05230837

app/logs/2025-05-23/south-forecast.2025-05-23.0.log

@@ -0,0 +1,361 @@
+2025-05-23 08:22:27,699 - 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
+2025-05-23 08:22:28,041 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
+2025-05-23 08:22:30,660 - 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
+2025-05-23 08:22:33,584 - 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
+2025-05-23 08:22:33,590 - 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
+2025-05-23 08:22:33,866 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
+2025-05-23 08:22:33,866 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
+2025-05-23 08:22:33,892 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:22:33,892 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:22:33,900 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:22:33,900 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:22:33,937 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:22:33,937 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:22:33,937 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:22:33,937 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:22:33,937 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:22:33,937 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:22:34,920 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:22:34,920 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:22:34,937 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:22:34,937 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:22:49,417 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:22:49,417 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:22:49,418 - tf_lstm.py - INFO - 训练集损失函数为：[8.9987e-01 3.1557e-01 9.8220e-02 2.7320e-02 7.0100e-03 1.9300e-03
+ 8.2000e-04 6.0000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04
+ 5.4000e-04 5.4000e-04 5.4000e-04 5.4000e-04 5.3000e-04 5.3000e-04
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+ 5.1000e-04 5.1000e-04 5.1000e-04 5.1000e-04] - training
+2025-05-23 08:22:49,418 - tf_lstm.py - INFO - 训练集损失函数为：[8.9367e-01 3.1262e-01 9.6830e-02 2.6540e-02 6.4300e-03 1.4200e-03
+ 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
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+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
+2025-05-23 08:22:49,418 - tf_lstm.py - INFO - 验证集损失函数为：[0.51006 0.16613 0.04846 0.01297 0.00362 0.00146 0.00101 0.00092 0.00091
+ 0.0009  0.00089 0.00089 0.00088 0.00088 0.00088 0.00088 0.00087 0.00087
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+ 0.00082] - training
+2025-05-23 08:22:49,418 - tf_lstm.py - INFO - 验证集损失函数为：[5.0543e-01 1.6387e-01 4.7130e-02 1.1960e-02 2.7300e-03 6.1000e-04
+ 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
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+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
+2025-05-23 08:22:49,454 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fbfd9db527f0bfe137dbd - insert_trained_model_into_mongo
+2025-05-23 08:22:49,467 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fbfd99a32f7c1241a3c80 - insert_trained_model_into_mongo
+2025-05-23 08:22:49,476 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fbfd99a32f7c1241a3c82 - insert_scaler_model_into_mongo
+2025-05-23 08:22:49,488 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fbfd9db527f0bfe137dbf - insert_scaler_model_into_mongo
+2025-05-23 08:22:50,948 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
+2025-05-23 08:22:50,948 - task_worker.py - ERROR - Area 1002 failed: 'station_id' - region_task
+2025-05-23 08:28:12,866 - 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
+2025-05-23 08:28:12,981 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
+2025-05-23 08:28:15,622 - 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
+2025-05-23 08:28:18,516 - 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
+2025-05-23 08:28:18,524 - 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
+2025-05-23 08:28:18,724 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
+2025-05-23 08:28:18,726 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
+2025-05-23 08:28:18,742 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:28:18,749 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:28:18,750 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:28:18,757 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:28:18,778 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:28:18,778 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:28:18,779 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:28:18,784 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:28:18,784 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:28:18,785 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:28:19,722 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:28:19,724 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:28:19,729 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:28:19,730 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:28:32,613 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:28:32,614 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:28:32,614 - tf_lstm.py - INFO - 训练集损失函数为：[8.9933e-01 3.1451e-01 9.7470e-02 2.6750e-02 6.5000e-03 1.4300e-03
+ 3.2000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
+ 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05
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+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
+2025-05-23 08:28:32,614 - tf_lstm.py - INFO - 验证集损失函数为：[5.0852e-01 1.6489e-01 4.7480e-02 1.2060e-02 2.7600e-03 6.1000e-04
+ 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
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+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
+2025-05-23 08:28:32,614 - tf_lstm.py - INFO - 训练集损失函数为：[8.9252e-01 3.1244e-01 9.7110e-02 2.6970e-02 6.9100e-03 1.9100e-03
+ 8.1000e-04 5.9000e-04 5.5000e-04 5.5000e-04 5.4000e-04 5.4000e-04
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+2025-05-23 08:28:32,614 - tf_lstm.py - INFO - 验证集损失函数为：[0.50531 0.16435 0.04786 0.01279 0.00357 0.00145 0.00101 0.00092 0.0009
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+ 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
+ 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
+ 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
+ 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
+ 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
+ 0.00082] - training
+2025-05-23 08:28:32,657 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fc130b586703a111ee9bb - insert_trained_model_into_mongo
+2025-05-23 08:28:32,692 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fc130da054ff2159993e3 - insert_trained_model_into_mongo
+2025-05-23 08:28:32,696 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fc130b586703a111ee9bd - insert_scaler_model_into_mongo
+2025-05-23 08:28:32,711 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fc130da054ff2159993e5 - insert_scaler_model_into_mongo
+2025-05-23 08:28:34,015 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
+2025-05-23 08:28:34,034 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:28:34,042 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:28:34,069 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:28:34,069 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:28:34,069 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:28:35,005 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:28:35,006 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:28:46,672 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:28:46,672 - tf_lstm.py - INFO - 训练集损失函数为：[9.1275e-01 3.2207e-01 1.0061e-01 2.7740e-02 6.7200e-03 1.4400e-03
+ 2.8000e-04 5.0000e-05 1.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
+2025-05-23 08:28:46,672 - tf_lstm.py - INFO - 验证集损失函数为：[5.1888e-01 1.6976e-01 4.9170e-02 1.2490e-02 2.8000e-03 5.6000e-04
+ 1.0000e-04 2.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
+2025-05-23 08:28:46,673 - tf_model_train.py - ERROR - Training failed: can only concatenate str (not "int") to str
+Traceback (most recent call last):
+  File "E:\compete\app\model\tf_model_train.py", line 94, in train
+    'model_table': self.config['model_table'] + f'_{pre_type}_' + pre_id,
+                   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~
+TypeError: can only concatenate str (not "int") to str
+ - _handle_error
+2025-05-23 08:35:44,723 - 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
+2025-05-23 08:35:44,847 - main.py - INFO - 输入文件目录: E:/compete/app/model/data/DQYC/qy/62/1002/2025-04-21/IN - main
+2025-05-23 08:35:47,468 - 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
+2025-05-23 08:35:50,450 - 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
+2025-05-23 08:35:50,453 - 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
+2025-05-23 08:35:50,668 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
+2025-05-23 08:35:50,670 - tf_model_train.py - INFO - GPU 2 allocated - _setup_resources
+2025-05-23 08:35:50,686 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:35:50,689 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:35:50,694 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:35:50,696 - data_cleaning.py - INFO - 行清洗：清洗的行数有：69，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:35:50,723 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:35:50,723 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:35:50,723 - data_handler.py - INFO - 数据总数：2907, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:35:50,723 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:35:50,724 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:35:50,724 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:35:51,687 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:35:51,689 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:35:51,695 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:35:51,696 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:36:06,600 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:36:06,600 - tf_lstm.py - INFO - 训练集损失函数为：[9.0568e-01 3.1741e-01 9.8600e-02 2.7150e-02 6.6200e-03 1.4700e-03
+ 3.3000e-04 1.0000e-04 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05
+ 6.0000e-05 6.0000e-05 6.0000e-05 6.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05
+ 5.0000e-05 5.0000e-05 5.0000e-05 5.0000e-05] - training
+2025-05-23 08:36:06,601 - tf_lstm.py - INFO - 验证集损失函数为：[0.50575 0.165   0.04822 0.01292 0.00361 0.00145 0.00101 0.00092 0.0009
+ 0.00089 0.00089 0.00088 0.00088 0.00088 0.00087 0.00087 0.00087 0.00087
+ 0.00087 0.00086 0.00086 0.00086 0.00086 0.00086 0.00086 0.00085 0.00085
+ 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00085 0.00084
+ 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084 0.00084
+ 0.00084 0.00084 0.00084 0.00084 0.00083 0.00083 0.00083 0.00083 0.00083
+ 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
+ 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083 0.00083
+ 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
+ 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
+ 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082 0.00082
+ 0.00082] - training
+2025-05-23 08:36:06,601 - tf_lstm.py - INFO - 验证集损失函数为：[5.1277e-01 1.6664e-01 4.8130e-02 1.2280e-02 2.8200e-03 6.3000e-04
+ 1.8000e-04 1.0000e-04 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05 8.0000e-05
+ 8.0000e-05 8.0000e-05 8.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05
+ 7.0000e-05 7.0000e-05 7.0000e-05 7.0000e-05] - training
+2025-05-23 08:36:06,647 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fc2f63ef80176b2f4bf7e - insert_trained_model_into_mongo
+2025-05-23 08:36:06,657 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fc2f63ef80176b2f4bf80 - insert_scaler_model_into_mongo
+2025-05-23 08:36:06,668 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fc2f649c4c6cb3fd6c5b2 - insert_trained_model_into_mongo
+2025-05-23 08:36:06,685 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fc2f649c4c6cb3fd6c5b4 - insert_scaler_model_into_mongo
+2025-05-23 08:36:08,118 - tf_model_train.py - INFO - GPU 1 allocated - _setup_resources
+2025-05-23 08:36:08,137 - data_cleaning.py - INFO - 开始清洗：训练集…… - cleaning
+2025-05-23 08:36:08,145 - data_cleaning.py - INFO - 行清洗：清洗的行数有：68，缺失的列有： - key_field_row_cleaning
+2025-05-23 08:36:08,172 - data_handler.py - INFO - 数据总数：2908, 时序缺失的间隔：0, 其中，较长的时间间隔：0 - missing_time_splite
+2025-05-23 08:36:08,172 - data_handler.py - INFO - 需要补值的总点数：0 - missing_time_splite
+2025-05-23 08:36:08,173 - data_handler.py - INFO - 再次测算，需要插值的总点数为：0.0 - fill_train_data
+2025-05-23 08:36:09,113 - dbmg.py - INFO - ⚠️ 未找到模型 'lstm' 的有效记录 - get_keras_model_from_mongo
+2025-05-23 08:36:09,114 - tf_lstm.py - INFO - 加强训练加载模型权重失败:('cannot unpack non-iterable NoneType object',) - train_init
+2025-05-23 08:36:22,336 - tf_lstm.py - INFO - -----模型训练经过100轮迭代----- - training
+2025-05-23 08:36:22,337 - tf_lstm.py - INFO - 训练集损失函数为：[9.1068e-01 3.2140e-01 1.0044e-01 2.7720e-02 6.7200e-03 1.4400e-03
+ 2.8000e-04 5.0000e-05 1.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
+2025-05-23 08:36:22,337 - tf_lstm.py - INFO - 验证集损失函数为：[5.1778e-01 1.6943e-01 4.9120e-02 1.2490e-02 2.8000e-03 5.6000e-04
+ 1.0000e-04 2.0000e-05 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00
+ 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00] - training
+2025-05-23 08:36:22,426 - dbmg.py - INFO - ✅ 模型 lstm 保存成功 | 文档ID: 682fc3061866c0ded50f27e9 - insert_trained_model_into_mongo
+2025-05-23 08:36:22,446 - dbmg.py - INFO - ✅ 缩放器 lstm 保存成功 | 文档ID: 682fc3061866c0ded50f27eb - insert_scaler_model_into_mongo

app/model/main.py

@@ -114,7 +114,7 @@ def main():
     weighted_nwp_h[use_cols] /= total_cap
     weighted_nwp_v[use_cols_v] /= total_cap
     weighted_nwp_v_h[use_cols_v] /= total_cap
-    data_nwps = types.SimpleNamespace(**{'nwp': weighted_nwp, 'nwp_h': weighted_nwp_h, 'nwp_v': weighted_nwp_v, 'nwp_v_h': weighted_nwp_v_h})
+    data_nwps = types.SimpleNamespace(**{'nwp': weighted_nwp, 'nwp_h': weighted_nwp_h, 'nwp_v': weighted_nwp_v, 'nwp_v_h': weighted_nwp_v_h, 'total_cap': total_cap})
     print(f"Final result: {completed} stations trained successfully")
     # 区域级功率预测训练
     task_config = deepcopy(config)

app/model/task_worker.py

@@ -57,11 +57,12 @@ class Task(object):
             # 数据合并
             print(data_nwps.nwp)
             print(data_nwps.nwp_v)
+            print("累加的区域装机量{}，实际区域装机量{}".format(data_nwps.total_cap, data_objects.area_cap))
             train_data = pd.merge(data_nwps.nwp_v_h, data_objects.power, on=config['col_time'])
             print("444")
             # 模型训练
-            model = ModelTrainer(train_data, capacity=data_objects.cap, config=config)
-            model.train()
+            model = ModelTrainer(train_data, capacity=data_objects.area_cap, config=config)
+            model.train(pre_area=True)
             print("555")
             return {'status': 'success', 'area_id': area_id}
         except Exception as e:

app/model/tf_model_train.py

@@ -91,8 +91,8 @@ class ModelTrainer:
                 'params': json.dumps(self.config['Model']),
                 'descr': f'南网竞赛-{pre_id}',
                 'gen_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()),
-                'model_table': self.config['model_table'] + f'_{pre_type}_' + pre_id,
-                'scaler_table': self.config['scaler_table'] + f'_{pre_type}_'+ pre_id
+                'model_table': self.config['model_table'] + f'_{pre_type}_' + str(pre_id),
+                'scaler_table': self.config['scaler_table'] + f'_{pre_type}_'+ str(pre_id)
             })
             self.mgUtils.insert_trained_model_into_mongo(trained_model, self.config)
             self.mgUtils.insert_scaler_model_into_mongo(scaled_train_bytes, scaled_target_bytes, self.config)