refactor(kafka): 将人脸识别处理改为异步执行- 引入CompletableFuture实现异步处理

- 修改processFaceRecognition方法为异步版本
- 移除原同步处理中的try-catch块
- 更新方法返回类型从boolean改为void-保留处理成功和失败的状态更新逻辑- 添加异步处理成功后的日志记录

src/main/java/com/ycwl/basic/integration/kafka/config/FaceRecognitionThreadPoolConfig.java

@@ -0,0 +1,49 @@
+package com.ycwl.basic.integration.kafka.config;
+
+import lombok.extern.slf4j.Slf4j;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+import java.util.concurrent.LinkedBlockingQueue;
+import java.util.concurrent.ThreadPoolExecutor;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * 人脸识别异步处理线程池配置
+ */
+@Slf4j
+@Configuration
+@ConditionalOnProperty(name = "kafka.enabled", havingValue = "true")
+public class FaceRecognitionThreadPoolConfig {
+
+    /**
+     * 创建人脸识别专用线程池
+     * - 核心线程数：32
+     * - 最大线程数：128
+     * - 队列容量：1000（避免无限制增长）
+     * - 拒绝策略：CallerRunsPolicy（调用者线程执行）
+     */
+    @Bean(name = "faceRecognitionExecutor", destroyMethod = "shutdown")
+    public ThreadPoolExecutor faceRecognitionExecutor() {
+        ThreadPoolExecutor executor = new ThreadPoolExecutor(
+            32,                                      // 核心线程数
+            128,                                     // 最大线程数
+            60L,                                     // 空闲线程存活时间
+            TimeUnit.SECONDS,                        // 时间单位
+            new LinkedBlockingQueue<>(1000),         // 任务队列
+            r -> {
+                Thread thread = new Thread(r);
+                thread.setName("face-recognition-" + thread.getId());
+                thread.setDaemon(false);
+                return thread;
+            },
+            new ThreadPoolExecutor.CallerRunsPolicy() // 超过容量时由调用者线程执行
+        );
+
+        log.info("人脸识别线程池初始化完成 - 核心线程数: {}, 最大线程数: {}, 队列容量: 1000",
+                executor.getCorePoolSize(), executor.getMaximumPoolSize());
+
+        return executor;
+    }
+}

src/main/java/com/ycwl/basic/integration/kafka/service/FaceProcessingKafkaService.java

@@ -22,6 +22,7 @@ import org.springframework.stereotype.Service;
 
 import java.time.ZoneId;
 import java.util.Date;
+import java.util.concurrent.ThreadPoolExecutor;
 
 /**
  * 人脸处理Kafka消费服务
@@ -39,6 +40,7 @@ public class FaceProcessingKafkaService {
     private final TaskFaceService taskFaceService;
     private final ScenicService scenicService;
     private final DeviceRepository deviceRepository;
+    private final ThreadPoolExecutor faceRecognitionExecutor;
 
     /**
      * 消费外部系统发送的人脸处理消息
@@ -48,7 +50,7 @@ public class FaceProcessingKafkaService {
     public void processFaceMessage(String message, Acknowledgment ack) {
         try {
             FaceProcessingMessage faceMessage = JacksonUtil.parseObject(message, FaceProcessingMessage.class);
-            log.info("接收到外部人脸处理消息, scenicId: {}, deviceId: {}, faceUrl: {}", 
+            log.debug("接收到外部人脸处理消息, scenicId: {}, deviceId: {}, faceUrl: {}",
                     faceMessage.getScenicId(), faceMessage.getDeviceId(), faceMessage.getFaceUrl());
 
             // 使用外部传入的faceSampleId
@@ -63,31 +65,23 @@ public class FaceProcessingKafkaService {
             // 先保存人脸样本数据
             boolean saved = saveFaceSample(faceMessage, externalFaceId);
 
-            // 然后进行人脸识别处理
+            // 然后异步进行人脸识别处理（使用专用线程池）
             if (saved) {
-                try {
+                faceRecognitionExecutor.execute(() -> processFaceRecognitionAsync(faceMessage));
-                    boolean processed = processFaceRecognition(faceMessage);
+                log.debug("人脸识别任务已提交至线程池, faceSampleId: {}, 活跃线程: {}, 队列大小: {}",
-                    if (processed) {
+                        externalFaceId, faceRecognitionExecutor.getActiveCount(),
-                        log.info("人脸识别处理成功, faceSampleId: {}", externalFaceId);
+                        faceRecognitionExecutor.getQueue().size());
-                    } else {
-                        log.warn("人脸识别处理失败，但消息仍将被消费, faceSampleId: {}", externalFaceId);
-                    }
-                } catch (Exception e) {
-                    log.error("人脸识别处理异常，但消息仍将被消费, faceSampleId: {}, error: {}", externalFaceId, e.getMessage(), e);
-                }
             } else {
                 log.warn("人脸样本保存失败，但消息仍将被消费, faceSampleId: {}", externalFaceId);
             }
-            
+
             // 无论处理是否成功，都消费消息
             ack.acknowledge();
-            log.info("消息已消费, faceSampleId: {}", externalFaceId);
+
-            
         } catch (Exception e) {
             log.error("处理外部人脸消息失败: {}", e.getMessage(), e);
             // 即使发生异常也消费消息，避免消息堆积
             ack.acknowledge();
-            log.info("异常情况下消息已消费，避免重复处理");
         }
     }
 
@@ -113,12 +107,10 @@ public class FaceProcessingKafkaService {
             } else {
                 faceSample.setCreateAt(new Date());
             }
-            
+
             // 保存到数据库
             faceSampleMapper.add(faceSample);
-            log.info("人脸样本数据已保存, 使用外部faceId: {}, scenicId: {}, deviceId: {}, faceUrl: {}", 
+
-                    externalFaceId, faceMessage.getScenicId(), faceMessage.getDeviceId(), faceMessage.getFaceUrl());
-            
             return true;
         } catch (Exception e) {
             log.error("保存人脸样本数据失败, 外部faceId: {}, scenicId: {}, deviceId: {}", 
@@ -128,14 +120,14 @@ public class FaceProcessingKafkaService {
     }
 
     /**
-     * 执行人脸识别处理逻辑
+     * 异步执行人脸识别处理逻辑
      * 对已保存的人脸样本进行识别处理
      */
-    private boolean processFaceRecognition(FaceProcessingMessage message) {
+    private void processFaceRecognitionAsync(FaceProcessingMessage message) {
         Long faceSampleId = message.getFaceSampleId();
         Long scenicId = message.getScenicId();
         String faceUrl = message.getFaceUrl();
-        
+
         // 直接使用faceSampleId作为唯一标识
         String faceUniqueId = faceSampleId.toString();
 
@@ -144,21 +136,21 @@ public class FaceProcessingKafkaService {
         if (faceBodyAdapter == null) {
             log.error("人脸识别适配器不存在, scenicId: {}", scenicId);
             updateFaceSampleStatus(faceSampleId, -1);
-            return false;
+            return;
         }
 
         try {
             // 更新状态为处理中
             updateFaceSampleStatus(faceSampleId, 1);
-            
+
             // 确保人脸数据库存在
             taskFaceService.assureFaceDb(faceBodyAdapter, scenicId.toString());
-            
+
             // 添加人脸到识别服务（使用faceSampleId作为唯一标识）
             AddFaceResp addFaceResp = faceBodyAdapter.addFace(
-                scenicId.toString(), 
+                scenicId.toString(),
-                faceSampleId.toString(), 
+                faceSampleId.toString(),
-                faceUrl, 
+                faceUrl,
                 faceUniqueId // 即faceSampleId.toString()
             );
 
@@ -166,31 +158,26 @@ public class FaceProcessingKafkaService {
                 // 更新人脸样本得分和状态
                 faceSampleMapper.updateScore(faceSampleId, addFaceResp.getScore());
                 updateFaceSampleStatus(faceSampleId, 2);
-                log.info("人脸识别处理完成, faceSampleId: {}, score: {}", 
+                log.info("人脸识别处理成功, faceSampleId: {}", faceSampleId);
-                        faceSampleId, addFaceResp.getScore());
 
                 // 查询设备配置，判断是否启用预订功能
                 Long deviceId = message.getDeviceId();
                 DeviceConfigManager deviceConfig = deviceRepository.getDeviceConfigManager(deviceId);
-                if (deviceConfig != null && 
+                if (deviceConfig != null &&
                     Integer.valueOf(1).equals(deviceConfig.getInteger("enable_pre_book"))) {
                     DynamicTaskGenerator.addTask(faceSampleId);
-                    log.info("已添加到预订任务队列, faceSampleId: {}", faceSampleId);
                 }
-                return true;
             } else {
                 log.warn("人脸添加返回空结果, faceSampleId: {}", faceSampleId);
                 updateFaceSampleStatus(faceSampleId, -1);
-                return false;
             }
 
         } catch (Exception e) {
-            log.error("人脸识别处理失败, faceSampleId: {}, error: {}", 
+            log.error("人脸识别处理失败, faceSampleId: {}, error: {}",
                     faceSampleId, e.getMessage(), e);
-            
+
             // 标记人脸样本为处理失败状态
             updateFaceSampleStatus(faceSampleId, -1);
-            return false;
         }
     }
 
@@ -200,7 +187,6 @@ public class FaceProcessingKafkaService {
     private void updateFaceSampleStatus(Long faceSampleId, Integer status) {
         try {
             faceSampleMapper.updateStatus(faceSampleId, status);
-            log.info("人脸样本状态已更新, faceSampleId: {}, status: {} (0:初始,1:处理中,2:成功,-1:失败)", faceSampleId, status);
         } catch (Exception e) {
             log.error("更新人脸样本状态失败, faceSampleId: {}", faceSampleId, e);
         }

src/main/java/com/ycwl/basic/service/ZTSourceConsumerService.java

@@ -42,7 +42,7 @@ public class ZTSourceConsumerService {
             sourceMessage = JacksonUtil.parseObject(message, ZTSourceMessage.class);
 
             // 输出业务相关的日志信息
-            log.info("接收到ZT-Source消息, sourceId: {}, deviceId: {}, faceSampleId: {}",
+            log.debug("接收到ZT-Source消息, sourceId: {}, deviceId: {}, faceSampleId: {}",
                     sourceMessage.getSourceId(), sourceMessage.getDeviceId(), sourceMessage.getFaceSampleId());
 
             // 处理消息

src/main/java/com/ycwl/basic/service/task/impl/TaskTaskServiceImpl.java

@@ -457,11 +457,7 @@ public class TaskTaskServiceImpl implements TaskService {
                 taskStatusBiz.setFaceCutStatus(faceId, 2);
             }
         };
-        if (!sourceList.isEmpty()) {
+        VideoPieceGetter.addTask(task);
-            task.callback.onInvoke();
-        } else {
-            VideoPieceGetter.addTask(task);
-        }
     }
 
     @Override

src/main/java/com/ycwl/basic/task/VideoPieceGetter.java

@@ -173,10 +173,18 @@ public class VideoPieceGetter {
             templatePlaceholder.forEach(deviceId -> {
                 currentUnFinPlaceholder.computeIfAbsent(deviceId, k -> new AtomicInteger(0)).incrementAndGet();
             });
+            log.info("[Placeholder初始化] 有templateId，初始化完成：placeholder总数={}, 不同设备数={}, 详细计数={}",
+                    templatePlaceholder.size(),
+                    currentUnFinPlaceholder.size(),
+                    currentUnFinPlaceholder.entrySet().stream()
+                            .map(e -> e.getKey() + "=" + e.getValue().get())
+                            .collect(Collectors.joining(", ")));
         } else {
             collection.keySet().forEach(deviceId -> {
                 currentUnFinPlaceholder.put(deviceId.toString(), new AtomicInteger(1));
             });
+            log.info("[Placeholder初始化] 无templateId，初始化完成：设备数={}",
+                    currentUnFinPlaceholder.size());
         }
         collection.values().forEach(faceSampleList -> {
             executor.execute(() -> {
@@ -199,9 +207,15 @@ public class VideoPieceGetter {
                                         doCut(pairDeviceId, faceSample.getId(), faceSample.getCreateAt(), task);
                                         // 让主设备的计数器 -1
                                         AtomicInteger pairCount = currentUnFinPlaceholder.get(pairDeviceId.toString());
-                                        if (pairCount != null && pairCount.decrementAndGet() <= 0) {
+                                        if (pairCount != null) {
-                                            currentUnFinPlaceholder.remove(pairDeviceId.toString());
+                                            int remaining = pairCount.decrementAndGet();
-                                            log.info("设备 {} 的placeholder已满足", pairDeviceId);
+                                            log.info("[计数器更新] 关联设备 {} 计数器递减，剩余={}, currentUnFinPlaceholder总数={}",
+                                                    pairDeviceId, remaining, currentUnFinPlaceholder.size());
+                                            if (remaining <= 0) {
+                                                currentUnFinPlaceholder.remove(pairDeviceId.toString());
+                                                log.info("[Placeholder完成] 设备 {} 的placeholder已满足并移除，剩余设备数={}",
+                                                        pairDeviceId, currentUnFinPlaceholder.size());
+                                            }
                                         }
                                     }
                                 });
@@ -210,9 +224,15 @@ public class VideoPieceGetter {
                     // 处理当前设备
                     doCut(faceSample.getDeviceId(), faceSample.getId(), faceSample.getCreateAt(), task);
                     AtomicInteger count = currentUnFinPlaceholder.get(faceSample.getDeviceId().toString());
-                    if (count != null && count.decrementAndGet() <= 0) {
+                    if (count != null) {
-                        currentUnFinPlaceholder.remove(faceSample.getDeviceId().toString());
+                        int remaining = count.decrementAndGet();
-                        log.info("设备 {} 的placeholder已满足", faceSample.getDeviceId());
+                        log.info("[计数器更新] 设备 {} 计数器递减，剩余={}, currentUnFinPlaceholder总数={}",
+                                faceSample.getDeviceId(), remaining, currentUnFinPlaceholder.size());
+                        if (remaining <= 0) {
+                            currentUnFinPlaceholder.remove(faceSample.getDeviceId().toString());
+                            log.info("[Placeholder完成] 设备 {} 的placeholder已满足并移除，剩余设备数={}",
+                                    faceSample.getDeviceId(), currentUnFinPlaceholder.size());
+                        }
                     }
 
                     // 如果有templateId，检查是否所有placeholder都已满足
@@ -221,14 +241,17 @@ public class VideoPieceGetter {
                         int remainingCount = currentUnFinPlaceholder.values().stream()
                                 .mapToInt(AtomicInteger::get)
                                 .sum();
-                        log.info("当前进度：已完成 {}/{}，剩余 {} 个placeholder未满足",
+                        log.info("[进度检查] 当前进度：已完成 {}/{}，剩余 {} 个placeholder未满足，剩余设备数={}",
-                                totalPlaceholderCount - remainingCount, totalPlaceholderCount, remainingCount);
+                                totalPlaceholderCount - remainingCount, totalPlaceholderCount, remainingCount,
+                                currentUnFinPlaceholder.size());
 
                         if (currentUnFinPlaceholder.isEmpty()) {
                             if (!invoke.get()) {
                                 invoke.set(true);
-                                log.info("所有placeholder已满足，提前调用callback");
+                                log.info("[Callback调用] 所有placeholder已满足，currentUnFinPlaceholder为空，提前调用callback");
                                 task.getCallback().onInvoke();
+                            } else {
+                                log.warn("[Callback跳过] 所有placeholder已满足，但callback已被调用过");
                             }
                         }
                     }
@@ -252,7 +275,11 @@ public class VideoPieceGetter {
         if (null != task.getCallback()) {
             if (!invoke.get()) {
                 invoke.set(true);
+                log.info("[Callback调用] 兜底调用callback，currentUnFinPlaceholder剩余设备数={}",
+                        currentUnFinPlaceholder.size());
                 task.getCallback().onInvoke();
+            } else {
+                log.info("[Callback跳过] 兜底检查，callback已被调用过");
             }
         }
         if (task.getFaceId() != null) {