流计算中的流式图处理是什么？请解释其作用和常用操作。

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
public class StreamGraphProcessingExample {
    public static void main(String[] args) throws Exception {
        // 创建流处理环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 定义数据源
        env.addSource(new MySourceFunction())
                // 数据转换操作
                .map(event -> event.getData())
                // 数据过滤操作
                .filter(data -> data > 0)
                // 数据窗口操作
                .windowAll(TumblingProcessingTimeWindows.of(Time.seconds(10)))
                // 数据聚合操作
                .process(new MyProcessWindowFunction())
                .print();
        // 执行任务
        env.execute("Stream Graph Processing Example");
    }
    // 自定义数据源函数
    public static class MySourceFunction implements SourceFunction<Event> {
        private volatile boolean running = true;
        @Override
        public void run(SourceContext<Event> ctx) throws Exception {
            while (running) {
                // 生成随机事件
                Event event = generateRandomEvent();
                // 发送事件到下游操作
                ctx.collect(event);
                // 控制发送速率
                Thread.sleep(1000);
            }
        }
        @Override
        public void cancel() {
            running = false;
        }
        private Event generateRandomEvent() {
            // 生成随机事件的逻辑
            return new Event();
        }
    }
    // 自定义事件类
    public static class Event {
        private int data;
        public int getData() {
            return data;
        }
        public void setData(int data) {
            this.data = data;
        }
    }
    // 自定义窗口处理函数
    public static class MyProcessWindowFunction extends ProcessWindowFunction<Integer, String, String, TimeWindow> {
        @Override
        public void process(String key, Context context, Iterable<Integer> elements, Collector<String> out) {
            int sum = 0;
            int count = 0;
            for (Integer element : elements) {
                sum += element;
                count++;
            }
            double average = (double) sum / count;
            out.collect("Average: " + average);
        }
    }
}