Use flink to calculate the probability of drunk driving in traffic

To calculate the probability of drunk driving in traffic, we need to have some data as input, including traffic violation records, alcohol test results, vehicle information, etc.
To simplify the problem, we take the traffic records of a city within a certain period of time as example data. Below is a possible implementation process I wrote out. If you have any different opinions, please feel free to comment

1. Data collection: First, we need to obtain data such as traffic violation records, alcohol test results, and vehicle information from relevant departments. This data can be stored in an input source such as Kafka, MQ, file system, etc.

2. Data preprocessing: For these input data, we need to preprocess it for further analysis. For example, information such as vehicle type and speed can be extracted from traffic violation records and vehicle information, and information such as alcohol content can be extracted from alcohol test results. Data can be manipulated using Flink's DataStream API.

3. Calculate the probability of drunk driving: Next, we need to calculate the probability of drunk driving based on the input data. This can be achieved by counting the number of drunk driving cases and total miles driven, and calculating their proportion. Since traffic violation records and vehicle information are generated in real time, we need to use window technology to achieve real-time calculations. You can use Flink's Window API to define calculation windows and use operator functions to calculate probabilities.

4. Data display: Finally, we need to display the calculation results to the user. The results can be output to an external storage system (such as Hive, HBase, etc.) or sent to the Web front-end for display using protocols such as WebSocket and HTTP.

The following is the specific code implementation process:

import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.scala.function.WindowFunction
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.util.Collector

case class TrafficRecord(vehicleType: String, speed: Double, isDrinkDriving: Boolean)

object DrinkDrivingProbability {
    
    
  def main(args: Array[String]): Unit = {
    
    
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    
    // 从Kafka中读取交通记录数据
    val records = env.addSource(new FlinkKafkaConsumer[String]("traffic-records", new SimpleStringSchema(), properties))

    // 将交通记录数据解析为TrafficRecord对象
    val trafficRecords = records.map(record => {
    
    
      val fields = record.split(",")
      TrafficRecord(fields(0), fields(1).toDouble, fields(2).toBoolean)
    })

    // 计算酒驾概率
    val probability = trafficRecords
      .keyBy(_.vehicleType)
      .timeWindow(Time.minutes(10))
      .apply(new ProbabilityFunction())
    
    // 输出结果到控制台
    probability.print()

    env.execute("Drink driving probability job")
  }
}

// 窗口函数，用于计算酒驾概率
class ProbabilityFunction extends WindowFunction[TrafficRecord, Double, String, TimeWindow] {
    
    
  override def apply(key: String, window: TimeWindow, input: Iterable[TrafficRecord], out: Collector[Double]): Unit = {
    
    
    val filteredRecords = input.filter(record => record.isDrinkDriving)

    val totalMileage = input.map(_.speed).sum

    val drinkDrivingMileage = filteredRecords.map(_.speed).sum

    val probability = drinkDrivingMileage / totalMileage

    out.collect(probability)
  }
}

In this example, we first read traffic record data from Kafka and parse it into TrafficRecord objects. Then the probability of drunk driving is continuously calculated for each vehicle type, and the results are output for each calculation. The probability calculation formula is: drunk driving miles/total driving miles. Finally, we print the calculation results to the console.