以下场景的异常分数计算错误，
繁殖
->我在本地运行这个项目，这两个插件部署在elastic search中->我创建了相同的异常检测器，如下所述用于测试和理解：https://opendistro.github.io/for-elasticsearch-docs/docs/ad/api/#create-异常探测器

"feature_attributes": [
    {
      "feature_name": "total_order",
      "feature_enabled": true,
      "aggregation_query": {
        "total_order": {
          "sum": {
            "field": "value"
          }
        }
      }
    }
  ]

另外，我的探测器间隔为1分钟，与文档中给出的相同
->我启动检测器，它正在运行->并行使用我自己的测试代码，我在ElasticSearch中填充“order”索引，每隔3秒填充一个1到10之间的“value”字段，当它达到100可除时，我更新“order”索引中“value”列中的巨大值以检查异常。

Map<String, Object> dataMap = new HashMap<String, Object>();
        dataMap.put("timestamp", DATE_FORMAT.format(new Date()));
        dataMap.put("value", random.nextInt(10));
        if(atomicValue.get() % 100 == 0) {
            dataMap.put("value", atomicValue.get());
        }

这意味着值将填充为
例子：

但当值增加时，异常得分不增加，请查看下面的输出
修改订单索引中“value”的和时的预期行为。期望异常分数发生变化，但异常分数未按预期计算，请查看屏幕截图
截屏
创建探测器

启动探测器

并行顺序索引是使用我自己的测试代码填充的
模型已创建

异常检测结果

"hits": [
            {
                "_index": ".opendistro-anomaly-results-history-2020.09.08-1",
                "_type": "_doc",
                "_id": "2REQb3QBhcHTa30MZQ4i",
                "_version": 1,
                "_seq_no": 9,
                "_primary_term": 1,
                "_score": null,
                "_source": {
                    "detector_id": "MhEJb3QBhcHTa30M5g77",
                    "anomaly_score": 5.088753089094179,
                    "execution_start_time": 1599591178599,
                    "data_end_time": 1599591118599,
                    "confidence": 0.8238839646482641,
                    "data_start_time": 1599591058599,
                    "feature_data": [
                        {
                            "feature_id": "LhEJb3QBhcHTa30M5g73",
                            "feature_name": "total_order",
                            "data": 401
                        }
                    ],
                    "execution_end_time": 1599591179553,
                    "anomaly_grade": 0.01234567901233773
                },
                "sort": [
                    1599591179553
                ]
            },
            {
                "_index": ".opendistro-anomaly-results-history-2020.09.08-1",
                "_type": "_doc",
                "_id": "wxEPb3QBhcHTa30MeQ5-",
                "_version": 1,
                "_seq_no": 8,
                "_primary_term": 1,
                "_score": null,
                "_source": {
                    "detector_id": "MhEJb3QBhcHTa30M5g77",
                    "anomaly_score": 4.534258135489182,
                    "execution_start_time": 1599591118600,
                    "data_end_time": 1599591058600,
                    "confidence": 0.8237597742294861,
                    "data_start_time": 1599590998600,
                    "feature_data": [
                        {
                            "feature_id": "LhEJb3QBhcHTa30M5g73",
                            "feature_name": "total_order",
                            "data": 94
                        }
                    ],
                    "execution_end_time": 1599591119230,
                    "anomaly_grade": 0
                },
                "sort": [
                    1599591119230
                ]
            },
            {
                "_index": ".opendistro-anomaly-results-history-2020.09.08-1",
                "_type": "_doc",
                "_id": "phEOb3QBhcHTa30MjQ7J",
                "_version": 1,
                "_seq_no": 7,
                "_primary_term": 1,
                "_score": null,
                "_source": {
                    "detector_id": "MhEJb3QBhcHTa30M5g77",
                    "anomaly_score": 4.636067976770329,
                    "execution_start_time": 1599591058600,
                    "data_end_time": 1599590998600,
                    "confidence": 0.823635410425475,
                    "data_start_time": 1599590938600,
                    "feature_data": [
                        {
                            "feature_id": "LhEJb3QBhcHTa30M5g73",
                            "feature_name": "total_order",
                            "data": 75
                        }
                    ],
                    "execution_end_time": 1599591058889,
                    "anomaly_grade": 0.007444168734482831
                },
                "sort": [
                    1599591058889
                ]
            },

我们可以看到总订单是每一分钟计算一次的，
有人能解释一下，与“值”[总订单]字段的总和相比，异常分数是多少吗？因为
当总订单=75时，异常得分=4.636067976770329当总订单=94时，异常得分=4.534258135489182当总订单=401时，异常得分=5.088753089094179
因为据我所知，异常分数没有按预期计算。当“值”字段的总和急剧增加（94到401）时，我预计异常值会大幅增加，但事实并非如此。
感谢您的帮助。
谢谢，
骚扰