diff --git a/api/api_v1/authz/authz.py b/api/api_v1/authz/authz.py
index d029dd3..72ed3ae 100644
--- a/api/api_v1/authz/authz.py
+++ b/api/api_v1/authz/authz.py
@@ -35,7 +35,7 @@ async def add_role_domain(
     #                                                 domain=item.game)
     #
     # return schemas.Msg(code=0, msg='添加成功', data=True)
-    res = await crud.url_list.get_all(db)
+    res = await crud.url_list.get_all(db)  # 获取所有级别权限的所有路由和路由状态
     role_id = {}
     for i in res:
         role_id[i['auth_id']] = i['name']
@@ -60,7 +60,7 @@ async def add_role_domain(
                                                                          quanxian_id=quanxian_id))
     return schemas.Msg(code=0, msg='添加成功', data=True)
 
-
+# 疑似弃用
 @router.post("/get_permissions_for_user_in_domain")
 async def get_permissions_for_user_in_domain(
         request: Request,
@@ -153,6 +153,7 @@ async def add_policy(
 async def remove_policy(
         request: Request,
         data_in: schemas.Del_role,
+        db: AsyncIOMotorDatabase = Depends(get_database),
         current_user: schemas.UserDB = Depends(deps.get_current_user)):
     """
     修改角色api权限
diff --git a/api/api_v1/ck_mana/event.py b/api/api_v1/ck_mana/event.py
index c81f727..3ab9cd3 100644
--- a/api/api_v1/ck_mana/event.py
+++ b/api/api_v1/ck_mana/event.py
@@ -19,7 +19,7 @@ from models.x_analysis import XAnalysis
 
 router = APIRouter()
 
-
+# 弃用
 @router.post("/update_event_view")
 async def update_event_view(
         request: Request,
diff --git a/api/api_v1/endpoints/dashboard.py b/api/api_v1/endpoints/dashboard.py
index 58d84c8..109e28a 100644
--- a/api/api_v1/endpoints/dashboard.py
+++ b/api/api_v1/endpoints/dashboard.py
@@ -32,6 +32,7 @@ async def edit_show_report(
         db: AsyncIOMotorDatabase = Depends(get_database),
         current_user: schemas.UserDB = Depends(deps.get_current_user)
 ) -> schemas.Msg:
+    """修改看板"""
     report_id = data_in.config.report_id
     res = await crud.dashboard.update_one(db, {'_id': data_in.dashboard_id, 'reports.report_id': report_id},
                                           {'$set': {f'reports.$.{k}': v for k, v in
diff --git a/api/api_v1/endpoints/data_auth.py b/api/api_v1/endpoints/data_auth.py
index 9a17b04..70945ae 100644
--- a/api/api_v1/endpoints/data_auth.py
+++ b/api/api_v1/endpoints/data_auth.py
@@ -20,7 +20,7 @@ from db.redisdb import get_redis_pool, RedisDrive
 
 router = APIRouter()
 
-
+# 疑似弃用
 @router.post('/add_data_auth')
 async def add_data_auth(request: Request,
                         data_id: schemas.DataAuthCreate,
@@ -32,7 +32,7 @@ async def add_data_auth(request: Request,
     await crud.data_auth.create(db, data_id, game)
     return schemas.Msg(code=0, msg='ok', data=data_id)
 
-
+# 疑似弃用
 @router.post('/edit_data_auth')
 async def edit_data_auth(request: Request,
                          data_id: schemas.DataAuthEdit,
@@ -44,7 +44,7 @@ async def edit_data_auth(request: Request,
     await crud.data_auth.edit_data_auth(db, data_id)
     return schemas.Msg(code=0, msg='ok', data=data_id)
 
-
+#各数据类型在分析指标时可支持的计算方法
 @router.get("/quotas_map")
 async def quotas_map(
         request: Request,
@@ -60,6 +60,7 @@ async def filter_map(
         game: str,
         current_user: schemas.UserDB = Depends(deps.get_current_user)
 ) -> schemas.Msg:
+    # ck中，不同的数据类型的不同计算规则
     return schemas.Msg(code=0, msg='ok', data=settings.CK_FILTER)
 
 
@@ -154,7 +155,7 @@ async def user_property(request: Request,
              'show_name': data_attr.get(k, {}).get('show_name', ''),
              }
         )
-    propertys = sorted(propertys, key=lambda x: x['show_name'])
+    propertys = sorted(propertys, key=lambda x: x['show_name'])  # 按show_name排序
 
     return schemas.Msg(code=0, msg='ok', data=propertys)
 
@@ -170,7 +171,7 @@ async def load_prop_quotas(request: Request,
                            ) -> schemas.Msg:
     """事件属性 聚合条件"""
 
-    event_columns = await ck.get_columns(game, 'event')
+    event_columns = await ck.get_columns(game, 'event')  # 获取字段名和字段类型
 
     data_attr = await crud.data_attr.find_many(db, {'game': game, 'cat': 'event'})
     data_attr = {item['name']: item for item in data_attr}
diff --git a/api/api_v1/endpoints/data_mana.py b/api/api_v1/endpoints/data_mana.py
index cff1e4c..f6a4226 100644
--- a/api/api_v1/endpoints/data_mana.py
+++ b/api/api_v1/endpoints/data_mana.py
@@ -136,7 +136,7 @@ async def edit_data_attr(
 #     await crud.select_map.save(db, data_in)
 #     return schemas.Msg(code=0, msg='ok', data=data_in)
 
-#在gametoos同步区服了，所以不需要这段代码
+# 在gametoos同步区服了，但在其他事件映射时需要用到
 @router.post("/add_select_map")
 async def add_select_map(
         request: Request,
@@ -148,7 +148,7 @@ async def add_select_map(
     """添加游戏区服信息选择映射"""
     dfs = pd.read_excel(file, engine='openpyxl', sheet_name=None)
     for attr_name, df in dfs.items():
-        #将id这列转换成字符串类型
+        #  将id这列转换成字符串类型
         if len(df) >0:
             df['id'] = df['id'].astype(str)
             map_ = df.to_dict('records')
@@ -165,7 +165,7 @@ async def select_list(
         current_user: schemas.UserDB = Depends(deps.get_current_user)
 ) -> schemas.Msg:
     """属性值选择映射列表"""
-    #当游戏为魔法门H5时，把game的值改为数据库中对应的值（mdb中的值和ck中的值是不一样的）
+    #  当游戏为魔法门H5时，把game的值改为数据库中对应的值（mdb中的值和ck中的值是不一样的）
     if game == 'mfmh5':
         game='mzmfmh5'
     resp = await crud.select_map.get_list(db, game)
@@ -207,13 +207,13 @@ async def add_attr(
     data = await rdb.get(f'{game}_{data_in.cat}')
     data = json.loads(data)
     if data_in.state =='add':
-        #判断传入数据类型
+        # 判断传入数据类型
         new_data_type=estimate_data(data_in.data_type)
-        #添加数据
+        # 添加数据
         data[data_in.new_attribute]=new_data_type
     else:
         del data[data_in.new_attribute]
-    #将字典转为字符串
+    # 将字典转为字符串
     str_data=dict_to_str(data)
     await rdb.set(f'{game}_{data_in.cat}',str_data)
     return schemas.Msg(code=0, msg='ok')
\ No newline at end of file
diff --git a/api/api_v1/endpoints/event_mana.py b/api/api_v1/endpoints/event_mana.py
index db2b2d8..41337f3 100644
--- a/api/api_v1/endpoints/event_mana.py
+++ b/api/api_v1/endpoints/event_mana.py
@@ -27,9 +27,9 @@ async def event_list(
         current_user: schemas.UserDB = Depends(deps.get_current_user)
 ) -> schemas.Msg:
     """事件列表"""
-    #获取事件名
+    # 获取事件名
     try:
-        event_list = await ckdb.distinct(game, 'event', '#event_name')
+        event_list = await ckdb.distinct(game, 'event', '#event_name')  # 获取事件名
         # 获取事件量
         event_count = await ckdb.yesterday_event_count(game)
         event_meta = await crud.event_mana.find_many(db, {'game': game}) or {}
diff --git a/api/api_v1/endpoints/project.py b/api/api_v1/endpoints/project.py
index 5456321..fb955cb 100644
--- a/api/api_v1/endpoints/project.py
+++ b/api/api_v1/endpoints/project.py
@@ -23,7 +23,7 @@ async def create(
 ) -> schemas.Msg:
     """创建项目"""
     try:
-        res_project = await crud.project.create(db, data_in, current_user=request.user)
+        res_project = await crud.project.create(db, data_in, current_user=request.user)  # 创建项目
         await crud.project_number.createxiangmu(db, data_in)
         # 同步插入项目
         # await crud.project_number.createxiangmu(db, data_in)
@@ -36,7 +36,7 @@ async def create(
         await crud.user_url.updata_quanxian(db, schemas.Url_quanxian(user=user_url['user'], user_id=user_url['user_id'],
                                                                      game=user_url['game'],
                                                                      quanxian_id=user_url['quanxian_id'],
-                                                                     quanxian=user_url['quanxian']))
+                                                                     quanxian=user_url['quanxian']))  # 配置权限
     except pymongo.errors.DuplicateKeyError:
         return schemas.Msg(code=-1, msg='项目名已存在', data='项目名已存在')
 
diff --git a/api/api_v1/endpoints/query.py b/api/api_v1/endpoints/query.py
index 0d7f8f1..24e7d6c 100644
--- a/api/api_v1/endpoints/query.py
+++ b/api/api_v1/endpoints/query.py
@@ -181,16 +181,16 @@ async def event_model(
         request: Request,
         game: str,
         data_in: schemas.CkQuery,
-        ckdb: CKDrive = Depends(get_ck_db),
-        db: AsyncIOMotorDatabase = Depends(get_database),
-        rdb: RedisDrive = Depends(get_redis_pool),
+        ckdb: CKDrive = Depends(get_ck_db),  # ck 驱动
+        db: AsyncIOMotorDatabase = Depends(get_database),  # mongodb 驱动
+        rdb: RedisDrive = Depends(get_redis_pool),  # redis 驱动
         analysis: BehaviorAnalysis = Depends(BehaviorAnalysis),
-        current_user: schemas.UserDB = Depends(deps.get_current_user)
+        current_user: schemas.UserDB = Depends(deps.get_current_user)  # 登录校验
 ) -> schemas.Msg:
     """ 事件分析"""
-    await analysis.init(data_where=current_user.data_where)
+    await analysis.init(data_where=current_user.data_where)  # 初始化请求参数
     try:
-        sqls = await analysis.event_model_sql()
+        sqls = await analysis.event_model_sql()  # 生成SQL，查询时间范围，日期类型
     except Exception as e:
         return schemas.Msg(code=-9, msg='报表配置参数异常')
     res = []
@@ -226,7 +226,7 @@ async def event_model(
                 if last_value > 0:
                     q['last_value'] = float(last_value)
                     break
-            if list(item.get('event_name'))[-1] == '率':
+            if list(item.get('event_name'))[-1] == '率':  # 比列类的全部加上百分比符号
                 for i in range(len(values)):
                     values[i] = str((values[i])) + '%'
                 q['values'].append(values)
@@ -243,10 +243,10 @@ async def event_model(
             continue
         # sql语句
         sql = item['sql']
-        groupby = item['groupby']
+        groupby = item['groupby']  # 分组项
         date_range = item['date_range']  # 获取的要查询的每一天的时间
         df = await ckdb.query_dataframe(sql)  # 以sql语句查出数据，df是二维列表
-        if item['event_name'] == '月充总额':
+        if item['event_name'] == '月充总额':  # 充值总额和月充总额是单独拿出来做处理返回数据
             date_range=df['date'].tolist()
             q['date_range']=[str(i).split('-')[0]+'-'+str(i).split('-')[1] for i in date_range]
         else:
@@ -255,7 +255,7 @@ async def event_model(
         # 映射对应中文返回给前端展示
         for i in groupby:
             if i == 'svrindex':
-                if game == 'mfmh5':
+                if game == 'mfmh5':  # 只有mfmh5单独处理，因为同步区服的的游戏名不一样
                     game = 'mzmfmh5'
                 chinese = {}
                 resp = await crud.select_map.get_one(db, game, i)
@@ -355,10 +355,10 @@ async def event_model(
                     else:
                         concat_data.append((i, group, 0))
                 df_group = pd.concat([df_group, pd.DataFrame(concat_data, columns=df_group.columns)])
-                df_group.sort_values('date', inplace=True)
+                df_group.sort_values('date', inplace=True)  # 按date这一列的值在原数据排序
                 q['values'].append(df_group['values'].to_list())
-                q['sum'].append(round(float(df_group['values'].sum()), 2))
-                q['avg'].append(round(float(df_group['values'].mean()), 2))
+                q['sum'].append(round(float(df_group['values'].sum()), 2))  # 求和
+                q['avg'].append(round(float(df_group['values'].mean()), 2))  #平均数
                 for last_value in df['values'].values[::-1]:
                     if last_value > 0:
                         q['last_value'] = float(last_value)
@@ -503,7 +503,7 @@ async def retention_model(request: Request,
     except Exception as e:
         return schemas.Msg(code=-9, msg='报表配置参数异常')
     sql = res['sql']  # 获取到sql语句
-    df = await ckdb.query_dataframe(sql)
+    df = await ckdb.query_dataframe(sql)  # 查询数据
     if df.empty:
         return schemas.Msg(code=-9, msg='无数据', data=None)
 
@@ -512,12 +512,12 @@ async def retention_model(request: Request,
     retention_n = res['retention_n']  # 列表 int
     filter_item_type = res['filter_item_type']  # all
     filter_item = res['filter_item']  # 列表  0,1,3,7,14,21,30
-    df.set_index('reg_date', inplace=True)
+    df.set_index('reg_date', inplace=True)  # 重新生成下标
     #  补齐没有数据的日期
     for d in set(res['date_range']) - set(df.index):
         df.loc[d] = 0
 
-    df.sort_index(inplace=True)
+    df.sort_index(inplace=True)  # 原数据排序
     summary_values = {'均值': {}}
     max_retention_n = 1
     # 留存人数
@@ -557,11 +557,11 @@ async def retention_model(request: Request,
                 retention_avg_dict[rn]['o_cnt0'] += rd['cnt0']
                 retention_avg_dict[rn]['o_cntn'] += rd[f'on{rn}']
     # 算均值
-    tmp['p'] = []
-    tmp['n'] = []
-    tmp['p_outflow'] = []
-    tmp['n_outflow'] = []
-    tmp['d0'] = 0
+    tmp['p'] = []  # 留存率
+    tmp['n'] = []  # 留存人数
+    tmp['p_outflow'] = []  # 流失率
+    tmp['n_outflow'] = []  # 流失人数
+    tmp['d0'] = 0  # 总人数
     for rt, rd in retention_avg_dict.items():
         tmp['d0'] = int(df['cnt0'].sum())
         n = round(rd['cntn'] * 100 / rd['cnt0'], 2)
@@ -582,15 +582,15 @@ async def retention_model(request: Request,
             items[key].extend(['-'] * (retention_length - len(items[key])))
 
     resp = {
-        'summary_values': summary_values,
+        'summary_values': summary_values,  # 留存/流失数据
         # 'values': values,
-        'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
-        'title': title,
+        'date_range': [d.strftime('%Y-%m-%d') for d in date_range],  # 时间范围
+        'title': title,  # 表头
         'filter_item_type': filter_item_type,
         'filter_item': filter_item,
-        'start_date': res['start_date'],
-        'end_date': res['end_date'],
-        'time_particle': res['time_particle']
+        'start_date': res['start_date'],  # 开始时间
+        'end_date': res['end_date'],  # 结束时间
+        'time_particle': res['time_particle']  # 时间类型
 
     }
     return schemas.Msg(code=0, msg='ok', data=resp)
@@ -668,13 +668,13 @@ async def retention_model(request: Request,
     if len(groupby_list) == 1:
         max_v = int(df[groupby_list[0]['columnName']].max())
         min_v = int(df[groupby_list[0]['columnName']].min())
-    for i in groupby:
+    for i in groupby:  # 映射区服
         if i == 'svrindex':
             if game == 'mfmh5':
                 game = 'mzmfmh5'
             chinese = {}
             resp = await crud.select_map.get_one(db, game, i)
-            if not resp:
+            if not resp:  # 如果没有配置相关的映射数据就跳过
                 continue
             for ii in resp:
                 chinese[ii['id']] = ii['title']
@@ -682,11 +682,11 @@ async def retention_model(request: Request,
                 # 开始映射
                 df.loc[df['svrindex'] == k, 'svrindex'] = v
     times = df['reg_date'][0]
-    df.set_index(groupby, inplace=True)
+    df.set_index(groupby, inplace=True)  # 重新生成下标
     # for d in set(res['date_range']) - set(df.index):
     #     df.loc[d] = 0
 
-    df.sort_index(inplace=True)
+    df.sort_index(inplace=True)  # 排序
     summary_values = {'均值': {}}
     max_retention_n = 1
     # 留存人数
@@ -862,7 +862,7 @@ async def retention_model(request: Request,
     }
     return schemas.Msg(code=0, msg='ok', data=resp)
 
-
+# 废弃
 async def retention_model01(request: Request,
                             game: str,
                             ckdb: CKDrive = Depends(get_ck_db),
@@ -986,7 +986,7 @@ async def retention_model_export(request: Request,
         export = d.to_stream()
     return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
 
-
+# 弃用
 @router.post("/retention_model_del", deprecated=True)
 async def retention_model_del(
         request: Request,
@@ -1095,7 +1095,7 @@ async def funnel_model(
     cond_level = res['cond_level']
     groupby = res['groupby']
     switch_test = analysis.event_view.get('switchTest', True)
-    if switch_test:
+    if switch_test: # 界面的开关是开的走这里
         df = await ckdb.query_dataframe(sql)
         if df.empty:
             return schemas.Msg(code=-9, msg='无数据', data=None)
@@ -1112,7 +1112,7 @@ async def funnel_model(
         # df.set_index('date',inplace=True)
         data_list = []
         date_data = {}
-        if df.shape == (0, 0):
+        if df.shape == (0, 0):  # 元组的第一个元素代表行数，第二个元素代表列数,判断数据大小
             return schemas.Msg(code=0, msg='ok', data={'list': data_list, 'level': cond_level})
 
         tmp = {'title': '总体'}
@@ -1123,7 +1123,7 @@ async def funnel_model(
         for i in tmp_df.index:
             tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
 
-        tmp['n'] = tmp_df['values'].to_list()
+        tmp['n'] = tmp_df['values'].to_list()  # 取出values这一列的所有数据返回成列表
         tmp['p1'] = [100]
         # tmp['p2'] = []
         for i, v in tmp_df.loc[2:, 'values'].items():
@@ -1142,7 +1142,7 @@ async def funnel_model(
             tmp_df = tmp_df.groupby('level').sum()
             tmp_df.sort_index(inplace=True)
             for i in tmp_df.index:
-                tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
+                tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()  # 数据求和
 
             tmp = dict()
 
@@ -1161,12 +1161,12 @@ async def funnel_model(
         if groupby:
             # 补齐数据
             concat_data = []
-            idx = set(df.set_index(['date'] + groupby).index)
+            idx = set(df.set_index(['date'] + groupby).index)  # 下标去重
             all_idx = {(*j, i) for i in range(1, len(cond_level) + 1) for j in idx}
             for i in all_idx - set(df.set_index(list(('date', *groupby, 'level'))).index):
                 concat_data.append((*i, 0))
 
-            df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
+            df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])  # 合并数据
             # df.sort_values(list((*groupby, 'level')), inplace=True, ascending=False)
             # 映射对应中文返回给前端展示
             for i in groupby:
@@ -1184,7 +1184,7 @@ async def funnel_model(
                         df.loc[df[i] == k, i] = v
             for key, tmp_df in df.groupby(groupby):
                 tmp = {'title': key}
-                tmp_df = tmp_df.groupby('level').sum()
+                tmp_df = tmp_df.groupby('level').sum()  #按level分组求和
                 tmp_df.sort_index(inplace=True)
                 for i in tmp_df.index:
                     tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
@@ -1220,14 +1220,14 @@ async def funnel_model(
                 _ = date_data.setdefault(key[0].strftime('%Y-%m-%d'), {})
                 # [key[1]] = tmp
         title = (groupby or ['总体']) + cond_level
-        resp = {'list': data_list,
+        resp = {'list': data_list, # 数据列表
                 'date_data': date_data,
-                'title': title,
-                'level': cond_level,
+                'title': title,  # 表头
+                'level': cond_level,  # 所选事件
                 'switch_test': switch_test,
-                'start_date': res['start_date'],
-                'end_date': res['end_date'],
-                'time_particle': res['time_particle']
+                'start_date': res['start_date'],  # 开始时间
+                'end_date': res['end_date'],  # 结束时间
+                'time_particle': res['time_particle']  # 时间类型
                 }
         return schemas.Msg(code=0, msg='ok', data=resp)
     else:
@@ -1243,8 +1243,8 @@ async def funnel_model(
 
         group_str = res['analysis']
         # 转int
-        df[group_str] = df[group_str].astype(int)
-        step_list = [str(i) for i in sorted(df[group_str].unique())]
+        df[group_str] = df[group_str].astype(int)  # 声明这一列的数据类型为int
+        step_list = [str(i) for i in sorted(df[group_str].unique())]  # 唯一性去重排序
         dict_k = {}
         df['values'] = 1
         for k, nedf in df.groupby("date"):
@@ -1431,9 +1431,9 @@ async def scatter_model(
         res = await analysis.scatter_model_sql()
     except Exception as e:
         return schemas.Msg(code=-9, msg='报表配置参数异常')
-    end_date = analysis.end_date
-    start_date = analysis.start_date
-    where = analysis.events[-1]['quotaname']
+    end_date = analysis.end_date  # 开始时间
+    start_date = analysis.start_date  # 结束时间
+    where = analysis.events[-1]['quotaname']  # 查询条件的字段名
     sql = res['sql']
     # columnName = analysis.events[-1]['label_id']
 
@@ -1450,29 +1450,29 @@ async def scatter_model(
     if 'list' in str(type(df['values'][0])):
         # f=lambda x:x[0]
         # df['values']=df['values'].map(f)
-        df = df.explode("values").reset_index(drop=True)
+        df = df.explode("values").reset_index(drop=True)  # 取字典类型数据并重置索引
         # df['values']=df['values'].astype(str)
-    df.fillna(0, inplace=True)
+    df.fillna(0, inplace=True)  # 在原数据把缺失值填充为0
     # 转换数据类型为int
-    if analysis.events[-1].get('analysis') != 'uniqExact':
+    if analysis.events[-1].get('analysis') != 'uniqExact':  # 如果不是去重数的话声明值为int类型
         df['values'] = df['values'].astype(int)
     else:
         df['values'] = df['values'].astype(str)  # 统一声明使用去重数的时候为str
-    interval_type = res['interval_type']
+    interval_type = res['interval_type']  # 在前端的展现形式，默认/离散/自定义
     analysi = res['analysis']
     groupby = res['groupby']
     quota_interval_arr = res['quota_interval_arr']
     # 兼容合计的
-    if res['time_particle'] == 'total':
+    if res['time_particle'] == 'total': # 计算所有的数据走这里
         df['date'] = '合计'
 
     if analysi != 'number_of_days' and interval_type != 'discrete':
         try:
-            max_v = int(df['values'].max())
+            max_v = int(df['values'].max()) # 数值中的最大值
         except Exception as e:
             return schemas.Msg(code=-9, msg='请用离散数字', data=None)
-        min_v = int(df['values'].min())
-        interval = (max_v - min_v) // 10 or 1
+        min_v = int(df['values'].min())  # 数值中的最小值
+        interval = (max_v - min_v) // 10 or 1  # 大小值之间10等分的区间数值
         resp = {'list': dict(),
                 'start_date': res['start_date'],
                 'end_date': res['end_date'],
@@ -1480,8 +1480,8 @@ async def scatter_model(
                 }
 
         if not quota_interval_arr:
-            resp['label'] = [f'[{i},{i + interval})' for i in range(min_v, max_v, interval)]
-            bins = [i for i in range(min_v, max_v + interval, interval)]
+            resp['label'] = [f'[{i},{i + interval})' for i in range(min_v, max_v, interval)]  # 表头
+            bins = [i for i in range(min_v, max_v + interval, interval)]  #区间
         else:
             quota_interval_arr = [-float('inf')] + quota_interval_arr + [float('inf')]
             resp['label'] = []
@@ -1493,16 +1493,16 @@ async def scatter_model(
         # 这是整体的
         for key, tmp_df in df.groupby('date'):
             bins_s = pd.cut(tmp_df['values'], bins=bins,
-                            right=False, include_lowest=True).value_counts()
-            bins_s.sort_index(inplace=True)
-            total = int(bins_s.sum())
-            if res['time_particle'] == 'total':
+                            right=False, include_lowest=True).value_counts()  # 按区间分开
+            bins_s.sort_index(inplace=True)  # 重新排序
+            total = int(bins_s.sum())  # 算出数量
+            if res['time_particle'] == 'total':  # 合计的数据
                 resp['list']['合计'] = dict()
                 p = list(round(bins_s * 100 / total, 2).to_list())
                 resp['list']['合计']['总体'] = {'n': bins_s.to_list(), 'total': total,
                                             'p': [str(i) + '%' for i in p],
                                             'title': '总体'}
-            else:
+            else:  # 按时间分开算数据
                 p = list(round(bins_s * 100 / total, 2).to_list())
                 resp['list'][key.strftime('%Y-%m-%d')] = dict()
                 resp['list'][key.strftime('%Y-%m-%d')]['总体'] = {'n': bins_s.to_list(), 'total': total,
@@ -1523,7 +1523,7 @@ async def scatter_model(
         #                                                           'title': title
         #                                                           }
         download = analysis.event_view.get('download', '')
-        if download == 1:
+        if download == 1: # 下载数据
             creat_df = create_df(resp)
             Download = Download_xlsx(creat_df, '分布分析')
             return Download
@@ -1540,7 +1540,7 @@ async def scatter_model(
         labels = [str(i) for i in sorted(df['values'].unique())]
         resp['label'] = labels
         shaixuan = analysis.events[0].get('analysis')
-        for key, tmp_df in df.groupby(['date']):
+        for key, tmp_df in df.groupby(['date']):  # 按日期分组
             if shaixuan == 'uniqExact':
                 total = len(set(tmp_df['uid']))
             else:
@@ -1585,7 +1585,7 @@ async def scatter_model(
                     number_str = str(number_int) + '%'
                     list_p.append(number_str)
                 resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
-                                           'p': list_p}}
+                                           'p': list_p}}  # 整合出数据
                 # 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]}}
         if where == "step_id" and event_type == "guide":
@@ -1617,7 +1617,7 @@ async def scatter_model(
         #                                                 'p': round(bins_s * 100 / total, 2).to_list(),
         #                                                 'title': '总体'}
 
-
+# 疑弃用，有同类型分布分析
 @router.post("/guide_model")
 async def guide_model(
         request: Request,
@@ -1657,9 +1657,9 @@ async def guide_model(
     p_data = {}
     data = {}
     for dict_key, dict_data in dict_k.items():
-        dict_data1 = deepcopy(dict_data)
+        dict_data1 = deepcopy(dict_data)  # 深拷贝一份数据
         dict_k1 = {int(k): v for k, v in dict_data1.items()}
-        sorted(dict_k1.keys())
+        sorted(dict_k1.keys())  # 按key排序
         data_values = list(dict_k1.values())
         p_values = [round(i / sum(data_values), 2) or 0 for i in data_values]
         p_values.insert(0, dict_key)
@@ -1814,8 +1814,8 @@ async def scatter_model(
             df['values'] = df['values'].astype(str)  # 统一声明使用去重数的时候为str
         interval_type = res['interval_type']
         analysi = res['analysis']
-        groupby = res['groupby']
-        true_df = df.groupby(groupby).sum()
+        groupby = res['groupby']  # 分组项
+        true_df = df.groupby(groupby).sum()  # 按分组求和
         group_label = res['group_label']
         quota_interval_arr = res['quota_interval_arr']
         # 兼容合计的
@@ -2070,7 +2070,7 @@ async def trace_model_sql(
     event_num_dict = {}
     event_next_event = {}
     nodes = {'流失'}
-    name_list = analysis.events['event_namesdes']
+    name_list = analysis.events['event_namesdes']  # 参与分析的事件
     name_dict = {}
     for i in name_list:
         name_dict[i['event_name']] = i['event_desc']
@@ -2126,7 +2126,7 @@ async def trace_model_sql(
             trail.append(i)
         else:
             nodes.append(i)
-    first.sort(reverse=True)
+    first.sort(reverse=True)  # 排序
     for i in first:
         nodes.insert(0, i)
     for i in trail:
@@ -2341,8 +2341,8 @@ async def user_property_model(
     account_id = list(df['#account_id'])
     new_sql = f"""select `#account_id`,`#ip`,`#distinct_id`,rmbmoney,owner_name,lv,zhanli,channel,
     channel,svrindex,maxmapid,name,`exp`,vip,jinbi,last_account_login_time,binduid from {game}.`user` where `#account_id` in ({account_id})"""
-    df1 = await ckdb.query_dataframe(new_sql)
-    new_values = df1.values.tolist()
+    df1 = await ckdb.query_dataframe(new_sql)  # 按对应用户查询数据
+    new_values = df1.values.tolist()  # 把所有值按列表返回
     for i in range(len(new_values)):
         if str(new_values[i][6]) == 'nan':
             new_values[i][6] = 0
diff --git a/crud/select_map.py b/crud/select_map.py
index 9594a61..c7b1200 100644
--- a/crud/select_map.py
+++ b/crud/select_map.py
@@ -7,6 +7,7 @@ __all__ = 'select_map',
 
 
 class CRUDSelectMap(CRUDBase):
+    # 更新数据
     async def save(self, db: AsyncIOMotorDatabase, data_in: schemas.SelectMap):
         where = {'attr_name': data_in.attr_name, 'game': data_in.game}
         return await self.update_one(db, where, {'$set': data_in.dict(skip_defaults=True)}, upsert=True)
@@ -20,7 +21,7 @@ class CRUDSelectMap(CRUDBase):
         where = {'game': game}
         res = await self.find_many(db, where, {'_id': 0})
         return res
-
+    # 查找一条数据，不返回_id
     async def get_select(self, db: AsyncIOMotorDatabase, data_in: schemas.SelectAttr, game: str):
         where = {'game': game, **data_in.dict()}
         res = await self.find_one(db, where, {'_id': 0})
diff --git a/models/behavior_analysis.py b/models/behavior_analysis.py
index a5f6087..c165279 100644
--- a/models/behavior_analysis.py
+++ b/models/behavior_analysis.py
@@ -137,7 +137,7 @@ class BehaviorAnalysis:
         self.data_where = []
 
     async def init(self, *args, **kwargs):
-
+        # 在自动获取看板和报表信息的时候会走这里
         if self.data_in.report_id:
             db = get_database()
             report = await crud.report.get(db, id=self.data_in.report_id)
@@ -152,7 +152,7 @@ class BehaviorAnalysis:
                 except:
                     # 兼容以前的
                     e_days, s_days = self.event_view['recentDay'].split('-')
-
+                # 根据本地电脑时间，按时间范围自动适应当前的时间范围
                 self.event_view['endTime'] = arrow.get().shift(days=-int(e_days)).strftime('%Y-%m-%d 23:59:59')
                 self.event_view['startTime'] = arrow.get().shift(days=-int(s_days)).strftime('%Y-%m-%d 00:00:00')
 
@@ -362,7 +362,7 @@ class BehaviorAnalysis:
                     user_filters.append(and_(*user_filter))
 
         return event_filters, user_filters
-
+    # 根据筛选的where条件生成对应SQL
     async def handler_filts(self, *filters, nu=0):
         """