terms分桶后的结果, 还需要根据每桶的doc_count再次分桶,该如何写后续的逻辑?

es版本是7.4.1
运行环境是centos7, docker
业务需求如下:
数据源是销售人员的拜访记录, 该记录的doc中包含createTime(拜访时间)和salesmanAutoId(业务员唯一标识);
需要按天分桶, 查出近两天的拜访总数;
然后按salesmanAutoId分桶, 主要是看销售人员的拜访次数;
再根据每个业务员拜访次数的区间分桶, 查看宏观拜访数据: 例如每天2访, 3访, 3访以上的各有多少人
最后这一步我无法实现, 或者是不是我不该按salesmanAutoId分桶?
请大神指点迷津, 万分感谢
以下是DSL

GET visited/_search

{

  "size": 0,

  "query": {

    "bool": {

      "must": [

        {

          "range": {

            "createTime": {

              "gte": "now-1d/d",

              "lte": "now/d"

            }

          }

        }

      ]

    }

  },

  "aggs": {

    "visit_per_day": {

      "date_histogram": {

        "field": "createTime",

        "calendar_interval": "day"

      },

      "aggs": {

        "times_per_user": {

          "terms": {

            "field": "salesmanAutoId"

          }

        }

      }

    }

  }

}

以下是结果数据:

{

  "took" : 1,

  "timed_out" : false,

  "_shards" : {

    "total" : 1,

    "successful" : 1,

    "skipped" : 0,

    "failed" : 0

  },

  "hits" : {

    "total" : {

      "value" : 15,

      "relation" : "eq"

    },

    "max_score" : null,

    "hits" : [ ]

  },

  "aggregations" : {

    "visit_per_day" : {

      "buckets" : [

        {

          "key_as_string" : "2019-11-07T00:00:00.000Z",

          "key" : 1573084800000,

          "doc_count" : 6,

          "times_per_user" : {

            "doc_count_error_upper_bound" : 0,

            "sum_other_doc_count" : 0,

            "buckets" : [

              {

                "key" : 620,

                "doc_count" : 3

              },

              {

                "key" : 17006,

                "doc_count" : 2

              },

              {

                "key" : 17323,

                "doc_count" : 1

              }

            ]

          }

        },

        {

          "key_as_string" : "2019-11-08T00:00:00.000Z",

          "key" : 1573171200000,

          "doc_count" : 9,

          "times_per_user" : {

            "doc_count_error_upper_bound" : 0,

            "sum_other_doc_count" : 0,

            "buckets" : [

              {

                "key" : 620,

                "doc_count" : 4

              },

              {

                "key" : 17006,

                "doc_count" : 3

              },

              {

                "key" : 17323,

                "doc_count" : 2

              }

            ]

          }

        }

      ]

    }

  }

}