04030930

app/logs/2025-04-03/south-forecast.2025-04-03.0.log

@@ -0,0 +1,226 @@
+2025-04-03 09:26:58,615 - 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
+2025-04-03 09:26:58,902 - tf_fmi_train.py - INFO - Program starts execution! - model_training
+2025-04-03 09:26:58,958 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-04-03 09:26:58,966 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubDireciton', 'HubSpeed'} - data_column_cleaning
+2025-04-03 09:26:58,984 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
+2025-04-03 09:26:58,997 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
+2025-04-03 09:26:58,998 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
+2025-04-03 09:26:59,031 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
+2025-04-03 09:26:59,031 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
+2025-04-03 09:26:59,031 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
+2025-04-03 09:26:59,033 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
+2025-04-03 09:26:59,033 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
+2025-04-03 09:26:59,033 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
+2025-04-03 09:26:59,036 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,037 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
+2025-04-03 09:26:59,042 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,042 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:26:59,043 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,043 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:26:59,044 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,044 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:26:59,045 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,046 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:26:59,046 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,046 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:26:59,047 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,048 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:26:59,048 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,049 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
+2025-04-03 09:26:59,049 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
+2025-04-03 09:26:59,062 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,062 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
+2025-04-03 09:26:59,063 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
+2025-04-03 09:26:59,063 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
+2025-04-03 09:26:59,065 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
+2025-04-03 09:26:59,065 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:26:59,066 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
+2025-04-03 09:26:59,066 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:26:59,066 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
+2025-04-03 09:26:59,066 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:26:59,067 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
+2025-04-03 09:26:59,068 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:26:59,068 - 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
+2025-04-03 09:26:59,068 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
+2025-04-03 09:26:59,070 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
+2025-04-03 09:26:59,075 - 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
+2025-04-03 09:26:59,079 - 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
+2025-04-03 09:26:59,081 - 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
+2025-04-03 09:26:59,082 - 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
+2025-04-03 09:26:59,083 - 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
+2025-04-03 09:26:59,084 - 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
+2025-04-03 09:26:59,085 - 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
+2025-04-03 09:26:59,086 - 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
+2025-04-03 09:26:59,088 - 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
+2025-04-03 09:26:59,089 - 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
+2025-04-03 09:26:59,090 - 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
+2025-04-03 09:26:59,091 - 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
+2025-04-03 09:26:59,092 - 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
+2025-04-03 09:26:59,093 - 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
+2025-04-03 09:26:59,094 - 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
+2025-04-03 09:26:59,094 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
+2025-04-03 09:27:01,585 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:27:01,586 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:27:01,919 - dbmg.py - INFO - ❌ 系统异常: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - get_keras_model_from_mongo
+2025-04-03 09:27:01,920 - tf_fmi.py - INFO - 加强训练加载模型权重失败:("Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}",) - train_init
+2025-04-03 09:27:14,119 - tf_fmi.py - INFO - -----模型训练经过31轮迭代----- - training
+2025-04-03 09:27:14,120 - tf_fmi.py - INFO - 训练集损失函数为：[53.51689 30.66457 18.66789 11.65896  7.50273  4.84363  3.09119  1.95949
+  1.25144  0.82907  0.58984  0.46101  0.39091  0.3534   0.33623  0.32777
+  0.31812  0.31297  0.30957  0.30708  0.30657  0.30585  0.30357  0.30433
+  0.30124  0.30502  0.29952  0.30102  0.30003  0.29883  0.30341] - training
+2025-04-03 09:27:14,120 - tf_fmi.py - INFO - 验证集损失函数为：[33.29909 22.46415 14.20688  9.14478  5.98742  3.91006  2.55501  1.67482
+  1.12169  0.7885   0.59235  0.48276  0.42948  0.41301  0.37812  0.35537
+  0.35367  0.35108  0.34602  0.34273  0.33807  0.34047  0.33835  0.33889
+  0.34738  0.34364  0.34219  0.34872  0.34872  0.34948  0.3491 ] - training
+2025-04-03 09:27:14,206 - dbmg.py - INFO - ❌ 数据库操作 - 详细错误: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - insert_trained_model_into_mongo
+2025-04-03 09:29:33,755 - 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
+2025-04-03 09:29:33,856 - tf_fmi_train.py - INFO - Program starts execution! - model_training
+2025-04-03 09:29:33,912 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-04-03 09:29:33,921 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
+2025-04-03 09:29:33,936 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
+2025-04-03 09:29:33,946 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
+2025-04-03 09:29:33,946 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
+2025-04-03 09:29:33,980 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
+2025-04-03 09:29:33,981 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
+2025-04-03 09:29:33,981 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
+2025-04-03 09:29:33,982 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
+2025-04-03 09:29:33,982 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
+2025-04-03 09:29:33,982 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
+2025-04-03 09:29:33,986 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,986 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
+2025-04-03 09:29:33,991 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,991 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:29:33,992 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,992 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:29:33,993 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,993 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:29:33,994 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,994 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:29:33,995 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,995 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:29:33,996 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,996 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:29:33,997 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
+2025-04-03 09:29:33,997 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
+2025-04-03 09:29:33,997 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
+2025-04-03 09:29:34,011 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
+2025-04-03 09:29:34,011 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
+2025-04-03 09:29:34,013 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
+2025-04-03 09:29:34,013 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
+2025-04-03 09:29:34,014 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
+2025-04-03 09:29:34,015 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:34,016 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
+2025-04-03 09:29:34,016 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:34,016 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
+2025-04-03 09:29:34,016 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:34,017 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
+2025-04-03 09:29:34,017 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:34,018 - 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
+2025-04-03 09:29:34,018 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
+2025-04-03 09:29:34,020 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
+2025-04-03 09:29:34,021 - 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
+2025-04-03 09:29:34,024 - 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
+2025-04-03 09:29:34,026 - 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
+2025-04-03 09:29:34,026 - 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
+2025-04-03 09:29:34,028 - 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
+2025-04-03 09:29:34,028 - 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
+2025-04-03 09:29:34,030 - 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
+2025-04-03 09:29:34,031 - 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
+2025-04-03 09:29:34,033 - 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
+2025-04-03 09:29:34,034 - 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
+2025-04-03 09:29:34,035 - 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
+2025-04-03 09:29:34,036 - 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
+2025-04-03 09:29:34,037 - 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
+2025-04-03 09:29:34,038 - 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
+2025-04-03 09:29:34,039 - 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
+2025-04-03 09:29:34,039 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
+2025-04-03 09:29:53,779 - 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
+2025-04-03 09:29:53,878 - tf_fmi_train.py - INFO - Program starts execution! - model_training
+2025-04-03 09:29:53,935 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-04-03 09:29:53,944 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
+2025-04-03 09:29:53,959 - data_cleaning.py - INFO - 行清洗：清洗的行数有：881，缺失的列有： - key_field_row_cleaning
+2025-04-03 09:29:53,969 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
+2025-04-03 09:29:53,969 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
+2025-04-03 09:29:54,003 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
+2025-04-03 09:29:54,003 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
+2025-04-03 09:29:54,003 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
+2025-04-03 09:29:54,005 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
+2025-04-03 09:29:54,005 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
+2025-04-03 09:29:54,005 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
+2025-04-03 09:29:54,009 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,009 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
+2025-04-03 09:29:54,014 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,014 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:29:54,016 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,016 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:29:54,017 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,017 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
+2025-04-03 09:29:54,018 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,018 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:29:54,019 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,019 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:29:54,020 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,020 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
+2025-04-03 09:29:54,021 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,021 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
+2025-04-03 09:29:54,021 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
+2025-04-03 09:29:54,035 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,035 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
+2025-04-03 09:29:54,036 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
+2025-04-03 09:29:54,036 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
+2025-04-03 09:29:54,038 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
+2025-04-03 09:29:54,039 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:54,039 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
+2025-04-03 09:29:54,040 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:54,040 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
+2025-04-03 09:29:54,041 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:54,041 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
+2025-04-03 09:29:54,042 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
+2025-04-03 09:29:54,042 - 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
+2025-04-03 09:29:54,042 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
+2025-04-03 09:29:54,043 - data_handler.py - INFO - 再次测算，需要插值的总点数为：4.0 - fill_train_data
+2025-04-03 09:29:54,045 - 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
+2025-04-03 09:29:54,048 - 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
+2025-04-03 09:29:54,049 - 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
+2025-04-03 09:29:54,051 - 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
+2025-04-03 09:29:54,052 - 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
+2025-04-03 09:29:54,053 - 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
+2025-04-03 09:29:54,054 - 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
+2025-04-03 09:29:54,055 - 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
+2025-04-03 09:29:54,057 - 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
+2025-04-03 09:29:54,058 - 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
+2025-04-03 09:29:54,060 - 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
+2025-04-03 09:29:54,061 - 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
+2025-04-03 09:29:54,062 - 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
+2025-04-03 09:29:54,063 - 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
+2025-04-03 09:29:54,064 - 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
+2025-04-03 09:29:54,064 - data_handler.py - INFO - 训练集分成了15段，实际一共补值4点 - data_fill
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
+2025-04-03 09:29:56,533 - data_handler.py - INFO - 特征处理-训练数据-无法进行最小分割 - get_train_data
+2025-04-03 09:29:56,751 - dbmg.py - INFO - ❌ 系统异常: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - get_keras_model_from_mongo
+2025-04-03 09:29:56,751 - tf_fmi.py - INFO - 加强训练加载模型权重失败:("Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}",) - train_init
+2025-04-03 09:30:07,199 - tf_fmi.py - INFO - -----模型训练经过26轮迭代----- - training
+2025-04-03 09:30:07,199 - tf_fmi.py - INFO - 训练集损失函数为：[51.47301 28.56529 17.00252 10.45491  6.56836  4.06275  2.4503   1.46647
+  0.90028  0.59797  0.44225  0.36502  0.32612  0.30597  0.29727  0.29397
+  0.29415  0.29314  0.29399  0.29276  0.29808  0.29488  0.29392  0.29338
+  0.29251  0.2949 ] - training
+2025-04-03 09:30:07,199 - tf_fmi.py - INFO - 验证集损失函数为：[32.08155 20.83876 12.88028  8.15244  5.13598  3.18782  1.98071  1.25191
+  0.83589  0.59431  0.47115  0.4052   0.37547  0.36021  0.34598  0.33878
+  0.35231  0.35726  0.3452   0.35579  0.36291  0.3551   0.35916  0.3541
+  0.34667  0.35812] - training
+2025-04-03 09:30:07,261 - dbmg.py - INFO - ❌ 数据库操作 - 详细错误: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'} - insert_trained_model_into_mongo

app/model/main.py

@@ -33,37 +33,37 @@ def material(input_file, isDq=True):
     env_wf, env_sf = 'DQYC_IN_ACTUAL_WEATHER_WIND', 'DQYC_IN_ACTUAL_WEATHER_SOLAR' # 实测气象
     input_file = Path(input_file)
     env_w, env_s = None, None
-    basic = pd.read_csv(input_file / basi, sep='\s+', header=0)
-    power = pd.read_csv(input_file / power, sep='\s+', header=0)
+    basic = pd.read_csv(input_file / basi, sep=r'\s+', header=0)
+    power = pd.read_csv(input_file / power, sep=r'\s+', header=0)
     plant_type = int(basic.loc[basic['PropertyID'].to_list().index(('PlantType')), 'Value'])
     if isDq:
-        nwp_v = pd.read_csv(input_file / '0' / nwp_v, sep='\s+', header=0)
-        nwp_v_h = pd.read_csv(input_file / '0' / nwp_v_h, sep='\s+', header=0)
-        nwp_own = pd.read_csv(input_file / '1' / nwp_own, sep='\s+', header=0)
-        nwp_own_h = pd.read_csv(input_file / '1' / nwp_own_h, sep='\s+', header=0)
+        nwp_v = pd.read_csv(input_file / '0' / nwp_v, sep=r'\s+', header=0)
+        nwp_v_h = pd.read_csv(input_file / '0' / nwp_v_h, sep=r'\s+', header=0)
+        nwp_own = pd.read_csv(input_file / '1' / nwp_own, sep=r'\s+', header=0)
+        nwp_own_h = pd.read_csv(input_file / '1' / nwp_own_h, sep=r'\s+', header=0)
         if args['switch_nwp_owner']:
             nwp_v, nwp_v_h = nwp_own, nwp_own_h
         # 如果是风电
         if plant_type == 0:
-            station_info = pd.read_csv(input_file / station_info_w, sep='\s+', header=0)
-            station_info_d = pd.read_csv(input_file / station_info_d_w, sep='\s+', header=0)
-            nwp = pd.read_csv(input_file / nwp_w, sep='\s+', header=0)
-            nwp_h = pd.read_csv(input_file / nwp_w_h, sep='\s+', header=0)
+            station_info = pd.read_csv(input_file / station_info_w, sep=r'\s+', header=0)
+            station_info_d = pd.read_csv(input_file / station_info_d_w, sep=r'\s+', header=0)
+            nwp = pd.read_csv(input_file / nwp_w, sep=r'\s+', header=0)
+            nwp_h = pd.read_csv(input_file / nwp_w_h, sep=r'\s+', header=0)
             if (input_file / env_wf).exists():
-                env_w = pd.read_csv(input_file / env_wf, sep='\s+', header=0)
+                env_w = pd.read_csv(input_file / env_wf, sep=r'\s+', header=0)
             return station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h, env_w
         # 如果是光伏
         elif plant_type == 1:
-            station_info = pd.read_csv(input_file / station_info_s, sep='\s+', header=0)
-            station_info_d = pd.read_csv(input_file / station_info_d_s, sep='\s+', header=0)
-            nwp = pd.read_csv(input_file / nwp_s, sep='\s+', header=0)
-            nwp_h = pd.read_csv(input_file / nwp_s_h, sep='\s+', header=0)
+            station_info = pd.read_csv(input_file / station_info_s, sep=r'\s+', header=0)
+            station_info_d = pd.read_csv(input_file / station_info_d_s, sep=r'\s+', header=0)
+            nwp = pd.read_csv(input_file / nwp_s, sep=r'\s+', header=0)
+            nwp_h = pd.read_csv(input_file / nwp_s_h, sep=r'\s+', header=0)
             if (input_file / env_sf).exists():
-                env_s = pd.read_csv(input_file / env_sf, sep='\s+', header=0)
+                env_s = pd.read_csv(input_file / env_sf, sep=r'\s+', header=0)
             return station_info, station_info_d, nwp, nwp_h, power, nwp_v, nwp_v_h, env_s
     else:
         # 区域级预测待定，可能需要遍历获取场站数据
-        basic_area = pd.read_csv(input_file / basi_area, sep='\s+', header=0)
+        basic_area = pd.read_csv(input_file / basi_area, sep=r'\s+', header=0)
         return basic_area
 
 def clean_power(power, env, plant_id):