diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 0a29cb4..7126085 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -508,12 +508,13 @@ def model_losss_juxiting(sqlitedb):
         modelnames.remove('y')
     df_combined3 = df_combined.copy()  # 备份df_combined,后面画图需要
 
-    # 每行预测值找到10%分位数和90%分位数
+
+    # 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.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].idxmin()
         max_model = row[row == q90].idxmin()
@@ -526,18 +527,32 @@ 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].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)
     
     
 
 
-
-
-
-
-
-
-
-
     # # 计算波动率
     # df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)
     # # 计算近60日的波动率 10% 90%分位数