Scala 遍历打印com.alibaba.fastjson.JSONObject所有的Key和Value

最新推荐文章于 2024-03-13 15:01:47 发布
原创 最新推荐文章于 2024-03-13 15:01:47 发布 · 1.5k 阅读 · 1 ·
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#scala #开发语言 #后端

该文章展示了如何在Scala中使用Fastjson库解析JSON字符串，并通过递归方法遍历JSONObject，打印所有的键值对。代码示例中包含了处理嵌套对象的方法。

在Scala中，可以使用Fastjson库处理JSON数据。要遍历打印Fastjson中的JSONObject所有的Key和Value，可以通过递归方式遍历，并使用getOrDefault方法逐一输出其中的键值对。
代码如下，：

import com.alibaba.fastjson.JSONObject

object Main extends App {
   
   
  val jsonString =
    """
      |{
      |  "name": "Tom",
      |  "age": 28,
      |  "sex": "male",
      |  "address": {
      |    "province": "Beijing",
      |    "city": "Haidian"
      |  }
      |}
      |""".stripMargin // 定义JSON字符串
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上面提出DA-LT-4BT0007点位离线，但未生成离线异常，请分析原因后生成完整修改后代码。生成package com.tongchuang.realtime.mds; import com.alibaba.fastjson.JSON; import com.alibaba.fastjson.JSONObject; import com.alibaba.fastjson.JSONObject; import com.tongchuang.realtime.bean.ULEParamConfig; import com.tongchuang.realtime.util.KafkaUtils; import org.apache.flink.api.common.eventtime.WatermarkStrategy; import org.apache.flink.api.common.state.*; import org.apache.flink.api.common.time.Time; import org.apache.flink.api.common.typeinfo.BasicTypeInfo; import org.apache.flink.api.common.typeinfo.TypeHint; import org.apache.flink.api.common.typeinfo.TypeInformation; import org.apache.flink.configuration.Configuration; import org.apache.flink.connector.kafka.source.KafkaSource; import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer; import org.apache.flink.streaming.api.datastream.*; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.api.functions.co.KeyedBroadcastProcessFunction; import org.apache.flink.streaming.api.functions.source.RichSourceFunction; import org.apache.flink.util.Collector; import org.apache.flink.util.OutputTag; import java.io.Serializable; import java.sql.*; import java.text.ParseException; import java.text.SimpleDateFormat; import java.time.Duration; import java.util.*; import java.util.Date; import java.util.concurrent.TimeUnit; public class ULEDataanomalyanalysis { public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setParallelism(1); env.getConfig().setAutoWatermarkInterval(1000); // Kafka消费者配置 KafkaSource<String> kafkaConsumer = KafkaUtils.getKafkaConsumer( "realdata_minute", "minutedata_uledataanomalyanalysis", OffsetsInitializer.latest() ); // 修改Watermark策略 DataStreamSource<String> kafkaDS = env.fromSource( kafkaConsumer, WatermarkStrategy.<String>forBoundedOutOfOrderness(Duration.ofMinutes(1)) .withTimestampAssigner((event, timestamp) -> { try { JSONObject json = JSON.parseObject(event); return new SimpleDateFormat("yyyy-MM-dd HH:mm").parse(json.getString("times")).getTime(); } catch (ParseException e) { return System.currentTimeMillis(); } }), "realdata_uledataanomalyanalysis" ); kafkaDS.print("分钟数据流"); // 解析JSON并拆分tag SingleOutputStreamOperator<JSONObject> splitStream = kafkaDS .map(JSON::parseObject) .returns(TypeInformation.of(JSONObject.class)) .flatMap((JSONObject value, Collector<JSONObject> out) -> { JSONObject data = value.getJSONObject("datas"); String time = value.getString("times"); for (String tag : data.keySet()) { JSONObject tagData = data.getJSONObject(tag); JSONObject newObj = new JSONObject(); newObj.put("time", time); newObj.put("tag", tag); newObj.put("ontime", tagData.getDouble("ontime")); newObj.put("avg", tagData.getDouble("avg")); out.collect(newObj); } }) .returns(TypeInformation.of(JSONObject.class)) .name("Split-By-Tag"); // 配置数据源 DataStream<ConfigCollection> configDataStream = env .addSource(new MysqlConfigSource()) .setParallelism(1) .filter(Objects::nonNull) .name("Config-Source"); // 标签值数据流 DataStream<Map<String, Object>> tagValueStream = splitStream .map(json -> { Map<String, Object> valueMap = new HashMap<>(); valueMap.put("tag", json.getString("tag")); valueMap.put("value", "436887485805570949".equals(json.getString("datatype")) ? json.getDouble("ontime") : json.getDouble("avg")); return valueMap; }) .returns(new TypeHint<Map<String, Object>>() {}) .name("Tag-Value-Stream"); // 合并配置流和标签值流 DataStream<Object> broadcastStream = configDataStream .map(config -> (Object) config) .returns(TypeInformation.of(Object.class)) .union( tagValueStream.map(tagValue -> (Object) tagValue) .returns(TypeInformation.of(Object.class)) ); // 广播流 BroadcastStream<Object> finalBroadcastStream = broadcastStream .broadcast(Descriptors.configStateDescriptor, Descriptors.tagValuesDescriptor); // 按tag分组 KeyedStream<JSONObject, String> keyedStream = splitStream .keyBy(json -> json.getString("tag")); BroadcastConnectedStream<JSONObject, Object> connectedStream = keyedStream.connect(finalBroadcastStream); // 异常检测处理 SingleOutputStreamOperator<JSONObject> anomalyStream = connectedStream .process(new OptimizedAnomalyDetectionFunction()) .name("Anomaly-Detection"); anomalyStream.print("异常检测结果"); // 离线检测结果侧输出 DataStream<String> offlineCheckStream = anomalyStream.getSideOutput(OptimizedAnomalyDetectionFunction.OFFLINE_CHECK_TAG); offlineCheckStream.print("离线检测结果"); env.execute("uledataanomalyanalysis"); } // 配置集合类 public static class ConfigCollection implements Serializable { private static final long serialVersionUID = 1L; public final Map<String, List<ULEParamConfig>> tagToConfigs; public final Map<String, ULEParamConfig> encodeToConfig; public final Set<String> allTags; public final long checkpointTime; public ConfigCollection(Map<String, List<ULEParamConfig>> tagToConfigs, Map<String, ULEParamConfig> encodeToConfig) { this.tagToConfigs = new HashMap<>(tagToConfigs); this.encodeToConfig = new HashMap<>(encodeToConfig); this.allTags = new HashSet<>(tagToConfigs.keySet()); this.checkpointTime = System.currentTimeMillis(); } } // MySQL配置源 public static class MysqlConfigSource extends RichSourceFunction<ConfigCollection> { private volatile boolean isRunning = true; private final long interval = TimeUnit.MINUTES.toMillis(5); @Override public void run(SourceContext<ConfigCollection> ctx) throws Exception { while (isRunning) { ConfigCollection newConfig = loadParams(); if (newConfig != null) { ctx.collect(newConfig); System.out.println("配置加载完成，检查点时间: " + new Date(newConfig.checkpointTime)); } else { System.out.println("配置加载失败"); } Thread.sleep(interval); } } private ConfigCollection loadParams() { Map<String, List<ULEParamConfig>> tagToConfigs = new HashMap<>(5000); Map<String, ULEParamConfig> encodeToConfig = new HashMap<>(5000); String url = "jdbc:mysql://10.51.37.73:3306/eps?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=true&serverTimezone=GMT%2B8"; String user = "root"; String password = "6CKIm5jDVsLrahSw"; String query = "SELECT F_tag AS tag, F_enCode AS encode, F_dataTypes AS datatype, " + "F_isConstantValue AS constantvalue, F_isOnline AS isonline, " + "F_isSync AS issync, F_syncParaEnCode AS syncparaencode, " + "F_isZero AS iszero, F_isHigh AS ishigh, F_highThreshold AS highthreshold, " + "F_isLow AS islow, F_lowThreshold AS lowthreshold, F_duration AS duration " + "FROM t_equipmentparameter " + "WHERE F_enabledmark = '1' AND (F_isConstantValue='1' OR F_isZero='1' " + "OR F_isHigh='1' OR F_isLow='1' OR F_isOnline='1' OR F_isSync='1')"; try (Connection conn = DriverManager.getConnection(url, user, password); Statement stmt = conn.createStatement(); ResultSet rs = stmt.executeQuery(query)) { while (rs.next()) { ULEParamConfig config = new ULEParamConfig(); config.tag = rs.getString("tag"); config.encode = rs.getString("encode"); config.datatype = rs.getString("datatype"); config.constantvalue = rs.getInt("constantvalue"); config.iszero = rs.getInt("iszero"); config.ishigh = rs.getInt("ishigh"); config.highthreshold = rs.getDouble("highthreshold"); config.islow = rs.getInt("islow"); config.lowthreshold = rs.getDouble("lowthreshold"); config.duration = rs.getLong("duration"); config.isonline = rs.getInt("isonline"); config.issync = rs.getInt("issync"); config.syncparaencode = rs.getString("syncparaencode"); if (config.encode == null || config.encode.isEmpty()) { System.err.println("忽略无效配置: 空encode"); continue; } String tag = config.tag; tagToConfigs.computeIfAbsent(tag, k -> new ArrayList<>(10)).add(config); encodeToConfig.put(config.encode, config); } System.out.println("加载配置: " + encodeToConfig.size() + " 个参数"); return new ConfigCollection(tagToConfigs, encodeToConfig); } catch (SQLException e) { System.err.println("加载参数配置错误:"); e.printStackTrace(); return null; } } @Override public void cancel() { isRunning = false; } } // 状态描述符 public static class Descriptors { public static final MapStateDescriptor<Void, ConfigCollection> configStateDescriptor = new MapStateDescriptor<>( "configState", TypeInformation.of(Void.class), TypeInformation.of(ConfigCollection.class) ); public static final MapStateDescriptor<String, Double> tagValuesDescriptor = new MapStateDescriptor<>( "tagValuesBroadcastState", BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.DOUBLE_TYPE_INFO ); } // 优化后的异常检测函数（重点修复离线检测） public static class OptimizedAnomalyDetectionFunction extends KeyedBroadcastProcessFunction<String, JSONObject, Object, JSONObject> { public static final OutputTag<String> OFFLINE_CHECK_TAG = new OutputTag<String>("offline-check"){}; // 状态管理 private transient MapState<String, AnomalyState> stateMap; private transient MapState<String, Double> lastValuesMap; private transient MapState<String, Long> lastDataTimeMap; private transient MapState<String, Long> offlineTimerState; private transient MapState<String, Long> tagInitTimeState; // 新增：标签初始化时间状态 private transient SimpleDateFormat timeFormat; private transient long lastSyncLogTime = 0; @Override public void open(Configuration parameters) { StateTtlConfig ttlConfig = StateTtlConfig.newBuilder(Time.days(30)) .setUpdateType(StateTtlConfig.UpdateType.OnCreateAndWrite) .setStateVisibility(StateTtlConfig.StateVisibility.NeverReturnExpired) .cleanupFullSnapshot() .build(); // 异常状态存储 MapStateDescriptor<String, AnomalyState> stateDesc = new MapStateDescriptor<>( "anomalyState", BasicTypeInfo.STRING_TYPE_INFO, TypeInformation.of(AnomalyState.class) ); stateDesc.enableTimeToLive(ttlConfig); stateMap = getRuntimeContext().getMapState(stateDesc); // 最新值存储 MapStateDescriptor<String, Double> valuesDesc = new MapStateDescriptor<>( "lastValuesState", BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.DOUBLE_TYPE_INFO ); valuesDesc.enableTimeToLive(ttlConfig); lastValuesMap = getRuntimeContext().getMapState(valuesDesc); // 最后数据时间存储 MapStateDescriptor<String, Long> timeDesc = new MapStateDescriptor<>( "lastDataTimeState", BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.LONG_TYPE_INFO ); timeDesc.enableTimeToLive(ttlConfig); lastDataTimeMap = getRuntimeContext().getMapState(timeDesc); // 离线检测定时器状态 MapStateDescriptor<String, Long> timerDesc = new MapStateDescriptor<>( "offlineTimerState", BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.LONG_TYPE_INFO ); timerDesc.enableTimeToLive(ttlConfig); offlineTimerState = getRuntimeContext().getMapState(timerDesc); // 新增：标签初始化时间状态 MapStateDescriptor<String, Long> initTimeDesc = new MapStateDescriptor<>( "tagInitTimeState", BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.LONG_TYPE_INFO ); initTimeDesc.enableTimeToLive(ttlConfig); tagInitTimeState = getRuntimeContext().getMapState(initTimeDesc); timeFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm"); } @Override public void processElement(JSONObject data, ReadOnlyContext ctx, Collector<JSONObject> out) throws Exception { String tag = ctx.getCurrentKey(); String timeStr = data.getString("time"); long eventTime = timeFormat.parse(timeStr).getTime(); // 更新最后数据时间 lastDataTimeMap.put(tag, eventTime); // 获取广播配置 ConfigCollection configCollection = getBroadcastConfig(ctx); if (configCollection == null) return; List<ULEParamConfig> configs = configCollection.tagToConfigs.get(tag); if (configs == null || configs.isEmpty()) return; // 清理无效状态 List<String> keysToRemove = new ArrayList<>(); for (String encode : stateMap.keys()) { boolean found = false; for (ULEParamConfig cfg : configs) { if (cfg.encode.equals(encode)) { found = true; break; } } if (!found) { System.out.println("清理过期状态: " + encode); keysToRemove.add(encode); } } for (String encode : keysToRemove) { stateMap.remove(encode); } double value = 0; boolean valueSet = false; // 检查是否需要离线检测 boolean hasOnlineConfig = false; long minDuration = Long.MAX_VALUE; for (ULEParamConfig config : configs) { if (config.isonline == 1) { hasOnlineConfig = true; minDuration = Math.min(minDuration, config.duration); // 初始化标签检测时间（如果尚未初始化） if (!tagInitTimeState.contains(tag)) { tagInitTimeState.put(tag, System.currentTimeMillis()); } } } // 管理离线检测定时器 if (hasOnlineConfig) { // 删除现有定时器 Long currentTimer = offlineTimerState.get(tag); if (currentTimer != null) { ctx.timerService().deleteEventTimeTimer(currentTimer); } // 注册新定时器（使用最小duration） long offlineTimeout = eventTime + minDuration * 60 * 1000; ctx.timerService().registerEventTimeTimer(offlineTimeout); offlineTimerState.put(tag, offlineTimeout); // 重置离线状态（数据到达表示恢复） for (ULEParamConfig config : configs) { if (config.isonline == 1) { AnomalyState state = getOrCreateState(config.encode); AnomalyStatus status = state.getStatus(6); if (status.reported) { reportAnomaly(6, 0, 0.0, timeStr, config, out); status.reset(); stateMap.put(config.encode, state); System.out.println("离线恢复: tag=" + tag + ", encode=" + config.encode); } } } } // 遍历配置项进行异常检测 for (ULEParamConfig config : configs) { if (!valueSet) { value = "436887485805570949".equals(config.datatype) ? data.getDouble("ontime") : data.getDouble("avg"); lastValuesMap.put(tag, value); valueSet = true; } AnomalyState state = getOrCreateState(config.encode); // 处理异常类型 checkConstantValueAnomaly(config, value, timeStr, state, out); // 1. 恒值检测 checkZeroValueAnomaly(config, value, timeStr, state, out); // 2. 零值检测 checkThresholdAnomaly(config, value, timeStr, state, out); // 3. 上阈值, 4. 下阈值 checkSyncAnomaly(config, value, timeStr, state, configCollection, ctx, out); // 5. 同步检测 stateMap.put(config.encode, state); } } @Override public void onTimer(long timestamp, OnTimerContext ctx, Collector<JSONObject> out) throws Exception { String tag = ctx.getCurrentKey(); Long lastEventTime = lastDataTimeMap.get(tag); ConfigCollection configCollection = getBroadcastConfig(ctx); if (configCollection == null) return; List<ULEParamConfig> configs = configCollection.tagToConfigs.get(tag); if (configs == null) return; // 检查所有需要离线检测的配置项 boolean hasOnlineConfig = false; for (ULEParamConfig config : configs) { if (config.isonline == 1) { hasOnlineConfig = true; AnomalyState state = getOrCreateState(config.encode); AnomalyStatus status = state.getStatus(6); // 核心修复：处理从未收到数据的情况 if (lastEventTime == null) { // 获取标签初始化时间 Long initTime = tagInitTimeState.get(tag); if (initTime == null) { // 如果状态中不存在，使用配置加载时间 initTime = configCollection.checkpointTime; tagInitTimeState.put(tag, initTime); } // 检查是否超过配置的duration long elapsed = System.currentTimeMillis() - initTime; long durationMs = config.duration * 60 * 1000; if (elapsed >= durationMs) { if (!status.reported) { String triggerTime = timeFormat.format(new Date()); reportAnomaly(6, 1, 0.0, triggerTime, config, out); status.reported = true; // 输出到侧输出流 ctx.output(OFFLINE_CHECK_TAG, String.format("离线异常(从未收到数据): tag=%s, encode=%s, 初始化时间=%s, 当前时间=%s, 时间差=%dms (阈值=%dms)", config.tag, config.encode, timeFormat.format(new Date(initTime)), triggerTime, elapsed, durationMs) ); System.out.println("检测到从未接收数据的离线异常: " + config.tag); } } } // 处理已有数据但超时的情况 else if (timestamp - lastEventTime >= config.duration * 60 * 1000) { if (!status.reported) { String triggerTime = timeFormat.format(new Date(timestamp)); reportAnomaly(6, 1, 0.0, triggerTime, config, out); status.reported = true; ctx.output(OFFLINE_CHECK_TAG, String.format("离线异常: tag=%s, encode=%s, 最后数据时间=%s, 超时时间=%s, 时间差=%dms (阈值=%dms)", config.tag, config.encode, timeFormat.format(new Date(lastEventTime)), triggerTime, timestamp - lastEventTime, config.duration * 60 * 1000) ); System.out.println("检测到数据中断的离线异常: " + config.tag); } } else { // 数据已恢复，重置状态 if (status.reported) { reportAnomaly(6, 0, 0.0, timeFormat.format(new Date()), config, out); status.reset(); System.out.println("离线状态恢复: " + config.tag); } } stateMap.put(config.encode, state); } } // 重新注册定时器（每分钟检查一次） if (hasOnlineConfig) { long newTimeout = timestamp + TimeUnit.MINUTES.toMillis(1); ctx.timerService().registerEventTimeTimer(newTimeout); offlineTimerState.put(tag, newTimeout); } else { offlineTimerState.remove(tag); } } // 恒值检测 - 异常类型1 private void checkConstantValueAnomaly(ULEParamConfig config, double currentValue, String timeStr, AnomalyState state, Collector<JSONObject> out) { if (config.constantvalue != 1) return; try { AnomalyStatus status = state.getStatus(1); long durationThreshold = config.duration * 60 * 1000; Date timestamp = timeFormat.parse(timeStr); if (status.lastValue == null) { status.lastValue = currentValue; status.lastChangeTime = timestamp; return; } if (Math.abs(currentValue - status.lastValue) > 0.001) { status.lastValue = currentValue; status.lastChangeTime = timestamp; if (status.reported) { reportAnomaly(1, 0, currentValue, timeStr, config, out); } status.reset(); return; } long elapsed = timestamp.getTime() - status.lastChangeTime.getTime(); if (elapsed > durationThreshold) { if (!status.reported) { reportAnomaly(1, 1, currentValue, timeStr, config, out); status.reported = true; } } } catch (Exception e) { System.err.println("恒值检测错误: " + config.encode + " - " + e.getMessage()); } } // 零值检测 - 异常类型2 private void checkZeroValueAnomaly(ULEParamConfig config, double currentValue, String timeStr, AnomalyState state, Collector<JSONObject> out) { if (config.iszero != 1) return; try { AnomalyStatus status = state.getStatus(2); Date timestamp = timeFormat.parse(timeStr); boolean isZero = Math.abs(currentValue) < 0.001; if (isZero) { if (status.startTime == null) { status.startTime = timestamp; } else if (!status.reported) { long elapsed = timestamp.getTime() - status.startTime.getTime(); if (elapsed >= config.duration * 60 * 1000) { reportAnomaly(2, 1, currentValue, timeStr, config, out); status.reported = true; } } } else { if (status.reported) { reportAnomaly(2, 0, currentValue, timeStr, config, out); status.reset(); } else if (status.startTime != null) { status.startTime = null; } } } catch (Exception e) { System.err.println("零值检测错误: " + config.encode + " - " + e.getMessage()); } } // 阈值检测 - 异常类型3(上阈值)和4(下阈值) private void checkThresholdAnomaly(ULEParamConfig config, double currentValue, String timeStr, AnomalyState state, Collector<JSONObject> out) { try { if (config.ishigh == 1) { AnomalyStatus highStatus = state.getStatus(3); processThresholdAnomaly(highStatus, currentValue, timeStr, currentValue > config.highthreshold, config, 3, out); } if (config.islow == 1) { AnomalyStatus lowStatus = state.getStatus(4); processThresholdAnomaly(lowStatus, currentValue, timeStr, currentValue < config.lowthreshold, config, 4, out); } } catch (Exception e) { System.err.println("阈值检测错误: " + config.encode + " - " + e.getMessage()); } } private void processThresholdAnomaly(AnomalyStatus status, double currentValue, String timeStr, boolean isAnomaly, ULEParamConfig config, int anomalyType, Collector<JSONObject> out) { try { Date timestamp = timeFormat.parse(timeStr); if (isAnomaly) { if (status.startTime == null) { status.startTime = timestamp; } else if (!status.reported) { long elapsed = timestamp.getTime() - status.startTime.getTime(); if (elapsed >= config.duration * 60 * 1000) { reportAnomaly(anomalyType, 1, currentValue, timeStr, config, out); status.reported = true; } } } else { if (status.reported) { reportAnomaly(anomalyType, 0, currentValue, timeStr, config, out); status.reset(); } else if (status.startTime != null) { status.startTime = null; } } } catch (Exception e) { System.err.println("阈值处理错误: " + config.encode + " - " + e.getMessage()); } } // 同步检测 - 异常类型5（优化日志输出） private void checkSyncAnomaly(ULEParamConfig config, double currentValue, String timeStr, AnomalyState state, ConfigCollection configCollection, ReadOnlyContext ctx, Collector<JSONObject> out) { if (config.issync != 1 || config.syncparaencode == null || config.syncparaencode.isEmpty()) { return; } try { // 通过encode获取关联配置 ULEParamConfig relatedConfig = configCollection.encodeToConfig.get(config.syncparaencode); if (relatedConfig == null) { if (System.currentTimeMillis() - lastSyncLogTime > 60000) { System.out.println("同步检测错误: 未找到关联配置, encode=" + config.syncparaencode); lastSyncLogTime = System.currentTimeMillis(); } return; } // 获取关联配置的tag String relatedTag = relatedConfig.tag; if (relatedTag == null || relatedTag.isEmpty()) { if (System.currentTimeMillis() - lastSyncLogTime > 60000) { System.out.println("同步检测错误: 关联配置没有tag, encode=" + config.syncparaencode); lastSyncLogTime = System.currentTimeMillis(); } return; } // 从广播状态获取关联值 ReadOnlyBroadcastState<String, Double> tagValuesState = ctx.getBroadcastState(Descriptors.tagValuesDescriptor); Double relatedValue = tagValuesState.get(relatedTag); if (relatedValue == null) { if (System.currentTimeMillis() - lastSyncLogTime > 60000) { // 优化日志：添加当前标签信息 System.out.printf("同步检测警告: 关联值未初始化 [主标签=%s(%s), 关联标签=%s(%s)]%n", config.tag, config.encode, relatedTag, config.syncparaencode); lastSyncLogTime = System.currentTimeMillis(); } return; } // 同步检测逻辑 AnomalyStatus status = state.getStatus(5); Date timestamp = timeFormat.parse(timeStr); // 业务逻辑：当前值接近1且关联值接近0时异常 boolean isAnomaly = (currentValue >= 0.99) && (Math.abs(relatedValue) < 0.01); // 日志记录 if (System.currentTimeMillis() - lastSyncLogTime > 60000) { System.out.printf("同步检测: %s (%.4f) vs %s (%.4f) -> %b%n", config.tag, currentValue, relatedTag, relatedValue, isAnomaly); lastSyncLogTime = System.currentTimeMillis(); } // 处理异常状态 if (isAnomaly) { if (status.startTime == null) { status.startTime = timestamp; } else if (!status.reported) { long elapsed = timestamp.getTime() - status.startTime.getTime(); if (elapsed >= config.duration * 60 * 1000) { reportAnomaly(5, 1, currentValue, timeStr, config, out); status.reported = true; } } } else { if (status.reported) { reportAnomaly(5, 0, currentValue, timeStr, config, out); status.reset(); } else if (status.startTime != null) { status.startTime = null; } } } catch (ParseException e) { System.err.println("同步检测时间解析错误: " + config.encode + " - " + e.getMessage()); } catch (Exception e) { System.err.println("同步检测错误: " + config.encode + " - " + e.getMessage()); } } // 报告异常（添加详细日志） private void reportAnomaly(int anomalyType, int statusFlag, double value, String time, ULEParamConfig config, Collector<JSONObject> out) { JSONObject event = new JSONObject(); event.put("tag", config.tag); event.put("paracode", config.encode); event.put("abnormaltype", anomalyType); event.put("statusflag", statusFlag); event.put("datavalue", value); event.put("triggertime", time); out.collect(event); // 添加详细日志输出 String statusDesc = statusFlag == 1 ? "异常开始" : "异常结束"; System.out.printf("报告异常: 类型=%d, 状态=%s, 标签=%s, 编码=%s, 时间=%s%n", anomalyType, statusDesc, config.tag, config.encode, time); } @Override public void processBroadcastElement(Object broadcastElement, Context ctx, Collector<JSONObject> out) throws Exception { // 处理配置更新 if (broadcastElement instanceof ConfigCollection) { ConfigCollection newConfig = (ConfigCollection) broadcastElement; BroadcastState<Void, ConfigCollection> configState = ctx.getBroadcastState(Descriptors.configStateDescriptor); // 获取旧配置 ConfigCollection oldConfig = configState.get(null); // 处理配置变更：清理不再启用的报警 if (oldConfig != null) { for (Map.Entry<String, ULEParamConfig> entry : oldConfig.encodeToConfig.entrySet()) { String encode = entry.getKey(); ULEParamConfig oldCfg = entry.getValue(); ULEParamConfig newCfg = newConfig.encodeToConfig.get(encode); if (newCfg == null || !isAlarmEnabled(newCfg, oldCfg)) { // 发送恢复事件 sendRecoveryEvents(encode, oldCfg, ctx, out); } } } // 更新广播状态 configState.put(null, newConfig); System.out.println("广播配置更新完成, 配置项: " + newConfig.encodeToConfig.size()); // 新增：初始化所有需要在线检测的标签 for (String tag : newConfig.allTags) { List<ULEParamConfig> configs = newConfig.tagToConfigs.get(tag); if (configs != null) { for (ULEParamConfig config : configs) { if (config.isonline == 1) { // 初始化标签检测时间 if (!tagInitTimeState.contains(tag)) { long initTime = System.currentTimeMillis(); tagInitTimeState.put(tag, initTime); System.out.println("初始化在线检测: tag=" + tag + ", 时间=" + timeFormat.format(new Date(initTime))); } } } } } } // 处理标签值更新 else if (broadcastElement instanceof Map) { @SuppressWarnings("unchecked") Map<String, Object> tagValue = (Map<String, Object>) broadcastElement; String tag = (String) tagValue.get("tag"); Double value = (Double) tagValue.get("value"); if (tag != null && value != null) { BroadcastState<String, Double> tagValuesState = ctx.getBroadcastState(Descriptors.tagValuesDescriptor); tagValuesState.put(tag, value); } } } // 检查报警是否启用 private boolean isAlarmEnabled(ULEParamConfig newCfg, ULEParamConfig oldCfg) { return (oldCfg.constantvalue == 1 && newCfg.constantvalue == 1) || (oldCfg.iszero == 1 && newCfg.iszero == 1) || (oldCfg.ishigh == 1 && newCfg.ishigh == 1) || (oldCfg.islow == 1 && newCfg.islow == 1) || (oldCfg.isonline == 1 && newCfg.isonline == 1) || (oldCfg.issync == 1 && newCfg.issync == 1); } // 发送恢复事件 private void sendRecoveryEvents(String encode, ULEParamConfig config, Context ctx, Collector<JSONObject> out) { try { AnomalyState state = stateMap.get(encode); if (state == null) return; // 遍历所有可能的报警类型 for (int type = 1; type <= 6; type++) { AnomalyStatus status = state.getStatus(type); if (status.reported) { JSONObject recoveryEvent = new JSONObject(); recoveryEvent.put("tag", config.tag); recoveryEvent.put("paracode", config.encode); recoveryEvent.put("abnormaltype", type); recoveryEvent.put("statusflag", 0); // 恢复事件 recoveryEvent.put("datavalue", 0.0); recoveryEvent.put("triggertime", timeFormat.format(new Date())); out.collect(recoveryEvent); System.out.println("发送恢复事件: 类型=" + type + ", 标签=" + config.tag); status.reset(); } } // 更新状态 stateMap.put(encode, state); } catch (Exception e) { System.err.println("发送恢复事件失败: " + e.getMessage()); } } // 辅助方法 private ConfigCollection getBroadcastConfig(ReadOnlyContext ctx) throws Exception { return ctx.getBroadcastState(Descriptors.configStateDescriptor).get(null); } private AnomalyState getOrCreateState(String encode) throws Exception { AnomalyState state = stateMap.get(encode); if (state == null) { state = new AnomalyState(); } return state; } } // 异常状态类 public static class AnomalyState implements Serializable { private static final long serialVersionUID = 1L; private final Map<Integer, AnomalyStatus> statusMap = new HashMap<>(); public AnomalyStatus getStatus(int type) { return statusMap.computeIfAbsent(type, k -> new AnomalyStatus()); } } // 异常状态详情 public static class AnomalyStatus implements Serializable { private static final long serialVersionUID = 1L; public Date startTime; // 异常开始时间 public Double lastValue; // 用于恒值检测 public Date lastChangeTime; // 值最后变化时间 public boolean reported; // 是否已报告 public void reset() { startTime = null; lastValue = null; lastChangeTime = null; reported = false; } } } 运行时日志为："C:\Program Files (x86)\Java\jdk1.8.0_102\bin\java.exe" -agentlib:jdwp=transport=dt_socket,address=127.0.0.1:21096,suspend=y,server=n -javaagent:C:\Users\Administrator\AppData\Local\JetBrains\IntelliJIdea2021.2\captureAgent\debugger-agent.jar=file:/C:/Users/Administrator/AppData/Local/Temp/capture.props -Dfile.encoding=UTF-8 -classpath C:\Users\Administrator\AppData\Local\Temp\classpath849000959.jar com.tongchuang.realtime.mds.ULEDataanomalyanalysis 已连接到目标 VM, 地址: ''127.0.0.1:21096'，传输: '套接字'' SLF4J: Class path contains multiple SLF4J bindings. SLF4J: Found binding in [jar:file:/F:/flink/flinkmaven/repository/org/apache/logging/log4j/log4j-slf4j-impl/2.10.0/log4j-slf4j-impl-2.10.0.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: Found binding in [jar:file:/F:/flink/flinkmaven/repository/org/slf4j/slf4j-log4j12/1.7.25/slf4j-log4j12-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class] SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. SLF4J: Actual binding is of type [org.apache.logging.slf4j.Log4jLoggerFactory] 加载配置: 30 个参数 配置加载完成，检查点时间: Wed Aug 06 07:55:06 CST 2025 广播配置更新完成, 配置项: 30 Exception in thread "main" org.apache.flink.runtime.client.JobExecutionException: Job execution failed. at org.apache.flink.runtime.jobmaster.JobResult.toJobExecutionResult(JobResult.java:144) at org.apache.flink.runtime.minicluster.MiniClusterJobClient.lambda$getJobExecutionResult$3(MiniClusterJobClient.java:137) at java.util.concurrent.CompletableFuture.uniApply(CompletableFuture.java:602) at java.util.concurrent.CompletableFuture$UniApply.tryFire(CompletableFuture.java:577) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.complete(CompletableFuture.java:1962) at org.apache.flink.runtime.rpc.akka.AkkaInvocationHandler.lambda$invokeRpc$0(AkkaInvocationHandler.java:237) at java.util.concurrent.CompletableFuture.uniWhenComplete(CompletableFuture.java:760) at java.util.concurrent.CompletableFuture$UniWhenComplete.tryFire(CompletableFuture.java:736) at java.util.concurrent.CompletableFuture.postComplete(CompletableFuture.java:474) at java.util.concurrent.CompletableFuture.complete(CompletableFuture.java:1962) at org.apache.flink.runtime.concurrent.FutureUtils$1.onComplete(FutureUtils.java:1081) at akka.dispatch.OnComplete.internal(Future.scala:264) at akka.dispatch.OnComplete.internal(Future.scala:261) at akka.dispatch.japi$CallbackBridge.apply(Future.scala:191) at akka.dispatch.japi$CallbackBridge.apply(Future.scala:188) at scala.concurrent.impl.CallbackRunnable.run$$$capture(Promise.scala:60) at scala.concurrent.impl.CallbackRunnable.run(Promise.scala) at org.apache.flink.runtime.concurrent.Executors$DirectExecutionContext.execute(Executors.java:73) at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:68) at scala.concurrent.impl.Promise$DefaultPromise.$anonfun$tryComplete$1(Promise.scala:284) at scala.concurrent.impl.Promise$DefaultPromise.$anonfun$tryComplete$1$adapted(Promise.scala:284) at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:284) at akka.pattern.PromiseActorRef.$bang(AskSupport.scala:573) at akka.pattern.PipeToSupport$PipeableFuture$$anonfun$pipeTo$1.applyOrElse(PipeToSupport.scala:22) at akka.pattern.PipeToSupport$PipeableFuture$$anonfun$pipeTo$1.applyOrElse(PipeToSupport.scala:21) at scala.concurrent.Future.$anonfun$andThen$1(Future.scala:532) at scala.concurrent.impl.Promise.liftedTree1$1(Promise.scala:29) at scala.concurrent.impl.Promise.$anonfun$transform$1(Promise.scala:29) at scala.concurrent.impl.CallbackRunnable.run$$$capture(Promise.scala:60) at scala.concurrent.impl.CallbackRunnable.run(Promise.scala) at akka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExecutor.scala:55) at akka.dispatch.BatchingExecutor$BlockableBatch.$anonfun$run$1(BatchingExecutor.scala:91) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:12) at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:81) at akka.dispatch.BatchingExecutor$BlockableBatch.run(BatchingExecutor.scala:91) at akka.dispatch.TaskInvocation.run(AbstractDispatcher.scala:40) at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(ForkJoinExecutorConfigurator.scala:44) at akka.dispatch.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) at akka.dispatch.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) at akka.dispatch.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at akka.dispatch.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) Caused by: org.apache.flink.runtime.JobException: Recovery is suppressed by NoRestartBackoffTimeStrategy at org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.handleFailure(ExecutionFailureHandler.java:138) at org.apache.flink.runtime.executiongraph.failover.flip1.ExecutionFailureHandler.getFailureHandlingResult(ExecutionFailureHandler.java:82) at org.apache.flink.runtime.scheduler.DefaultScheduler.handleTaskFailure(DefaultScheduler.java:216) at org.apache.flink.runtime.scheduler.DefaultScheduler.maybeHandleTaskFailure(DefaultScheduler.java:206) at org.apache.flink.runtime.scheduler.DefaultScheduler.updateTaskExecutionStateInternal(DefaultScheduler.java:197) at org.apache.flink.runtime.scheduler.SchedulerBase.updateTaskExecutionState(SchedulerBase.java:682) at org.apache.flink.runtime.scheduler.SchedulerNG.updateTaskExecutionState(SchedulerNG.java:79) at org.apache.flink.runtime.jobmaster.JobMaster.updateTaskExecutionState(JobMaster.java:435) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcInvocation(AkkaRpcActor.java:305) at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleRpcMessage(AkkaRpcActor.java:212) at org.apache.flink.runtime.rpc.akka.FencedAkkaRpcActor.handleRpcMessage(FencedAkkaRpcActor.java:77) at org.apache.flink.runtime.rpc.akka.AkkaRpcActor.handleMessage(AkkaRpcActor.java:158) at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:26) at akka.japi.pf.UnitCaseStatement.apply(CaseStatements.scala:21) at scala.PartialFunction.applyOrElse(PartialFunction.scala:123) at scala.PartialFunction.applyOrElse$(PartialFunction.scala:122) at akka.japi.pf.UnitCaseStatement.applyOrElse(CaseStatements.scala:21) at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:171) at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:172) at scala.PartialFunction$OrElse.applyOrElse(PartialFunction.scala:172) at akka.actor.Actor.aroundReceive(Actor.scala:517) at akka.actor.Actor.aroundReceive$(Actor.scala:515) at akka.actor.AbstractActor.aroundReceive(AbstractActor.scala:225) at akka.actor.ActorCell.receiveMessage$$$capture(ActorCell.scala:592) at akka.actor.ActorCell.receiveMessage(ActorCell.scala) at akka.actor.ActorCell.invoke(ActorCell.scala:561) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:258) at akka.dispatch.Mailbox.run(Mailbox.scala:225) at akka.dispatch.Mailbox.exec(Mailbox.scala:235) ... 4 more Caused by: java.lang.NullPointerException: No key set. This method should not be called outside of a keyed context. at org.apache.flink.util.Preconditions.checkNotNull(Preconditions.java:76) at org.apache.flink.runtime.state.heap.StateTable.checkKeyNamespacePreconditions(StateTable.java:270) at org.apache.flink.runtime.state.heap.StateTable.get(StateTable.java:260) at org.apache.flink.runtime.state.heap.StateTable.get(StateTable.java:143) at org.apache.flink.runtime.state.heap.HeapMapState.get(HeapMapState.java:86) at org.apache.flink.runtime.state.ttl.TtlMapState.lambda$getWrapped$0(TtlMapState.java:64) at org.apache.flink.runtime.state.ttl.AbstractTtlDecorator.getWrappedWithTtlCheckAndUpdate(AbstractTtlDecorator.java:97) at org.apache.flink.runtime.state.ttl.TtlMapState.getWrapped(TtlMapState.java:63) at org.apache.flink.runtime.state.ttl.TtlMapState.contains(TtlMapState.java:96) at org.apache.flink.runtime.state.UserFacingMapState.contains(UserFacingMapState.java:72) at com.tongchuang.realtime.mds.ULEDataanomalyanalysis$OptimizedAnomalyDetectionFunction.processBroadcastElement(ULEDataanomalyanalysis.java:767) at org.apache.flink.streaming.api.operators.co.CoBroadcastWithKeyedOperator.processElement2(CoBroadcastWithKeyedOperator.java:133) at org.apache.flink.streaming.runtime.io.StreamTwoInputProcessorFactory.processRecord2(StreamTwoInputProcessorFactory.java:221) at org.apache.flink.streaming.runtime.io.StreamTwoInputProcessorFactory.lambda$create$1(StreamTwoInputProcessorFactory.java:190) at org.apache.flink.streaming.runtime.io.StreamTwoInputProcessorFactory$StreamTaskNetworkOutput.emitRecord(StreamTwoInputProcessorFactory.java:291) at org.apache.flink.streaming.runtime.io.AbstractStreamTaskNetworkInput.processElement(AbstractStreamTaskNetworkInput.java:134) at org.apache.flink.streaming.runtime.io.AbstractStreamTaskNetworkInput.emitNext(AbstractStreamTaskNetworkInput.java:105) at org.apache.flink.streaming.runtime.io.StreamOneInputProcessor.processInput(StreamOneInputProcessor.java:66) at org.apache.flink.streaming.runtime.io.StreamTwoInputProcessor.processInput(StreamTwoInputProcessor.java:98) at org.apache.flink.streaming.runtime.tasks.StreamTask.processInput(StreamTask.java:423) at org.apache.flink.streaming.runtime.tasks.mailbox.MailboxProcessor.runMailboxLoop(MailboxProcessor.java:204) at org.apache.flink.streaming.runtime.tasks.StreamTask.runMailboxLoop(StreamTask.java:684) at org.apache.flink.streaming.runtime.tasks.StreamTask.executeInvoke(StreamTask.java:639) at org.apache.flink.streaming.runtime.tasks.StreamTask.runWithCleanUpOnFail(StreamTask.java:650) at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:623) at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:779) at org.apache.flink.runtime.taskmanager.Task.run(Task.java:566) at java.lang.Thread.run(Thread.java:745) 与目标 VM 断开连接, 地址为: ''127.0.0.1:21096'，传输: '套接字'' 进程已结束，退出代码为 1 请生成完整修复后代码。
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最新发布
11-22
<think> 我们遇到了一个类型转换问题：尝试将 `com.alibaba.fastjson.JSONObject` 转换为 `cn.hutool.json.JSONObject`。这两个类来自不同的JSON库（Fastjson和Hutool），它们之间没有继承关系，因此直接强制转换会导致 `ClassCastException`。 ### 问题分析 - **根本原因**：`com.alibaba.fastjson.JSONObject` 和 `cn.hutool.json.JSONObject` 是两个完全独立的类，尽管它们都表示JSON对象，但它们在内存中的结构不同，不能直接互相转换。 - **典型错误代码**： ```java com.alibaba.fastjson.JSONObject fastjsonObj = ...; // 一个Fastjson的JSONObject cn.hutool.json.JSONObject hutoolObj = (cn.hutool.json.JSONObject) fastjsonObj; // 这里会抛出ClassCastException ``` ### 解决方案 我们需要通过中间格式（如字符串）或使用转换工具来转换这两个不同的JSON对象。以下是两种推荐方法： #### 方法1：通过JSON字符串转换（推荐） 将Fastjson的JSONObject先转换成JSON字符串，再用Hutool的JSONUtil解析成Hutool的JSONObject。 ```java import com.alibaba.fastjson.JSON; import com.alibaba.fastjson.JSONObject; import cn.hutool.json.JSONObject; import cn.hutool.json.JSONUtil; // 假设有一个Fastjson的JSONObject实例 com.alibaba.fastjson.JSONObject fastjsonObj = ...; // 将Fastjson的JSONObject转换为JSON字符串 String jsonString = fastjsonObj.toJSONString(); // 使用Hutool的JSONUtil将字符串解析为Hutool的JSONObject cn.hutool.json.JSONObject hutoolObj = JSONUtil.parseObj(jsonString); ``` #### 方法2：使用Hutool的转换工具（如果适用） Hutool提供了`JSONUtil`，它也可以直接转换其他类型的JSON对象，但需要确保传入的对象可以被正确识别。不过，更通用的做法还是先转成字符串。 ```java // 或者使用Hutool的JSONUtil的parseObj方法，但注意它接受的是字符串或Map等 cn.hutool.json.JSONObject hutoolObj = JSONUtil.parseObj(fastjsonObj); ``` 注意：`JSONUtil.parseObj`方法有多个重载，其中一个可以接受`Object`对象，但它的行为取决于传入对象的类型。如果传入的是Fastjson的JSONObject，它内部会调用其`toString()`方法，本质上还是转成了字符串再解析。所以，方法1更直接且明确。 ### 示例代码 假设我们有一个Fastjson的JSONObject，现在需要转换为Hutool的JSONObject： ```java import com.alibaba.fastjson.JSONObject as FastjsonJSONObject; import cn.hutool.json.JSONObject as HutoolJSONObject; import cn.hutool.json.JSONUtil; public class JsonConversionExample { public static void main(String[] args) { // 创建一个Fastjson的JSONObject FastjsonJSONObject fastjsonObj = new FastjsonJSONObject(); fastjsonObj.put("name", "John"); fastjsonObj.put("age", 30); // 转换为Hutool的JSONObject String jsonStr = fastjsonObj.toJSONString(); HutoolJSONObject hutoolObj = JSONUtil.parseObj(jsonStr); // 现在可以正常使用hutoolObj System.out.println(hutoolObj.getStr("name")); // 输出: John } } ``` ### 注意事项 1. **性能考虑**：如果频繁转换，要注意字符串转换可能带来的性能开销（尤其是在高并发场景下）。如果可能，尽量统一使用同一个JSON库。 2. **依赖冲突**：确保项目中同时引入了Fastjson和Hutool的依赖，并且版本兼容。 3. **空值处理**：注意两个库对空值（null）的处理可能不同。例如，Hutool使用`JSONNull`表示空值，而Fastjson使用Java的`null`。这可能会导致转换后行为不一致[^3]。 4. **复杂对象转换**：如果JSON对象中包含自定义的Java对象，转换过程可能会丢失类型信息，需要额外处理。 ### 统一JSON库建议 为了避免这类问题，建议在项目中统一使用一个JSON库（如全部使用Hutool或全部使用Fastjson）。如果项目已经混合使用，可以考虑逐步迁移。 §§相关问题§§ 1. 如何将Hutool的JSONObject转换为Fastjson的JSONObject？ 2. 如何解决Hutool和Fastjson在空值处理上的差异？ 3. 如何避免项目中多个JSON库的冲突？ 4. 如何将JSON字符串转换为Hutool的JSONArray？ 5. 如何将包含自定义对象的JSON字符串用Hutool正确解析？ [^1]: 类型转换错误场景 [^2]: Fastjson的基本功能 [^3]: Hutool与Jackson的空值处理差异 </think>### 解决 `com.alibaba.fastjson.JSONObject` 无法转换为 `cn.hutool.json.JSONObject` 的方法 #### 🔍 核心问题分析 根本原因是 **Fastjson 和 Hutool 的 JSON 实现互不兼容**： ```java // 错误示例：直接强制转换会报 ClassCastException com.alibaba.fastjson.JSONObject fastjsonObj = ...; cn.hutool.json.JSONObject hutoolObj = (cn.hutool.json.JSONObject) fastjsonObj; // 报错! ``` 两个库的 `JSONObject` 没有继承关系，内存结构不同，无法直接转换[^1]。 --- ### ✅ 解决方案（4种方法） #### 方法1：通过 JSON 字符串中转（推荐） ```java // Fastjson对象 → JSON字符串 → Hutool对象 com.alibaba.fastjson.JSONObject fastjsonObj = ...; String jsonStr = fastjsonObj.toJSONString(); // 转成JSON字符串 cn.hutool.json.JSONObject hutoolObj = JSONUtil.parseObj(jsonStr); // Hutool解析 ``` #### 方法2：使用 Hutool 的转换方法 ```java // 直接使用 Hutool 的转换工具 cn.hutool.json.JSONObject hutoolObj = JSONUtil.parseObj(fastjsonObj); ``` #### 方法3：手动转换（适用于简单对象） ```java cn.hutool.json.JSONObject hutoolObj = new cn.hutool.json.JSONObject(); // 遍历复制属性 for (String key : fastjsonObj.keySet()) { hutoolObj.set(key, fastjsonObj.get(key)); } ``` #### 方法4：类型转换时指定泛型（避免强制转换） ```diff // 修改原始代码 - List<PageDeviseVo> list = (List<PageDeviseVo>) pageComponent.get("componentsList"); + List<PageDeviseVo> list = pageComponent.getObject("componentsList", new TypeReference<List<PageDeviseVo>>(){}); ``` --- ### ⚠️ 关键注意事项 1. **空值处理差异** Hutool 用 `JSONNull` 表示空值，而 Fastjson 用
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