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
2025-04-03 13:33:12,420 - 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 13:33:12,613 - tf_cnn_train.py - INFO - Program starts execution! - model_training
2025-04-03 13:33:12,674 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
2025-04-03 13:33:12,683 - data_cleaning.py - INFO - 列清洗：清洗的列有：{'HubSpeed', 'HubDireciton'} - data_column_cleaning
2025-04-03 13:33:12,700 - 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 13:33:12,721 - data_handler.py - INFO - 2022-05-05 23:45:00 ~ 2022-05-09 00:00:00 - missing_time_splite
2025-04-03 13:33:12,721 - data_handler.py - INFO - 缺失点数：288.0 - missing_time_splite
2025-04-03 13:33:12,754 - data_handler.py - INFO - 2022-06-25 07:45:00 ~ 2022-06-25 08:15:00 - missing_time_splite
2025-04-03 13:33:12,754 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
2025-04-03 13:33:12,754 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
2025-04-03 13:33:12,756 - data_handler.py - INFO - 2022-06-27 06:45:00 ~ 2022-06-27 07:15:00 - missing_time_splite
2025-04-03 13:33:12,756 - data_handler.py - INFO - 缺失点数：1.0 - missing_time_splite
2025-04-03 13:33:12,756 - data_handler.py - INFO - 需要补值的点数：1.0 - missing_time_splite
2025-04-03 13:33:12,760 - data_handler.py - INFO - 2022-07-01 06:30:00 ~ 2022-07-01 23:45:00 - missing_time_splite
2025-04-03 13:33:12,760 - data_handler.py - INFO - 缺失点数：68.0 - missing_time_splite
2025-04-03 13:33:12,765 - data_handler.py - INFO - 2022-07-08 06:15:00 ~ 2022-07-08 23:45:00 - missing_time_splite
2025-04-03 13:33:12,765 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
2025-04-03 13:33:12,767 - data_handler.py - INFO - 2022-07-09 06:15:00 ~ 2022-07-09 23:45:00 - missing_time_splite
2025-04-03 13:33:12,767 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
2025-04-03 13:33:12,768 - data_handler.py - INFO - 2022-07-10 06:15:00 ~ 2022-07-10 23:45:00 - missing_time_splite
2025-04-03 13:33:12,768 - data_handler.py - INFO - 缺失点数：69.0 - missing_time_splite
2025-04-03 13:33:12,769 - data_handler.py - INFO - 2022-07-11 06:00:00 ~ 2022-07-11 23:45:00 - missing_time_splite
2025-04-03 13:33:12,769 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
2025-04-03 13:33:12,770 - data_handler.py - INFO - 2022-07-12 06:00:00 ~ 2022-07-12 23:45:00 - missing_time_splite
2025-04-03 13:33:12,770 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
2025-04-03 13:33:12,772 - data_handler.py - INFO - 2022-07-13 06:00:00 ~ 2022-07-13 23:45:00 - missing_time_splite
2025-04-03 13:33:12,772 - data_handler.py - INFO - 缺失点数：70.0 - missing_time_splite
2025-04-03 13:33:12,773 - data_handler.py - INFO - 2022-07-14 23:00:00 ~ 2022-07-14 23:45:00 - missing_time_splite
2025-04-03 13:33:12,773 - data_handler.py - INFO - 缺失点数：2.0 - missing_time_splite
2025-04-03 13:33:12,773 - data_handler.py - INFO - 需要补值的点数：2.0 - missing_time_splite
2025-04-03 13:33:12,785 - data_handler.py - INFO - 2022-08-01 20:00:00 ~ 2022-08-01 23:45:00 - missing_time_splite
2025-04-03 13:33:12,785 - data_handler.py - INFO - 缺失点数：14.0 - missing_time_splite
2025-04-03 13:33:12,787 - data_handler.py - INFO - 2022-08-02 21:00:00 ~ 2022-08-02 23:45:00 - missing_time_splite
2025-04-03 13:33:12,788 - data_handler.py - INFO - 缺失点数：10.0 - missing_time_splite
2025-04-03 13:33:12,789 - data_handler.py - INFO - 2022-08-04 00:00:00 ~ 2022-08-04 23:15:00 - missing_time_splite
2025-04-03 13:33:12,789 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
2025-04-03 13:33:12,790 - data_handler.py - INFO - 2022-08-05 00:00:00 ~ 2022-08-05 23:15:00 - missing_time_splite
2025-04-03 13:33:12,790 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
2025-04-03 13:33:12,791 - data_handler.py - INFO - 2022-08-06 00:00:00 ~ 2022-08-06 23:15:00 - missing_time_splite
2025-04-03 13:33:12,791 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
2025-04-03 13:33:12,792 - data_handler.py - INFO - 2022-08-07 00:00:00 ~ 2022-08-07 23:15:00 - missing_time_splite
2025-04-03 13:33:12,792 - data_handler.py - INFO - 缺失点数：92.0 - missing_time_splite
2025-04-03 13:33:12,793 - 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 13:33:12,793 - data_handler.py - INFO - 需要补值的总点数：4.0 - missing_time_splite
2025-04-03 13:33:12,793 - 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 13:33:12,795 - 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 13:33:12,799 - 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 13:33:12,800 - 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 13:33:12,801 - 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 13:33:12,802 - 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 13:33:12,803 - 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 13:33:12,804 - 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 13:33:12,805 - 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 13:33:12,807 - 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 13:33:12,808 - 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 13:33:12,809 - 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 13:33:12,810 - 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 13:33:12,811 - 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 13:33:12,812 - 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 13:33:12,813 - 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 13:33:12,813 - 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 13:33:15,280 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
2025-04-03 13:33:15,281 - 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 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
2025-04-03 13:33:15,281 - 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 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
2025-04-03 13:33:15,281 - 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 13:33:15,281 - data_handler.py - INFO - 特征处理-训练数据-不满足time_step - get_train_data
2025-04-03 13:33:15,281 - 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 13:33:15,493 - 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 13:33:15,493 - tf_cnn.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 13:33:26,756 - tf_cnn.py - INFO - -----模型训练经过76轮迭代----- - training
2025-04-03 13:33:26,756 - tf_cnn.py - INFO - 训练集损失函数为：[5.3196  2.96034 1.83915 1.399   1.15213 0.97754 0.84255 0.73772 0.66046
 0.60034 0.55292 0.51518 0.48333 0.45676 0.43364 0.41425 0.39769 0.3834
 0.37138 0.36097 0.35203 0.3447  0.33776 0.33146 0.32607 0.32143 0.31722
 0.31405 0.31122 0.30854 0.306   0.30391 0.30194 0.30016 0.29861 0.29733
 0.29619 0.29507 0.29394 0.29281 0.2917  0.29055 0.28939 0.28831 0.28746
 0.28662 0.28581 0.285   0.2843  0.28371 0.28309 0.28248 0.28181 0.28114
 0.28057 0.28002 0.27949 0.27907 0.27876 0.27847 0.27816 0.27786 0.27756
 0.27726 0.27697 0.27668 0.27638 0.27609 0.27581 0.27552 0.27525 0.27498
 0.27482 0.27454 0.27427 0.27401] - training
2025-04-03 13:33:26,757 - tf_cnn.py - INFO - 验证集损失函数为：[3.92513 2.23538 1.60418 1.28774 1.08642 0.93493 0.81426 0.72426 0.65625
 0.6028  0.5611  0.52649 0.49804 0.47311 0.45227 0.43444 0.41905 0.40589
 0.39496 0.38506 0.37721 0.36995 0.3632  0.35745 0.35234 0.3479  0.34436
 0.34159 0.33884 0.33624 0.33404 0.33224 0.33051 0.32884 0.32741 0.32638
 0.32527 0.32424 0.32322 0.32202 0.32096 0.3199  0.31874 0.31774 0.31694
 0.31608 0.31535 0.31433 0.31367 0.31316 0.31234 0.3115  0.31067 0.30995
 0.30914 0.30852 0.30783 0.30739 0.30679 0.30646 0.30603 0.30568 0.30537
 0.3051  0.30508 0.3049  0.30503 0.30513 0.30515 0.30524 0.3053  0.30525
 0.30495 0.30523 0.30534 0.30523] - training
2025-04-03 13:33:26,794 - 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 13:51:00,688 - 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 13:51:26,825 - 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 13:51:26,937 - tf_cnn_pre.py - INFO - 算法状态异常：Traceback (most recent call last):
  File "E:\compete\app\common\dbmg.py", line 436, in get_scaler_model_from_mongo
    scaler_doc = collection.find_one(
                 ^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\collection.py", line 1495, in find_one
    for result in cursor.limit(-1):
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1243, in next
    if len(self.__data) or self._refresh():
                           ^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1160, in _refresh
    self.__send_message(q)
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1039, in __send_message
    response = client._run_operation(
               ^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    return func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1425, in _run_operation
    return self._retryable_read(
           ^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1534, in _retryable_read
    return self._retry_internal(
           ^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    return func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1501, in _retry_internal
    ).run()
      ^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2347, in run
    return self._read() if self._is_read else self._write()
           ^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2479, in _read
    with self._client._conn_from_server(self._read_pref, self._server, self._session) as (
  File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1351, in _conn_from_server
    with self._checkout(server, session) as conn:
  File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1260, in _checkout
    with server.checkout(handler=err_handler) as conn:
  File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1763, in checkout
    conn = self._get_conn(checkout_started_time, handler=handler)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1921, in _get_conn
    conn = self.connect(handler=handler)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1725, in connect
    conn.authenticate()
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1098, in authenticate
    auth.authenticate(creds, self, reauthenticate=reauthenticate)
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 656, in authenticate
    auth_func(credentials, conn)
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 560, in _authenticate_default
    return _authenticate_scram(credentials, conn, "SCRAM-SHA-1")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 299, in _authenticate_scram
    res = conn.command(source, cmd)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 342, in inner
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 988, in command
    return command(
           ^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\network.py", line 212, in command
    helpers._check_command_response(
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 248, in _check_command_response
    raise OperationFailure(errmsg, code, response, max_wire_version)
pymongo.errors.OperationFailure: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "E:\compete\app\predict\tf_cnn_pre.py", line 41, in model_prediction
    feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(params)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "E:\compete\app\common\dbmg.py", line 474, in get_scaler_model_from_mongo
    raise RuntimeError(f"🔌 数据库操作失败: {str(e)}") from e
RuntimeError: 🔌 数据库操作失败: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}
 - model_prediction
2025-04-03 13:51:26,942 - tf_cnn_pre.py - INFO - cnn预测任务：用了 0.01752614974975586 秒  - model_prediction
2025-04-03 13:59:47,178 - 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 13:59:47,295 - tf_cnn_pre.py - INFO - 算法状态异常：Traceback (most recent call last):
  File "E:\compete\app\common\dbmg.py", line 436, in get_scaler_model_from_mongo
    scaler_doc = collection.find_one(
                 ^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\collection.py", line 1495, in find_one
    for result in cursor.limit(-1):
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1243, in next
    if len(self.__data) or self._refresh():
                           ^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1160, in _refresh
    self.__send_message(q)
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\cursor.py", line 1039, in __send_message
    response = client._run_operation(
               ^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    return func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1425, in _run_operation
    return self._retryable_read(
           ^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1534, in _retryable_read
    return self._retry_internal(
           ^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\_csot.py", line 108, in csot_wrapper
    return func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1501, in _retry_internal
    ).run()
      ^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2347, in run
    return self._read() if self._is_read else self._write()
           ^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 2479, in _read
    with self._client._conn_from_server(self._read_pref, self._server, self._session) as (
  File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1351, in _conn_from_server
    with self._checkout(server, session) as conn:
  File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\mongo_client.py", line 1260, in _checkout
    with server.checkout(handler=err_handler) as conn:
  File "D:\anaconda3\envs\py312\Lib\contextlib.py", line 137, in __enter__
    return next(self.gen)
           ^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1763, in checkout
    conn = self._get_conn(checkout_started_time, handler=handler)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1921, in _get_conn
    conn = self.connect(handler=handler)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1725, in connect
    conn.authenticate()
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 1098, in authenticate
    auth.authenticate(creds, self, reauthenticate=reauthenticate)
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 656, in authenticate
    auth_func(credentials, conn)
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 560, in _authenticate_default
    return _authenticate_scram(credentials, conn, "SCRAM-SHA-1")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\auth.py", line 299, in _authenticate_scram
    res = conn.command(source, cmd)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 342, in inner
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\pool.py", line 988, in command
    return command(
           ^^^^^^^^
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\network.py", line 212, in command
    helpers._check_command_response(
  File "D:\anaconda3\envs\py312\Lib\site-packages\pymongo\helpers.py", line 248, in _check_command_response
    raise OperationFailure(errmsg, code, response, max_wire_version)
pymongo.errors.OperationFailure: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "E:\compete\app\predict\tf_cnn_pre.py", line 41, in model_prediction
    feature_scaler, target_scaler = mgUtils.get_scaler_model_from_mongo(params)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "E:\compete\app\common\dbmg.py", line 474, in get_scaler_model_from_mongo
    raise RuntimeError(f"🔌 数据库操作失败: {str(e)}") from e
RuntimeError: 🔌 数据库操作失败: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}
 - model_prediction
2025-04-03 13:59:47,299 - tf_cnn_pre.py - INFO - cnn预测任务：用了 0.01714491844177246 秒  - model_prediction