1. 统计程序-模板格式定义

1.1. 修订记录

日期 版本 说明 编写
2019-02-22 1.4.0(R5) 增加分析统计 Zoe
2019-02-27 1.5.0(R1) 1.统计配置脚本增加rewrite_collectos字段</br>2.修改所有设备读取的一样的离线数据的问题 Zoe
2019-04-03 2.1.0(R1) 1.统计增加方差(variance)和协方差(covariance)函数</br>2.修改statistics_extend定时器定时触发逻辑，首次运行会立马执行定时任务,created固定为统一约定的时间</br>3.增加范围过滤,由filter来指定,兼容force_filter Zoe
2019-05-14 2.2.0(R1) 1.统计增加标准差(stddev)函数</br>2.统计增加欧几里得距离(euclidean_distance)函数</br>3.统计增加皮尔逊相关系数(pearson)函数</br>4.统计增加综合趋势微分(integrated_trend_differential)函数 Zoe
2020-05-06 2.2.0(R2) 文档格式修改 Roy

1.2. 文档说明

本文档适用于 statistics_keeper V2.2.0版本

1.3. 概念介绍

一个统计程序由一个统计由3大要素组成:统计对象，统计方法,统计区间。

1.3.1. 统计对象

  1. 支持针对标准统计的GR,MO,CU,EQ,EV,FA,AL,ME,ST,CO 10类统计对象和分析统计的Index,Stability,Tendency,Risk 4类统计对象 总共 14类统计对象的统计。
  2. 支持四则表达式运算。

1.3.2. 统计方法

表示对统计对象做什么样的统计,计数(count),累加(sum)还是平均数(average)等等,目前支持15种。

1.3.3. 统计区间

表示对统计对象的取值的采样区间,使用cycle表示。

1.4. 示例

{
    "params": {
        "connector": {
            "INFLUXDB": {
                "host": "127.0.0.1:8086"
            },
            "MYSQL": {
                "host": "127.0.0.1:3306"
            }
        },
        "source": {
            "MO": {
                "object": [
                    "S54;S54 > 150 || S54 < 120"
                ]
            },
            "CO": {
                "object": ["PowerMeterRatio;collect_id = 'PowerMeterRatio'"]
            }
        },
        "expr": "accumulate(S54)",
        "selection": {
            "filter_not": {
                "customerID": [
                    "1004"
                ]
            },
            "filter_and": {
                "mappingID": [
                    "1002"
                ]
            }
        },
        "track": true,
        "duration": {
            "start": "2018-07-01 00:00:00",
            "end": "2018-08-01 12:00:00"
        },
        "result": {
            "precision": 3
        },
        "cycle": "60m"
    },
    "force_filter": false,
    "tag": "ABC",
    "rewrite_collectos":{
        "collect_id":"PowerMeterRatio2",
        "enable":true
    },
    "fiilter":{
        "force_filter":true,
        "min":0,
        "max":1000
    },
    "statistics_id": "totalCumulativeActivePower",
    "statos_name": "每小时用电量",
    "description": "每小时用电量的统计",
    "name": "totalCumulativeActivePower",
    "type": "statistics"
}

1.5. 参数说明

1.5.1. connector

  • INFLUXDB:
    • 定义连接influxdb的参数,格式:ip:port
  • MYSQL:
    • 定义连接MYSQL的参数,格式:ip:port

1.5.2. source

定义统计的数据源,就是对哪些数据进行统计,支持MO,CU,EQ,EV,FA,AL,ME,ST,CO,四则运算(+,-,*,/)总共11大类。

  1. MO:就是 MOSAIC ,获取 mosaic 的数据
  2. CU:就是 CUSTOMER ,对应的是数据库的 admin_customer
  3. EQ:就是 EQUIPMENT ,对应的是数据库的 admin_equipment
  4. EV:就是 EVENT ,对应的是数据库的 admin_event
  5. FA:就是 FAULT ,对应的是数据库的 admin_fault
  6. AL:就是 ALARM ,对应的是数据库的 admin_alarm
  7. ME:就是 MESSAGE ,对应的是数据库的 admin_message
  8. ST:就是 STATOS ,对应的是数据库的 admin_statos
  9. CO:就是 COLLECTOS ,对应的是数据库的 admin_collectos
  10. Index: 对应的是分析项目的 Index(指数)表
  11. Stability：对应的是分析项目的 Stability(稳定性)表
  12. Tendency:对应的是分析项目的 Tendency(趋势)表
  13. Risk:对应的是分析项目的 Risk(风险)表
  14. object: 表示实体对象。
    1. 格式 name[=default_value];condition
  15. 支持逻辑表达式 && , ||, >,>=, <, <=

  16. MO的source目前只能配置一个,其他的source源可以配置多个。

  17. 示例：配置MO/GR数据源：

"source": {
    "MO": {
        "object": [
            "S02;S02 > 100 && S02 < 120"
            "S03"            // 配置错误,MO的source配置仅支持一个源
        ]
    }
}
定义数据源S02,并且取值值区间范围为 S02 > 100 && S02 < 120
  • CU,EQ,EV,FA,AL,ME,ST,CO 这8类统计数据源是数据库的某个field,支持条件
  • field必须是数据库存在的.

  • 针对这类统计,表达式expr必须是有意义的.

  • 示例：配置CU数据源：

"expr":"count(euipment_id)",
"source": {
    "CU": {
        "object": ["customer_id; customer_id >= '1001'"]
    }
}
定义数据源customer_id,并且取值条件为：customer_id >= '1001'
统计customer_id>=1001
另外：1001 必须添加单引号
  • 示例：配置EQ数据源：
"expr":"count(euipment_id)",
"source": {
    "EQ": {
        "object": ["euipment_id; euipment_id >= '1004' && customer_id = '1001' "]
    }
}
定义数据源customer_id,并且取值条件为：customer_id >= '1001'
统计设备号>='1004' 并且customer_id = '1001' 的设备有多少
  • 错误示例：配置CU数据源：
"expr":"accumulate(euipment_id)",
"source": {
    "CU": {
        "object": ["customer_id; customer_id >= '1001'"]
    }
}
定义数据源customer_id,并且取值条件为：customer_id >= '1001'
统计方法accumulate是错误的表述,相同的有错误表述的统计方法有:max,min,average,sum,accumulate,integral,hour
  • 示例：配置CO默认值：
"expr":"accumulate(S04) * A",
"source": {
    "MO": {
        "object": ["S04"]
    },
    "CO": {
        "object":["A = 1;collect_id = 'OriginalGasElectricityRatio'"]
    }
}
配置source
MO的key是S04
CO的key是A,并且A的默认值是1,当任何原因导致没有查询到CO的值的时候,会用设置的默认值替代,如何没有设置默认值,则默认值为0
  • 示例：配置分析项目：
"expr":"max(Grad)",
"source": {
    "Index": {
        "object": ["Grad"]
    }
}
配置source
Index的key是Grad

1.5.3. expr

表达式,统计的表达式,针对source块里面定义的值的表达式

  1. 支持聚合函数 max,min,count,average,sum,accumulate,integral,hour,interval 函数内不可嵌套四则运算
  2. 支持四则运算
  3. 示例:
"expr":"max(A) + min(B)"
"expr":"accumulate(A) * B"
"expr":"max(A + B)"  // 不支持

1.5.3.1. max

  • 格式:max(object)
  • 说明:在指定范围内对object取最大值
  • 示例:
{
    "expr":"max(S01)",
    "cycle":"60m"
}
取出1小时内的S01值得最大值

1.5.3.2. min

  • 格式:min(object)
  • 说明:在指定范围内对object取最小值
  • 示例:
{
    "expr":"min(S01)",
    "cycle":"60m"
}
取出1小时内的S01值得最小值

1.5.3.3. count

  • 格式:count(object)
  • 说明:在指定范围内对object做计数统计
  • 示例:
{
    "expr":"count(S01)",
    "cycle":"60m"
}
取出1小时内的S01值的计数统计

1.5.3.4. average

  • 格式:average(object)
  • 说明:在指定范围内对object做平均数统计
  • 示例:
{
    "expr":"average(S01)",
    "cycle":"60m"
}
取出1小时内的S01值做平均数统计

1.5.3.5. sum

  • 格式:sum(object)
  • 说明:在指定范围内对object做求和统计
  • 示例:
{
    "expr":"sum(S01)",
    "cycle":"60m"
}
取出1小时内的S01值做求和统计

1.5.3.6. accumulate

  • 格式:accumulate(object)
  • 说明:在指定范围内对object做累计值统计(读数转用量)
  • 示例:
{
    "expr":"accumulate(S01)",
    "cycle":"60m"
}
取出1小时内的S01值做累计值统计

1.5.3.7. integral

  • 格式:integral(object)
  • 说明:在指定范围内对object做积分统计
  • 示例:
{
    "expr":"integral(S01)",
    "cycle":"60m"
}
取出1小时内的S01值做积分统计

1.5.3.8. hour

  • 格式:hour(object)
  • 说明:在指定范围内对object做耗时统计(非连续)
  • 示例:
{
    "expr":"hour(S01)",
    "cycle":"60m"
}
取出1小时内的S01值做耗时统计(非连续)

1.5.3.9. interval

  • 格式:interval(object1,object2)
  • 说明:在指定范围内 object1 到 object2 持续多长时间
  • 示例:
"source": {
    "EV": {
        "object": ["code_start;code = 1161",
           "code_stop;code = 1160"]
    }
},
"expr": "interval(code_start,code_stop)",
"cycle":"60m"
取出1小时内 code_start 到 code_stop 持续多少时间

1.5.3.10. variance

  • 格式:variance(object)
  • 说明:计算object的方差,object必须是mosaic的单值或者vector,不可配置matrix
  • 示例:
> select * from mosaic_1002 where time >= '2019-03-22 18:00:00' and time <= '2019-03-22 19:00:00' order by time desc limit 10;
name: mosaic_1002
time                           equipmentid gridtime            sorttime            value
----                           ----------- --------            --------            -----
2019-03-22T18:58:09.298177821Z 1002        1553281089297178893 1553252289298177821 {"S01":4717,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4717,2.3,4717,1],"S22":[[4717,2.3],[3,4]]}
2019-03-22T18:58:08.28036485Z  1002        1553281088279344937 1553252288280364850 {"S01":4716,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4716,2.3,4716,1],"S22":[[4716,2.3],[3,4]]}
2019-03-22T18:58:07.260233525Z 1002        1553281087259394265 1553252287260233525 {"S01":4715,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4715,2.3,4715,1],"S22":[[4715,2.3],[3,4]]}
2019-03-22T18:58:06.092396052Z 1002        1553281086091579615 1553252286092396052 {"S01":4714,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4714,2.3,4714,1],"S22":[[4714,2.3],[3,4]]}
2019-03-22T18:58:04.864876839Z 1002        1553281084863705716 1553252284864876839 {"S01":4713,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4713,2.3,4713,1],"S22":[[4713,2.3],[3,4]]}
2019-03-22T18:58:03.854390154Z 1002        1553281083853549670 1553252283854390154 {"S01":4712,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4712,2.3,4712,1],"S22":[[4712,2.3],[3,4]]}
2019-03-22T18:58:02.819916627Z 1002        1553281082818837575 1553252282819916627 {"S01":4711,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4711,2.3,4711,1],"S22":[[4711,2.3],[3,4]]}
2019-03-22T18:58:01.81190973Z  1002        1553281081811070227 1553252281811909730 {"S01":4710,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4710,2.3,4710,1],"S22":[[4710,2.3],[3,4]]}
2019-03-22T18:58:00.803793213Z 1002        1553281080802776365 1553252280803793213 {"S01":4709,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4709,2.3,4709,1],"S22":[[4709,2.3],[3,4]]}
2019-03-22T18:57:59.792919202Z 1002        1553281079792024744 1553252279792919202 {"S01":4708,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4708,2.3,4708,1],"S22":[[4708,2.3],[3,4]]}

示例一：

"source": {
    "MO": {
        "object": ["S01"]
    }
},
"expr": "variance(S01)",
"cycle":"60m"
输出结果是单值 output = variance(vector(S01))

示例二:

"source": {
    "MO": {
        "object": ["S21"]
    }
},
"expr": "variance(S21)",
"cycle":"60m"
S21 是由S01,S02,S01,1组成,所以输出结果是vector
output = [variance(vector(S01)),variance(vector(S02)),variance(vector(S01)),variance(vector(1))]

示例三:

"source": {
    "MO": {
        "object": ["S22"]
    }
},
"expr": "variance(S22)",
"cycle":"60m"
配置方法错误,variance不支持配置的source为matrix

1.5.3.11. covariance

  • 格式:covariance(object)
  • 说明:计算object的协方差矩阵,object只能是mosaic的vector,不可配置matrix和单值.
  • 示例:
> select * from mosaic_1002 where time >= '2019-03-22 18:00:00' and time <= '2019-03-22 19:00:00' order by time desc limit 10;
name: mosaic_1002
time                           equipmentid gridtime            sorttime            value
----                           ----------- --------            --------            -----
2019-03-22T18:58:09.298177821Z 1002        1553281089297178893 1553252289298177821 {"S01":4717,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4717,2.3,4717,1],"S22":[[4717,2.3],[3,4]]}
2019-03-22T18:58:08.28036485Z  1002        1553281088279344937 1553252288280364850 {"S01":4716,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4716,2.3,4716,1],"S22":[[4716,2.3],[3,4]]}
2019-03-22T18:58:07.260233525Z 1002        1553281087259394265 1553252287260233525 {"S01":4715,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4715,2.3,4715,1],"S22":[[4715,2.3],[3,4]]}
2019-03-22T18:58:06.092396052Z 1002        1553281086091579615 1553252286092396052 {"S01":4714,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4714,2.3,4714,1],"S22":[[4714,2.3],[3,4]]}
2019-03-22T18:58:04.864876839Z 1002        1553281084863705716 1553252284864876839 {"S01":4713,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4713,2.3,4713,1],"S22":[[4713,2.3],[3,4]]}
2019-03-22T18:58:03.854390154Z 1002        1553281083853549670 1553252283854390154 {"S01":4712,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4712,2.3,4712,1],"S22":[[4712,2.3],[3,4]]}
2019-03-22T18:58:02.819916627Z 1002        1553281082818837575 1553252282819916627 {"S01":4711,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4711,2.3,4711,1],"S22":[[4711,2.3],[3,4]]}
2019-03-22T18:58:01.81190973Z  1002        1553281081811070227 1553252281811909730 {"S01":4710,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4710,2.3,4710,1],"S22":[[4710,2.3],[3,4]]}
2019-03-22T18:58:00.803793213Z 1002        1553281080802776365 1553252280803793213 {"S01":4709,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4709,2.3,4709,1],"S22":[[4709,2.3],[3,4]]}
2019-03-22T18:57:59.792919202Z 1002        1553281079792024744 1553252279792919202 {"S01":4708,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4708,2.3,4708,1],"S22":[[4708,2.3],[3,4]]}

"source": {
    "MO": {
        "object": ["S21"]
    }
},
"expr": "covariance(S21)",
"cycle":"60m"
S21 为 mosaic的vector
表示计算60m内S22 的协方差矩阵
结果为矩阵

1.5.3.12. stddev

  • 格式:stddev(object)
  • 说明:计算object的标准差,object只能是mosaic的单值或者vector,不可配置matrix.
  • 示例: 参考variance

1.5.3.13. euclidean_distance

  • 格式:euclidean_distance(object,{targetvalue1,targetvalue2,targetvalue3...targetvalueN})
  • 说明:
    • 计算object的欧几里得距离,object只能是mosaic的单值或者vector,不可配置matrix.
    • {targetvalue1,targetvalue2,targetvalue3...targetvalueN}为设置的目标值，如果object为单值，则设置目标值为{targetvalue1},如果object为vector,则设置目标值原色个数根据object个数来定
  • 示例:
> select * from mosaic_1002 where time >= '2019-03-22 18:00:00' and time <= '2019-03-22 19:00:00' order by time desc limit 10;
name: mosaic_1002
time                           equipmentid gridtime            sorttime            value
----                           ----------- --------            --------            -----
2019-03-22T18:58:09.298177821Z 1002        1553281089297178893 1553252289298177821 {"S01":4717,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4717,2.3,4717,1],"S22":[[4717,2.3],[3,4]]}
2019-03-22T18:58:08.28036485Z  1002        1553281088279344937 1553252288280364850 {"S01":4716,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4716,2.3,4716,1],"S22":[[4716,2.3],[3,4]]}
2019-03-22T18:58:07.260233525Z 1002        1553281087259394265 1553252287260233525 {"S01":4715,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4715,2.3,4715,1],"S22":[[4715,2.3],[3,4]]}
2019-03-22T18:58:06.092396052Z 1002        1553281086091579615 1553252286092396052 {"S01":4714,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4714,2.3,4714,1],"S22":[[4714,2.3],[3,4]]}
2019-03-22T18:58:04.864876839Z 1002        1553281084863705716 1553252284864876839 {"S01":4713,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4713,2.3,4713,1],"S22":[[4713,2.3],[3,4]]}
2019-03-22T18:58:03.854390154Z 1002        1553281083853549670 1553252283854390154 {"S01":4712,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4712,2.3,4712,1],"S22":[[4712,2.3],[3,4]]}
2019-03-22T18:58:02.819916627Z 1002        1553281082818837575 1553252282819916627 {"S01":4711,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4711,2.3,4711,1],"S22":[[4711,2.3],[3,4]]}
2019-03-22T18:58:01.81190973Z  1002        1553281081811070227 1553252281811909730 {"S01":4710,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4710,2.3,4710,1],"S22":[[4710,2.3],[3,4]]}
2019-03-22T18:58:00.803793213Z 1002        1553281080802776365 1553252280803793213 {"S01":4709,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4709,2.3,4709,1],"S22":[[4709,2.3],[3,4]]}
2019-03-22T18:57:59.792919202Z 1002        1553281079792024744 1553252279792919202 {"S01":4708,"S02":2.3,"S03":3,"S04":4,"S05":["111","222","333"],"S06":1,"S07":200,"S08":300,"S21":[4708,2.3,4708,1],"S22":[[4708,2.3],[3,4]]}

示例一：

"source": {
    "MO": {
        "object": ["S02"]
    }
},
"expr": "euclidean_distance(S02,{2.3})",
"cycle":"60m"
S02 为 mosaic的 单值
表示计算60m内S02 的欧几里得距离
结果为[0]

示例二：

"source": {
    "MO": {
        "object": ["S05"]
    }
},
"expr": "euclidean_distance(S05,{111,222,33})",
"cycle":"60m"
S05 为 mosaic的 vector
表示计算60m内S05 的欧几里得距离
结果为[0,0,0]

1.5.3.14. pearson

  • 格式:pearson(object)
  • 说明:计算object的皮尔逊相关系数,object只能是mosaic的vector,并且vector只能有两个元素;不可配置单值或者matrix
  • 示例: 配置方法参考variance

1.5.3.15. integrated_trend_differential

  • 格式:integrated_trend_differential(object)
  • 说明:计算object的综合趋势微分,object只能是mosaic的单值.
  • 示例: 配置方法参考variance

1.5.4. selection

对哪些设备进行统计.

  • filter_and: 表示可以选择多个,and 的意思理解为和
  • filter_not: 表示排除之外选择。

支持四种类型的方式过滤设备 equipmentID,customerID,mappingID,projectID

  • projectID 只针对分析项目的统计
  • 分析统计项目的projectID 有几种写法：
    1. 分析项目:设备 表示对分析项目下的某个设备进行分析统计,例如 "projectID":["test:1010"]
    2. 分析项目: 表示对分析项目下的所有设备进行分析统计,例如 "projectID":["test:"]
    3. 填写多个分析项目 例如 "projectID":["test:","ABC:1001"]

1.5.4.1. 示例1

"selection": {
    "filter_not":{
        "customerID":["1004"]
    },
    "filter_and":{
        "equipmentID":["1006","1007","1008","1002"]
    }
}
对customerID=1004之外设备并且 加上equipmentID=1006,1007,1008,1002的设备进行统计

1.5.4.2. 示例2

"selection": {
    "filter_not":{
    }
}
对目前系统的所有设备进行统计

1.5.4.3. 示例3

"selection": {
    "filter_and":{
        "projectID":["test:1010","abc:1010","ABC:1100","jichai:"]
    }
}
表示对test分析项目下面的1010 设备,abc分析项目下面的1010设备,ABC分析项目下面的1100设备,jichai分析项目下面的所有设备进行分析统计

1.5.4.4. 示例4

"selection": {
    "filter_and":{
        "equipmentID":["1006","1007","1008","1002"]
    }
}
对equipmentID=1006,1007,1008,1002的设备进行统计

1.5.5. rewrite_collectos

是否将expr的结果同时回写到admin_collectos数据库表，只有配置了CO的source，并且CO的source源只有一个的时候，rewrite_collectos的选项才能够起作用，rewrite_collectos一般用作维保任务。

"expr": "PowerMeterRatio - int(max(PowerConsumption))",
"source": {
    "MO": {
        "object": ["PowerConsumption"]
    },
    "CO": {
        "object": ["PowerMeterRatio;collect_id = 'PowerMeterRatio'"]
    }
},
"rewrite_collectos":{
    "collect_id":"PowerMeterRatio2",
    "enable":true
}

上面的配置表示将expr计算的结果的值会同时保存admin_statos和admin_collectos表,保存admin_collectos表的时候，collect_id是PowerMeterRatio2

1.5.6. track

调试开关,开启之后程序会打印更多的信息.

1.5.7. duration

统计的时间范围,主要用于手动运行统计程序.配合cycle一起使用

1.5.7.1. 示例

"duration":{
    "start":"2018-07-01 00:00:00",
    "end":"2018-08-01 00:00:00"
},
"cycle":"60m"
表示统计时间段2018-07-01 00:00:00 - 2018-08-01 00:00:00,统计程序取数据的范围是60m

1.5.8. cycle

统计程序统计累积量的计算范围.

1.5.9. statistics_id

统计程序的统计ID

1.5.10. statos_name

统计程序的名称

1.5.11. name

统计程序可执行文件的名称

1.5.12. result

1.5.12.1. precision

统计结果的精度

1.5.13. type

标识这个统计脚本是用于统计还是计算 type的值有 statistics,calculate;statistics用于统计,calculate用于计算

1.5.14. force_filter

是否启动强制过滤的功能,读数转用量的函数accumulate,对于适配器上报的数据,是否需要对增长幅度进行过滤,默认值为false

1.5.15. filter

范围过滤,需要指定最大值(默认不指定为600000),最小值(默认不指定为0)

示例:

"force":{
    "force_filter":true,
    "min":0,
    "max":10000
}
只有指定force_filter = true 的时候,范围过滤才有效。
范围过滤针对accumulate函数,对用量进行过滤min和max之间指的是不被过滤掉的范围.

1.5.16. 注意

  1. 如果要单独执行统计程序要将statistics_id,statos_name,name 写入到parmas的json块，并且把params保存为json文件
  2. 分析项目的函数只有max,min,average,integral,hour 5种
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