pir sensor和ir sensor的区别

Posted 2023-05-06

Sensor 是传感器的统称，把现实中物理存在的信号转换成电信号。 Switch 是开关的意思，它是被包含在Sensor中的，比如光电开关，它其实就是一种传感器。 参考技术A PIR主要强调在P(passive)被动上，因为仅被动接收红外线，不主动发射任何辐射，所以成本低、功耗小、尺寸小巧。PIR内部一般是2个探测元反向串联（高级的也有4个的，更多的不常见）。物体静止或没有物体时，两个探测元的输入一致，极性相反，电压抵消；物体通过时，两个探测元感受到的红外信号不平衡，产生电压差，就认为检测到有物体经过。但是也正因如此，他的检测方式受限制，仅能探测物体（与环境背景温度有偏差的物体，一般PIRs要求物体温度高于环境至少4~8℃）运动，但是如果物体静止就检测不到了。如果你以前看过一些关于蛇的纪录片，应该知道蛇眼中的世界，很类似PIR。比如一个青蛙，如果运动时，蛇可以看到；静止时，则好像突然消失了。
IR sensor就是正常的红外传感器，军事上常用的热成像仪，即使物体(同样需要与环境背景温度有差别)不移动，由于有温度差还是能检测到。对比PIR好处时实时能检测，配合算法可以自动判断出物体类型，运动状态等；坏处是尺寸大、价格高、功耗惊人(对比PIRs来说)、同时很多需要注意制冷散热(近几年很多厂家倒是出了不少无需制冷散热的sensor)。

flink笔记10 [实验]体验ProcessingTime和指定EventTime下的区别

体验ProcessingTime和指定EventTime下的区别

 

实验数据

实验代码

实验结果

实验分析

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实验数据

sensor_1,1619492107,36.2
sensor_1,1619492108,36.0
sensor_1,1619492109,36.5
sensor_1,1619492110,34.3
sensor_1,1619492111,34.3
sensor_1,1619492112,34.3
sensor_1,1619492113,34.3
sensor_1,1619492114,34.3
sensor_1,1619492115,34.3
sensor_1,1619492116,34.3
sensor_1,1619492117,34.3
sensor_1,1619492118,34.3
sensor_1,1619492119,34.3
sensor_1,1619492120,36.2
sensor_1,1619492121,36.1
sensor_1,1619492122,36.5
sensor_1,1619492123,35.2
sensor_1,1619492124,36.2
sensor_1,1619492125,36.1
sensor_1,1619492126,36.5
sensor_1,1619492127,36.2
sensor_1,1619492128,36.1
sensor_1,1619492129,36.3
sensor_1,1619492130,36.2
sensor_1,1619492131,36.2
sensor_1,1619492132,36.2
sensor_1,1619492170,36.2
sensor_1,1619492171,36.5
sensor_1,1619492172,37.3
sensor_1,1619492173,36.2
sensor_1,1619492174,36.3
sensor_1,1619492175,36.1
sensor_1,1619492176,36.6
sensor_1,1619492177,36.5
sensor_1,1619492178,36.1
sensor_1,1619492179,36.8
sensor_1,1619492180,36.1
sensor_1,1619492209,36.5

实验代码

(一) EventTime

import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, createTypeInformation}
import org.apache.flink.streaming.api.windowing.time.Time
import org.tysy.window_api.sensorReading

import java.util.Date

object window_eventtime {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)

    val inputStream = env.socketTextStream("localhost",7777)

    val resultStream = inputStream
      .map(data =>{
        val arr = data.split(",")
        sensorReading(arr(0),arr(1).toLong,arr(2).toDouble)
      })
      .assignAscendingTimestamps(_.timestamp * 1000)
      .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[sensorReading](Time.seconds(3)) {
        override def extractTimestamp(element: sensorReading): Long = element.timestamp * 1000L
      } )
      .keyBy(_.id)
      .timeWindow(Time.seconds(15))
      .reduce((data1,data2)=>sensorReading(data1.id,data2.timestamp,data1.temperature.min(data2.temperature)))
      .map(data =>(data,new Date().getTime))

    resultStream.print()

    env.execute("window_eventtime test")
  }
}

(二) ProcessingTime

import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, createTypeInformation}
import org.apache.flink.streaming.api.windowing.time.Time
import org.tysy.window_api.sensorReading

import java.util.Date

object window_processingtime {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    val inputStream = env.socketTextStream("localhost",7777)

    val resultStream = inputStream
      .map(data =>{
        val arr = data.split(",")
        sensorReading(arr(0),arr(1).toLong,arr(2).toDouble)
      })
      .keyBy(_.id)
      .timeWindow(Time.seconds(15))
      .reduce((data1,data2)=>sensorReading(data1.id,data2.timestamp,data1.temperature.min(data2.temperature)))
      .map(data =>(data,new Date().getTime))

    resultStream.print()

    env.execute("window_processingtime test")
  }
}

实验结果

实验分析

①数据的最后一列是系统的时间戳。
②当使用的是ProcessingTime时，在同一个window里有多少条数据，和测试代码时在该window的时间内输入的代码条数有关，最后一列的时间戳可看出窗口大小15秒。
③当使用的是EventTime时，和输入数据的速度没有关系，只和实验中指定的时间戳大小有关。