diff --git a/main.py b/main.py
index c51a009..d06d24b 100644
--- a/main.py
+++ b/main.py
@@ -138,15 +138,23 @@ def predict_main():
             is_eta=is_eta,
             )
 
+
+    logger.info('模型训练完成')
     # # 模型评估
+    
+    logger.info('训练数据绘图ing')
     model_results3 = model_losss_juxiting(sqlitedb)
+    
+    logger.info('训练数据绘图end')
     # 模型报告
     
+    logger.info('制作报告ing')
     title = f'{settings}--{now}-预测报告' # 报告标题
     brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
                 reportname=reportname,sqlitedb=sqlitedb),
     # pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
                 # reportname=reportname),
+    logger.info('制作报告end')
     logger.info('模型训练完成')
     
     # tansuanli_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,end_time=end_time,reportname=reportname)
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 7ec6e36..74dc7d6 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -510,36 +510,14 @@ def model_losss_juxiting(sqlitedb):
 
 
     # 使用最佳五个模型进行绘图
-    # best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
-    # def find_min_max_within_quantile(row):
-    #     row = row[best_models]
-    #     q10 = row.min()
-    #     q90 = row.max()
-    #     # 获取 row行10%分位值对应的模型名称
-    #     min_model = row[row == q10].idxmin()
-    #     max_model = row[row == q90].idxmin()
-        
-    #     # # 判断flot值是否为空值
-    #     # if pd.isna(q10) or pd.isna(q90):
-    #     return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
-
-    # # 遍历行
-    # df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
-    # df_combined = df_combined.round(4)
-    # print(df_combined3)
-
-
-
-
-    # 通道使用预测模型的80%置信度
+    best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
     def find_min_max_within_quantile(row):
-        row.drop(['ds','y'], inplace=True)
-        # 获取分位数10%和90%的值
-        q10 = row.quantile(0.1)
-        q90 = row.quantile(0.9)
+        row = row[best_models]
+        q10 = row.min()
+        q90 = row.max()
         # 获取 row行10%分位值对应的模型名称
-        min_model = row[row == q10].index[0]
-        max_model = row[row == q90].index[0]
+        min_model = row[row == q10].idxmin()
+        max_model = row[row == q90].idxmin()
         
         # # 判断flot值是否为空值
         # if pd.isna(q10) or pd.isna(q90):
@@ -549,6 +527,28 @@ def model_losss_juxiting(sqlitedb):
     df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
     df_combined = df_combined.round(4)
     print(df_combined3)
+
+
+
+
+    # # 通道使用预测模型的80%置信度
+    # def find_min_max_within_quantile(row):
+    #     row.drop(['ds','y'], inplace=True)
+    #     # 获取分位数10%和90%的值
+    #     q10 = row.quantile(0.1)
+    #     q90 = row.quantile(0.9)
+    #     # 获取 row行10%分位值对应的模型名称
+    #     min_model = row[row == q10].index[0]
+    #     max_model = row[row == q90].index[0]
+        
+    #     # # 判断flot值是否为空值
+    #     # if pd.isna(q10) or pd.isna(q90):
+    #     return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
+
+    # # 遍历行
+    # df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
+    # df_combined = df_combined.round(4)
+    # print(df_combined3)