基于 Java 17 Spring Boot 3.2 Flink 1.18 开发的智慧实验室管理系统核心代码实现

// 基于Java 17 + Spring Boot 3.2 + Flink 1.18的智慧实验室管理系统核心代码

// 1. 设备接入层 - 支持OPC UA、MQTT等12种工业协议
public interface DeviceConnector {
    CompletableFuture<DeviceStatus> connect(String deviceId);
    Flux<DeviceData> streamData(String deviceId);
    Mono<Void> sendCommand(String deviceId, Command cmd);
}

// 2. 实时数据处理 - 设备状态异常检测
@Service
public class DeviceAnomalyDetector {
    private final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
    public void startAnomalyDetection() throws Exception {
        DataStream<DeviceData> dataStream = env
            .addSource(new KafkaSource<>(deviceDataConsumerConfig()))
            .assignTimestampsAndWatermarks(WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofSeconds(5)));
            
        // 滑动窗口异常检测 - 连续3次读数超过阈值
        dataStream
            .keyBy(DeviceData::getDeviceId)
            .window(SlidingEventTimeWindows.of(Time.seconds(30), Time.seconds(10)))
            .process(new AnomalyDetectionProcessFunction())
            .addSink(new AlertSink());
            
        env.execute("DeviceAnomalyDetectionJob");
    }
}

// 3. 智能调度引擎 - 基于强化学习的设备资源分配
@Service
public class RLSchedulingEngine {
    private final QLearningAgent agent = new QLearningAgent(
        stateSpaceDimension: 12,
        actionSpaceDimension: 100,
        learningRate: 0.01,
        discountFactor: 0.95
    );
    
    // 资源分配决策
    public DeviceAllocation decisionMaking(Course course, List<Device> availableDevices) {
        State currentState = buildState(course, availableDevices);
        int action = agent.selectAction(currentState);
        return mapActionToAllocation(action, availableDevices);
    }
    
    // 训练调度模型
    public void trainModel() {
        for (int episode = 0; episode < 10000; episode++) {
            State state = resetEnvironment();
            double totalReward = 0;
            
            while (!isTerminal(state)) {
                int action = agent.selectAction(state);
                State nextState = executeAction(state, action);
                double reward = calculateReward(state, action, nextState);
                agent.update(state, action, reward, nextState);
                state = nextState;
                totalReward += reward;
            }
            
            log.info("Episode {} completed with reward: {}", episode, totalReward);
        }
    }
}

// 4. 三维可视化 - 使用JavaFX和WebGL渲染实验室空间
public class Lab3DVisualizer extends Application {
    @Override
    public void start(Stage primaryStage) {
        Group root = new Group();
        Scene scene = new Scene(root, 1200, 800, true);
        
        // 加载实验室3D模型
        ModelLoader loader = new ModelLoader();
        Node labModel = loader.loadModel("lab_model.glb");
        
        // 添加设备状态指示器
        List<DeviceStatusIndicator> indicators = createDeviceIndicators();
        root.getChildren().addAll(labModel, indicators);
        
        // 添加交互控制器
        addInteractionControls(scene, labModel);
        
        primaryStage.setScene(scene);
        primaryStage.show();
    }
}

// 5. 微服务架构 - 使用Spring Cloud构建分布式系统
@SpringBootApplication
@EnableEurekaClient
@EnableFeignClients
public class LabManagementApplication {
    public static void main(String[] args) {
        SpringApplication.run(LabManagementApplication.class, args);
    }
}

// 6. 数据仓库 - 构建实验室数据湖
public class LabDataLake {
    private final SparkSession spark = SparkSession.builder()
        .appName("LabDataLake")
        .config("hive.metastore.uris", "thrift://hive-metastore:9083")
        .enableHiveSupport()
        .getOrCreate();
        
    // 构建设备使用数据仓库
    public void buildDeviceUsageDataWarehouse() {
        Dataset<Row> deviceLogDF = spark.read()
            .format("parquet")
            .load("hdfs://namenode:8020/lab_data/device_logs");
            
        // 构建星型模式
        deviceLogDF.createOrReplaceTempView("device_logs");
        Dataset<Row> factTable = spark.sql("""
            SELECT 
                dl.device_id,
                dl.usage_start_time,
                dl.usage_end_time,
                dl.usage_duration,
                dl.experiment_id,
                e.course_id,
                e.student_id,
                d.device_type,
                d.location
            FROM device_logs dl
            JOIN devices d ON dl.device_id = d.device_id
            JOIN experiments e ON dl.experiment_id = e.experiment_id
        """);
        
        factTable.write()
            .format("hive")
            .saveAsTable("lab_warehouse.device_usage_fact");
    }
}