diff --git a/main_juxiting_zhoudu.py b/main_juxiting_zhoudu.py
index 26a5db0..5048ad6 100644
--- a/main_juxiting_zhoudu.py
+++ b/main_juxiting_zhoudu.py
@@ -301,42 +301,42 @@ def predict_main():
 
     # 判断当前日期是不是周一
     is_weekday = datetime.datetime.now().weekday() == 0
-    if is_weekday:
-        logger.info('今天是周一，更新预测模型')
-        # 计算最近60天预测残差最低的模型名称
-        model_results = sqlitedb.select_data(
-            'trueandpredict', order_by="ds DESC", limit="60")
-        # 删除空值率为90%以上的列
-        if len(model_results) > 10:
-            model_results = model_results.dropna(
-                thresh=len(model_results)*0.1, axis=1)
-        # 删除空行
-        model_results = model_results.dropna()
-        modelnames = model_results.columns.to_list()[2:-2]
-        for col in model_results[modelnames].select_dtypes(include=['object']).columns:
-            model_results[col] = model_results[col].astype(np.float32)
-        # 计算每个预测值与真实值之间的偏差率
-        for model in modelnames:
-            model_results[f'{model}_abs_error_rate'] = abs(
-                model_results['y'] - model_results[model]) / model_results['y']
-        # 获取每行对应的最小偏差率值
-        min_abs_error_rate_values = model_results.apply(
-            lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
-        # 获取每行对应的最小偏差率值对应的列名
-        min_abs_error_rate_column_name = model_results.apply(
-            lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
-        # 将列名索引转换为列名
-        min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
-            lambda x: x.split('_')[0])
-        # 取出现次数最多的模型名称
-        most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
-        logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
-        # 保存结果到数据库
-        if not sqlitedb.check_table_exists('most_model'):
-            sqlitedb.create_table(
-                'most_model', columns="ds datetime, most_common_model TEXT")
-        sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
-            '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
+    # if is_weekday:
+    #     logger.info('今天是周一，更新预测模型')
+    #     # 计算最近60天预测残差最低的模型名称
+    #     model_results = sqlitedb.select_data(
+    #         'trueandpredict', order_by="ds DESC", limit="60")
+    #     # 删除空值率为90%以上的列
+    #     if len(model_results) > 10:
+    #         model_results = model_results.dropna(
+    #             thresh=len(model_results)*0.1, axis=1)
+    #     # 删除空行
+    #     model_results = model_results.dropna()
+    #     modelnames = model_results.columns.to_list()[2:-2]
+    #     for col in model_results[modelnames].select_dtypes(include=['object']).columns:
+    #         model_results[col] = model_results[col].astype(np.float32)
+    #     # 计算每个预测值与真实值之间的偏差率
+    #     for model in modelnames:
+    #         model_results[f'{model}_abs_error_rate'] = abs(
+    #             model_results['y'] - model_results[model]) / model_results['y']
+    #     # 获取每行对应的最小偏差率值
+    #     min_abs_error_rate_values = model_results.apply(
+    #         lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
+    #     # 获取每行对应的最小偏差率值对应的列名
+    #     min_abs_error_rate_column_name = model_results.apply(
+    #         lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
+    #     # 将列名索引转换为列名
+    #     min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
+    #         lambda x: x.split('_')[0])
+    #     # 取出现次数最多的模型名称
+    #     most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
+    #     logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
+    #     # 保存结果到数据库
+    #     if not sqlitedb.check_table_exists('most_model'):
+    #         sqlitedb.create_table(
+    #             'most_model', columns="ds datetime, most_common_model TEXT")
+    #     sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
+    #         '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
 
     if is_corr:
         df = corr_feature(df=df)
@@ -375,17 +375,17 @@ def predict_main():
     logger.info('训练数据绘图end')
 
     # # 模型报告
-    logger.info('制作报告ing')
-    title = f'{settings}--{end_time}-预测报告'  # 报告标题
-    reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf'  # 报告文件名
-    reportname = reportname.replace(':', '-')  # 替换冒号
-    pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
-                  reportname=reportname, sqlitedb=sqlitedb),
+    # logger.info('制作报告ing')
+    # title = f'{settings}--{end_time}-预测报告'  # 报告标题
+    # reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf'  # 报告文件名
+    # reportname = reportname.replace(':', '-')  # 替换冒号
+    # pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
+    #               reportname=reportname, sqlitedb=sqlitedb),
 
-    logger.info('制作报告end')
-    logger.info('模型训练完成')
+    # logger.info('制作报告end')
+    # 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)
@@ -412,12 +412,11 @@ 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('2022-1-1', '2025-3-26', freq='M'):
-    #     try:
-    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #         predict_main()
-    #     except Exception as e:
-    #         logger.info(f'预测失败：{e}')
-    #         continue
-
-    predict_main()
+    for i_time in pd.date_range('2025-3-1', '2025-5-26', freq='W'):
+        try:
+            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+            predict_main()
+        except Exception as e:
+            logger.info(f'预测失败：{e}')
+            continue
+    # predict_main()