diff --git a/api/api_v1/endpoints/query.py b/api/api_v1/endpoints/query.py
index bf0b9db..60c2622 100644
--- a/api/api_v1/endpoints/query.py
+++ b/api/api_v1/endpoints/query.py
@@ -606,7 +606,7 @@ async def scatter_model(
     analysis = res['analysis']
     groupby = res['groupby']
     quota_interval_arr = res['quota_interval_arr']
-    if analysis != 'number_of_days':
+    if analysis != 'number_of_days' and interval_type != 'discrete':
         max_v = int(df['values'].max())
         min_v = int(df['values'].min())
         interval = (max_v - min_v) // 10 or 1
@@ -655,10 +655,25 @@ async def scatter_model(
 
     # elif analysis == 'number_of_days':
     else:
-        resp = {'list': {}}
-        for key, tmp_df in df.groupby('date'):
-            total = int(tmp_df['values'].sum())
-            resp['list'][key.strftime('%Y-%m-%d')] = {'n': total, 'total': total, 'p': 100}
+        resp = {'list': {}, 'label': [],
+                'start_date': res['start_date'],
+                'end_date': res['end_date'],
+                'time_particle': res['time_particle']
+                }
+        total_dict = {}
+        labels = [str(i) for i in sorted(df['values'].unique())]
+        resp['label'] = labels
+        for key, tmp_df in df.groupby(['date']):
+            total = len(tmp_df)
+            dt = key.strftime('%Y-%m-%d')
+            labels_dict = {}
+            for key2, tmp_df2 in tmp_df.groupby('values'):
+                label = str(key2)
+                n = len(tmp_df2)
+                labels_dict[label] = n
+
+            resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
+                                       'p': [round(labels_dict.get(i, 0)*100 / total, 2) for i in labels]}}
         return schemas.Msg(code=0, msg='ok', data=resp)
 
         # bins_s = pd.cut(tmp_df['values'], bins=bins,