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嗨，我目前正在 R 中执行此操作，但想知道是否有办法在Flux中执行此操作：

我有一个跟踪值的时间序列，并且仅在信号打开和​​关闭时存储。问题是我正在跟踪的机器的性质只允许以这种方式完成。这会产生一个数据表/测量值，其中两行显示一个值（例如故障的开始和结束）。如何使用通量查询数据以组合这两行？（以“开始”和“停止”作为标签/字段）

我目前使用elapsed()-function 来计算我的值的时间差/持续时间

time                      value     field          measurement    equipmentNumber    workplace    duration
2021-01-29 07:11:17.496   1         FAULT_LASER    FAULT_LASER    L5211M0855         0            188
2021-01-29 07:12:03.332   0         FAULT_LASER    FAULT_LASER    L5211M0855         0            45835
2021-01-29 07:12:19.618   1         FAULT_LASER    FAULT_LASER    L5211M0855         0            16285
2021-01-29 07:12:19.618   0         FAULT_LASER    FAULT_LASER    L5211M0855         0            161725

我现在在 R 中这样做：

for(i in 1:nrow(df_f)){
  if(df_f[i, "duration"] > 0){
    df_fdur[i, "start"] <- df_f[i, "time"]
    df_fdur[i, "stop"] <- df_f[i+1, "time"]
    df_fdur[i, "type"] <- df_f[i, "value"]
    df_fdur[i, "duration"] <- df_f[i, "duration"]
    df_fdur[i, "workplace"] <- df_f[i, "workplace"]
    df_fdur[i, "equipmentNumber"] <- df_f[i, "equipmentNumber"]
  }
}

关于我如何做到这一点的任何想法？

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1 回答 1

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这并不能直接解决问题，但它解决了我正在处理的问题。也许它对其他人有用。祝你有美好的一天！

// Get all the data from the bucket filtered for FAULT_LASER
data = from(bucket: "plcview_4/autogen")
  |> range(start: 2021-01-29T00:00:00.000Z, stop: now())                // regular time range filter
  |> filter(fn: (r) => r._measurement == "FAULT_LASER")                 // filter for the measurement
  |> elapsed(unit: 1ms, timeColumn: "_time", columnName: "duration")    // calculate time difference between rows
  |> yield(name: "data")
// returns data tables for every unique set of tags (workplace and equipmentNumber)

// Filter for all "No-Fault-Values" and sum their durations
operational = data
  |> filter(fn: (r) => r._value == 0)   // filter for all rows where FAULT_LASER = 0 --> No Faults
  |> group()                            // group all data tables together
  |> sum(column: "duration")            // sum all the durations from all data tables 
  |> yield(name: "operational")

// Count the number of faults
nfaults = data
  |> filter(fn: (r) => r._value == 1)   // filter for all rows where FAULT_LASER = 1 --> Faults
  |> group()                            // group all data tables together
  |> count()                            // count the number of records
  |> yield(name: "nfaults")
于 2021-04-21T08:21:36.597 回答