diff --git a/config_jingbo_zhoudu.py b/config_jingbo_zhoudu.py
index 189bf30..2cd60d1 100644
--- a/config_jingbo_zhoudu.py
+++ b/config_jingbo_zhoudu.py
@@ -216,7 +216,7 @@ data_set = '原油指标数据.xlsx' # 数据集文件
 dataset = 'yuanyouzhoududataset' # 数据集文件夹
 
 # 数据库名称
-db_name = os.path.join(dataset,'jbsh_yuanyou.db')
+db_name = os.path.join(dataset,'jbsh_yuanyou_zhoudu.db')
 sqlitedb = SQLiteHandler(db_name) 
 sqlitedb.connect()
 
diff --git a/main_yuanyou_zhoudu.py b/main_yuanyou_zhoudu.py
index 7cf59ea..98dce8a 100644
--- a/main_yuanyou_zhoudu.py
+++ b/main_yuanyou_zhoudu.py
@@ -254,16 +254,16 @@ 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
-    logger.info('制作报告ing')
-    title = f'{settings}--{end_time}-预测报告' # 报告标题
-    reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
-    reportname = reportname.replace(':', '-') # 替换冒号
-    brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
-                reportname=reportname,sqlitedb=sqlitedb),
+    # # 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'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
+    # reportname = reportname.replace(':', '-') # 替换冒号
+    # brent_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')
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
@@ -288,11 +288,11 @@ def predict_main():
 
 
 if __name__ == '__main__':
-    # global end_time
-    # is_on = True
-    # # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    # for i_time in pd.date_range('2025-1-20', '2025-2-6', freq='B'):
-    #     end_time = i_time.strftime('%Y-%m-%d')
-    #     predict_main()
+    global end_time
+    is_on = True
+    # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
+    for i_time in pd.date_range('2024-10-8', '2025-2-12', freq='B'):
+        end_time = i_time.strftime('%Y-%m-%d')
+        predict_main()
 
-    predict_main()
\ No newline at end of file
+    # predict_main()
\ No newline at end of file
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 1336783..8714f30 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -188,9 +188,9 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
         logger.info('读取模型：'+ filename)
         nf = load(filename)
     # # 测试集预测
-    nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
-    # 测试集预测结果保存
-    nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
+    # nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
+    # # 测试集预测结果保存
+    # nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
 
     df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
 
@@ -257,7 +257,7 @@ def model_losss_yongan(sqlitedb,end_time,table_name_prefix):
     # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列
     df_combined = df_combined[df_combined['CREAT_DATE'] == df_combined['max_cutoff']]
     # 删除模型生成的cutoff列
-    df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True) 
+    df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean']) 
     # 获取模型名称
     modelnames  = df_combined.columns.to_list()[1:] 
     if 'y' in modelnames:
@@ -657,14 +657,14 @@ def model_losss(sqlitedb,end_time):
     df_combined.dropna(inplace=True) 
     logger.info(df_combined.shape)
     # 其他列转为数值类型
-    df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['CREAT_DATE','ds','created_dt'] })
+    df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['CREAT_DATE','ds','created_dt','cutoff'] })
     # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值
     df_combined['max_cutoff'] = df_combined.groupby('ds')['CREAT_DATE'].transform('max')
 
     # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列
     df_combined = df_combined[df_combined['CREAT_DATE'] == df_combined['max_cutoff']]
     # 删除模型生成的cutoff列
-    df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True) 
+    df_combined.drop(columns=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True,errors='ignore') 
     # 获取模型名称
     modelnames  = df_combined.columns.to_list()[1:] 
     if 'y' in modelnames: