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03241758

David 3 months ago
parent
bc80bfa092
commit
7df76b70c7
4 changed files with 1296 additions and 683 deletions
Unified View Show Diff Stats
  1. 610 0
      app/logs/2025-03-24/south-forecast.2025-03-24.0.log
  2. 11 6
      app/model/tf_lstm_train.py
  3. 672 672
      app/predict/data/DQYC/20220807/2365/OUT/DQYC_OUT_PREDICT_POWER.txt
  4. 3 5
      app/predict/tf_lstm_pre.py

+ 610 - 0
app/logs/2025-03-24/south-forecast.2025-03-24.0.log
View File 

@@ -1851,3 +1851,613 @@
 
  0.32354 0.32525] - training
 
  0.32354 0.32525] - training
 
 2025-03-24 15:25:56,689 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e1090464fdbe5a30d5a307 - insert_trained_model_into_mongo
 
 2025-03-24 15:25:56,689 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e1090464fdbe5a30d5a307 - insert_trained_model_into_mongo
 
 2025-03-24 15:25:56,697 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e1090464fdbe5a30d5a309 - insert_scaler_model_into_mongo
 
 2025-03-24 15:25:56,697 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e1090464fdbe5a30d5a309 - insert_scaler_model_into_mongo
 
+2025-03-24 15:37:30,960 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:38:50,548 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:38:50,926 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:39:54,363 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:40:28,533 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:41:51,230 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:42:36,739 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:43:28,115 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:43:34,170 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:45:36,573 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:45:41,835 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:46:35,207 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:46:49,802 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:47:07,043 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 15:47:07,044 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpuzv867x2.keras - get_keras_model_from_mongo
 
+2025-03-24 15:48:02,671 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:48:08,321 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:48:10,104 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 15:48:10,104 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmppp2bur2b.keras - get_keras_model_from_mongo
 
+2025-03-24 15:48:46,701 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 15:49:15,435 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 15:49:17,502 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 15:49:17,503 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp6jjhhllp.keras - get_keras_model_from_mongo
 
+2025-03-24 16:25:48,997 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:25:54,990 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 15:25:56) - get_scaler_model_from_mongo
 
+2025-03-24 16:25:57,123 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 16:25:57,124 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp55bwpy6h.keras - get_keras_model_from_mongo
 
+2025-03-24 16:27:06,421 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:27:34,098 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:28:03,494 - tf_lstm_train.py - INFO - Program starts execution! - model_training
 
+2025-03-24 16:28:13,423 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
 
+2025-03-24 16:28:14,328 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
 
+2025-03-24 16:28:15,007 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
 
+2025-03-24 16:28:29,728 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
 
+2025-03-24 16:28:29,729 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
 
+2025-03-24 16:28:29,762 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
 
+2025-03-24 16:28:29,763 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:28:29,763 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:28:29,764 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
 
+2025-03-24 16:28:29,764 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:28:29,765 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:28:29,768 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,768 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
 
+2025-03-24 16:28:29,774 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,775 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:28:29,776 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,776 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:28:29,777 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,777 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:28:29,778 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,779 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:28:29,779 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,780 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:28:29,781 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,781 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:28:29,781 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,782 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
 
+2025-03-24 16:28:29,782 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
 
+2025-03-24 16:28:29,795 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,795 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
 
+2025-03-24 16:28:29,797 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
 
+2025-03-24 16:28:29,797 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
 
+2025-03-24 16:28:29,800 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
 
+2025-03-24 16:28:29,800 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:28:29,801 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
 
+2025-03-24 16:28:29,801 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:28:29,802 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
 
+2025-03-24 16:28:29,803 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:28:29,939 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
 
+2025-03-24 16:28:29,939 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:28:29,941 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
 
+2025-03-24 16:28:29,941 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
 
+2025-03-24 16:28:29,943 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
 
+2025-03-24 16:28:31,432 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,438 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:28:31,442 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,447 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,452 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,456 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,461 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,466 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,470 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:28:31,475 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,479 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,485 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,489 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,493 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,497 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:28:31,498 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
 
+2025-03-24 16:29:15,220 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:29:15,221 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:29:26,828 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
 
+2025-03-24 16:29:27,632 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
 
+2025-03-24 16:29:57,341 - tf_lstm.py - INFO - -----模型训练经过28轮迭代----- - training
 
+2025-03-24 16:29:57,342 - tf_lstm.py - INFO - 训练集损失函数为：[1.02629 0.51363 0.40185 0.35459 0.32764 0.30722 0.29113 0.28102 0.27487
 
+ 0.27069 0.26745 0.26378 0.2614  0.25867 0.25592 0.25758 0.2541  0.25267
 
+ 0.24981 0.24854 0.24725 0.24632 0.24614 0.24585 0.24434 0.2443  0.24277
 
+ 0.24312] - training
 
+2025-03-24 16:29:57,342 - tf_lstm.py - INFO - 验证集损失函数为：[0.64746 0.46841 0.41888 0.38172 0.35578 0.33682 0.32683 0.32256 0.31833
 
+ 0.31401 0.31055 0.30685 0.30428 0.30192 0.30389 0.29887 0.29866 0.29472
 
+ 0.29633 0.29759 0.29883 0.30783 0.31148 0.30816 0.30389 0.30156 0.30865
 
+ 0.30822] - training
 
+2025-03-24 16:30:33,003 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e118290a53203110baf559 - insert_trained_model_into_mongo
 
+2025-03-24 16:30:33,547 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e118290a53203110baf55b - insert_scaler_model_into_mongo
 
+2025-03-24 16:31:27,711 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:31:37,265 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 16:30:28) - get_scaler_model_from_mongo
 
+2025-03-24 16:31:48,232 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 16:31:48,234 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp7xxoukqg.keras - get_keras_model_from_mongo
 
+2025-03-24 16:36:18,503 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:36:18,697 - tf_lstm_train.py - INFO - Program starts execution! - model_training
 
+2025-03-24 16:36:18,750 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
 
+2025-03-24 16:36:18,759 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
 
+2025-03-24 16:36:18,774 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
 
+2025-03-24 16:36:18,784 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
 
+2025-03-24 16:36:18,784 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
 
+2025-03-24 16:36:18,817 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
 
+2025-03-24 16:36:18,817 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:36:18,817 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:36:18,818 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
 
+2025-03-24 16:36:18,819 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:36:18,819 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:36:18,822 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,822 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
 
+2025-03-24 16:36:18,827 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,827 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:36:18,828 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,829 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:36:18,829 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,830 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:36:18,830 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,831 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:36:18,831 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,832 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:36:18,832 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,833 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:36:18,833 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,833 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
 
+2025-03-24 16:36:18,834 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
 
+2025-03-24 16:36:18,847 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,847 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
 
+2025-03-24 16:36:18,848 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
 
+2025-03-24 16:36:18,848 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
 
+2025-03-24 16:36:18,850 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
 
+2025-03-24 16:36:18,850 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:36:18,851 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
 
+2025-03-24 16:36:18,851 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:36:18,851 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
 
+2025-03-24 16:36:18,852 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:36:18,852 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
 
+2025-03-24 16:36:18,853 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:36:18,854 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
 
+2025-03-24 16:36:18,854 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
 
+2025-03-24 16:36:18,855 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
 
+2025-03-24 16:36:18,857 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,860 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:36:18,861 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,862 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,863 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,864 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,865 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,866 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,868 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:36:18,869 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,870 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,871 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,872 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,873 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,875 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:36:18,875 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:36:21,335 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:36:21,531 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
 
+2025-03-24 16:36:21,531 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
 
+2025-03-24 16:36:30,042 - tf_lstm.py - INFO - -----模型训练经过34轮迭代----- - training
 
+2025-03-24 16:36:30,043 - tf_lstm.py - INFO - 训练集损失函数为：[0.9643  0.47406 0.38006 0.33788 0.31271 0.29495 0.28256 0.27419 0.26928
 
+ 0.26494 0.26215 0.25938 0.25772 0.25598 0.25533 0.25299 0.25207 0.25119
 
+ 0.25038 0.24868 0.24694 0.25517 0.24927 0.24506 0.24366 0.24517 0.24472
 
+ 0.24305 0.24234 0.24261 0.24163 0.24408 0.24209 0.24256] - training
 
+2025-03-24 16:36:30,043 - tf_lstm.py - INFO - 验证集损失函数为：[0.61301 0.4526  0.3929  0.36492 0.34239 0.32703 0.31823 0.31337 0.30822
 
+ 0.30591 0.30293 0.30064 0.29839 0.29845 0.29586 0.29498 0.29576 0.29323
 
+ 0.29608 0.29809 0.32526 0.29454 0.2927  0.29199 0.3023  0.30548 0.30169
 
+ 0.30032 0.29995 0.30027 0.3121  0.30454 0.31515 0.3114 ] - training
 
+2025-03-24 16:36:30,101 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e1198e20b7faa5a8a3e4de - insert_trained_model_into_mongo
 
+2025-03-24 16:36:30,130 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e1198e20b7faa5a8a3e4e0 - insert_scaler_model_into_mongo
 
+2025-03-24 16:37:17,731 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:37:22,653 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 16:36:30) - get_scaler_model_from_mongo
 
+2025-03-24 16:37:36,982 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 16:38:22,317 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp8iqm1cck.keras - get_keras_model_from_mongo
 
+2025-03-24 16:42:04,247 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:42:39,643 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:42:39,835 - tf_lstm_train.py - INFO - Program starts execution! - model_training
 
+2025-03-24 16:42:39,889 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
 
+2025-03-24 16:42:39,897 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
 
+2025-03-24 16:42:39,912 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
 
+2025-03-24 16:42:39,921 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
 
+2025-03-24 16:42:39,922 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
 
+2025-03-24 16:42:39,955 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
 
+2025-03-24 16:42:39,955 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:42:39,955 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:42:39,956 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
 
+2025-03-24 16:42:39,956 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:42:39,956 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:42:39,960 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,960 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
 
+2025-03-24 16:42:39,965 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,965 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:42:39,966 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,966 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:42:39,967 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,967 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:42:39,968 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,968 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:42:39,969 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,969 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:42:39,970 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,971 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:42:39,972 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,972 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
 
+2025-03-24 16:42:39,972 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
 
+2025-03-24 16:42:39,987 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,988 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
 
+2025-03-24 16:42:39,989 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
 
+2025-03-24 16:42:39,989 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
 
+2025-03-24 16:42:39,991 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
 
+2025-03-24 16:42:39,991 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:42:39,992 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
 
+2025-03-24 16:42:39,992 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:42:39,993 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
 
+2025-03-24 16:42:39,993 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:42:39,993 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
 
+2025-03-24 16:42:39,994 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:42:39,995 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
 
+2025-03-24 16:42:39,995 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
 
+2025-03-24 16:42:39,995 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
 
+2025-03-24 16:42:39,998 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,000 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:42:40,001 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,003 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,004 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,005 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,007 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,008 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,009 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:42:40,010 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,011 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,012 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,014 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,015 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,015 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:42:40,015 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:42:42,468 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:42:42,660 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
 
+2025-03-24 16:42:42,660 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
 
+2025-03-24 16:42:49,897 - tf_lstm.py - INFO - -----模型训练经过27轮迭代----- - training
 
+2025-03-24 16:42:49,897 - tf_lstm.py - INFO - 训练集损失函数为：[0.96267 0.4795  0.3828  0.33981 0.31385 0.29333 0.28207 0.27515 0.26941
 
+ 0.26535 0.26145 0.25899 0.25735 0.25456 0.25272 0.25108 0.24981 0.24775
 
+ 0.24647 0.24539 0.24586 0.24603 0.24397 0.24372 0.24296 0.24248 0.24168] - training
 
+2025-03-24 16:42:49,897 - tf_lstm.py - INFO - 验证集损失函数为：[0.59539 0.44493 0.39523 0.36333 0.33706 0.32722 0.32066 0.31653 0.31189
 
+ 0.3084  0.30568 0.30329 0.3006  0.29764 0.29469 0.29404 0.29394 0.29482
 
+ 0.29847 0.30606 0.30585 0.30305 0.3043  0.30375 0.30399 0.30229 0.30546] - training
 
+2025-03-24 16:42:49,937 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e11b09e07150fcb0cf945b - insert_trained_model_into_mongo
 
+2025-03-24 16:42:49,962 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e11b09e07150fcb0cf945d - insert_scaler_model_into_mongo
 
+2025-03-24 16:43:02,092 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:43:02,317 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 16:42:49) - get_scaler_model_from_mongo
 
+2025-03-24 16:43:02,413 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 16:43:02,414 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpx3q11gfa.keras - get_keras_model_from_mongo
 
+2025-03-24 16:43:38,278 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:43:59,173 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 16:42:49) - get_scaler_model_from_mongo
 
+2025-03-24 16:44:02,875 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 16:44:04,753 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpllyi19g9.keras - get_keras_model_from_mongo
 
+2025-03-24 16:46:37,754 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 16:46:54,113 - tf_lstm_train.py - INFO - Program starts execution! - model_training
 
+2025-03-24 16:47:01,978 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
 
+2025-03-24 16:47:02,494 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
 
+2025-03-24 16:47:03,101 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
 
+2025-03-24 16:47:05,929 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
 
+2025-03-24 16:47:05,929 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
 
+2025-03-24 16:47:05,962 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
 
+2025-03-24 16:47:05,962 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:47:05,963 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:47:05,964 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
 
+2025-03-24 16:47:05,964 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 16:47:05,964 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 16:47:05,968 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,968 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
 
+2025-03-24 16:47:05,975 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,975 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:47:05,976 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,976 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:47:05,977 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,977 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 16:47:05,979 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,979 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:47:05,980 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,980 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:47:05,981 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,981 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 16:47:05,982 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:05,982 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
 
+2025-03-24 16:47:05,982 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
 
+2025-03-24 16:47:06,132 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:06,133 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
 
+2025-03-24 16:47:06,134 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
 
+2025-03-24 16:47:06,135 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
 
+2025-03-24 16:47:06,136 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
 
+2025-03-24 16:47:06,137 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:47:06,138 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
 
+2025-03-24 16:47:06,138 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:47:06,139 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
 
+2025-03-24 16:47:06,139 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:47:06,140 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
 
+2025-03-24 16:47:06,140 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 16:47:06,141 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
 
+2025-03-24 16:47:06,141 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
 
+2025-03-24 16:47:06,143 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
 
+2025-03-24 16:47:06,750 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,758 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:47:06,762 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,767 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,771 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,776 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,781 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,785 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,790 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
 
+2025-03-24 16:47:06,795 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,800 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,804 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,808 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,813 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,818 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
 
+2025-03-24 16:47:06,818 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
 
+2025-03-24 16:47:18,426 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 16:47:18,427 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 16:50:06,912 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
 
+2025-03-24 16:50:13,396 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
 
+2025-03-24 16:57:55,524 - tf_lstm.py - INFO - -----模型训练经过29轮迭代----- - training
 
+2025-03-24 16:57:55,586 - tf_lstm.py - INFO - 训练集损失函数为：[0.97667 0.481   0.38455 0.34302 0.31664 0.29749 0.28507 0.27773 0.27194
 
+ 0.26851 0.26433 0.26113 0.25886 0.25782 0.25613 0.25257 0.25237 0.25089
 
+ 0.24923 0.24812 0.24534 0.24582 0.24415 0.24344 0.24284 0.24193 0.24205
 
+ 0.24151 0.24144] - training
 
+2025-03-24 16:57:55,632 - tf_lstm.py - INFO - 验证集损失函数为：[0.60394 0.44582 0.40218 0.36976 0.34505 0.33046 0.32577 0.31802 0.31591
 
+ 0.31033 0.30696 0.30481 0.30502 0.30265 0.29893 0.29964 0.29727 0.29644
 
+ 0.29476 0.29623 0.30499 0.30848 0.30849 0.3045  0.30433 0.30477 0.30999
 
+ 0.30893 0.30762] - training
 
+2025-03-24 17:03:00,603 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e11fc4bc458d27a489190c - insert_trained_model_into_mongo
 
+2025-03-24 17:03:01,163 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e11fc5bc458d27a489190e - insert_scaler_model_into_mongo
 
+2025-03-24 17:06:59,385 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:06:59,604 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:02:16) - get_scaler_model_from_mongo
 
+2025-03-24 17:06:59,720 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:06:59,721 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpdm2thyh7.keras - get_keras_model_from_mongo
 
+2025-03-24 17:08:17,377 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:08:17,564 - tf_lstm_train.py - INFO - Program starts execution! - model_training
 
+2025-03-24 17:08:17,618 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
 
+2025-03-24 17:08:17,627 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
 
+2025-03-24 17:08:17,642 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
 
+2025-03-24 17:08:17,652 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
 
+2025-03-24 17:08:17,652 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
 
+2025-03-24 17:08:17,685 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
 
+2025-03-24 17:08:17,685 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 17:08:17,685 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 17:08:17,686 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
 
+2025-03-24 17:08:17,687 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 17:08:17,687 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 17:08:17,690 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,690 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
 
+2025-03-24 17:08:17,698 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,698 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 17:08:17,699 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,699 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 17:08:17,701 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,701 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 17:08:17,702 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,702 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 17:08:17,704 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,704 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 17:08:17,705 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,705 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 17:08:17,706 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,706 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
 
+2025-03-24 17:08:17,706 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
 
+2025-03-24 17:08:17,719 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,719 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
 
+2025-03-24 17:08:17,720 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
 
+2025-03-24 17:08:17,720 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
 
+2025-03-24 17:08:17,722 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
 
+2025-03-24 17:08:17,722 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:08:17,723 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
 
+2025-03-24 17:08:17,723 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:08:17,724 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
 
+2025-03-24 17:08:17,724 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:08:17,725 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
 
+2025-03-24 17:08:17,725 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:08:17,727 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
 
+2025-03-24 17:08:17,727 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
 
+2025-03-24 17:08:17,728 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
 
+2025-03-24 17:08:17,731 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,735 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
 
+2025-03-24 17:08:17,736 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,737 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,738 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,739 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,740 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,741 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,743 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
 
+2025-03-24 17:08:17,744 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,745 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,746 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,747 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,748 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,749 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:08:17,750 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:08:20,183 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:08:20,384 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
 
+2025-03-24 17:08:20,384 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
 
+2025-03-24 17:08:27,815 - tf_lstm.py - INFO - -----模型训练经过28轮迭代----- - training
 
+2025-03-24 17:08:27,815 - tf_lstm.py - INFO - 训练集损失函数为：[1.00304 0.50132 0.40073 0.35612 0.32649 0.30222 0.288   0.27935 0.27276
 
+ 0.26797 0.26366 0.26215 0.25902 0.25727 0.25586 0.2526  0.25153 0.24999
 
+ 0.2468  0.2471  0.24597 0.2473  0.24438 0.24328 0.24272 0.24299 0.2421
 
+ 0.24151] - training
 
+2025-03-24 17:08:27,816 - tf_lstm.py - INFO - 验证集损失函数为：[0.63754 0.46007 0.41528 0.3836  0.34785 0.33632 0.33146 0.32125 0.31666
 
+ 0.31205 0.30924 0.30597 0.30214 0.30073 0.29796 0.29722 0.29637 0.29324
 
+ 0.29767 0.29886 0.30864 0.30684 0.30177 0.29866 0.30389 0.30451 0.30523
 
+ 0.30582] - training
 
+2025-03-24 17:08:27,858 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e1210bd9445a7794dc508b - insert_trained_model_into_mongo
 
+2025-03-24 17:08:27,878 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e1210bd9445a7794dc508d - insert_scaler_model_into_mongo
 
+2025-03-24 17:09:12,279 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:09:12,496 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:08:27) - get_scaler_model_from_mongo
 
+2025-03-24 17:09:12,590 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:09:12,591 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpdtwtlo2m.keras - get_keras_model_from_mongo
 
+2025-03-24 17:11:00,249 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:11:00,443 - tf_lstm_train.py - INFO - Program starts execution! - model_training
 
+2025-03-24 17:11:00,496 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
 
+2025-03-24 17:11:00,506 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
 
+2025-03-24 17:11:00,525 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
 
+2025-03-24 17:11:00,536 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
 
+2025-03-24 17:11:00,537 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
 
+2025-03-24 17:11:00,570 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
 
+2025-03-24 17:11:00,571 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 17:11:00,571 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 17:11:00,572 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
 
+2025-03-24 17:11:00,572 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
 
+2025-03-24 17:11:00,572 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
 
+2025-03-24 17:11:00,576 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,576 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
 
+2025-03-24 17:11:00,581 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,581 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 17:11:00,583 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,583 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 17:11:00,584 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,584 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
 
+2025-03-24 17:11:00,585 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,585 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 17:11:00,586 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,586 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 17:11:00,588 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,588 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
 
+2025-03-24 17:11:00,589 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,589 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
 
+2025-03-24 17:11:00,589 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
 
+2025-03-24 17:11:00,603 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,603 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
 
+2025-03-24 17:11:00,604 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
 
+2025-03-24 17:11:00,604 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
 
+2025-03-24 17:11:00,606 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
 
+2025-03-24 17:11:00,606 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:11:00,607 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
 
+2025-03-24 17:11:00,607 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:11:00,608 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
 
+2025-03-24 17:11:00,608 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:11:00,609 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
 
+2025-03-24 17:11:00,609 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
 
+2025-03-24 17:11:00,610 - data_handler.py - INFO - 数据总数：8207, 时序缺失的间隔：3, 其中，较长的时间间隔：14 - missing_time_splite
 
+2025-03-24 17:11:00,610 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
 
+2025-03-24 17:11:00,611 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
 
+2025-03-24 17:11:00,613 - data_handler.py - INFO - 2022-05-02 08:00:00 ~ 2022-05-05 23:45:00 有 352 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,617 - data_handler.py - INFO - 2022-05-09 00:00:00 ~ 2022-07-01 06:30:00 有 5115 个点, 填补 2 个点. - data_fill
 
+2025-03-24 17:11:00,619 - data_handler.py - INFO - 2022-07-01 23:45:00 ~ 2022-07-08 06:15:00 有 603 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,620 - data_handler.py - INFO - 2022-07-08 23:45:00 ~ 2022-07-09 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,621 - data_handler.py - INFO - 2022-07-09 23:45:00 ~ 2022-07-10 06:15:00 有 27 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,622 - data_handler.py - INFO - 2022-07-10 23:45:00 ~ 2022-07-11 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,623 - data_handler.py - INFO - 2022-07-11 23:45:00 ~ 2022-07-12 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,624 - data_handler.py - INFO - 2022-07-12 23:45:00 ~ 2022-07-13 06:00:00 有 26 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,625 - data_handler.py - INFO - 2022-07-13 23:45:00 ~ 2022-08-01 20:00:00 有 1810 个点, 填补 2 个点. - data_fill
 
+2025-03-24 17:11:00,627 - data_handler.py - INFO - 2022-08-01 23:45:00 ~ 2022-08-02 21:00:00 有 86 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,628 - data_handler.py - INFO - 2022-08-02 23:45:00 ~ 2022-08-04 00:00:00 有 98 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,629 - data_handler.py - INFO - 2022-08-04 23:15:00 ~ 2022-08-05 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,630 - data_handler.py - INFO - 2022-08-05 23:15:00 ~ 2022-08-06 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,631 - data_handler.py - INFO - 2022-08-06 23:15:00 ~ 2022-08-07 00:00:00 有 4 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,632 - data_handler.py - INFO - 2022-08-07 23:15:00 ~ 2022-08-07 23:45:00 有 3 个点, 填补 0 个点. - data_fill
 
+2025-03-24 17:11:00,632 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
 
+2025-03-24 17:11:03,098 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
 
+2025-03-24 17:11:03,294 - dbmg.py - INFO - ⚠️ 未找到模型 'fmi' 的有效记录 - get_keras_model_from_mongo
 
+2025-03-24 17:11:03,295 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
 
+2025-03-24 17:11:10,984 - tf_lstm.py - INFO - -----模型训练经过29轮迭代----- - training
 
+2025-03-24 17:11:10,985 - tf_lstm.py - INFO - 训练集损失函数为：[1.0187  0.50769 0.4003  0.34891 0.31975 0.30011 0.2885  0.28014 0.27268
 
+ 0.26722 0.26358 0.26146 0.25936 0.25838 0.25643 0.25476 0.25288 0.25224
 
+ 0.25059 0.2489  0.24688 0.24658 0.24647 0.2449  0.24442 0.24386 0.24395
 
+ 0.24276 0.243  ] - training
 
+2025-03-24 17:11:10,985 - tf_lstm.py - INFO - 验证集损失函数为：[0.64388 0.46151 0.40515 0.37103 0.35298 0.33836 0.32787 0.31869 0.3132
 
+ 0.30919 0.30599 0.30453 0.30324 0.30072 0.29934 0.29613 0.29618 0.29528
 
+ 0.29365 0.29378 0.30024 0.30205 0.29393 0.29537 0.29821 0.30199 0.30141
 
+ 0.3054  0.30375] - training
 
+2025-03-24 17:11:11,026 - dbmg.py - INFO - ✅ 模型 fmi 保存成功 | 文档ID: 67e121af8347b570c0051984 - insert_trained_model_into_mongo
 
+2025-03-24 17:11:11,046 - dbmg.py - INFO - ✅ 缩放器 fmi 保存成功 | 文档ID: 67e121af8347b570c0051986 - insert_scaler_model_into_mongo
 
+2025-03-24 17:14:35,654 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:14:35,856 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:14:35,953 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:14:35,954 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpvvvszb77.keras - get_keras_model_from_mongo
 
+2025-03-24 17:30:37,721 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:31:04,516 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:36:19,276 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:36:45,665 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:37:32,496 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:37:38,183 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:37:44,340 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:37:45,648 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpxnt3vn8d.keras - get_keras_model_from_mongo
 
+2025-03-24 17:48:35,080 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:48:39,970 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:48:44,344 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:48:46,276 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpc5qqpto_.keras - get_keras_model_from_mongo
 
+2025-03-24 17:49:12,303 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:49:16,406 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:49:24,029 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:49:31,117 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp3dcxyg51.keras - get_keras_model_from_mongo
 
+2025-03-24 17:50:55,775 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:50:59,392 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:51:03,446 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:51:04,445 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmpr1zyclo4.keras - get_keras_model_from_mongo
 
+2025-03-24 17:51:56,991 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:52:00,812 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:52:05,476 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:52:06,146 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp9z7uk0pw.keras - get_keras_model_from_mongo
 
+2025-03-24 17:52:43,974 - module_wrapper.py - WARNING - From E:\compete\app\model\losses.py:10: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
 
+ - _tfmw_add_deprecation_warning
 
+2025-03-24 17:52:44,193 - dbmg.py - INFO - ✅ 成功加载 fmi 的缩放器 (版本时间: 2025-03-24 17:11:10) - get_scaler_model_from_mongo
 
+2025-03-24 17:52:44,285 - dbmg.py - INFO - fmi 模型成功从 MongoDB 加载！ - get_keras_model_from_mongo
 
+2025-03-24 17:52:44,285 - dbmg.py - INFO - 🧹 已清理临时文件: C:\Users\ADMINI~1\AppData\Local\Temp\tmp94emdn_b.keras - get_keras_model_from_mongo
 
+2025-03-24 17:52:44,582 - tf_lstm.py - INFO - 执行预测方法 - predict

+ 11 - 6
app/model/tf_lstm_train.py
View File 

@@ -8,6 +8,9 @@ import json, os
 
 import numpy as np
 
 import numpy as np
 
 import traceback
 
 import traceback
 
 import logging
 
 import logging
 
+
 
+from pyexpat import features
 
+
 
 from app.common.logs import args
 
 from app.common.logs import args
 
 from app.common.data_handler import DataHandler, write_number_to_file
 
 from app.common.data_handler import DataHandler, write_number_to_file
 
 import time
 
 import time
 
@@ -57,12 +60,14 @@ def model_training(train_data, input_file, cap):
 
         # 更新算法状态：1. 启动成功
 
         # 更新算法状态：1. 启动成功
 
         write_number_to_file(os.path.join(output_file, status_file), 1, 1, 'rewrite')
 
         write_number_to_file(os.path.join(output_file, status_file), 1, 1, 'rewrite')
 
         # ------------ 组装模型数据 ------------
 
         # ------------ 组装模型数据 ------------
 
-        args['params'] = json.dumps(args)
 
-        args['descr'] = '南网竞赛-{}'.format(farm_id)
 
-        args['gen_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
 
-        args['model_table'] += farm_id
 
-        args['scaler_table'] += farm_id
 
-        args['features'] = ','.join(dh.opt.features)
 
+        args['Model']['features'] = ','.join(dh.opt.features)
 
+        args.update({
 
+            'params': json.dumps(args),
 
+            'descr': f'南网竞赛-{farm_id}',
 
+            'gen_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()),
 
+            'model_table': args['model_table'] + farm_id,
 
+            'scaler_table': args['scaler_table'] + farm_id
 
+        })
 
         mgUtils.insert_trained_model_into_mongo(ts_model, args)
 
         mgUtils.insert_trained_model_into_mongo(ts_model, args)
 
         mgUtils.insert_scaler_model_into_mongo(scaled_train_bytes, scaled_target_bytes, args)
 
         mgUtils.insert_scaler_model_into_mongo(scaled_train_bytes, scaled_target_bytes, args)
 
         # 更新算法状态：正常结束
 
         # 更新算法状态：正常结束

+ 672 - 672
app/predict/data/DQYC/20220807/2365/OUT/DQYC_OUT_PREDICT_POWER.txt
View File 

@@ -1,673 +1,673 @@
 
 PlantID Datetime Power
 
 PlantID Datetime Power
 
-2365 "2022-08-08 00:00:00" 11.48
 
-2365 "2022-08-08 00:15:00" 11.58
 
-2365 "2022-08-08 00:30:00" 11.42
 
-2365 "2022-08-08 00:45:00" 12.1
 
-2365 "2022-08-08 01:00:00" 11.77
 
-2365 "2022-08-08 01:15:00" 12.47
 
-2365 "2022-08-08 01:30:00" 11.26
 
-2365 "2022-08-08 01:45:00" 12.04
 
-2365 "2022-08-08 02:00:00" 12.06
 
-2365 "2022-08-08 02:15:00" 11.15
 
-2365 "2022-08-08 02:30:00" 11.42
 
-2365 "2022-08-08 02:45:00" 11.37
 
-2365 "2022-08-08 03:00:00" 11.84
 
-2365 "2022-08-08 03:15:00" 11.24
 
-2365 "2022-08-08 03:30:00" 12.24
 
-2365 "2022-08-08 03:45:00" 11.4
 
-2365 "2022-08-08 04:00:00" 11.48
 
-2365 "2022-08-08 04:15:00" 11.58
 
-2365 "2022-08-08 04:30:00" 11.42
 
-2365 "2022-08-08 04:45:00" 12.1
 
-2365 "2022-08-08 05:00:00" 11.77
 
-2365 "2022-08-08 05:15:00" 12.47
 
-2365 "2022-08-08 05:30:00" 11.26
 
-2365 "2022-08-08 05:45:00" 12.04
 
-2365 "2022-08-08 06:00:00" 12.06
 
-2365 "2022-08-08 06:15:00" 11.15
 
-2365 "2022-08-08 06:30:00" 11.41
 
-2365 "2022-08-08 06:45:00" 11.37
 
-2365 "2022-08-08 07:00:00" 11.84
 
-2365 "2022-08-08 07:15:00" 11.24
 
-2365 "2022-08-08 07:30:00" 12.24
 
-2365 "2022-08-08 07:45:00" 11.4
 
-2365 "2022-08-08 08:00:00" 11.48
 
-2365 "2022-08-08 08:15:00" 11.58
 
-2365 "2022-08-08 08:30:00" 11.42
 
-2365 "2022-08-08 08:45:00" 12.1
 
-2365 "2022-08-08 09:00:00" 11.77
 
-2365 "2022-08-08 09:15:00" 12.47
 
-2365 "2022-08-08 09:30:00" 11.26
 
-2365 "2022-08-08 09:45:00" 12.04
 
-2365 "2022-08-08 10:00:00" 12.06
 
-2365 "2022-08-08 10:15:00" 11.15
 
-2365 "2022-08-08 10:30:00" 11.41
 
-2365 "2022-08-08 10:45:00" 11.37
 
-2365 "2022-08-08 11:00:00" 11.84
 
-2365 "2022-08-08 11:15:00" 11.24
 
-2365 "2022-08-08 11:30:00" 12.24
 
-2365 "2022-08-08 11:45:00" 11.4
 
-2365 "2022-08-08 12:00:00" 11.48
 
-2365 "2022-08-08 12:15:00" 11.58
 
-2365 "2022-08-08 12:30:00" 11.42
 
-2365 "2022-08-08 12:45:00" 12.1
 
-2365 "2022-08-08 13:00:00" 11.77
 
-2365 "2022-08-08 13:15:00" 12.47
 
-2365 "2022-08-08 13:30:00" 11.26
 
-2365 "2022-08-08 13:45:00" 12.04
 
-2365 "2022-08-08 14:00:00" 12.06
 
-2365 "2022-08-08 14:15:00" 11.15
 
-2365 "2022-08-08 14:30:00" 11.42
 
-2365 "2022-08-08 14:45:00" 11.37
 
-2365 "2022-08-08 15:00:00" 11.84
 
-2365 "2022-08-08 15:15:00" 11.24
 
-2365 "2022-08-08 15:30:00" 12.24
 
-2365 "2022-08-08 15:45:00" 11.4
 
-2365 "2022-08-08 16:00:00" 11.48
 
-2365 "2022-08-08 16:15:00" 11.58
 
-2365 "2022-08-08 16:30:00" 11.42
 
-2365 "2022-08-08 16:45:00" 12.1
 
-2365 "2022-08-08 17:00:00" 11.77
 
-2365 "2022-08-08 17:15:00" 12.47
 
-2365 "2022-08-08 17:30:00" 11.26
 
-2365 "2022-08-08 17:45:00" 12.04
 
-2365 "2022-08-08 18:00:00" 12.06
 
-2365 "2022-08-08 18:15:00" 11.15
 
-2365 "2022-08-08 18:30:00" 11.42
 
-2365 "2022-08-08 18:45:00" 11.37
 
-2365 "2022-08-08 19:00:00" 11.84
 
-2365 "2022-08-08 19:15:00" 11.24
 
-2365 "2022-08-08 19:30:00" 12.24
 
-2365 "2022-08-08 19:45:00" 11.4
 
-2365 "2022-08-08 20:00:00" 11.48
 
-2365 "2022-08-08 20:15:00" 11.58
 
-2365 "2022-08-08 20:30:00" 11.42
 
-2365 "2022-08-08 20:45:00" 12.1
 
-2365 "2022-08-08 21:00:00" 11.77
 
-2365 "2022-08-08 21:15:00" 12.47
 
-2365 "2022-08-08 21:30:00" 11.26
 
-2365 "2022-08-08 21:45:00" 12.04
 
-2365 "2022-08-08 22:00:00" 12.06
 
-2365 "2022-08-08 22:15:00" 11.15
 
-2365 "2022-08-08 22:30:00" 11.41
 
-2365 "2022-08-08 22:45:00" 11.37
 
-2365 "2022-08-08 23:00:00" 11.84
 
-2365 "2022-08-08 23:15:00" 11.24
 
-2365 "2022-08-08 23:30:00" 12.24
 
-2365 "2022-08-08 23:45:00" 11.4
 
-2365 "2022-08-09 00:00:00" 11.48
 
-2365 "2022-08-09 00:15:00" 11.58
 
-2365 "2022-08-09 00:30:00" 11.42
 
-2365 "2022-08-09 00:45:00" 12.1
 
-2365 "2022-08-09 01:00:00" 11.77
 
-2365 "2022-08-09 01:15:00" 12.47
 
-2365 "2022-08-09 01:30:00" 11.26
 
-2365 "2022-08-09 01:45:00" 12.04
 
-2365 "2022-08-09 02:00:00" 12.06
 
-2365 "2022-08-09 02:15:00" 11.15
 
-2365 "2022-08-09 02:30:00" 11.42
 
-2365 "2022-08-09 02:45:00" 11.37
 
-2365 "2022-08-09 03:00:00" 11.84
 
-2365 "2022-08-09 03:15:00" 11.24
 
-2365 "2022-08-09 03:30:00" 12.24
 
-2365 "2022-08-09 03:45:00" 11.4
 
-2365 "2022-08-09 04:00:00" 11.48
 
-2365 "2022-08-09 04:15:00" 11.58
 
-2365 "2022-08-09 04:30:00" 11.42
 
-2365 "2022-08-09 04:45:00" 12.1
 
-2365 "2022-08-09 05:00:00" 11.77
 
-2365 "2022-08-09 05:15:00" 12.47
 
-2365 "2022-08-09 05:30:00" 11.26
 
-2365 "2022-08-09 05:45:00" 12.04
 
-2365 "2022-08-09 06:00:00" 12.06
 
-2365 "2022-08-09 06:15:00" 11.15
 
-2365 "2022-08-09 06:30:00" 11.42
 
-2365 "2022-08-09 06:45:00" 11.37
 
-2365 "2022-08-09 07:00:00" 11.84
 
-2365 "2022-08-09 07:15:00" 11.24
 
-2365 "2022-08-09 07:30:00" 12.24
 
-2365 "2022-08-09 07:45:00" 11.4
 
-2365 "2022-08-09 08:00:00" 11.48
 
-2365 "2022-08-09 08:15:00" 11.58
 
-2365 "2022-08-09 08:30:00" 11.42
 
-2365 "2022-08-09 08:45:00" 12.1
 
-2365 "2022-08-09 09:00:00" 11.77
 
-2365 "2022-08-09 09:15:00" 12.47
 
-2365 "2022-08-09 09:30:00" 11.26
 
-2365 "2022-08-09 09:45:00" 12.04
 
-2365 "2022-08-09 10:00:00" 12.06
 
-2365 "2022-08-09 10:15:00" 11.15
 
-2365 "2022-08-09 10:30:00" 11.42
 
-2365 "2022-08-09 10:45:00" 11.37
 
-2365 "2022-08-09 11:00:00" 11.84
 
-2365 "2022-08-09 11:15:00" 11.24
 
-2365 "2022-08-09 11:30:00" 12.24
 
-2365 "2022-08-09 11:45:00" 11.4
 
-2365 "2022-08-09 12:00:00" 11.48
 
-2365 "2022-08-09 12:15:00" 11.58
 
-2365 "2022-08-09 12:30:00" 11.42
 
-2365 "2022-08-09 12:45:00" 12.1
 
-2365 "2022-08-09 13:00:00" 11.77
 
-2365 "2022-08-09 13:15:00" 12.47
 
-2365 "2022-08-09 13:30:00" 11.26
 
-2365 "2022-08-09 13:45:00" 12.04
 
-2365 "2022-08-09 14:00:00" 12.06
 
-2365 "2022-08-09 14:15:00" 11.15
 
-2365 "2022-08-09 14:30:00" 11.42
 
-2365 "2022-08-09 14:45:00" 11.37
 
-2365 "2022-08-09 15:00:00" 11.84
 
-2365 "2022-08-09 15:15:00" 11.24
 
-2365 "2022-08-09 15:30:00" 12.24
 
-2365 "2022-08-09 15:45:00" 11.4
 
-2365 "2022-08-09 16:00:00" 11.48
 
-2365 "2022-08-09 16:15:00" 11.58
 
-2365 "2022-08-09 16:30:00" 11.42
 
-2365 "2022-08-09 16:45:00" 12.1
 
-2365 "2022-08-09 17:00:00" 11.77
 
-2365 "2022-08-09 17:15:00" 12.47
 
-2365 "2022-08-09 17:30:00" 11.26
 
-2365 "2022-08-09 17:45:00" 12.04
 
-2365 "2022-08-09 18:00:00" 12.06
 
-2365 "2022-08-09 18:15:00" 11.15
 
-2365 "2022-08-09 18:30:00" 11.42
 
-2365 "2022-08-09 18:45:00" 11.37
 
-2365 "2022-08-09 19:00:00" 11.84
 
-2365 "2022-08-09 19:15:00" 11.24
 
-2365 "2022-08-09 19:30:00" 12.24
 
-2365 "2022-08-09 19:45:00" 11.4
 
-2365 "2022-08-09 20:00:00" 11.48
 
-2365 "2022-08-09 20:15:00" 11.58
 
-2365 "2022-08-09 20:30:00" 11.42
 
-2365 "2022-08-09 20:45:00" 12.1
 
-2365 "2022-08-09 21:00:00" 11.77
 
-2365 "2022-08-09 21:15:00" 12.47
 
-2365 "2022-08-09 21:30:00" 11.26
 
-2365 "2022-08-09 21:45:00" 12.04
 
-2365 "2022-08-09 22:00:00" 12.06
 
-2365 "2022-08-09 22:15:00" 11.15
 
-2365 "2022-08-09 22:30:00" 11.42
 
-2365 "2022-08-09 22:45:00" 11.37
 
-2365 "2022-08-09 23:00:00" 11.84
 
-2365 "2022-08-09 23:15:00" 11.24
 
-2365 "2022-08-09 23:30:00" 12.24
 
-2365 "2022-08-09 23:45:00" 11.4
 
-2365 "2022-08-10 00:00:00" 11.48
 
-2365 "2022-08-10 00:15:00" 11.58
 
-2365 "2022-08-10 00:30:00" 11.42
 
-2365 "2022-08-10 00:45:00" 12.1
 
-2365 "2022-08-10 01:00:00" 11.77
 
-2365 "2022-08-10 01:15:00" 12.47
 
-2365 "2022-08-10 01:30:00" 11.26
 
-2365 "2022-08-10 01:45:00" 12.04
 
-2365 "2022-08-10 02:00:00" 12.06
 
-2365 "2022-08-10 02:15:00" 11.15
 
-2365 "2022-08-10 02:30:00" 11.42
 
-2365 "2022-08-10 02:45:00" 11.37
 
-2365 "2022-08-10 03:00:00" 11.84
 
-2365 "2022-08-10 03:15:00" 11.24
 
-2365 "2022-08-10 03:30:00" 12.24
 
-2365 "2022-08-10 03:45:00" 11.4
 
-2365 "2022-08-10 04:00:00" 11.48
 
-2365 "2022-08-10 04:15:00" 11.58
 
-2365 "2022-08-10 04:30:00" 11.42
 
-2365 "2022-08-10 04:45:00" 12.1
 
-2365 "2022-08-10 05:00:00" 11.77
 
-2365 "2022-08-10 05:15:00" 12.47
 
-2365 "2022-08-10 05:30:00" 11.26
 
-2365 "2022-08-10 05:45:00" 12.04
 
-2365 "2022-08-10 06:00:00" 12.06
 
-2365 "2022-08-10 06:15:00" 11.15
 
-2365 "2022-08-10 06:30:00" 11.42
 
-2365 "2022-08-10 06:45:00" 11.37
 
-2365 "2022-08-10 07:00:00" 11.84
 
-2365 "2022-08-10 07:15:00" 11.24
 
-2365 "2022-08-10 07:30:00" 12.24
 
-2365 "2022-08-10 07:45:00" 11.4
 
-2365 "2022-08-10 08:00:00" 11.48
 
-2365 "2022-08-10 08:15:00" 11.58
 
-2365 "2022-08-10 08:30:00" 11.42
 
-2365 "2022-08-10 08:45:00" 12.1
 
-2365 "2022-08-10 09:00:00" 11.77
 
-2365 "2022-08-10 09:15:00" 12.47
 
-2365 "2022-08-10 09:30:00" 11.26
 
-2365 "2022-08-10 09:45:00" 12.04
 
-2365 "2022-08-10 10:00:00" 12.06
 
-2365 "2022-08-10 10:15:00" 11.15
 
-2365 "2022-08-10 10:30:00" 11.42
 
-2365 "2022-08-10 10:45:00" 11.37
 
-2365 "2022-08-10 11:00:00" 11.84
 
-2365 "2022-08-10 11:15:00" 11.24
 
-2365 "2022-08-10 11:30:00" 12.24
 
-2365 "2022-08-10 11:45:00" 11.4
 
-2365 "2022-08-10 12:00:00" 11.48
 
-2365 "2022-08-10 12:15:00" 11.58
 
-2365 "2022-08-10 12:30:00" 11.42
 
-2365 "2022-08-10 12:45:00" 12.1
 
-2365 "2022-08-10 13:00:00" 11.77
 
-2365 "2022-08-10 13:15:00" 12.47
 
-2365 "2022-08-10 13:30:00" 11.26
 
-2365 "2022-08-10 13:45:00" 12.04
 
-2365 "2022-08-10 14:00:00" 12.06
 
-2365 "2022-08-10 14:15:00" 11.15
 
-2365 "2022-08-10 14:30:00" 11.42
 
-2365 "2022-08-10 14:45:00" 11.37
 
-2365 "2022-08-10 15:00:00" 11.84
 
-2365 "2022-08-10 15:15:00" 11.24
 
-2365 "2022-08-10 15:30:00" 12.24
 
-2365 "2022-08-10 15:45:00" 11.4
 
-2365 "2022-08-10 16:00:00" 11.48
 
-2365 "2022-08-10 16:15:00" 11.58
 
-2365 "2022-08-10 16:30:00" 11.42
 
-2365 "2022-08-10 16:45:00" 12.1
 
-2365 "2022-08-10 17:00:00" 11.77
 
-2365 "2022-08-10 17:15:00" 12.47
 
-2365 "2022-08-10 17:30:00" 11.26
 
-2365 "2022-08-10 17:45:00" 12.04
 
-2365 "2022-08-10 18:00:00" 12.06
 
-2365 "2022-08-10 18:15:00" 11.15
 
-2365 "2022-08-10 18:30:00" 11.42
 
-2365 "2022-08-10 18:45:00" 11.37
 
-2365 "2022-08-10 19:00:00" 11.84
 
-2365 "2022-08-10 19:15:00" 11.24
 
-2365 "2022-08-10 19:30:00" 12.24
 
-2365 "2022-08-10 19:45:00" 11.4
 
-2365 "2022-08-10 20:00:00" 11.48
 
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-2365 "2022-08-14 18:45:00" 11.37
 
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-2365 "2022-08-14 20:00:00" 11.48
 
-2365 "2022-08-14 20:15:00" 11.58
 
-2365 "2022-08-14 20:30:00" 11.42
 
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+2365 "2022-08-12 23:15:00" 1.99
 
+2365 "2022-08-12 23:30:00" 2.01
 
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+2365 "2022-08-13 02:00:00" 0.3
 
+2365 "2022-08-13 02:15:00" 0.35
 
+2365 "2022-08-13 02:30:00" 1.1
 
+2365 "2022-08-13 02:45:00" 0.86
 
+2365 "2022-08-13 03:00:00" 1.24
 
+2365 "2022-08-13 03:15:00" 1.55
 
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+2365 "2022-08-13 03:45:00" 1.75
 
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+2365 "2022-08-13 04:30:00" 0.86
 
+2365 "2022-08-13 04:45:00" 0.49
 
+2365 "2022-08-13 05:00:00" 0.4
 
+2365 "2022-08-13 05:15:00" 0.48
 
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+2365 "2022-08-13 06:00:00" 0.55
 
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+2365 "2022-08-13 06:45:00" 1.17
 
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+2365 "2022-08-13 16:45:00" 7.5
 
+2365 "2022-08-13 17:00:00" 7.47
 
+2365 "2022-08-13 17:15:00" 7.37
 
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+2365 "2022-08-14 13:30:00" 50.0
 
+2365 "2022-08-14 13:45:00" 50.0
 
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+2365 "2022-08-14 14:15:00" 50.0
 
+2365 "2022-08-14 14:30:00" 50.0
 
+2365 "2022-08-14 14:45:00" 50.0
 
+2365 "2022-08-14 15:00:00" 50.0
 
+2365 "2022-08-14 15:15:00" 50.0
 
+2365 "2022-08-14 15:30:00" 31.89
 
+2365 "2022-08-14 15:45:00" 33.66
 
+2365 "2022-08-14 16:00:00" 5.81
 
+2365 "2022-08-14 16:15:00" 5.63
 
+2365 "2022-08-14 16:30:00" 5.42
 
+2365 "2022-08-14 16:45:00" 5.21
 
+2365 "2022-08-14 17:00:00" 5.19
 
+2365 "2022-08-14 17:15:00" 4.99
 
+2365 "2022-08-14 17:30:00" 4.66
 
+2365 "2022-08-14 17:45:00" 4.7
 
+2365 "2022-08-14 18:00:00" 4.16
 
+2365 "2022-08-14 18:15:00" 4.17
 
+2365 "2022-08-14 18:30:00" 4.27
 
+2365 "2022-08-14 18:45:00" 4.08
 
+2365 "2022-08-14 19:00:00" 4.37
 
+2365 "2022-08-14 19:15:00" 4.38
 
+2365 "2022-08-14 19:30:00" 4.39
 
+2365 "2022-08-14 19:45:00" 4.34
 
+2365 "2022-08-14 20:00:00" 4.57
 
+2365 "2022-08-14 20:15:00" 4.34
 
+2365 "2022-08-14 20:30:00" 4.14
 
+2365 "2022-08-14 20:45:00" 3.79
 
+2365 "2022-08-14 21:00:00" 3.7
 
+2365 "2022-08-14 21:15:00" 3.53
 
+2365 "2022-08-14 21:30:00" 3.34
 
+2365 "2022-08-14 21:45:00" 3.3
 
+2365 "2022-08-14 22:00:00" 2.78
 
+2365 "2022-08-14 22:15:00" 2.9
 
+2365 "2022-08-14 22:30:00" 3.04
 
+2365 "2022-08-14 22:45:00" 2.89
 
+2365 "2022-08-14 23:00:00" 3.34
 
+2365 "2022-08-14 23:15:00" 3.4
 
+2365 "2022-08-14 23:30:00" 3.58
 
+2365 "2022-08-14 23:45:00" 3.52

+ 3 - 5
app/predict/tf_lstm_pre.py
View File 

@@ -14,10 +14,9 @@ import time, json
 
 
 
 model_lock = Lock()
 
 model_lock = Lock()
 
 from itertools import chain
 
 from itertools import chain
 
-from app.common.logs import args
 
+from app.common.logs import logger, args
 
 from app.model.tf_lstm import TSHandler
 
 from app.model.tf_lstm import TSHandler
 
 from app.common.dbmg import MongoUtils
 
 from app.common.dbmg import MongoUtils
 
-from app.predict.main import logger
 
 
 
 np.random.seed(42)  # NumPy随机种子
 
 np.random.seed(42)  # NumPy随机种子
 
 
 
@@ -41,10 +40,9 @@ def model_prediction(pre_data, input_file, cap):
 
         args['model_table'] += farm_id
 
         args['model_table'] += farm_id
 
         args['scaler_table'] += farm_id
 
         args['scaler_table'] += farm_id
 
         feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(args)
 
         feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(args)
 
-        ts.opt.cap = round(target_scaler.transform(cap), 2)
 
-
 
+        ts.opt.cap = round(target_scaler.transform(np.array([[float(cap)]]))[0, 0], 2)
 
         ts.get_model(args)
 
         ts.get_model(args)
 
-        dh.opt.features = json.loads(ts.model_params).get('features', ts.opt.features)
 
+        dh.opt.features = json.loads(ts.model_params).get('Model').get('features', ts.opt.features).split(',')
 
         scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler)
 
         scaled_pre_x, pre_data = dh.pre_data_handler(pre_data, feature_scaler)
 
 
 
         success = 1
 
         success = 1