diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 64d2e8c..6fa1757 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -400,7 +400,7 @@ def model_losss(sqlitedb,end_time):
 
     # 保存到数据库
     if not sqlitedb.check_table_exists('accuracy'):
-        columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE'])
+        columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE','min_price','max_price'])
         sqlitedb.create_table('accuracy',columns=columns)
     existing_data = sqlitedb.select_data(table_name = "accuracy")
 
@@ -429,9 +429,11 @@ def model_losss(sqlitedb,end_time):
     # 模型评估前五最大最小
     # predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']]
     # 模型评估前五均值      df_combined3['mean'] = df_combined3[modelnames].mean(axis=1)
-
-    predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
-    predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
+    # predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
+    # predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
+    # 模型评估前十均值
+    predict_y['min_price'] = predict_y[allmodelnames[0:5]].min(axis=1) 
+    predict_y['max_price'] = predict_y[allmodelnames[0:5]].max(axis=1)
     for id in ids:
         row = predict_y[predict_y['id'] == id]
         try:
@@ -447,12 +449,19 @@ def model_losss(sqlitedb,end_time):
     df['ds'] =  pd.to_datetime(df['ds'])
     df = df.reindex()
     def calculate_accuracy(row):
-        if row['HIGH_PRICE'] > row['min_price'] or row['max_price'] > row['LOW_PRICE']:
+        # 子集情况：
+        if (row['HIGH_PRICE'] >= row['max_price'] and row['min_price'] >= row['LOW_PRICE']) or \
+            (row['LOW_PRICE'] >= row['min_price'] and row['max_price'] >= row['HIGH_PRICE']):
+            return 1    
+        # 有交集情况：
+        if row['HIGH_PRICE'] > row['min_price'] or \
+            row['max_price'] > row['LOW_PRICE']:
             sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']])
             middle_diff = sorted_prices[2] - sorted_prices[1]
             price_range = row['HIGH_PRICE'] - row['LOW_PRICE']
             accuracy = middle_diff / price_range
             return accuracy
+        # 无交集情况：
         else:
             return 0
     columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price']
@@ -586,7 +595,7 @@ def model_losss(sqlitedb,end_time):
     _plt_model_results3()
 
     return model_results3
-
+ 
 
 # 聚烯烃计算预测评估指数
 def model_losss_juxiting(sqlitedb):