燃煤发电厂智能监控系统（四）：系统集成与数据分析平台

系统集成与数据分析是智能监控系统的核心，负责整合各子系统数据，提供统一的监控界面和智能分析功能。本文将详细介绍如何构建一个完整的数据采集、存储、分析和可视化平台。

1 系统集成架构

1.1 整体架构设计

智能监控系统采用分层架构，包括数据采集层、数据处理层、业务逻辑层和展示层：

数据采集层：通过Modbus、OPC UA等协议从各种传感器和控制器采集实时数据。

数据处理层：对原始数据进行清洗、验证、转换和存储，支持实时流处理和批处理。

业务逻辑层：实现各种业务功能，包括监控、控制、报警、分析等。

展示层：提供Web界面、移动端应用和大屏展示，支持实时监控和历史数据查询。

1.2 数据采集服务

/**
 * 统一数据采集服务
 * 负责从各个子系统采集数据并进行统一处理
 */
@Service
@Slf4j
public class DataCollectionService {
    
    @Autowired
    private ModbusClient modbusClient;
    
    @Autowired
    private OpcUaClient opcUaClient;
    
    @Autowired
    private InfluxDBTemplate influxDBTemplate;
    
    @Autowired
    private RedisTemplate<String, Object> redisTemplate;
    
    @Autowired
    private KafkaTemplate<String, Object> kafkaTemplate;
    
    @Autowired
    private DataValidationService validationService;
    
    @Autowired
    private AlarmService alarmService;
    
    // 数据采集配置
    private final Map<String, DataPointConfig> dataPointConfigs = new ConcurrentHashMap<>();
    
    // 数据缓存
    private final Map<String, SensorData> dataCache = new ConcurrentHashMap<>();
    
    @PostConstruct
    public void initializeDataPoints() {
        // 初始化数据点配置
        loadDataPointConfigurations();
        
        // 启动数据采集任务
        startDataCollectionTasks();
    }
    
    /**
     * 主数据采集任务
     * 每秒执行一次，采集所有实时数据
     */
    @Scheduled(fixedRate = 1000)
    public void collectRealTimeData() {
        try {
            List<CompletableFuture<Void>> futures = new ArrayList<>();
            
            // 并行采集各系统数据
            futures.add(CompletableFuture.runAsync(this::collectCombustionSystemData));
            futures.add(CompletableFuture.runAsync(this::collectSteamWaterSystemData));
            futures.add(CompletableFuture.runAsync(this::collectGeneratorData));
            futures.add(CompletableFuture.runAsync(this::collectCoolingSystemData));
            futures.add(CompletableFuture.runAsync(this::collectEnvironmentalData));
            
            // 等待所有采集任务完成
            CompletableFuture.allOf(futures.toArray(new CompletableFuture[0]))
                    .get(5, TimeUnit.SECONDS);
            
            // 数据处理和存储
            processAndStoreData();
            
        } catch (Exception e) {
            log.error("数据采集异常", e);
            alarmService.sendAlarm("数据采集系统异常: " + e.getMessage(), "HIGH");
        }
    }
    
    /**
     * 采集燃烧系统数据
     */
    private void collectCombustionSystemData() {
        try {
            Map<String, Object> data = new HashMap<>();
            
            // 煤粉制备系统
            data.put("coal_feed_rate", modbusClient.readHoldingRegister(1001) / 10.0);
            data.put("mill_speed", modbusClient.readHoldingRegister(1002));
            data.put("mill_current", modbusClient.readHoldingRegister(1003) / 10.0);
            data.put("coal_fineness", modbusClient.readHoldingRegister(1004) / 10.0);
            
            // 炉膛燃烧系统
            data.put("furnace_temp_1", modbusClient.readHoldingRegister(1011) / 10.0);
            data.put("furnace_temp_2", modbusClient.readHoldingRegister(1012) / 10.0);
            data.put("furnace_temp_3", modbusClient.readHoldingRegister(1013) / 10.0);
            data.put("furnace_temp_4", modbusClient.readHoldingRegister(1014) / 10.0);
            data.put("oxygen_content", modbusClient.readHoldingRegister(1015) / 100.0);
            data.put("co_content", modbusClient.readHoldingRegister(1016));
            data.put("nox_content", modbusClient.readHoldingRegister(1017));
            data.put("so2_content", modbusClient.readHoldingRegister(1018));
            
            // 风烟系统
            data.put("primary_air_flow", modbusClient.readHoldingRegister(1021) / 10.0);
            data.put("secondary_air_flow", modbusClient.readHoldingRegister(1022) / 10.0);
            data.put("flue_gas_flow", modbusClient.readHoldingRegister(1023) / 10.0);
            data.put("draft_pressure", modbusClient.readHoldingRegister(1024) / 100.0);
            
            // 数据验证和存储
            SensorData sensorData = new SensorData("COMBUSTION_SYSTEM", data, LocalDateTime.now());
            if (validationService.validateData(sensorData)) {
                dataCache.put("COMBUSTION_SYSTEM", sensorData);
                publishDataToKafka("combustion-data", sensorData);
            }
            
        } catch (Exception e) {
            log.error("采集燃烧系统数据失败", e);
        }
    }
    
    /**
     * 采集汽水系统数据
     */
    private void collectSteamWaterSystemData() {
        try {
            Map<String, Object> data = new HashMap<>();
            
            // 汽包参数
            data.put("drum_water_level", modbusClient.readHoldingRegister(2001) / 100.0);
            data.put("drum_pressure", modbusClient.readHoldingRegister(2002) / 100.0);
            data.put("drum_temperature", modbusClient.readHoldingRegister(2003) / 10.0);
            
            // 蒸汽参数
            data.put("steam_temperature", modbusClient.readHoldingRegister(2011) / 10.0);
            data.put("steam_pressure", modbusClient.readHoldingRegister(2012) / 100.0);
            data.put("steam_flow", modbusClient.readHoldingRegister(2013) / 10.0);
            data.put("steam_quality", modbusClient.readHoldingRegister(2014) / 100.0);
            
            // 给水系统
            data.put("feedwater_flow", modbusClient.readHoldingRegister(2021) / 10.0);
            data.put("feedwater_temperature", modbusClient.readHoldingRegister(2022) / 10.0);
            data.put("feedwater_pressure", modbusClient.readHoldingRegister(2023) / 100.0);
            data.put("feedwater_valve_opening", modbusClient.readHoldingRegister(2024));
            
            // 过热器和再热器
            data.put("superheater_outlet_temp", modbusClient.readHoldingRegister(2031) / 10.0);
            data.put("reheater_outlet_temp", modbusClient.readHoldingRegister(2032) / 10.0);
            data.put("desuperheater_spray_flow", modbusClient.readHoldingRegister(2033) / 10.0);
            
            SensorData sensorData = new SensorData("STEAM_WATER_SYSTEM", data, LocalDateTime.now());
            if (validationService.validateData(sensorData)) {
                dataCache.put("STEAM_WATER_SYSTEM", sensorData);
                publishDataToKafka("steam-water-data", sensorData);
            }
            
        } catch (Exception e) {
            log.error("采集汽水系统数据失败", e);
        }
    }
    
    /**
     * 采集发电机组数据
     */
    private void collectGeneratorData() {
        try {
            Map<String, Object> data = new HashMap<>();
            
            // 汽轮机参数
            data.put("turbine_speed", modbusClient.readHoldingRegister(3001));
            data.put("turbine_vibration_1", modbusClient.readHoldingRegister(3002) / 100.0);
            data.put("turbine_vibration_2", modbusClient.readHoldingRegister(3003) / 100.0);
            data.put("turbine_bearing_temp_1", modbusClient.readHoldingRegister(3004) / 10.0);
            data.put("turbine_bearing_temp_2", modbusClient.readHoldingRegister(3005) / 10.0);
            data.put("turbine_efficiency", modbusClient.readHoldingRegister(3006) / 100.0);
            
            // 发电机参数
            data.put("generator_speed", modbusClient.readHoldingRegister(3011));
            data.put("generator_voltage", modbusClient.readHoldingRegister(3012) / 10.0);
            data.put("generator_current", modbusClient.readHoldingRegister(3013) / 10.0);
            data.put("generator_frequency", modbusClient.readHoldingRegister(3014) / 100.0);
            data.put("generator_power_factor", modbusClient.readHoldingRegister(3015) / 1000.0);
            data.put("active_power", modbusClient.readHoldingRegister(3016) / 10.0);
            data.put("reactive_power", modbusClient.readHoldingRegister(3017) / 10.0);
            
            // 励磁系统
            data.put("excitation_voltage", modbusClient.readHoldingRegister(3021) / 10.0);
            data.put("excitation_current", modbusClient.readHoldingRegister(3022) / 10.0);
            data.put("field_voltage", modbusClient.readHoldingRegister(3023) / 10.0);
            
            SensorData sensorData = new SensorData("GENERATOR_SYSTEM", data, LocalDateTime.now());
            if (validationService.validateData(sensorData)) {
                dataCache.put("GENERATOR_SYSTEM", sensorData);
                publishDataToKafka("generator-data", sensorData);
            }
            
        } catch (Exception e) {
            log.error("采集发电机组数据失败", e);
        }
    }
    
    /**
     * 采集冷却系统数据
     */
    private void collectCoolingSystemData() {
        try {
            Map<String, Object> data = new HashMap<>();
            
            // 凝汽器参数
            data.put("condenser_pressure", modbusClient.readHoldingRegister(4001) / 100.0);
            data.put("condenser_vacuum", modbusClient.readHoldingRegister(4002) / 100.0);
            data.put("condenser_inlet_temp", modbusClient.readHoldingRegister(4003) / 10.0);
            data.put("condenser_outlet_temp", modbusClient.readHoldingRegister(4004) / 10.0);
            
            // 冷却塔参数
            data.put("cooling_tower_inlet_temp", modbusClient.readHoldingRegister(4011) / 10.0);
            data.put("cooling_tower_outlet_temp", modbusClient.readHoldingRegister(4012) / 10.0);
            data.put("cooling_tower_water_level", modbusClient.readHoldingRegister(4013) / 10.0);
            data.put("cooling_tower_makeup_flow", modbusClient.readHoldingRegister(4014) / 10.0);
            
            // 循环水系统
            data.put("circulating_water_flow", modbusClient.readHoldingRegister(4021) / 10.0);
            data.put("circulating_water_pressure", modbusClient.readHoldingRegister(4022) / 100.0);
            data.put("circulating_water_temp", modbusClient.readHoldingRegister(4023) / 10.0);
            
            // 风机和水泵状态
            for (int i = 0; i < 4; i++) {
                data.put("fan_" + (i+1) + "_speed", modbusClient.readHoldingRegister(4031 + i));
                data.put("fan_" + (i+1) + "_current", modbusClient.readHoldingRegister(4041 + i) / 10.0);
            }
            
            for (int i = 0; i < 3; i++) {
                data.put("pump_" + (i+1) + "_speed", modbusClient.readHoldingRegister(4051 + i));
                data.put("pump_" + (i+1) + "_current", modbusClient.readHoldingRegister(4061 + i) / 10.0);
            }
            
            SensorData sensorData = new SensorData("COOLING_SYSTEM", data, LocalDateTime.now());
            if (validationService.validateData(sensorData)) {
                dataCache.put("COOLING_SYSTEM", sensorData);
                publishDataToKafka("cooling-data", sensorData);
            }
            
        } catch (Exception e) {
            log.error("采集冷却系统数据失败", e);
        }
    }
    
    /**
     * 采集环境数据
     */
    private void collectEnvironmentalData() {
        try {
            Map<String, Object> data = new HashMap<>();
            
            // 气象参数
            data.put("ambient_temperature", modbusClient.readHoldingRegister(5001) / 10.0);
            data.put("humidity", modbusClient.readHoldingRegister(5002) / 10.0);
            data.put("wind_speed", modbusClient.readHoldingRegister(5003) / 10.0);
            data.put("wind_direction", modbusClient.readHoldingRegister(5004));
            data.put("atmospheric_pressure", modbusClient.readHoldingRegister(5005) / 100.0);
            
            // 环保参数
            data.put("stack_so2", modbusClient.readHoldingRegister(5011));
            data.put("stack_nox", modbusClient.readHoldingRegister(5012));
            data.put("stack_dust", modbusClient.readHoldingRegister(5013) / 10.0);
            data.put("stack_temperature", modbusClient.readHoldingRegister(5014) / 10.0);
            data.put("stack_flow", modbusClient.readHoldingRegister(5015) / 10.0);
            
            SensorData sensorData = new SensorData("ENVIRONMENTAL_SYSTEM", data, LocalDateTime.now());
            if (validationService.validateData(sensorData)) {
                dataCache.put("ENVIRONMENTAL_SYSTEM", sensorData);
                publishDataToKafka("environmental-data", sensorData);
            }
            
        } catch (Exception e) {
            log.error("采集环境数据失败", e);
        }
    }
    
    /**
     * 数据处理和存储
     */
    private void processAndStoreData() {
        try {
            // 实时数据存储到Redis
            for (Map.Entry<String, SensorData> entry : dataCache.entrySet()) {
                String key = "realtime:" + entry.getKey();
                redisTemplate.opsForValue().set(key, entry.getValue(), Duration.ofMinutes(5));
            }
            
            // 历史数据存储到InfluxDB
            List<Point> points = new ArrayList<>();
            for (SensorData data : dataCache.values()) {
                Point.Builder pointBuilder = Point.measurement(data.getSystemName())
                        .time(data.getTimestamp().toInstant(ZoneOffset.UTC), WritePrecision.MS);
                
                for (Map.Entry<String, Object> field : data.getData().entrySet()) {
                    if (field.getValue() instanceof Number) {
                        pointBuilder.addField(field.getKey(), ((Number) field.getValue()).doubleValue());
                    } else {
                        pointBuilder.addField(field.getKey(), field.getValue().toString());
                    }
                }
                
                points.add(pointBuilder.build());
            }
            
            influxDBTemplate.writePoints(points);
            
            // 计算系统KPI
            calculateAndStoreKPIs();
            
        } catch (Exception e) {
            log.error("数据处理和存储失败", e);
        }
    }
    
    /**
     * 发布数据到Kafka
     */
    private void publishDataToKafka(String topic, SensorData data) {
        try {
            kafkaTemplate.send(topic, data);
        } catch (Exception e) {
            log.error("发布数据到Kafka失败: topic={}", topic, e);
        }
    }
    
    /**
     * 计算和存储KPI指标
     */
    private void calculateAndStoreKPIs() {
        try {
            Map<String, Double> kpis = new HashMap<>();
            
            // 整体效率计算
            SensorData combustionData = dataCache.get("COMBUSTION_SYSTEM");
            SensorData steamData = dataCache.get("STEAM_WATER_SYSTEM");
            SensorData generatorData = dataCache.get("GENERATOR_SYSTEM");
            
            if (combustionData != null && steamData != null && generatorData != null) {
                // 发电效率 = 发电功率 / 燃料热值
                double activePower = (Double) generatorData.getData().get("active_power");
                double coalFeedRate = (Double) combustionData.getData().get("coal_feed_rate");
                double efficiency = activePower / (coalFeedRate * 29.3) * 100; // 假设煤热值29.3MJ/kg
                kpis.put("overall_efficiency", efficiency);
                
                // 汽轮机效率
                double steamFlow = (Double) steamData.getData().get("steam_flow");
                double steamTemp = (Double) steamData.getData().get("steam_temperature");
                double turbineEfficiency = calculateTurbineEfficiency(activePower, steamFlow, steamTemp);
                kpis.put("turbine_efficiency", turbineEfficiency);
                
                // 锅炉效率
                double furnaceTemp = (Double) combustionData.getData().get("furnace_temp_1");
                double oxygenContent = (Double) combustionData.getData().get("oxygen_content");
                double boilerEfficiency = calculateBoilerEfficiency(furnaceTemp, oxygenContent);
                kpis.put("boiler_efficiency", boilerEfficiency);
            }
            
            // 环保指标
            SensorData envData = dataCache.get("ENVIRONMENTAL_SYSTEM");
            if (envData != null) {
                double so2Emission = (Double) envData.getData().get("stack_so2");
                double noxEmission = (Double) envData.getData().get("stack_nox");
                double dustEmission = (Double) envData.getData().get("stack_dust");
                
                kpis.put("so2_emission_rate", so2Emission);
                kpis.put("nox_emission_rate", noxEmission);
                kpis.put("dust_emission_rate", dustEmission);
            }
            
            // 存储KPI到Redis和InfluxDB
            redisTemplate.opsForValue().set("kpis:current", kpis, Duration.ofMinutes(5));
            
            Point kpiPoint = Point.measurement("system_kpis")
                    .time(Instant.now(), WritePrecision.MS);
            for (Map.Entry<String, Double> kpi : kpis.entrySet()) {
                kpiPoint.addField(kpi.getKey(), kpi.getValue());
            }
            influxDBTemplate.writePoint(kpiPoint);
            
        } catch (Exception e) {
            log.error("计算KPI失败", e);
        }
    }
    
    // 辅助方法
    private void loadDataPointConfigurations() {
        // 从配置文件或数据库加载数据点配置
        // 这里简化处理
    }
    
    private void startDataCollectionTasks() {
        // 启动额外的数据采集任务
        // 例如慢速采集任务、批量数据处理等
    }
    
    private double calculateTurbineEfficiency(double power, double steamFlow, double steamTemp) {
        // 汽轮机效率计算公式（简化）
        double theoreticalPower = steamFlow * (steamTemp - 30) * 0.5; // 简化计算
        return power / theoreticalPower * 100;
    }
    
    private double calculateBoilerEfficiency(double furnaceTemp, double oxygenContent) {
        // 锅炉效率计算公式（简化）
        double baseEfficiency = 88.0;
        double tempFactor = (furnaceTemp - 1200) / 1200 * 5; // 温度影响
        double oxygenFactor = (oxygenContent - 3) * 2; // 氧含量影响
        return baseEfficiency + tempFactor - oxygenFactor;
    }
}

2 智能报警系统

2.1 多级报警机制

/**
 * 智能报警系统
 * 提供多级报警、智能分析和预测性报警功能
 */
@Service
@Slf4j
public class IntelligentAlarmSystem {
    
    @Autowired
    private RedisTemplate<String, Object> redisTemplate;
    
    @Autowired
    private InfluxDBTemplate influxDBTemplate;
    
    @Autowired
    private NotificationService notificationService;
    
    @Autowired
    private MachineLearningService mlService;
    
    @Autowired
    private RuleEngine ruleEngine;
    
    // 报警级别定义
    public enum AlarmLevel {
        INFO(1, "信息", "#2196F3"),
        LOW(2, "低级", "#4CAF50"),
        MEDIUM(3, "中级", "#FF9800"),
        HIGH(4, "高级", "#F44336"),
        CRITICAL(5, "紧急", "#9C27B0");
        
        private final int priority;
        private final String description;
        private final String color;
        
        AlarmLevel(int priority, String description, String color) {
            this.priority = priority;
            this.description = description;
            this.color = color;
        }
        
        // getters...
    }
    
    // 报警状态
    public enum AlarmStatus {
        ACTIVE,     // 活动
        ACKNOWLEDGED, // 已确认
        RESOLVED,   // 已解决
        SUPPRESSED  // 已抑制
    }
    
    /**
     * 实时报警检测
     * 每5秒执行一次
     */
    @Scheduled(fixedRate = 5000)
    public void performRealTimeAlarmDetection() {
        try {
            // 获取实时数据
            Map<String, SensorData> realtimeData = getCurrentRealtimeData();
            
            // 并行执行各种报警检测
            List<CompletableFuture<List<Alarm>>> futures = Arrays.asList(
                CompletableFuture.supplyAsync(() -> detectThresholdAlarms(realtimeData)),
                CompletableFuture.supplyAsync(() -> detectTrendAlarms(realtimeData)),
                CompletableFuture.supplyAsync(() -> detectAnomalyAlarms(realtimeData)),
                CompletableFuture.supplyAsync(() -> detectCorrelationAlarms(realtimeData)),
                CompletableFuture.supplyAsync(() -> detectPredictiveAlarms(realtimeData))
            );
            
            // 收集所有报警
            List<Alarm> allAlarms = futures.stream()
                    .map(CompletableFuture::join)
                    .flatMap(List::stream)
                    .collect(Collectors.toList());
            
            // 处理报警
            processAlarms(allAlarms);
            
        } catch (Exception e) {
            log.error("实时报警检测异常", e);
        }
    }
    
    /**
     * 阈值报警检测
     */
    private List<Alarm> detectThresholdAlarms(Map<String, SensorData> data) {
        List<Alarm> alarms = new ArrayList<>();
        
        try {
            for (Map.Entry<String, SensorData> entry : data.entrySet()) {
                String systemName = entry.getKey();
                SensorData sensorData = entry.getValue();
                
                for (Map.Entry<String, Object> dataPoint : sensorData.getData().entrySet()) {
                    String paramName = dataPoint.getKey();
                    Object value = dataPoint.getValue();
                    
                    if (value instanceof Number) {
                        double numValue = ((Number) value).doubleValue();
                        
                        // 获取阈值配置
                        ThresholdConfig config = getThresholdConfig(systemName, paramName);
                        if (config != null) {
                            Alarm alarm = checkThreshold(systemName, paramName, numValue, config);
                            if (alarm != null) {
                                alarms.add(alarm);
                            }
                        }
                    }
                }
            }
        } catch (Exception e) {
            log.error("阈值报警检测失败", e);
        }
        
        return alarms;
    }
    
    /**
     * 趋势报警检测
     */
    private List<Alarm> detectTrendAlarms(Map<String, SensorData> data) {
        List<Alarm> alarms = new ArrayList<>();
        
        try {
            for (Map.Entry<String, SensorData> entry : data.entrySet()) {
                String systemName = entry.getKey();
                SensorData currentData = entry.getValue();
                
                // 获取历史数据进行趋势分析
                List<SensorData> historicalData = getHistoricalData(systemName, Duration.ofMinutes(30));
                
                for (String paramName : currentData.getData().keySet()) {
                    TrendAnalysisResult trend = analyzeTrend(historicalData, paramName);
                    
                    if (trend.isAbnormal()) {
                        Alarm alarm = createTrendAlarm(systemName, paramName, trend);
                        alarms.add(alarm);
                    }
                }
            }
        } catch (Exception e) {
            log.error("趋势报警检测失败", e);
        }
        
        return alarms;
    }
    
    /**
     * 异常检测报警
     * 使用机器学习模型检测异常
     */
    private List<Alarm> detectAnomalyAlarms(Map<String, SensorData> data) {
        List<Alarm> alarms = new ArrayList<>();
        
        try {
            for (Map.Entry<String, SensorData> entry : data.entrySet()) {
                String systemName = entry.getKey();
                SensorData sensorData = entry.getValue();
                
                // 使用机器学习模型检测异常
                AnomalyDetectionResult result = mlService.detectAnomalies(systemName, sensorData);
                
                if (result.hasAnomalies()) {
                    for (AnomalyPoint anomaly : result.getAnomalies()) {
                        Alarm alarm = createAnomalyAlarm(systemName, anomaly);
                        alarms.add(alarm);
                    }
                }
            }
        } catch (Exception e) {
            log.error("异常检测报警失败", e);
        }
        
        return alarms;
    }
    
    /**
     * 关联性报警检测
     * 检测多个参数之间的异常关联
     */
    private List<Alarm> detectCorrelationAlarms(Map<String, SensorData> data) {
        List<Alarm> alarms = new ArrayList<>();
        
        try {
            // 检测燃烧系统与汽水系统的关联
            SensorData combustionData = data.get("COMBUSTION_SYSTEM");
            SensorData steamData = data.get("STEAM_WATER_SYSTEM");
            
            if (combustionData != null && steamData != null) {
                // 检查煤粉流量与蒸汽流量的关联
                double coalFeedRate = (Double) combustionData.getData().get("coal_feed_rate");
                double steamFlow = (Double) steamData.getData().get("steam_flow");
                
                double expectedSteamFlow = coalFeedRate * 8.5; // 经验公式
                double deviation = Math.abs(steamFlow - expectedSteamFlow) / expectedSteamFlow;
                
                if (deviation > 0.15) { // 偏差超过15%
                    Alarm alarm = new Alarm();
                    alarm.setId(UUID.randomUUID().toString());
                    alarm.setSystemName("CORRELATION_ANALYSIS");
                    alarm.setParameterName("coal_steam_correlation");
                    alarm.setLevel(AlarmLevel.MEDIUM);
                    alarm.setMessage(String.format("煤粉流量与蒸汽流量关联异常，偏差: %.1f%%", deviation * 100));
                    alarm.setTimestamp(LocalDateTime.now());
                    alarm.setStatus(AlarmStatus.ACTIVE);
                    
                    alarms.add(alarm);
                }
            }
            
            // 检测发电机与冷却系统的关联
            SensorData generatorData = data.get("GENERATOR_SYSTEM");
            SensorData coolingData = data.get("COOLING_SYSTEM");
            
            if (generatorData != null && coolingData != null) {
                double activePower = (Double) generatorData.getData().get("active_power");
                double condenserPressure = (Double) coolingData.getData().get("condenser_pressure");
                
                // 负荷与凝汽器压力的关联检查
                double expectedPressure = 3.5 + activePower / 600 * 1.5; // 经验公式
                double pressureDeviation = Math.abs(condenserPressure - expectedPressure);
                
                if (pressureDeviation > 0.8) {
                    Alarm alarm = new Alarm();
                    alarm.setId(UUID.randomUUID().toString());
                    alarm.setSystemName("CORRELATION_ANALYSIS");
                    alarm.setParameterName("power_condenser_correlation");
                    alarm.setLevel(AlarmLevel.MEDIUM);
                    alarm.setMessage(String.format("发电负荷与凝汽器压力关联异常，压力偏差: %.2f kPa", pressureDeviation));
                    alarm.setTimestamp(LocalDateTime.now());
                    alarm.setStatus(AlarmStatus.ACTIVE);
                    
                    alarms.add(alarm);
                }
            }
            
        } catch (Exception e) {
            log.error("关联性报警检测失败", e);
        }
        
        return alarms;
    }
    
    /**
     * 预测性报警检测
     * 基于历史数据和机器学习模型预测未来可能的故障
     */
    private List<Alarm> detectPredictiveAlarms(Map<String, SensorData> data) {
        List<Alarm> alarms = new ArrayList<>();
        
        try {
            for (Map.Entry<String, SensorData> entry : data.entrySet()) {
                String systemName = entry.getKey();
                SensorData currentData = entry.getValue();
                
                // 获取历史数据用于预测
                List<SensorData> historicalData = getHistoricalData(systemName, Duration.ofHours(24));
                
                // 使用机器学习模型进行故障预测
                FaultPredictionResult prediction = mlService.predictFaults(systemName, historicalData, currentData);
                
                if (prediction.hasPotentialFaults()) {
                    for (PredictedFault fault : prediction.getPredictedFaults()) {
                        if (fault.getProbability() > 0.7) { // 概率超过70%
                            Alarm alarm = createPredictiveAlarm(systemName, fault);
                            alarms.add(alarm);
                        }
                    }
                }
            }
        } catch (Exception e) {
            log.error("预测性报警检测失败", e);
        }
        
        return alarms;
    }
    
    /**
     * 处理报警
     */
    private void processAlarms(List<Alarm> alarms) {
        for (Alarm alarm : alarms) {
            try {
                // 检查是否为重复报警
                if (isDuplicateAlarm(alarm)) {
                    continue;
                }
                
                // 报警抑制逻辑
                if (isAlarmSuppressed(alarm)) {
                    alarm.setStatus(AlarmStatus.SUPPRESSED);
                }
                
                // 存储报警
                storeAlarm(alarm);
                
                // 发送通知
                if (alarm.getStatus() == AlarmStatus.ACTIVE) {
                    sendAlarmNotification(alarm);
                }
                
                // 触发自动响应
                triggerAutomaticResponse(alarm);
                
                log.info("处理报警: {} - {} - {}", alarm.getLevel(), alarm.getSystemName(), alarm.getMessage());
                
            } catch (Exception e) {
                log.error("处理报警失败: {}", alarm, e);
            }
        }
    }
    
    /**
     * 发送报警通知
     */
    private void sendAlarmNotification(Alarm alarm) {
        try {
            // 根据报警级别确定通知方式
            List<NotificationChannel> channels = determineNotificationChannels(alarm.getLevel());
            
            for (NotificationChannel channel : channels) {
                NotificationMessage message = createNotificationMessage(alarm, channel);
                notificationService.sendNotification(channel, message);
            }
            
        } catch (Exception e) {
            log.error("发送报警通知失败: {}", alarm, e);
        }
    }
    
    /**
     * 触发自动响应
     */
    private void triggerAutomaticResponse(Alarm alarm) {
        try {
            // 根据报警类型和级别触发自动响应
            AutoResponseRule rule = ruleEngine.getAutoResponseRule(alarm);
            
            if (rule != null && rule.isEnabled()) {
                AutoResponseResult result = rule.execute(alarm);
                
                if (result.isSuccess()) {
                    log.info("自动响应执行成功: {} - {}", alarm.getId(), result.getDescription());
                    
                    // 记录自动响应历史
                    recordAutoResponse(alarm, result);
                } else {
                    log.warn("自动响应执行失败: {} - {}", alarm.getId(), result.getErrorMessage());
                }
            }
            
        } catch (Exception e) {
            log.error("触发自动响应失败: {}", alarm, e);
        }
    }
    
    /**
     * 报警确认
     */
    public boolean acknowledgeAlarm(String alarmId, String operator, String comment) {
        try {
            Alarm alarm = getAlarmById(alarmId);
            if (alarm == null) {
                return false;
            }
            
            alarm.setStatus(AlarmStatus.ACKNOWLEDGED);
            alarm.setAcknowledgedBy(operator);
            alarm.setAcknowledgedAt(LocalDateTime.now());
            alarm.setAcknowledgeComment(comment);
            
            updateAlarm(alarm);
            
            log.info("报警已确认: {} by {}", alarmId, operator);
            return true;
            
        } catch (Exception e) {
            log.error("确认报警失败: {}", alarmId, e);
            return false;
        }
    }
    
    /**
     * 报警解决
     */
    public boolean resolveAlarm(String alarmId, String operator, String solution) {
        try {
            Alarm alarm = getAlarmById(alarmId);
            if (alarm == null) {
                return false;
            }
            
            alarm.setStatus(AlarmStatus.RESOLVED);
            alarm.setResolvedBy(operator);
            alarm.setResolvedAt(LocalDateTime.now());
            alarm.setResolutionComment(solution);
            
            updateAlarm(alarm);
            
            log.info("报警已解决: {} by {}", alarmId, operator);
            return true;
            
        } catch (Exception e) {
            log.error("解决报警失败: {}", alarmId, e);
            return false;
        }
    }
    
    // 辅助方法
    private Map<String, SensorData> getCurrentRealtimeData() {
        Map<String, SensorData> data = new HashMap<>();
        
        String[] systems = {"COMBUSTION_SYSTEM", "STEAM_WATER_SYSTEM", "GENERATOR_SYSTEM", "COOLING_SYSTEM", "ENVIRONMENTAL_SYSTEM"};
        
        for (String system : systems) {
            String key = "realtime:" + system;
            SensorData sensorData = (SensorData) redisTemplate.opsForValue().get(key);
            if (sensorData != null) {
                data.put(system, sensorData);
            }
        }
        
        return data;
    }
    
    private ThresholdConfig getThresholdConfig(String systemName, String paramName) {
        // 从配置中获取阈值设置
        String configKey = "threshold:" + systemName + ":" + paramName;
        return (ThresholdConfig) redisTemplate.opsForValue().get(configKey);
    }
    
    private Alarm checkThreshold(String systemName, String paramName, double value, ThresholdConfig config) {
        AlarmLevel level = null;
        String message = null;
        
        if (value > config.getCriticalHigh()) {
            level = AlarmLevel.CRITICAL;
            message = String.format("%s 严重超高限: %.2f (限值: %.2f)", paramName, value, config.getCriticalHigh());
        } else if (value < config.getCriticalLow()) {
            level = AlarmLevel.CRITICAL;
            message = String.format("%s 严重超低限: %.2f (限值: %.2f)", paramName, value, config.getCriticalLow());
        } else if (value > config.getHighHigh()) {
            level = AlarmLevel.HIGH;
            message = String.format("%s 超高限: %.2f (限值: %.2f)", paramName, value, config.getHighHigh());
        } else if (value < config.getLowLow()) {
            level = AlarmLevel.HIGH;
            message = String.format("%s 超低限: %.2f (限值: %.2f)", paramName, value, config.getLowLow());
        } else if (value > config.getHigh()) {
            level = AlarmLevel.MEDIUM;
            message = String.format("%s 超高: %.2f (限值: %.2f)", paramName, value, config.getHigh());
        } else if (value < config.getLow()) {
            level = AlarmLevel.MEDIUM;
            message = String.format("%s 超低: %.2f (限值: %.2f)", paramName, value, config.getLow());
        }
        
        if (level != null) {
            Alarm alarm = new Alarm();
            alarm.setId(UUID.randomUUID().toString());
            alarm.setSystemName(systemName);
            alarm.setParameterName(paramName);
            alarm.setLevel(level);
            alarm.setMessage(message);
            alarm.setValue(value);
            alarm.setTimestamp(LocalDateTime.now());
            alarm.setStatus(AlarmStatus.ACTIVE);
            alarm.setType("THRESHOLD");
            
            return alarm;
        }
        
        return null;
    }
    
    private List<SensorData> getHistoricalData(String systemName, Duration duration) {
        // 从InfluxDB获取历史数据
        // 这里简化处理
        return new ArrayList<>();
    }
    
    private TrendAnalysisResult analyzeTrend(List<SensorData> data, String paramName) {
        // 趋势分析逻辑
        return new TrendAnalysisResult(false, 0.0, "正常");
    }
    
    private Alarm createTrendAlarm(String systemName, String paramName, TrendAnalysisResult trend) {
        Alarm alarm = new Alarm();
        alarm.setId(UUID.randomUUID().toString());
        alarm.setSystemName(systemName);
        alarm.setParameterName(paramName);
        alarm.setLevel(AlarmLevel.MEDIUM);
        alarm.setMessage(String.format("%s 趋势异常: %s", paramName, trend.getDescription()));
        alarm.setTimestamp(LocalDateTime.now());
        alarm.setStatus(AlarmStatus.ACTIVE);
        alarm.setType("TREND");
        
        return alarm;
    }
    
    private Alarm createAnomalyAlarm(String systemName, AnomalyPoint anomaly) {
        Alarm alarm = new Alarm();
        alarm.setId(UUID.randomUUID().toString());
        alarm.setSystemName(systemName);
        alarm.setParameterName(anomaly.getParameterName());
        alarm.setLevel(AlarmLevel.MEDIUM);
        alarm.setMessage(String.format("%s 异常检测: 异常分数 %.2f", anomaly.getParameterName(), anomaly.getAnomalyScore()));
        alarm.setTimestamp(LocalDateTime.now());
        alarm.setStatus(AlarmStatus.ACTIVE);
        alarm.setType("ANOMALY");
        
        return alarm;
    }
    
    private Alarm createPredictiveAlarm(String systemName, PredictedFault fault) {
        Alarm alarm = new Alarm();
        alarm.setId(UUID.randomUUID().toString());
        alarm.setSystemName(systemName);
        alarm.setParameterName(fault.getComponentName());
        alarm.setLevel(AlarmLevel.LOW);
        alarm.setMessage(String.format("预测性报警: %s 可能在 %d 小时内发生故障 (概率: %.1f%%)", 
                                     fault.getFaultType(), fault.getTimeToFailure(), fault.getProbability() * 100));
        alarm.setTimestamp(LocalDateTime.now());
        alarm.setStatus(AlarmStatus.ACTIVE);
        alarm.setType("PREDICTIVE");
        
        return alarm;
    }
    
    private boolean isDuplicateAlarm(Alarm alarm) {
        // 检查是否为重复报警
        String key = "alarm:active:" + alarm.getSystemName() + ":" + alarm.getParameterName();
        return redisTemplate.hasKey(key);
    }
    
    private boolean isAlarmSuppressed(Alarm alarm) {
        // 检查报警抑制规则
        String suppressKey = "alarm:suppress:" + alarm.getSystemName();
        return redisTemplate.hasKey(suppressKey);
    }
    
    private void storeAlarm(Alarm alarm) {
        // 存储到Redis（活动报警）
        String activeKey = "alarm:active:" + alarm.getSystemName() + ":" + alarm.getParameterName();
        redisTemplate.opsForValue().set(activeKey, alarm, Duration.ofHours(24));
        
        // 存储到InfluxDB（历史报警）
        Point point = Point.measurement("alarms")
                .time(alarm.getTimestamp().toInstant(ZoneOffset.UTC), WritePrecision.MS)
                .tag("system", alarm.getSystemName())
                .tag("parameter", alarm.getParameterName())
                .tag("level", alarm.getLevel().name())
                .tag("type", alarm.getType())
                .addField("message", alarm.getMessage())
                .addField("value", alarm.getValue() != null ? alarm.getValue() : 0.0)
                .build();
        
        influxDBTemplate.writePoint(point);
    }
    
    private List<NotificationChannel> determineNotificationChannels(AlarmLevel level) {
        List<NotificationChannel> channels = new ArrayList<>();
        
        switch (level) {
            case CRITICAL:
                channels.add(NotificationChannel.SMS);
                channels.add(NotificationChannel.EMAIL);
                channels.add(NotificationChannel.VOICE_CALL);
                channels.add(NotificationChannel.WEB_PUSH);
                break;
            case HIGH:
                channels.add(NotificationChannel.EMAIL);
                channels.add(NotificationChannel.WEB_PUSH);
                break;
            case MEDIUM:
                channels.add(NotificationChannel.WEB_PUSH);
                break;
            case LOW:
            case INFO:
                channels.add(NotificationChannel.WEB_PUSH);
                break;
        }
        
        return channels;
    }
    
    private NotificationMessage createNotificationMessage(Alarm alarm, NotificationChannel channel) {
        NotificationMessage message = new NotificationMessage();
        message.setTitle("发电厂报警 - " + alarm.getLevel().getDescription());
        message.setContent(alarm.getMessage());
        message.setUrgency(alarm.getLevel().getPriority());
        message.setTimestamp(alarm.getTimestamp());
        
        return message;
    }
    
    private Alarm getAlarmById(String alarmId) {
        // 从数据库获取报警信息
        return null; // 简化处理
    }
    
    private void updateAlarm(Alarm alarm) {
        // 更新报警状态到数据库
    }
    
    private void recordAutoResponse(Alarm alarm, AutoResponseResult result) {
        // 记录自动响应历史
    }
}

3 数据可视化与报表

3.1 实时监控大屏

/**
 * 实时监控数据服务
 * 为大屏展示提供数据支持
 */
@RestController
@RequestMapping("/api/dashboard")
@CrossOrigin(origins = "*")
public class DashboardController {
    
    @Autowired
    private DashboardService dashboardService;
    
    @Autowired
    private KPICalculationService kpiService;
    
    @Autowired
    private AlarmService alarmService;
    
    /**
     * 获取系统总览数据
     */
    @GetMapping("/overview")
    public ResponseEntity<SystemOverview> getSystemOverview() {
        try {
            SystemOverview overview = dashboardService.getSystemOverview();
            return ResponseEntity.ok(overview);
        } catch (Exception e) {
            log.error("获取系统总览失败", e);
            return ResponseEntity.status(500).build();
        }
    }
    
    /**
     * 获取实时KPI数据
     */
    @GetMapping("/kpis")
    public ResponseEntity<Map<String, Double>> getRealTimeKPIs() {
        try {
            Map<String, Double> kpis = kpiService.getCurrentKPIs();
            return ResponseEntity.ok(kpis);
        } catch (Exception e) {
            log.error("获取KPI数据失败", e);
            return ResponseEntity.status(500).build();
        }
    }
    
    /**
     * 获取活动报警列表
     */
    @GetMapping("/alarms/active")
    public ResponseEntity<List<AlarmSummary>> getActiveAlarms() {
        try {
            List<AlarmSummary> alarms = alarmService.getActiveAlarms();
            return ResponseEntity.ok(alarms);
        } catch (Exception e) {
            log.error("获取活动报警失败", e);
            return ResponseEntity.status(500).build();
        }
    }
    
    /**
     * 获取趋势数据
     */
    @GetMapping("/trends/{parameter}")
    public ResponseEntity<TrendData> getTrendData(
            @PathVariable String parameter,
            @RequestParam @DateTimeFormat(iso = DateTimeFormat.ISO.DATE_TIME) LocalDateTime startTime,
            @RequestParam @DateTimeFormat(iso = DateTimeFormat.ISO.DATE_TIME) LocalDateTime endTime) {
        
        try {
            TrendData trendData = dashboardService.getTrendData(parameter, startTime, endTime);
            return ResponseEntity.ok(trendData);
        } catch (Exception e) {
            log.error("获取趋势数据失败", e);
            return ResponseEntity.status(500).build();
        }
    }
    
    /**
     * 获取设备状态分布
     */
    @GetMapping("/equipment/status")
    public ResponseEntity<EquipmentStatusDistribution> getEquipmentStatus() {
        try {
            EquipmentStatusDistribution status = dashboardService.getEquipmentStatusDistribution();
            return ResponseEntity.ok(status);
        } catch (Exception e) {
            log.error("获取设备状态失败", e);
            return ResponseEntity.status(500).build();
        }
    }
    
    /**
     * 获取能耗分析数据
     */
    @GetMapping("/energy/analysis")
    public ResponseEntity<EnergyAnalysis> getEnergyAnalysis(
            @RequestParam @DateTimeFormat(iso = DateTimeFormat.ISO.DATE) LocalDate date) {
        
        try {
            EnergyAnalysis analysis = dashboardService.getEnergyAnalysis(date);
            return ResponseEntity.ok(analysis);
        } catch (Exception e) {
            log.error("获取能耗分析失败", e);
            return ResponseEntity.status(500).build();
        }
    }
}

4 总结

本系列文章详细介绍了燃煤发电厂智能监控系统的完整实现方案，涵盖了：

1. 燃烧系统监控：煤粉制备、炉膛燃烧的实时监控和自动控制
2. 汽水系统控制：汽包水位、蒸汽温度压力的精确控制和发电机组监测
3. 并网控制与冷却优化：电网同步并网和冷却系统智能优化
4. 系统集成与数据分析：统一数据采集、智能报警和可视化展示

整个系统采用现代化的Java技术栈，包括Spring Boot、Redis、InfluxDB、Kafka等，实现了：

• 实时数据采集：毫秒级数据采集和处理
• 智能控制算法：PID控制、多目标优化等
• 多级报警机制：阈值、趋势、异常、预测性报警
• 可视化监控：实时大屏、历史趋势、报表分析
• 系统集成：统一的数据模型和接口标准

该系统为燃煤发电厂的数字化转型提供了完整的解决方案，有效提升了运行效率、安全性和环保水平。