原油日度预测上传市场信息平台数据

lib/dataread.py

@@ -2276,60 +2276,6 @@ def get_baichuan_data(baichuanidnamedict):
 
 
 
-def push_market_value():
-    config.logger.info('发送预测结果到市场信息平台')
-    # 读取预测数据和模型评估数据
-    predict_file_path = os.path.join(config.dataset, 'predict.csv')
-    model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
-    try:
-        predictdata_df = pd.read_csv(predict_file_path)
-        top_models_df = pd.read_csv(model_eval_file_path)
-    except FileNotFoundError as e:
-        config.logger.error(f"文件未找到: {e}")
-        return
-
-    predictdata = predictdata_df.copy()
-
-    # 取模型前十
-    top_models = top_models_df['模型(Model)'].head(10).tolist()
-
-    # 计算前十模型的均值
-    predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
-
-    # 打印日期和前十模型均值
-    print(predictdata_df[['ds', 'top_models_mean']])
-
-    # 准备要推送的数据
-    first_mean = predictdata_df['top_models_mean'].iloc[0]
-    last_mean = predictdata_df['top_models_mean'].iloc[-1]
-    # 保留两位小数
-    first_mean = round(first_mean, 2)
-    last_mean = round(last_mean, 2)
-
-    predictdata = [
-        {
-            "dataItemNo": global_config['bdwd_items']['ciri'],
-            "dataDate": global_config['end_time'].replace('-',''),
-            "dataStatus": "add",
-            "dataValue": first_mean
-        },
-        {
-            "dataItemNo": global_config['bdwd_items']['benzhou'],
-            "dataDate": global_config['end_time'].replace('-',''),
-            "dataStatus": "add",
-            "dataValue": last_mean
-        }
-    ]
-
-    print(predictdata)
-
-    # 推送数据到市场信息平台
-    try:
-        push_market_data(predictdata)
-    except Exception as e:
-        config.logger.error(f"推送数据失败: {e}")
-
-
 def get_bdwd_predict_data():
     # 获取认证头部信息
     token = get_head_auth_report()

main_yuanyou.py

@@ -80,6 +80,63 @@ global_config.update({
 })
 
 
+
+def push_market_value():
+    config.logger.info('发送预测结果到市场信息平台')
+    # 读取预测数据和模型评估数据
+    predict_file_path = os.path.join(config.dataset, 'predict.csv')
+    model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
+    try:
+        predictdata_df = pd.read_csv(predict_file_path)
+        top_models_df = pd.read_csv(model_eval_file_path)
+    except FileNotFoundError as e:
+        config.logger.error(f"文件未找到: {e}")
+        return
+
+    predictdata = predictdata_df.copy()
+
+    # 取模型前十
+    top_models = top_models_df['模型(Model)'].head(10).tolist()
+
+    # 计算前十模型的均值
+    predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
+
+    # 打印日期和前十模型均值
+    print(predictdata_df[['ds', 'top_models_mean']])
+
+    # 准备要推送的数据
+    first_mean = predictdata_df['top_models_mean'].iloc[0]
+    last_mean = predictdata_df['top_models_mean'].iloc[-1]
+    # 保留两位小数
+    first_mean = round(first_mean, 2)
+    last_mean = round(last_mean, 2)
+
+    predictdata = [
+        {
+            "dataItemNo": global_config['bdwd_items']['ciri'],
+            "dataDate": global_config['end_time'].replace('-',''),
+            "dataStatus": "add",
+            "dataValue": first_mean
+        },
+        {
+            "dataItemNo": global_config['bdwd_items']['benzhou'],
+            "dataDate": global_config['end_time'].replace('-',''),
+            "dataStatus": "add",
+            "dataValue": last_mean
+        }
+    ]
+
+    print(predictdata)
+
+    # 推送数据到市场信息平台
+    try:
+        push_market_data(predictdata)
+    except Exception as e:
+        config.logger.error(f"推送数据失败: {e}")
+
+
+
+
 def predict_main():
     """
     主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。

main_yuanyou_yuedu.py

@@ -3,7 +3,7 @@
 from lib.dataread import *
 from config_jingbo_yuedu import *
 from lib.tools import SendMail, exception_logger
-from models.nerulforcastmodels import ex_Model, model_losss
+from models.nerulforcastmodels import ex_Model, model_losss,brent_export_pdf
 import datetime
 import torch
 torch.set_float32_matmul_precision("high")
@@ -417,18 +417,18 @@ def predict_main():
     model_results3 = model_losss(sqlitedb, end_time=end_time)
     logger.info('训练数据绘图end')
 
-    # # 模型报告
+    # 模型报告
-    # logger.info('制作报告ing')
+    logger.info('制作报告ing')
-    # title = f'{settings}--{end_time}-预测报告'  # 报告标题
+    title = f'{settings}--{end_time}-预测报告'  # 报告标题
-    # reportname = f'Brent原油大模型月度预测--{end_time}.pdf'  # 报告文件名
+    reportname = f'Brent原油大模型月度预测--{end_time}.pdf'  # 报告文件名
-    # reportname = reportname.replace(':', '-')  # 替换冒号
+    reportname = reportname.replace(':', '-')  # 替换冒号
-    # brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
+    brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
-    #                  reportname=reportname, sqlitedb=sqlitedb),
+                     reportname=reportname, sqlitedb=sqlitedb),
 
-    # logger.info('制作报告end')
+    logger.info('制作报告end')
-    # logger.info('模型训练完成')
+    logger.info('模型训练完成')
 
-    # push_market_value()
+    push_market_value()
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
@@ -455,13 +455,13 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2025-1-1', '2025-3-26', freq='B'):
+    # for i_time in pd.date_range('2025-1-1', '2025-3-26', freq='B'):
-        # try:
+    #     # try:
-        global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+    #     global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-        predict_main()
+    #     predict_main()
     #     except Exception as e:
     #         logger.info(f'预测失败：{e}')
     #         continue
 
-    # predict_main()
+    predict_main()