-BiLSTM-Attention, XGBoost +* **数据存储**: SQLite (用于历史记录), Parquet (用于核心时序数据) +* **版本与进程管理**: 自研的 `ModelManager` 和 `TrainingProcessManager` --- -## 核心概念扫盲：算法、模型、训练与预测 +## 2. 核心架构设计 -对于初次接触AI和数据科学的开发者来说，这些术语可能会令人困惑。本章节旨在用最通俗易懂的方式，解释本项目核心的AI部分是如何工作的。 +系统采用经典的前后端分离架构，并通过模块化的设计，实现了业务逻辑、AI能力与数据处理的有效解耦。 -### 1. 一个简单的比喻：学生备考 +### 2.1. 架构分层图 -让我们把整个AI预测系统想象成一个准备参加数学考试的学生： +``` ++---------------------+ +-----------------------+ +---------------------+ +| 前端 (UI) | <--> | API层 (Flask) | <--> | 核心逻辑层 | +| (Vue.js, Chart.js) | | (api.py, Socket.IO) | | (predictor.py) | ++---------------------+ +-----------------------+ +---------------------+ + | | + | | + +-------------------------------------------------------+ + | + v ++--------------------------+ +---------------------------+ +| AI能力层 (Trainers) | <--> | 模型与算法 (Models) | +| (kan_trainer.py, etc.) | | (kan_model.py, etc.) | ++--------------------------+ +---------------------------+ + | | + | | + v v ++-------------------------------------------------------------------+ +| 数据与工具层 (Utils) | +| (new_data_loader.py, model_manager.py, training_process_manager.py) | ++-------------------------------------------------------------------+ + | + v ++--------------------------+ +---------------------------+ +| 数据存储 (Storage) | | 原始数据 (Data) | +| (prediction_history.db) | | (*.parquet) | ++--------------------------+ +---------------------------+ +``` -* **历史数据 (Data)**：就像是学生的**教科书和练习册** (`pharmacy_sales.xlsx` 文件)。里面包含了大量的历史题目（过去的销售情况）和对应的正确答案。 -* **算法 (Algorithm)**：是学生的**学习方法和大脑结构** (本项目中的 `KAN` 神经网络)。它定义了学生如何去理解问题、寻找规律，但算法本身不包含任何知识。它只是一套空的学习框架。 -* **训练 (Training)**：就是学生**学习和复习**的过程 (`server/trainers/kan_trainer.py` 脚本)。学生通过不断地做练习册上的题（读取历史数据），然后对照答案（真实的销售额），分析自己错在哪里（计算损失/Loss），然后反思并修正自己的解题思路（反向传播与优化）。这个过程会重复很多遍（Epochs），直到学生在模拟考试（测试集）中取得好成绩。 -* **模型 (Model)**：是学生学习完成后，**学到的所有知识和解题技巧** (保存在 `saved_models/` 下的 `.pth` 文件)。它不再是空的大脑框架，而是包含了所有规律和知识、准备好上考场的“成型的知识体系”。 -* **预测 (Prediction)**：就是学生**正式上考场考试**的过程 (`server/predictors/model_predictor.py` 脚本)。我们给学生一套他从未见过的新题目（例如，提供最近30天的数据，让他预测未来7天），学生利用他已经学到的知识（加载`.pth`模型文件），给出他认为最可能的答案（未来的销售额）。 +### 2.2. 核心工作流：“数据 -> 智能”的闭环 -### 2. 训练和预测在本项目中的区别与联系 +1. **数据加载**: 所有的数据请求都由 `server/utils/new_data_loader.py` 统一处理。 +2. **模型训练**: + * 前端通过 **API层** 发起训练请求。 + * **进程管理器** (`TrainingProcessManager`) 创建一个独立的后台进程来处理该任务。 + * **核心逻辑层** (`predictor.py`) 根据请求参数（如训练模式、范围），从数据加载器获取并预处理数据。 + * **AI能力层** (具体的 `trainer.py`) 被调用，负责执行模型的训练、评估和保存。 + * **模型管理器** (`ModelManager`) 负责将训练好的模型，以统一、规范的命名格式保存到磁盘。 +3. **模型预测**: + * 前端通过 **API层** 发起预测请求。 + * **模型管理器** 根据请求参数找到正确的模型文件。 + * **核心预测器** (`model_predictor.py`) 加载模型，并从数据加载器获取预测所需的历史数据。 + * 根据模型类型（自回归 vs 直接输出），执行相应的预测逻辑。 + * 结果通过 **API层** 返回给前端，并在数据库中留下历史记录。 +4. **结果可视化**: 前端使用 `Chart.js` 将API返回的历史数据和预测数据渲染成趋势图。 -**Q: 训练和预测所使用的算法有区别吗？** +--- -**A:** 核心算法（大脑结构）是**完全相同**的，都是 `KAN` 网络。但它们所做的事情（功能）截然不同。 +## 3. 项目演进全景回顾 -* **训练 (`train_product_model_with_kan`)** - * **目标**：**创造一个模型**。 - * **输入**：大量的历史数据（特征+答案）。 - * **过程**：是一个**学习和迭代**的过程。它会反复调整算法内部的参数（权重），目标是让预测结果与真实答案的差距越来越小。这是一个计算量巨大、非常耗时的过程。 - * **输出**：一个 `.pth` 文件，这就是训练好的模型。 +### 第一阶段：奠基与探索 (早期修复与重构) -* **预测 (`load_model_and_predict`)** - * **目标**：**使用一个已有的模型**。 - * **输入**：一小段最近的历史数据（只有特征，没有答案）。 - * **过程**：是一个**计算和应用**的过程。它加载训练好的`.pth`模型文件，将输入数据“喂”给模型，模型根据已经固化的参数，直接计算出结果。这个过程非常快。 - * **输出**：对未来的预测数据（JSON格式）。 +此阶段的核心目标是**打通系统最基础的“训练->预测”闭环**，并解决一系列阻碍核心功能运行的初始Bug。 -**总结**：训练是“从0到1”打造模型的过程，预测是“使用这个1”来解决实际问题的过程。 +* **设计思路**：采用“问题驱动”的模式，从最表层的UI失败开始，逐层深入，定位并修复了从数据流、模型算法到前后端通信的多个核心问题。 +* **关键改动与修复**： + 1. **数据流重构**：修复了 `predictor.py` 中数据处理流程中断，导致特征丢失的根本性问题。 + 2. **模型算法修复**： + * 根治了所有PyTorch模型因**维度不匹配**而无法学习（R²恒为0）的严重Bug。 + * 修复了 `mLSTM` 模型因**算法设计缺陷**导致预测结果无效的问题。 + 3. **配置持久化**：系统性地修复了所有训练器，确保在保存模型时，会将**完整的、可用于重新实例化模型的配置**写入检查点，解决了除Transformer外所有模型预测失败的问题。 + 4. **版本管理统一**：引入了 `ModelManager` 作为系统中**唯一**负责版本管理、模型命名和IO的组件，彻底解决了因命名和路径不统一导致模型无法被找到的混乱状况。 + 5. **前后端链路打通**：通过修复**JSON序列化**、**API响应结构**、**数据时间戳连续性**和**图表日期格式化**等一系列问题，最终成功实现了所有预测视图的图表渲染。 -### 3. 模型的具体实现是怎样的？ +### 第二阶段：涅槃与重生 (核心数据源替换) -1. **我们如何得到模型？** - * 我们运行训练脚本 (`uv run train_model`)。 - * 该脚本调用 `kan_trainer.py` 中的 `train_product_model_with_kan` 函数。 - * 这个函数会加载销售数据，对数据进行预处理（例如，将“星期一”转换为数字1，将所有数据缩放到0-1之间以便于神经网络处理）。 - * 然后，它将数据切分成“用连续30天的数据，预测未来7天销量”这样的样本对。 - * 最后，它通过成千上万次的学习迭代，将学到的知识保存为一个 `.pth` 文件。 +此阶段的核心目标是将项目的数据源，从一个简单的、理想化的样本数据，彻底更换为**全新的、结构更复杂、更贴近真实业务场景的核心数据集**。这是一次“换引擎”级别的大手术。 -2. **预测的具体实现是怎样的？** - * 我们通过API请求预测。 - * API调用 `model_predictor.py` 中的 `load_model_and_predict` 函数。 - * 该函数首先根据请求，找到对应的 `.pth` 模型文件并加载它。 - * 然后，它获取预测开始日期前最新的30天真实数据。 - * 它将这30天数据输入模型，得到对未来第1天的预测销量。 - * **关键一步（自回归）**：它将第1天的预测结果，当作“真实发生过”的数据，拼接到历史数据的末尾，并丢掉最老的一天，形成一个新的30天数据序列。 - * 它用这个新的序列去预测第2天的销量。 - * ……如此循环往复，直到预测出未来7天的全部结果。 +#### **“更换数据源”标准作业程序 (SOP)** -### 4. 我需要具备哪些技术才能理解？ +我们在此次重构中，沉淀出了一套可复用的、用于安全更换核心数据源的SOP。 -这是一个很好的问题，可以分层来看： +**第1步：分析与评估 (Analysis & Assessment)** -* **初级（能使用和调试）**： - * **Python基础**：能读懂基本的Python代码和逻辑。 - * **Pandas库**：了解如何操作和处理表格数据（DataFrame）。我们项目中的数据预处理大量使用Pandas。 - * **理解本章节内容**：对训练和预测有宏观概念，知道输入什么、输出什么即可。 +* **目标**：全面理解新旧数据源的差异，识别风险，制定策略。 +* **动作**： + * 对比新旧 `.parquet` 文件的Schema（表结构）。 + * 识别出**“断裂性变更”**，例如：列名不同 (`subbh` vs `store_id`)、数据类型变化、特征缺失（如 `is_promotion`）。 + * **决策**：放弃“直接修改代码以适应新数据”的高风险方案，选择**“适配器模式”**，以最小的侵入性实现兼容。 -* **中级（能修改和优化）**： - * **PyTorch框架**：了解PyTorch的基本概念，如 `Tensor`（张量）、`nn.Module`（模型结构）、`Optimizer`（优化器）、`DataLoader`（数据加载器）。我们的 `KAN` 模型就是用PyTorch构建的。 - * **机器学习基础概念**：了解什么是特征工程、训练集/测试集、损失函数、过拟合与欠拟合等。 +**第2步：构建数据适配器 (Adapter Construction)** -* **高级（能设计和创新）**： - * **深度学习理论**：深入理解神经网络、反向传播等原理。 - * **时间序列分析**：了解ARIMA、LSTM、Transformer等其他时间序列预测模型的原理和优劣。 +* **目标**：创建一个独立的、统一的数据入口，将“新数据”动态转换为“旧代码能理解的格式”。 +* **动作**： + * 创建 `server/utils/new_data_loader.py`。 + * 在该模块中，集中实现所有的数据转换逻辑： + * **列名重映射**：`{'subbh': 'store_id', 'hh': 'product_id', ...}`。 + * **特征填充**：为缺失的 `is_promotion` 列创建代理并填充默认值 `0`。 + * **数据类型转换**：将日期列统一转换为 `datetime` 对象。 -**给您的建议**： -您现在完全不需要焦虑。从“初级”开始，先尝试理解数据的流转，即数据是如何从文件加载，被Pandas处理，然后输入给训练器或预测器的。当您能熟练地进行使用和调试后，再逐步去了解PyTorch和机器学习的细节。边学边做是最高效的方式。 \ No newline at end of file +**第3步：全面重构 (Refactoring)** + +* **目标**：将系统中所有直接读取旧数据的代码，全部重构为通过新的数据适配器获取数据。 +* **动作**： + * 系统性地修改了所有数据消费端，包括：`api.py`, `core/predictor.py`, 以及 `trainers/` 目录下的**所有**训练器脚本。 + * 删除了所有旧的、已废弃的数据加载工具，确保了数据源的唯一性。 + +**第4步：端到端测试与迭代修复 (E2E Testing & Iterative Fixing)** + +* **目标**：在新数据源上，对系统的所有核心功能（所有训练模式、所有预测模式）进行完整测试，并修复因此次变更而引发的所有连锁Bug。 +* **关键修复成果**： + * **`NaN`值处理**：在聚合运算（如“按店铺训练”）后，增加了 `.fillna(0)` 数据清理步骤，解决了因数据稀疏性引入`NaN`值而导致的训练失败。 + * **架构错配修复**： + * **问题**：`XGBoost` 和 `CnnBiLstmAttention` 模型在预测时，被错误地调用了为“自回归”模型设计的循环预测逻辑，导致在自定义预测天数时崩溃或返回无效结果。 + * **修复**：在 `model_predictor.py` 中为这两类“直接多步输出”模型创建了**专属的、正确的、非循环的**预测逻辑分支。 + * **全链路参数传递修复**： + * **问题**：“自定义全局训练”功能因新增的 `selected_stores` 和 `selected_products` 参数在 `api.py` -> `training_process_manager.py` -> `predictor.py` 的调用链中丢失而失败。 + * **修复**：对整条调用链上的所有相关模块进行了**同步升级**，确保了复杂参数的完整、正确传递。 + * **数据特性澄清**：确认了“负销量”是源于真实业务（如退货）的数据特性，并决策在代码层面**尊重**而非修改它，这是一个重要的技术决策。 + +**第5步：环境清理 (Environment Cleanup)** + +* **目标**：在最终验证前，确保一个完全纯净的、不受任何旧数据或旧模型干扰的系统环境。 +* **动作**： + * 删除了 `saved_models` 和 `saved_predictions` 文件夹中的所有历史产物。 + * 删除了 `prediction_history.db` 数据库文件，并验证了系统具备自动重建的能力。 + +### 第三阶段：展望 (模型优化与能力升级) + +在当前稳定的系统基础上，我们规划了以下四个方向的重点工作，以进一步提升系统的核心价值。 + +1. **特征工程**：通过创造时间特征、滞后特征和滑动平均特征，充分挖掘新数据源的潜力，提升模型预测精度。 +2. **模型可解释性**：引入SHAP等工具，让AI模型不再是“黑盒”，为每一次预测提供清晰、量化的归因分析。 +3. **超参数调优**：引入Optuna等自动化调优框架，为每个模型找到其最佳的超参数组合，进一步压榨模型性能。 +4. **架构限制突破**：通过改造训练器和API，最终实现让所有模型都能在**训练时**自由指定预测天数，提升系统的灵活性和易用性。 + +--- + +## 4. 系统维护与扩展 + +### 4.1. 如何引入一个新模型？ + +1. **创建模型文件**：在 `server/models/` 目录下，创建一个新的模型定义文件（如 `new_model.py`），确保它是一个标准的PyTorch `nn.Module`。 +2. **创建训练器文件**：在 `server/trainers/` 目录下，创建一个新的训练器文件（如 `new_model_trainer.py`）。 + * 参考现有训练器的结构，实现完整的“数据准备->训练循环->评估->保存”逻辑。 + * 在文件末尾，使用 `@register_trainer('new_model_id', train_function)` 将其注册到系统中。 +3. **（可选）创建专属预测器**：如果你的新模型不是标准的“自回归”模型（像XGBoost那样），则需要在 `server/predictors/model_predictor.py` 中为其创建一个专属的预测逻辑分支。 +4. **更新API**：`api.py` 会自动通过注册表发现你的新模型，无需修改。 + +### 4.2. 系统清理与数据管理 + +* **清理旧模型**：可以直接删除 `saved_models/` 目录下的 `.pth` 文件。 +* **清理旧预测**：可以安全地删除 `saved_predictions/` 目录下的 `.json` 文件和根目录下的 `prediction_history.db` 数据库文件。系统会在下次运行时自动重建。 \ No newline at end of file