路径写入数据库

.idea/Video.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="video" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="video" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="video" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
 </project>

config.ini

@@ -13,7 +13,3 @@ charset = utf8mb4
 secret_key = 6tsieyd87wefdw2wgeduwte23rfcsd
 algorithm = HS256
 access_token_expire_minutes = 30
-
-[live]
-rtmp_url = rtmp://192.168.110.65:1935/live/
-webrtc_url = http://192.168.110.65:1985/rtc/v1/whep/?app=live&stream=

core/all.py

@@ -0,0 +1,106 @@
+import cv2
+import numpy as np
+from PIL.Image import Image
+
+from core.establish import get_image_save_path
+from core.ocr import load_model as ocrLoadModel, detect as ocrDetect
+from core.face import load_model as faceLoadModel, detect as faceDetect
+from core.yolo import load_model as yoloLoadModel, detect as yoloDetect
+# 导入保存路径函数（根据实际文件位置调整导入路径）
+import numpy as np
+import base64
+from io import BytesIO
+from PIL import Image
+from ds.db import db
+from mysql.connector import Error as MySQLError
+
+# 模型加载状态标记（避免重复加载）
+
+
+_model_loaded = False
+
+
+def load_model():
+    """加载所有检测模型（仅首次调用时执行）"""
+    global _model_loaded
+    if _model_loaded:
+        print("模型已加载，无需重复执行")
+        return
+
+    # 依次加载OCR、人脸、YOLO模型
+    ocrLoadModel()
+    faceLoadModel()
+    yoloLoadModel()
+
+    _model_loaded = True
+    print("所有检测模型加载完成")
+
+
+def save_db(model_type, client_ip, result):
+    conn = None
+    cursor = None
+    try:
+        # 连接数据库
+        conn = db.get_connection()
+        # 往表插入数据
+        cursor = conn.cursor(dictionary=True)  # 返回字典格式结果
+        insert_query = """
+            INSERT INTO device_danger (client_ip, type, result)
+            VALUES (%s, %s, %s)
+        """
+        cursor.execute(insert_query, (client_ip, model_type, result))
+        conn.commit()
+    except MySQLError as e:
+        raise Exception(f"获取设备列表失败: {str(e)}") from e
+    finally:
+        db.close_connection(conn, cursor)
+
+
+
+def detect(client_ip, frame):
+    """
+    执行模型检测，检测到违规时按指定格式保存图片
+    参数：
+        frame: 待检测的图像帧（OpenCV格式，numpy.ndarray类型）
+    返回：
+        (检测结果布尔值, 检测详情, 检测模型类型)
+    """
+    # 1. YOLO检测（优先级1）
+    yolo_flag, yolo_result = yoloDetect(frame)
+    print(f"YOLO检测结果：{yolo_result}")
+    if yolo_flag:
+        full_save_path, display_path = get_image_save_path(model_type="yolo", client_ip=client_ip)
+        if full_save_path:  # 只判断完整路径是否有效（用于保存）
+            cv2.imwrite(full_save_path, frame)
+            # 打印时使用「显示用短路径」，符合需求格式
+            print(f"✅ YOLO违规图片已保存：{display_path}")
+        save_db(model_type="yolo", client_ip=client_ip, result=str(full_save_path))
+        return (True, yolo_result, "yolo")
+    #
+    # # 2. 人脸检测（优先级2）
+    face_flag, face_result = faceDetect(frame)
+    print(f"人脸检测结果：{face_result}")
+    if face_flag:
+        full_save_path, display_path = get_image_save_path(model_type="yolo", client_ip=client_ip)
+        if full_save_path:  # 只判断完整路径是否有效（用于保存）
+            cv2.imwrite(full_save_path, frame)
+            # 打印时使用「显示用短路径」，符合需求格式
+            print(f"✅ face违规图片已保存：{display_path}")
+        save_db(model_type="face", client_ip=client_ip, result=str(full_save_path))
+        return (True, face_result, "face")
+
+    # 3. OCR检测（优先级3）
+    ocr_flag, ocr_result = ocrDetect(frame)
+    print(f"OCR检测结果：{ocr_result}")
+    if ocr_flag:
+        # 解构元组，保存用完整路径，打印用短路径
+        full_save_path, display_path = get_image_save_path(model_type="yolo", client_ip=client_ip)
+        if full_save_path:  # 只判断完整路径是否有效（用于保存）
+            cv2.imwrite(full_save_path, frame)
+            # 打印时使用「显示用短路径」，符合需求格式
+            print(f"✅ ocr违规图片已保存：{display_path}")
+        save_db(model_type="ocr", client_ip=client_ip, result=str(full_save_path))
+        return (True, ocr_result, "ocr")
+    # 4. 无违规内容（不保存图片）
+    print(f"❌ 未检测到任何违规内容，不保存图片")
+    return (False, "未检测到任何内容", "none")

core/establish.py

@@ -0,0 +1,120 @@
+import os
+import datetime
+from pathlib import Path
+
+from service.device_service import get_unique_client_ips
+def create_directory_structure():
+    """创建项目所需的目录结构，为所有客户端IP预创建基础目录"""
+    try:
+        # 1. 创建根目录下的resource文件夹（存在则跳过，不覆盖子内容）
+        resource_dir = Path("resource")
+        resource_dir.mkdir(exist_ok=True)
+        print(f"确保resource目录存在: {resource_dir.absolute()}")
+
+        # 2. 在resource下创建dect文件夹
+        dect_dir = resource_dir / "dect"
+        dect_dir.mkdir(exist_ok=True)
+        print(f"确保dect目录存在: {dect_dir.absolute()}")
+
+        # 3. 在dect下创建三个模型文件夹
+        model_dirs = ["ocr", "face", "yolo"]
+        for model in model_dirs:
+            model_dir = dect_dir / model
+            model_dir.mkdir(exist_ok=True)
+            print(f"确保{model}模型目录存在: {model_dir.absolute()}")
+
+        # 4. 调用外部方法获取所有客户端IP地址
+        try:
+            # 调用外部ip_read()方法获取所有客户端IP地址列表
+            all_ip_addresses = get_unique_client_ips()
+
+            # 确保返回的是列表类型
+            if not isinstance(all_ip_addresses, list):
+                all_ip_addresses = [all_ip_addresses]
+
+            # 过滤有效IP（去除空字符串和空格）
+            valid_ips = [ip.strip() for ip in all_ip_addresses if ip.strip()]
+
+            if not valid_ips:
+                print("警告: 未获取到有效的客户端IP地址")
+                return
+
+            print(f"获取到的所有客户端IP地址: {valid_ips}")
+
+            # 5. 获取当前日期（年、月）
+            now = datetime.datetime.now()
+            current_year = str(now.year)
+            current_month = str(now.month)
+
+            # 6. 为每个客户端IP在每个模型文件夹下创建年->月的基础目录结构
+            for ip in valid_ips:
+                # 处理IP地址中的特殊字符（将.替换为_，避免路径问题）
+                safe_ip = ip.replace(".", "_")
+
+                for model in model_dirs:
+                    # 构建路径: resource/dect/{model}/{safe_ip}/{year}/{month}
+                    ip_dir = dect_dir / model / safe_ip
+                    year_dir = ip_dir / current_year
+                    month_dir = year_dir / current_month
+
+                    # 递归创建目录（存在则跳过，不覆盖）
+                    month_dir.mkdir(parents=True, exist_ok=True)
+                    print(f"为客户端IP {ip} 创建/确保目录存在: {month_dir.absolute()}")
+
+        except Exception as e:
+            print(f"处理客户端IP和日期目录时发生错误: {str(e)}")
+
+    except Exception as e:
+        print(f"创建基础目录结构时发生错误: {str(e)}")
+
+
+def get_image_save_path(model_type: str, client_ip: str) -> tuple:
+    """
+    获取图片保存的「完整路径」和「显示用短路径」
+
+    参数:
+        model_type: 模型类型，应为"ocr"、"face"或"yolo"
+        client_ip: 检测到违禁的客户端IP地址（原始格式，如192.168.1.101）
+
+    返回:
+        元组 (完整保存路径, 显示用短路径)；若出错则返回 ("", "")
+    """
+    try:
+        # 1. 验证客户端IP有效性（检查是否在已知IP列表中）
+        all_ip_addresses = get_unique_client_ips()
+        if not isinstance(all_ip_addresses, list):
+            all_ip_addresses = [all_ip_addresses]
+        valid_ips = [ip.strip() for ip in all_ip_addresses if ip.strip()]
+
+        if client_ip.strip() not in valid_ips:
+            raise ValueError(f"客户端IP {client_ip} 不在已知IP列表中，无法保存文件")
+
+        # 2. 处理IP地址（与目录创建逻辑一致，将.替换为_）
+        safe_ip = client_ip.strip().replace(".", "_")
+
+        # 3. 获取当前日期和毫秒级时间戳（确保文件名唯一）
+        now = datetime.datetime.now()
+        current_year = str(now.year)
+        current_month = str(now.month)
+        current_day = str(now.day)
+        # 时间戳格式：年月日时分秒毫秒（如20250910143050123）
+        timestamp = now.strftime("%Y%m%d%H%M%S%f")[:-3]
+
+        # 4. 定义基础目录（用于生成相对路径）
+        base_dir = Path("resource") / "dect"  # 显示路径会去掉这个前缀
+        # 构建日级目录（完整路径：resource/dect/{model}/{safe_ip}/{年}/{月}/{日}）
+        day_dir = base_dir / model_type / safe_ip / current_year / current_month / current_day
+        day_dir.mkdir(parents=True, exist_ok=True)  # 确保日目录存在
+
+        # 5. 构建唯一文件名
+        image_filename = f"dect_{model_type}_{safe_ip}_{current_year}{current_month}{current_day}_{timestamp}.jpg"
+
+        # 6. 生成完整路径（用于实际保存图片）和显示路径（用于打印）
+        full_path = day_dir / image_filename  # 完整路径：resource/dect/.../xxx.jpg
+        display_path = full_path.relative_to(base_dir)  # 短路径：{model}/.../xxx.jpg（去掉resource/dect）
+
+        return str(full_path), str(display_path)
+
+    except Exception as e:
+        print(f"获取图片保存路径时发生错误: {str(e)}")
+        return "", ""

core/face.py

@@ -0,0 +1,326 @@
+import os
+import numpy as np
+import cv2
+import gc
+import time
+import threading
+from PIL import Image
+from insightface.app import FaceAnalysis
+# 假设service.face_service中get_all_face_name_with_eigenvalue可获取人脸数据
+from service.face_service import get_all_face_name_with_eigenvalue
+
+# GPU状态检查支持
+try:
+    import pynvml
+
+    pynvml.nvmlInit()
+    _nvml_available = True
+except ImportError:
+    print("警告: pynvml库未安装，无法检测GPU状态，默认尝试使用GPU")
+    _nvml_available = False
+
+# 全局人脸引擎与特征库
+_face_app = None
+_known_faces_embeddings = {}  # 姓名 -> 归一化特征值的映射
+_known_faces_names = []  # 已知人脸姓名列表
+
+# GPU使用状态标记
+_using_gpu = False  # 是否使用GPU
+_used_gpu_id = -1  # 使用的GPU ID（-1表示CPU）
+
+# 资源管理变量
+_ref_count = 0  # 引擎引用计数（记录当前使用次数）
+_last_used_time = 0  # 最后一次使用引擎的时间
+_lock = threading.Lock()  # 线程安全锁
+_release_timeout = 8  # 闲置超时时间（秒）
+_is_releasing = False  # 资源释放中标记
+_monitor_thread_running = False  # 监控线程运行标记
+
+# 调试计数器
+_debug_counter = {
+    "engine_created": 0,  # 引擎创建次数
+    "engine_released": 0,  # 引擎释放次数
+    "detection_calls": 0  # 检测函数调用次数
+}
+
+
+def check_gpu_availability(gpu_id, memory_threshold=0.7):
+    """检查指定GPU的内存使用率是否低于阈值（判定为“可用”）"""
+    if not _nvml_available:
+        return True
+    try:
+        handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
+        mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+        memory_usage = mem_info.used / mem_info.total
+        return memory_usage < memory_threshold
+    except Exception as e:
+        print(f"检查GPU {gpu_id} 状态失败: {e}")
+        return False
+
+
+def select_best_gpu(preferred_gpus=[0, 1]):
+    """按优先级选择可用GPU，优先0号；均不可用则返回-1（CPU）"""
+    for gpu_id in preferred_gpus:
+        try:
+            # 验证GPU是否存在
+            if _nvml_available:
+                pynvml.nvmlDeviceGetHandleByIndex(gpu_id)
+            # 验证GPU内存是否充足
+            if check_gpu_availability(gpu_id):
+                print(f"GPU {gpu_id} 可用，将使用该GPU")
+                return gpu_id
+            else:
+                if gpu_id == 0:
+                    print("GPU 0 内存使用率过高，尝试其他GPU")
+        except Exception as e:
+            print(f"GPU {gpu_id} 不可用或访问失败: {e}")
+    print("所有指定GPU均不可用，将使用CPU计算")
+    return -1
+
+
+def _release_engine_resources():
+    """释放人脸引擎的所有资源（模型、特征库、GPU缓存等）"""
+    global _face_app, _is_releasing, _known_faces_embeddings, _known_faces_names
+    if not _face_app or _is_releasing:
+        return
+
+    try:
+        _is_releasing = True
+        print("开始释放人脸引擎资源...")
+
+        # 释放InsightFace模型资源
+        if hasattr(_face_app, "model"):
+            _face_app.model = None  # 显式置空模型引用
+        _face_app = None  # 释放引擎实例
+
+        # 清空人脸特征库
+        _known_faces_embeddings.clear()
+        _known_faces_names.clear()
+
+        _debug_counter["engine_released"] += 1
+        print(f"人脸引擎已释放，调试统计: {_debug_counter}")
+
+        # 强制垃圾回收
+        gc.collect()
+
+        # 清理各深度学习框架的GPU缓存
+        # Torch 缓存清理
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+                print("Torch GPU缓存已清理")
+        except ImportError:
+            pass
+
+        # TensorFlow 缓存清理
+        try:
+            import tensorflow as tf
+            tf.keras.backend.clear_session()
+            print("TensorFlow会话已清理")
+        except ImportError:
+            pass
+
+        # MXNet 缓存清理（InsightFace底层常用MXNet）
+        try:
+            import mxnet as mx
+            mx.nd.waitall()  # 等待所有计算完成并释放资源
+            print("MXNet资源已等待释放")
+        except ImportError:
+            pass
+
+    except Exception as e:
+        print(f"释放资源过程中出错: {e}")
+    finally:
+        _is_releasing = False
+
+
+def _resource_monitor_thread():
+    """后台监控线程：检测引擎闲置超时，触发资源释放"""
+    global _ref_count, _last_used_time, _face_app, _monitor_thread_running
+    _monitor_thread_running = True
+    while _monitor_thread_running:
+        time.sleep(2)  # 缩短检查间隔，加快闲置检测响应
+        with _lock:
+            # 当“引擎存在 + 无引用 + 未在释放中”时，检查闲置时间
+            if _face_app and _ref_count == 0 and not _is_releasing:
+                idle_time = time.time() - _last_used_time
+                if idle_time > _release_timeout:
+                    print(f"引擎闲置超时（{idle_time:.1f}s > {_release_timeout}s），释放资源")
+                    _release_engine_resources()
+
+
+def load_model(prefer_gpu=True, preferred_gpus=[0, 1]):
+    """加载人脸识别引擎及已知人脸特征库（默认优先用0号GPU）"""
+    global _face_app, _known_faces_embeddings, _known_faces_names, _using_gpu, _used_gpu_id
+
+    # 启动后台监控线程（确保仅启动一次）
+    if not _monitor_thread_running:
+        threading.Thread(
+            target=_resource_monitor_thread,
+            daemon=True,
+            name="FaceEngineMonitor"
+        ).start()
+        print("人脸引擎监控线程已启动")
+
+    # 若正在释放资源，等待释放完成
+    while _is_releasing:
+        time.sleep(0.1)
+
+    # 若引擎已初始化，直接返回
+    if _face_app:
+        return True
+
+    # 初始化InsightFace引擎
+    try:
+        print("正在初始化InsightFace人脸识别引擎...")
+        _face_app = FaceAnalysis(name="buffalo_l", root=os.path.expanduser("~/.insightface"))
+
+        # 选择GPU（优先用0号）
+        ctx_id = 0
+        if prefer_gpu:
+            ctx_id = select_best_gpu(preferred_gpus)
+            _using_gpu = ctx_id != -1
+            _used_gpu_id = ctx_id if _using_gpu else -1
+
+        if _using_gpu:
+            print(f"引擎初始化成功，将使用GPU {ctx_id} 计算")
+        else:
+            print("引擎初始化成功，将使用CPU计算")
+
+        # 准备模型（加载到指定设备）
+        _face_app.prepare(ctx_id=ctx_id, det_size=(640, 640))
+        print("InsightFace引擎初始化完成")
+        _debug_counter["engine_created"] += 1
+        print(f"引擎调试统计: {_debug_counter}")
+
+    except Exception as e:
+        print(f"引擎初始化失败: {e}")
+        return False
+
+    # 从服务加载已知人脸的姓名和特征值
+    try:
+        face_data = get_all_face_name_with_eigenvalue()
+        for person_name, eigenvalue_data in face_data.items():
+            # 兼容“numpy数组”和“字符串”格式的特征值
+            if isinstance(eigenvalue_data, np.ndarray):
+                eigenvalue = eigenvalue_data.astype(np.float32)
+            elif isinstance(eigenvalue_data, str):
+                # 清理字符串中的括号、换行等干扰符
+                cleaned = eigenvalue_data.replace("[", "").replace("]", "").replace("\n", "").strip()
+                # 分割并转换为浮点数数组
+                values = [v for v in cleaned.split() if v]  # 兼容空格/逗号分隔
+                eigenvalue = np.array(list(map(float, values)), dtype=np.float32)
+            else:
+                print(f"不支持的特征值类型（{type(eigenvalue_data)}），跳过 {person_name}")
+                continue
+
+            # 特征值归一化（保证后续相似度计算的一致性）
+            norm = np.linalg.norm(eigenvalue)
+            if norm != 0:
+                eigenvalue = eigenvalue / norm
+
+            _known_faces_embeddings[person_name] = eigenvalue
+            _known_faces_names.append(person_name)
+
+        print(f"成功加载 {len(_known_faces_names)} 个人脸的特征库")
+
+    except Exception as e:
+        print(f"加载人脸特征库失败: {e}")
+
+    return _face_app is not None
+
+
+def detect(frame, similarity_threshold=0.4):
+    """
+    检测并识别人脸
+    返回：(是否匹配到已知人脸, 结果描述字符串)
+    """
+    global _face_app, _known_faces_embeddings, _known_faces_names, _ref_count, _last_used_time
+
+    # 校验输入帧有效性
+    if frame is None or frame.size == 0:
+        return (False, "无效的输入帧数据")
+
+    # 加锁并更新引用计数、最后使用时间
+    engine = None
+    with _lock:
+        _ref_count += 1
+        _last_used_time = time.time()
+        _debug_counter["detection_calls"] += 1
+
+        # 若引擎未初始化且未在释放中，尝试初始化
+        if not _face_app and not _is_releasing:
+            if not load_model(prefer_gpu=True):
+                # 初始化失败，恢复引用计数
+                with _lock:
+                    _ref_count = max(0, _ref_count - 1)
+                return (False, "人脸引擎初始化失败")
+
+        engine = _face_app  # 获取引擎引用
+
+    # 校验引擎可用性
+    if not engine or len(_known_faces_names) == 0:
+        with _lock:
+            _ref_count = max(0, _ref_count - 1)
+        return (False, "人脸引擎不可用或特征库为空")
+
+    try:
+        # GPU计算时，确保帧数据是连续内存（避免CUDA错误）
+        if _using_gpu and engine is not None and not frame.flags.contiguous:
+            frame = np.ascontiguousarray(frame)
+
+        # 执行人脸检测与特征提取
+        faces = engine.get(frame)
+    except Exception as e:
+        print(f"人脸检测过程出错: {e}")
+        # 出错时尝试重新初始化引擎（可能是GPU状态变化导致）
+        print("尝试重新初始化人脸引擎...")
+        with _lock:
+            _ref_count = max(0, _ref_count - 1)
+        load_model(prefer_gpu=True)
+        return (False, f"检测错误: {str(e)}")
+
+    result_parts = []
+    has_matched_known_face = False  # 是否有任意人脸匹配到已知库
+
+    for face in faces:
+        # 归一化当前检测到的人脸特征
+        face_embedding = face.embedding.astype(np.float32)
+        norm = np.linalg.norm(face_embedding)
+        if norm == 0:
+            continue
+        face_embedding = face_embedding / norm
+
+        # 与已知人脸特征逐一比对
+        max_similarity, best_match_name = -1.0, "Unknown"
+        for name in _known_faces_names:
+            known_emb = _known_faces_embeddings[name]
+            similarity = np.dot(face_embedding, known_emb)  # 余弦相似度
+            if similarity > max_similarity:
+                max_similarity = similarity
+                best_match_name = name
+
+        # 判断是否匹配成功
+        is_matched = max_similarity >= similarity_threshold
+        if is_matched:
+            has_matched_known_face = True
+
+        # 记录该人脸的检测结果
+        bbox = face.bbox  # 人脸边界框
+        result_parts.append(
+            f"{'匹配' if is_matched else '未匹配'}: {best_match_name} "
+            f"(相似度: {max_similarity:.2f}, 边界框: {bbox.astype(int).tolist()})"
+        )
+
+    # 构建最终结果字符串
+    result_str = "未检测到人脸" if not result_parts else "; ".join(result_parts)
+
+    # 释放引用计数（线程安全）
+    with _lock:
+        _ref_count = max(0, _ref_count - 1)
+        # 若仍有引用，更新最后使用时间；若引用为0，也立即标记（加快闲置检测）
+        _last_used_time = time.time()
+
+    return (has_matched_known_face, result_str)

core/ocr.py

@@ -0,0 +1,253 @@
+import os
+import cv2
+import gc
+import time
+import threading
+import numpy as np
+from paddleocr import PaddleOCR
+from service.sensitive_service import get_all_sensitive_words
+
+# 解决NumPy 1.20+版本中np.int已移除的兼容性问题
+try:
+    if not hasattr(np, 'int'):
+        np.int = int
+except Exception as e:
+    print(f"处理NumPy兼容性时出错: {e}")
+
+# 全局变量
+_ocr_engine = None
+_forbidden_words = set()
+_conf_threshold = 0.5
+
+# 资源管理变量
+_ref_count = 0
+_last_used_time = 0
+_lock = threading.Lock()
+_release_timeout = 5  # 30秒无使用则释放
+_is_releasing = False  # 标记是否正在释放
+
+# 并行处理配置
+_max_workers = 4  # 并行处理的线程数
+
+# 调试用计数器
+_debug_counter = {
+    "created": 0,
+    "released": 0,
+    "detected": 0
+}
+
+
+def _release_engine():
+    """释放OCR引擎资源"""
+    global _ocr_engine, _is_releasing
+    if not _ocr_engine or _is_releasing:
+        return
+
+    try:
+        _is_releasing = True
+        _ocr_engine = None
+        _debug_counter["released"] += 1
+        print(f"OCR engine released. Stats: {_debug_counter}")
+
+        # 清理GPU缓存
+        gc.collect()
+        try:
+            import torch
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+                torch.cuda.ipc_collect()
+        except ImportError:
+            pass
+        try:
+            import paddle
+            if paddle.is_compiled_with_cuda():
+                paddle.device.cuda.empty_cache()
+        except ImportError:
+            pass
+    finally:
+        _is_releasing = False
+
+
+def _monitor_thread():
+    """监控线程，优化检查逻辑"""
+    global _ref_count, _last_used_time, _ocr_engine
+    while True:
+        time.sleep(5)  # 每5秒检查一次
+        with _lock:
+            if _ocr_engine and _ref_count == 0 and not _is_releasing:
+                elapsed = time.time() - _last_used_time
+                if elapsed > _release_timeout:
+                    print(f"Idle timeout ({elapsed:.1f}s > {_release_timeout}s), releasing engine")
+                    _release_engine()
+
+
+def load_model():
+    """加载违禁词列表和初始化监控线程"""
+    global _forbidden_words
+
+    # 确保监控线程只启动一次
+    if not any(t.name == "OCRMonitor" for t in threading.enumerate()):
+        threading.Thread(target=_monitor_thread, daemon=True, name="OCRMonitor").start()
+        print("OCR monitor thread started")
+
+    # 加载违禁词
+    try:
+        _forbidden_words = get_all_sensitive_words()
+        print(f"Loaded {len(_forbidden_words)} forbidden words")
+    except Exception as e:
+        print(f"Forbidden words load error: {e}")
+        return False
+
+    return True
+
+
+def detect(frame):
+    """OCR检测，支持并行处理"""
+    global _ocr_engine, _forbidden_words, _conf_threshold, _ref_count, _last_used_time, _max_workers
+
+    # 验证前置条件
+    if not _forbidden_words:
+        return (False, "违禁词未初始化")
+    if frame is None or frame.size == 0:
+        return (False, "无效帧数据")
+
+    # 增加引用计数并获取引擎实例
+    engine = None
+    with _lock:
+        _ref_count += 1
+        _last_used_time = time.time()
+        _debug_counter["detected"] += 1
+
+        # 初始化引擎（如果未初始化且不在释放中）
+        if not _ocr_engine and not _is_releasing:
+            try:
+                # 初始化PaddleOCR，设置并行处理参数
+                _ocr_engine = PaddleOCR(
+                    use_angle_cls=True,
+                    lang="ch",
+                    show_log=False,
+                    use_gpu=True,
+                    max_text_length=1024,
+                    threads=_max_workers
+                )
+                _debug_counter["created"] += 1
+                print(f"PaddleOCR engine initialized with {_max_workers} workers. Stats: {_debug_counter}")
+            except Exception as e:
+                print(f"OCR model load failed: {e}")
+                _ref_count -= 1
+                return (False, f"引擎初始化失败: {str(e)}")
+
+        engine = _ocr_engine
+
+    # 检查引擎是否可用
+    if not engine:
+        with _lock:
+            _ref_count -= 1
+        return (False, "OCR引擎不可用")
+
+    try:
+        # 执行OCR检测
+        ocr_res = engine.ocr(frame, cls=True)
+
+        # 验证OCR结果格式
+        if not ocr_res or not isinstance(ocr_res, list):
+            return (False, "无OCR结果")
+
+        # 处理OCR结果 - 兼容多种格式
+        texts = []
+        confs = []
+        for line in ocr_res:
+            if line is None:
+                continue
+
+            # 处理line可能是列表或直接是文本信息的情况
+            if isinstance(line, list):
+                items_to_process = line
+            else:
+                items_to_process = [line]
+
+            for item in items_to_process:
+                # 精确识别并忽略图片坐标位置信息 [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
+                if isinstance(item, list) and len(item) == 4:  # 四边形有4个顶点
+                    is_coordinate = True
+                    for point in item:
+                        # 每个顶点应该是包含2个数字的列表
+                        if not (isinstance(point, list) and len(point) == 2 and
+                                all(isinstance(coord, (int, float)) for coord in point)):
+                            is_coordinate = False
+                            break
+                    if is_coordinate:
+                        continue  # 是坐标信息，直接忽略
+
+                # 跳过纯数字列表（其他可能的坐标形式）
+                if isinstance(item, list) and all(isinstance(x, (int, float)) for x in item):
+                    continue
+
+                # 处理元组形式的文本和置信度 (text, confidence)
+                if isinstance(item, tuple) and len(item) == 2:
+                    text, conf = item
+                    if isinstance(text, str) and isinstance(conf, (int, float)):
+                        texts.append(text.strip())
+                        confs.append(float(conf))
+                        continue
+
+                # 处理列表形式的[坐标信息, (text, confidence)]
+                if isinstance(item, list) and len(item) >= 2:
+                    # 尝试从列表中提取文本和置信度
+                    text_data = item[1]
+                    if isinstance(text_data, tuple) and len(text_data) == 2:
+                        text, conf = text_data
+                        if isinstance(text, str) and isinstance(conf, (int, float)):
+                            texts.append(text.strip())
+                            confs.append(float(conf))
+                            continue
+                    elif isinstance(text_data, str):
+                        # 只有文本没有置信度的情况
+                        texts.append(text_data.strip())
+                        confs.append(1.0)  # 默认最高置信度
+                        continue
+
+                # 无法识别的格式，记录日志
+                print(f"无法解析的OCR结果格式: {item}")
+
+        if len(texts) != len(confs):
+            return (False, "OCR结果格式异常")
+
+        # 筛选违禁词
+        vio_info = []
+        for txt, conf in zip(texts, confs):
+            if conf < _conf_threshold:
+                continue
+            matched = [w for w in _forbidden_words if w in txt]
+            if matched:
+                vio_info.append(f"文本: '{txt}' 包含违禁词: {', '.join(matched)} (置信度: {conf:.2f})")
+
+        # 构建结果
+        has_text = len(texts) > 0
+        has_violation = len(vio_info) > 0
+
+        if not has_text:
+            return (False, "未识别到文本")
+        elif has_violation:
+            return (True, "; ".join(vio_info))
+        else:
+            return (False, "未检测到违禁词")
+
+    except Exception as e:
+        print(f"OCR detect error: {e}")
+        return (False, f"检测错误: {str(e)}")
+
+    finally:
+        # 减少引用计数，确保线程安全
+        with _lock:
+            _ref_count = max(0, _ref_count - 1)
+            if _ref_count > 0:
+                _last_used_time = time.time()
+
+
+def batch_detect(frames):
+    """批量检测接口，充分利用并行能力"""
+    results = []
+    for frame in frames:
+        results.append(detect(frame))
+    return results

core/rtc.py

@@ -1,137 +0,0 @@
-import asyncio
-import logging
-from aiortc import RTCPeerConnection, RTCSessionDescription
-import aiohttp
-from ocr.ocr_violation_detector import OCRViolationDetector
-
-import logging
-
-# 创建检测器实例
-detector = OCRViolationDetector(
-        forbidden_words_path=r"D:\Git\bin\video\ocr\forbidden_words.txt",
-        ocr_confidence_threshold=0.7,
-        log_level=logging.INFO,
-        log_file="ocr_detection.log"
-)
-
-# 配置日志
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("whep_video_puller")
-
-
-async def whep_pull_video_stream(ip,whep_url):
-    """
-    通过WHEP从指定URL拉取视频流并在收到每一帧时打印消息
-
-    Args:
-        whep_url: WHEP端点的URL
-    """
-    pc = RTCPeerConnection()
-
-    # 添加连接状态变化监听
-    @pc.on("connectionstatechange")
-    async def on_connectionstatechange():
-        print(f"连接状态: {pc.connectionState}")
-
-    # 添加ICE连接状态变化监听
-    @pc.on("iceconnectionstatechange")
-    async def on_iceconnectionstatechange():
-        print(f"ICE连接状态: {pc.iceConnectionState}")
-
-    # 添加视频接收器
-    pc.addTransceiver("video", direction="recvonly")
-
-    # 处理接收到的视频轨道
-    @pc.on("track")
-    def on_track(track):
-        print(f"接收到轨道: {track.kind}")
-        if track.kind == "video":
-            print(f"轨道ID: {track.id}")
-            print(f"轨道就绪状态: {track.readyState}")
-            # 创建异步任务来处理视频帧
-            asyncio.ensure_future(handle_video_track(track))
-
-    async def handle_video_track(track):
-        """处理视频轨道，接收并打印每一帧"""
-        frame_count = 0
-        print("开始处理视频轨道...")
-
-        while True:
-            try:
-                # 尝试接收帧
-                frame = await track.recv()
-                frame_count += 1
-                print(f"收到原始帧 (第{frame_count}帧)")
-
-                # 打印帧的基本信息
-                if hasattr(frame, 'width') and hasattr(frame, 'height'):
-                    print(f"  尺寸: {frame.width}x{frame.height}")
-                if hasattr(frame, 'time_base'):
-                    print(f"  时间基准: {frame.time_base}")
-                if hasattr(frame, 'pts'):
-                    print(f"  显示时间戳: {frame.pts}")
-
-                has_violation, violations, confidences = OCRViolationDetector.detect(frame)
-
-                # 输出检测结果
-                if has_violation:
-                    detector.logger.info(f"在图片中检测到 {len(violations)} 个违禁词:")
-                    for word, conf in zip(violations, confidences):
-                        detector.logger.info(f"- {word} (置信度: {conf:.4f})")
-                else:
-                    detector.logger.info("图片中未检测到违禁词")
-            except Exception as e:
-                print(f"接收帧时出错: {e}")
-                # 等待一段时间后重试
-                await asyncio.sleep(0.1)
-                continue
-
-    # 创建offer
-    offer = await pc.createOffer()
-    await pc.setLocalDescription(offer)
-
-    print(f"本地SDP信息:\n{offer.sdp}")
-
-    # 通过HTTP POST发送offer到WHEP端点
-    async with aiohttp.ClientSession() as session:
-        async with session.post(
-                whep_url,
-                data=offer.sdp,
-                headers={"Content-Type": "application/sdp"}
-        ) as response:
-            if response.status != 201:
-                print(f"WHEP服务器返回错误: {response.status}")
-                print(f"响应内容: {await response.text()}")
-                raise Exception(f"WHEP服务器返回错误: {response.status}")
-
-            # 获取answer SDP
-            answer_sdp = await response.text()
-
-            # 创建RTCSessionDescription对象
-            answer = RTCSessionDescription(sdp=answer_sdp, type="answer")
-
-            print(f"收到远程SDP:\n{answer_sdp}")
-
-    # 设置远程描述
-    await pc.setRemoteDescription(answer)
-
-    print("连接已建立，开始接收视频流...")
-
-    # 保持连接，直到用户中断
-    try:
-        while True:
-            await asyncio.sleep(1)
-            # 检查连接状态
-            print(f"当前连接状态: {pc.connectionState}")
-    except KeyboardInterrupt:
-        print("用户中断，关闭连接...")
-    finally:
-        await pc.close()
-
-
-if __name__ == "__main__":
-    # 替换为你的WHEP端点URL
-    WHEP_URL = "http://192.168.110.25:1985/rtc/v1/whep/?app=live&stream=473b95a47e338301cbd96809ea7ac416"
-
-    # 运行拉流任务
-    asyncio.run(whep_pull_video_stream(WHEP_URL))

core/rtmp.py

@@ -1,112 +0,0 @@
-import asyncio
-import logging
-import cv2
-import time
-from ocr.model_violation_detector import MultiModelViolationDetector
-
-
-# 配置文件相对路径（根据实际目录结构调整）
-YOLO_MODEL_PATH = "../ocr/models/best.pt"  # 关键修正：从core目录向上一级找ocr文件夹
-FORBIDDEN_WORDS_PATH = "../ocr/forbidden_words.txt"
-OCR_CONFIG_PATH = "../ocr/config/1.yaml"
-KNOWN_FACES_DIR = "../ocr/known_faces"
-
-# 创建检测器实例
-detector = MultiModelViolationDetector(
-    forbidden_words_path=FORBIDDEN_WORDS_PATH,
-    ocr_config_path=OCR_CONFIG_PATH,
-    yolo_model_path=YOLO_MODEL_PATH,
-    known_faces_dir=KNOWN_FACES_DIR,
-    ocr_confidence_threshold=0.5
-)
-
-# 配置日志
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("rtmp_video_puller")
-
-
-async def rtmp_pull_video_stream(rtmp_url):
-    """
-    通过RTMP从指定URL拉取视频流并进行违规检测
-    """
-    cap = None  # 初始化视频捕获对象
-    try:
-        # 异步打开RTMP流
-        cap = await asyncio.to_thread(
-            cv2.VideoCapture,
-            rtmp_url,
-            cv2.CAP_FFMPEG  # 指定FFmpeg后端确保RTMP兼容性
-        )
-
-        # 检查RTMP流是否成功打开
-        is_opened = await asyncio.to_thread(cap.isOpened)
-        if not is_opened:
-            raise Exception(f"RTMP流打开失败: {rtmp_url}（请检查URL有效性和FFmpeg环境）")
-
-        # 获取RTMP流基础信息
-        width = await asyncio.to_thread(cap.get, cv2.CAP_PROP_FRAME_WIDTH)
-        height = await asyncio.to_thread(cap.get, cv2.CAP_PROP_FRAME_HEIGHT)
-        fps = await asyncio.to_thread(cap.get, cv2.CAP_PROP_FPS)
-
-        # 处理异常情况
-        fps = fps if fps > 0 else 30.0
-        width, height = int(width), int(height)
-
-        # 打印流初始化成功信息
-        print(f"RTMP流状态: 已成功连接")
-        print(f"流基础信息: 分辨率 {width}x{height} | 配置帧率 {fps:.2f} FPS")
-        print("开始接收视频帧...（按 Ctrl+C 中断）")
-
-        # 初始化帧统计参数
-        frame_count = 0
-        start_time = time.time()
-
-        # 循环读取视频帧
-        while True:
-            ret, frame = await asyncio.to_thread(cap.read)
-
-            if not ret:
-                print(f"RTMP流状态: 帧读取失败（可能流已中断或结束）")
-                break
-
-            frame_count += 1
-
-            # 打印当前帧信息
-            print(f"收到帧 (第{frame_count}帧)")
-            print(f"  帧尺寸: {width}x{height}")
-            print(f"  配置帧率: {fps:.2f} FPS")
-
-            if frame is not None:
-                has_violation, violation_type, details = detector.detect_violations(frame)
-                if has_violation:
-                    print(f"检测到违规 - 类型: {violation_type}, 详情: {details}")
-                else:
-                    print("未检测到任何违规内容")
-            else:
-                print(f"无法读取测试图像")
-
-            # 每100帧统计一次实际接收帧率
-            if frame_count % 100 == 0:
-                elapsed_time = time.time() - start_time
-                actual_fps = frame_count / elapsed_time
-                print(f"---- 帧统计: 累计{frame_count}帧 | 实际平均帧率 {actual_fps:.2f} FPS ----")
-
-    except KeyboardInterrupt:
-        print(f"\n用户操作: 已通过 Ctrl+C 中断程序")
-    except Exception as e:
-        logger.error(f"RTMP流处理异常: {str(e)}", exc_info=True)
-        print(f"错误信息: {str(e)}")
-    finally:
-        if cap is not None:
-            await asyncio.to_thread(cap.release)
-            print(f"\n资源释放: RTMP流已关闭")
-            print(f"最终统计: 共接收 {frame_count if 'frame_count' in locals() else 0} 帧")
-
-
-if __name__ == "__main__":
-    RTMP_URL = "rtmp://192.168.110.25:1935/live/473b95a47e338301cbd96809ea7ac416"
-
-    try:
-        asyncio.run(rtmp_pull_video_stream(RTMP_URL))
-    except Exception as e:
-        print(f"程序启动失败: {str(e)}")

core/yolo.py

@@ -0,0 +1,54 @@
+import os
+
+from ultralytics import YOLO
+
+# 全局变量
+_yolo_model = None
+
+
+model_path = os.path.join(os.path.dirname(__file__), "models", "best.pt")
+
+
+def load_model():
+    """加载YOLO目标检测模型"""
+    global _yolo_model
+
+    try:
+        _yolo_model = YOLO(model_path)
+    except Exception as e:
+        print(f"YOLO model load failed: {e}")
+        return False
+
+    return True if _yolo_model else False
+
+
+def detect(frame, conf_threshold=0.2):
+    """YOLO目标检测、返回(是否识别到, 结果字符串)"""
+    global _yolo_model
+
+    if not _yolo_model or frame is None:
+        return (False, "未初始化或无效帧")
+
+    try:
+        results = _yolo_model(frame, conf=conf_threshold)
+        # 检查是否有检测结果
+        has_results = len(results[0].boxes) > 0 if results else False
+
+        if not has_results:
+            return (False, "未检测到目标")
+
+        # 构建结果字符串
+        result_parts = []
+        for box in results[0].boxes:
+            cls = int(box.cls[0])
+            conf = float(box.conf[0])
+            bbox = [float(x) for x in box.xyxy[0]]
+            class_name = _yolo_model.names[cls] if hasattr(_yolo_model, 'names') else f"类别{cls}"
+            result_parts.append(f"{class_name} (置信度: {conf:.2f}, 边界框: {bbox})")
+
+        result_str = "; ".join(result_parts)
+        return (has_results, result_str)
+
+    except Exception as e:
+        print(f"YOLO detect error: {e}")
+        return (False, f"检测错误: {str(e)}")

ds/config.py

@@ -14,4 +14,3 @@ config.read(config_path, encoding="utf-8")
 SERVER_CONFIG = config["server"]
 MYSQL_CONFIG = config["mysql"]
 JWT_CONFIG = config["jwt"]
-LIVE_CONFIG = config["live"]

main.py

@@ -1,11 +1,18 @@
-import uvicorn
-from fastapi import FastAPI
+from PIL import Image  # 正确导入
+import numpy as np
 
+import uvicorn
+from PIL import Image
+from fastapi import FastAPI
+from core.all import load_model,detect
 from ds.config import SERVER_CONFIG
 from middle.error_handler import global_exception_handler
 from service.user_service import router as user_router
+from service.sensitive_service import router as sensitive_router
+from service.face_service import router as face_router
 from service.device_service import router as device_router
 from ws.ws import ws_router, lifespan
+from core.establish import create_directory_structure
 
 # ------------------------------
 # 初始化 FastAPI 应用、指定生命周期管理
@@ -22,6 +29,8 @@ app = FastAPI(
 # ------------------------------
 app.include_router(user_router)
 app.include_router(device_router)
+app.include_router(face_router)
+app.include_router(sensitive_router)
 app.include_router(ws_router)
 
 # ------------------------------
@@ -33,11 +42,21 @@ app.add_exception_handler(Exception, global_exception_handler)
 # 启动服务
 # ------------------------------
 if __name__ == "__main__":
+    # -------------------------- 配置调整 --------------------------
+    # 模型配置路径（建议改为环境变量）
+    YOLO_MODEL_PATH = r"/core/models\best.pt"
+    OCR_CONFIG_PATH = r"/core/config\config.yaml"
+
+    create_directory_structure()
+
+    # 初始化项目（默认端口设为8000、避免初始化失败时port未定义）
     port = int(SERVER_CONFIG.get("port", 8000))
+
+    # 启动 UVicorn 服务
     uvicorn.run(
         app="main:app",
         host="0.0.0.0",
         port=port,
-        reload=True,
+        workers=8,
         ws="websockets"
     )

middle/auth_middleware.py

@@ -8,7 +8,6 @@ from passlib.context import CryptContext
 
 from ds.config import JWT_CONFIG
 from ds.db import db
-from service.user_service import UserResponse
 
 # ------------------------------
 # 密码加密配置
@@ -22,9 +21,10 @@ SECRET_KEY = JWT_CONFIG["secret_key"]
 ALGORITHM = JWT_CONFIG["algorithm"]
 ACCESS_TOKEN_EXPIRE_MINUTES = int(JWT_CONFIG["access_token_expire_minutes"])
 
-# OAuth2 依赖（从请求头获取 Token、格式：Bearer <token>）
+# OAuth2 依赖（从请求头获取 Token、格式: Bearer <token>）
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/users/login")
 
+
 # ------------------------------
 # 密码工具函数
 # ------------------------------
@@ -32,10 +32,12 @@ def verify_password(plain_password: str, hashed_password: str) -> bool:
     """验证明文密码与加密密码是否匹配"""
     return pwd_context.verify(plain_password, hashed_password)
 
+
 def get_password_hash(password: str) -> str:
     """对明文密码进行 bcrypt 加密"""
     return pwd_context.hash(password)
 
+
 # ------------------------------
 # JWT 工具函数
 # ------------------------------
@@ -53,11 +55,15 @@ def create_access_token(data: dict, expires_delta: Optional[timedelta] = None) -
     encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
     return encoded_jwt
 
+
 # ------------------------------
 # 认证依赖（获取当前登录用户）
 # ------------------------------
-def get_current_user(token: str = Depends(oauth2_scheme)) -> UserResponse:
+def get_current_user(token: str = Depends(oauth2_scheme)):  # 移除返回类型注解
     """从 Token 中解析用户信息、验证通过后返回当前用户"""
+    # 延迟导入、打破循环依赖
+    from schema.user_schema import UserResponse  # 在这里导入
+
     # 认证失败异常
     credentials_exception = HTTPException(
         status_code=status.HTTP_401_UNAUTHORIZED,

middle/error_handler.py

@@ -8,7 +8,7 @@ from schema.response_schema import APIResponse
 
 
 async def global_exception_handler(request: Request, exc: Exception):
-    """全局异常处理器：所有未捕获的异常都会在这里统一处理"""
+    """全局异常处理器: 所有未捕获的异常都会在这里统一处理"""
     # 1. 请求参数验证错误（Pydantic 校验失败）
     if isinstance(exc, RequestValidationError):
         error_details = []
@@ -18,7 +18,7 @@ async def global_exception_handler(request: Request, exc: Exception):
             status_code=status.HTTP_400_BAD_REQUEST,
             content=APIResponse(
                 code=400,
-                message=f"请求参数错误：{'; '.join(error_details)}",
+                message=f"请求参数错误: {'; '.join(error_details)}",
                 data=None
             ).model_dump()
         )
@@ -52,7 +52,7 @@ async def global_exception_handler(request: Request, exc: Exception):
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             content=APIResponse(
                 code=500,
-                message=f"数据库错误：{str(exc)}",
+                message=f"数据库错误: {str(exc)}",
                 data=None
             ).model_dump()
         )
@@ -62,7 +62,7 @@ async def global_exception_handler(request: Request, exc: Exception):
         status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
         content=APIResponse(
             code=500,
-            message=f"服务器内部错误：{str(exc)}",
+            message=f"服务器内部错误: {str(exc)}",
             data=None
         ).model_dump()
     )

ocr/face_recognizer.py

@@ -1,139 +0,0 @@
-import os
-import cv2
-import numpy as np
-import insightface
-from insightface.app import FaceAnalysis
-
-
-class FaceRecognizer:
-    """
-    封装InsightFace人脸识别功能，支持从文件夹加载已知人脸。
-    """
-
-    def __init__(self, known_faces_dir: str):
-        self.known_faces_dir = known_faces_dir
-        self.app = self._initialize_insightface()
-        self.known_faces_embeddings = {}
-        self.known_faces_names = []
-        self._load_known_faces()
-
-    def _initialize_insightface(self):
-        """初始化InsightFace FaceAnalysis应用"""
-        print("初始化InsightFace引擎...")
-        try:
-            app = FaceAnalysis(name='buffalo_l', root='~/.insightface')
-            app.prepare(ctx_id=0, det_size=(640, 640))
-            print("InsightFace引擎初始化完成")
-            return app
-        except Exception as e:
-            print(f"InsightFace初始化失败: {e}")
-            print("请检查依赖是否安装及模型是否可访问")
-            return None
-
-    def _load_known_faces(self):
-        """加载已知人脸特征"""
-        if not os.path.exists(self.known_faces_dir):
-            print(f"已知人脸目录不存在，已创建: {self.known_faces_dir}")
-            os.makedirs(self.known_faces_dir, exist_ok=True)
-            return
-
-        print(f"从目录加载人脸特征: {self.known_faces_dir}")
-        for person_name in os.listdir(self.known_faces_dir):
-            person_dir = os.path.join(self.known_faces_dir, person_name)
-            if os.path.isdir(person_dir):
-                print(f"处理人物: {person_name}")
-                embeddings = []
-                for filename in os.listdir(person_dir):
-                    if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
-                        image_path = os.path.join(person_dir, filename)
-                        try:
-                            img = cv2.imread(image_path)
-                            if img is None:
-                                print(f"无法读取图片: {image_path}，已跳过")
-                                continue
-
-                            faces = self.app.get(img)
-                            if faces:
-                                embeddings.append(faces[0].embedding)
-                                print(f"提取特征成功: {filename}")
-                            else:
-                                print(f"未检测到人脸: {filename}，已跳过")
-                        except Exception as e:
-                            print(f"处理图片出错 {image_path}: {e}")
-
-                if embeddings:
-                    self.known_faces_embeddings[person_name] = np.array(embeddings).mean(axis=0)
-                    self.known_faces_names.append(person_name)
-                    print(f"人物 {person_name} 加载完成，共 {len(embeddings)} 张照片")
-                else:
-                    print(f"人物 {person_name} 无有效特征，已跳过")
-        print(f"人脸加载完成，共 {len(self.known_faces_names)} 人")
-
-    def recognize(self, frame, threshold=0.4):
-        """识别人脸并返回结果"""
-        if not self.app or not self.known_faces_names:
-            return False, None, None
-
-        faces = self.app.get(frame)
-        if not faces:
-            return False, None, None
-
-        for face in faces:
-            for known_name in self.known_faces_names:
-                known_embedding = self.known_faces_embeddings[known_name]
-
-                embedding1 = face.embedding.astype(np.float32)
-                embedding2 = known_embedding.astype(np.float32)
-
-                dot_product = np.dot(embedding1, embedding2)
-                norm_embedding1 = np.linalg.norm(embedding1)
-                norm_embedding2 = np.linalg.norm(embedding2)
-
-                similarity = 0.0 if (norm_embedding1 == 0 or norm_embedding2 == 0) else (
-                        dot_product / (norm_embedding1 * norm_embedding2)
-                )
-
-                if similarity >= threshold:
-                    print(f"检测到已知人物: {known_name} (相似度: {similarity:.4f})")
-                    return True, known_name, similarity
-
-        return False, None, None
-
-    def test_single_image(self, image_path: str, threshold=0.4):
-        """测试单张图片识别"""
-        if not os.path.exists(image_path):
-            print(f"图片不存在: {image_path}")
-            return False, None, None
-
-        frame = cv2.imread(image_path)
-        if frame is None:
-            print(f"无法读取图片: {image_path}")
-            return False, None, None
-
-        result, name, similarity = self.recognize(frame, threshold)
-
-        if result:
-            print(f"识别结果: {name} (相似度: {similarity:.4f})")
-
-            faces = self.app.get(frame)
-            for face in faces:
-                bbox = face.bbox.astype(int)
-                cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)
-                text = f"{name}: {similarity:.2f}"
-                cv2.putText(frame, text, (bbox[0], bbox[1] - 10),
-                            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
-
-            cv2.imshow('识别结果', frame)
-            print("按任意键关闭窗口...")
-            cv2.waitKey(0)
-            cv2.destroyAllWindows()
-        else:
-            print("未识别到已知人脸")
-
-        return result, name, similarity
-
-#
-# if __name__ == "__main__":
-#     recognizer = FaceRecognizer(known_faces_dir="known_faces")
-#     test_image_path = r"F:\OCR\RapidOCR-main\known_faces\B\14sino-qiu02-master1050.jpg"
-#     recognizer.test_single_image(test_image_path, threshold=0.4)

ocr/forbidden_words.txt

@@ -1,18 +0,0 @@
-打倒习近平
-打到习近平
-打倒毛泽东
-打到毛泽东
-打到主席
-打倒主席
-打到共产主义
-打倒共产主义
-打到共产党
-打倒共产党
-胖猫
-法轮功
-法轮大法
-法轮大法好
-法轮功大法好
-法轮
-李洪志
-习近平

ocr/logger_config.py

@@ -1,49 +0,0 @@
-#日志文件
-import logging
-import sys
-
-def setup_logger():
-    """
-    配置一个全局日志记录器，支持输出到控制台和文件。
-    """
-    # 创建一个日志记录器
-
-    # 配置日志
-    logging.basicConfig(level=logging.INFO)
-    logger = logging.getLogger(__name__)
-
-    logger = logging.getLogger("ViolationDetectorLogger")
-    logger.setLevel(logging.DEBUG)  # 设置最低级别为DEBUG
-
-    # 如果已经有处理器了，就不要重复添加，防止日志重复打印
-    if logger.hasHandlers():
-        return logger
-
-    # --- 控制台处理器 ---
-    console_handler = logging.StreamHandler(sys.stdout)
-    # 对于控制台，我们只显示INFO及以上级别的信息
-    console_handler.setLevel(logging.INFO)
-    console_formatter = logging.Formatter(
-        '%(asctime)s - %(levelname)s - [%(module)s:%(lineno)d] - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S'
-    )
-    console_handler.setFormatter(console_formatter)
-
-    # --- 文件处理器 ---
-    file_handler = logging.FileHandler("violation_detector.log", mode='a', encoding='utf-8')
-    # 对于文件，我们记录所有DEBUG及以上级别的信息
-    file_handler.setLevel(logging.DEBUG)
-    file_formatter = logging.Formatter(
-        '%(asctime)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S'
-    )
-    file_handler.setFormatter(file_formatter)
-
-    # 将处理器添加到日志记录器
-    logger.addHandler(console_handler)
-    logger.addHandler(file_handler)
-
-    return logger
-
-# 创建并导出logger实例
-logger = setup_logger()

ocr/model_violation_detector.py

@@ -1,136 +0,0 @@
-import os
-
-import cv2
-import yaml
-from pathlib import Path
-from .ocr_violation_detector import OCRViolationDetector
-from .yolo_violation_detector import ViolationDetector as YoloViolationDetector
-from .face_recognizer import FaceRecognizer
-
-class MultiModelViolationDetector:
-    """
-    多模型违规检测封装类，串行调用OCR、人脸识别和YOLO模型，任一模型检测到违规即返回结果
-    """
-
-    def __init__(self,
-                 forbidden_words_path: str,
-                 ocr_config_path: str,
-                 yolo_model_path: str,
-                 known_faces_dir: str,
-                 ocr_confidence_threshold: float = 0.5):
-        """
-        初始化所有检测模型
-        """
-        # 初始化OCR检测器
-        self.ocr_detector = OCRViolationDetector(
-            forbidden_words_path=forbidden_words_path,
-            ocr_config_path=ocr_config_path,
-            ocr_confidence_threshold=ocr_confidence_threshold
-        )
-
-        # 初始化人脸识别器
-        self.face_recognizer = FaceRecognizer(
-            known_faces_dir=known_faces_dir
-        )
-
-        # 初始化YOLO检测器
-        self.yolo_detector = YoloViolationDetector(
-            model_path=yolo_model_path
-        )
-
-        print("多模型违规检测器初始化完成")
-
-    def detect_violations(self, frame):
-        """
-        串行调用三个检测模型（OCR → 人脸识别 → YOLO），任一检测到违规即返回结果
-        """
-        # 1. 首先进行OCR违禁词检测
-        try:
-            ocr_has_violation, ocr_words, ocr_confs = self.ocr_detector.detect(frame)
-            if ocr_has_violation:
-                details = {
-                    "words": ocr_words,
-                    "confidences": ocr_confs
-                }
-                print(f"警告: OCR检测到违禁内容: {details}")
-                return (True, "ocr", details)
-        except Exception as e:
-            print(f"错误: OCR检测出错: {str(e)}")
-
-        # 2. 接着进行人脸识别检测
-        try:
-            face_has_violation, face_name, face_similarity = self.face_recognizer.recognize(frame)
-            if face_has_violation:
-                details = {
-                    "name": face_name,
-                    "similarity": face_similarity
-                }
-                print(f"警告: 人脸识别到违规人员: {details}")
-                return (True, "face", details)
-        except Exception as e:
-            print(f"错误: 人脸识别出错: {str(e)}")
-
-        # 3. 最后进行YOLO目标检测
-        try:
-            yolo_results = self.yolo_detector.detect(frame)
-            if len(yolo_results.boxes) > 0:
-                details = {
-                    "classes": yolo_results.names,
-                    "boxes": yolo_results.boxes.xyxy.tolist(),
-                    "confidences": yolo_results.boxes.conf.tolist(),
-                    "class_ids": yolo_results.boxes.cls.tolist()
-                }
-                print(f"警告: YOLO检测到违规目标: {details}")
-                return (True, "yolo", details)
-        except Exception as e:
-            print(f"错误: YOLO检测出错: {str(e)}")
-
-        # 所有检测均未发现违规
-        return (False, None, None)
-
-
-def load_config(config_path: str) -> dict:
-    """加载YAML配置文件"""
-    try:
-        with open(config_path, 'r', encoding='utf-8') as f:
-            return yaml.safe_load(f)
-    except FileNotFoundError:
-        print(f"错误: 配置文件未找到: {config_path}")
-        raise
-    except yaml.YAMLError as e:
-        print(f"错误: 配置文件格式错误: {config_path}, 错误: {str(e)}")
-        raise
-    except Exception as e:
-        print(f"错误: 加载配置文件出错: {str(e)}")
-        raise
-
-
-# 使用示例
-# if __name__ == "__main__":
-#     # 加载配置文件
-#     config = load_config("config.yaml")  # 配置文件路径，可根据实际情况修改
-#
-#     # 初始化多模型检测器
-#     detector = MultiModelViolationDetector(
-#         forbidden_words_path=config["forbidden_words_path"],
-#         ocr_config_path=config["ocr_config_path"],
-#         yolo_model_path=config["yolo_model_path"],
-#         known_faces_dir=config["known_faces_dir"],
-#         ocr_confidence_threshold=config.get("ocr_confidence_threshold", 0.5)
-#     )
-#
-#     # 读取测试图像（可替换为视频帧读取逻辑）
-#     test_image_path = config.get("test_image_path")  # 从配置文件获取测试图片路径
-#     if test_image_path:
-#         frame = cv2.imread(test_image_path)
-#
-#         if frame is not None:
-#             has_violation, violation_type, details = detector.detect_violations(frame)
-#             if has_violation:
-#                 print(f"检测到违规 - 类型: {violation_type}, 详情: {details}")
-#             else:
-#                 print("未检测到任何违规内容")
-#         else:
-#             print(f"无法读取测试图像: {test_image_path}")
-#     else:
-#         print("配置文件中未指定测试图像路径")

ocr/ocr_violation_detector.py

@@ -1,178 +0,0 @@
-import os
-import cv2
-from rapidocr import RapidOCR
-
-
-class OCRViolationDetector:
-    """
-    封装RapidOCR引擎，用于检测图像帧中的违禁词。
-    核心功能：加载违禁词、初始化OCR引擎、单帧图像违禁词检测
-    """
-
-    def __init__(self,
-                 forbidden_words_path: str,
-                 ocr_config_path: str,
-                 ocr_confidence_threshold: float = 0.5):
-        """
-        初始化OCR引擎和违禁词列表。
-
-        Args:
-            forbidden_words_path (str): 违禁词列表 .txt 文件的路径。
-            ocr_config_path (str): OCR配置文件（如1.yaml）的路径。
-            ocr_confidence_threshold (float): OCR识别结果的置信度阈值（0~1）。
-        """
-        # 加载违禁词
-        self.forbidden_words = self._load_forbidden_words(forbidden_words_path)
-
-        # 初始化RapidOCR引擎
-        self.ocr_engine = self._initialize_ocr(ocr_config_path)
-
-        # 校验核心依赖是否就绪
-        self._check_dependencies()
-
-        # 设置置信度阈值（限制在0~1范围）
-        self.OCR_CONFIDENCE_THRESHOLD = max(0.0, min(ocr_confidence_threshold, 1.0))
-        print(f"OCR置信度阈值已设置（范围0~1）: {self.OCR_CONFIDENCE_THRESHOLD:.4f}")
-
-    def _load_forbidden_words(self, path: str) -> set:
-        """
-        从TXT文件加载违禁词（去重、过滤空行，支持UTF-8编码）
-        """
-        forbidden_words = set()
-
-        # 检查文件是否存在
-        if not os.path.exists(path):
-            print(f"错误：违禁词文件不存在: {path}")
-            return forbidden_words
-
-        # 读取文件并处理内容
-        try:
-            with open(path, 'r', encoding='utf-8') as f:
-                forbidden_words = {
-                    line.strip() for line in f
-                    if line.strip()  # 跳过空行或纯空格行
-                }
-            print(f"成功加载违禁词: {len(forbidden_words)} 个（已去重）")
-        except UnicodeDecodeError:
-            print(f"错误：违禁词文件编码错误（需UTF-8）: {path}")
-        except PermissionError:
-            print(f"错误：无权限读取违禁词文件: {path}")
-        except Exception as e:
-            print(f"错误：加载违禁词失败: {str(e)}")
-
-        return forbidden_words
-
-    def _initialize_ocr(self, config_path: str) -> RapidOCR | None:
-        """
-        初始化RapidOCR引擎（校验配置文件、捕获初始化异常）
-        """
-        print("开始初始化RapidOCR引擎...")
-
-        # 检查配置文件是否存在
-        if not os.path.exists(config_path):
-            print(f"错误：OCR配置文件不存在: {config_path}")
-            return None
-
-        # 初始化OCR引擎
-        try:
-            ocr_engine = RapidOCR(config_path=config_path)
-            print("RapidOCR引擎初始化成功")
-            return ocr_engine
-        except ImportError:
-            print("错误：RapidOCR依赖未安装（需执行：pip install rapidocr-onnxruntime）")
-        except Exception as e:
-            print(f"错误：RapidOCR初始化失败: {str(e)}")
-
-        return None
-
-    def _check_dependencies(self) -> None:
-        """校验OCR引擎和违禁词列表是否就绪"""
-        if not self.ocr_engine:
-            print("警告：⚠️ OCR引擎未就绪，违禁词检测功能将禁用")
-        if not self.forbidden_words:
-            print("警告：⚠️ 违禁词列表为空，违禁词检测功能将禁用")
-
-    def detect(self, frame) -> tuple[bool, list, list]:
-        """
-        对单帧图像进行OCR违禁词检测（核心方法）
-
-        Args:
-            frame: 输入图像帧（NumPy数组，BGR格式，cv2读取的图像）。
-
-        Returns:
-            tuple[bool, list, list]:
-                - 第一个元素：是否检测到违禁词（True/False）；
-                - 第二个元素：检测到的违禁词列表（空列表表示无违禁词）；
-                - 第三个元素：对应违禁词的置信度列表（与违禁词列表一一对应）。
-        """
-        # 初始化返回结果
-        has_violation = False
-        violation_words = []
-        violation_confs = []
-
-        # 前置校验
-        if frame is None or frame.size == 0:
-            print("警告：输入图像帧为空或无效，跳过OCR检测")
-            return has_violation, violation_words, violation_confs
-        if not self.ocr_engine or not self.forbidden_words:
-            print("OCR引擎未就绪或违禁词为空，跳过OCR检测")
-            return has_violation, violation_words, violation_confs
-
-        try:
-            # 执行OCR识别
-            print("开始执行OCR识别...")
-            ocr_result = self.ocr_engine(frame)
-            print(f"RapidOCR原始结果: {ocr_result}")
-
-            # 校验OCR结果是否有效
-            if ocr_result is None:
-                print("OCR识别未返回任何结果（图像无文本或识别失败）")
-                return has_violation, violation_words, violation_confs
-
-            # 检查txts和scores是否存在且不为None
-            if not hasattr(ocr_result, 'txts') or ocr_result.txts is None:
-                print("警告：OCR结果中txts为None或不存在")
-                return has_violation, violation_words, violation_confs
-
-            if not hasattr(ocr_result, 'scores') or ocr_result.scores is None:
-                print("警告：OCR结果中scores为None或不存在")
-                return has_violation, violation_words, violation_confs
-
-            # 转为列表并去None
-            if not isinstance(ocr_result.txts, (list, tuple)):
-                print(f"警告：OCR txts不是可迭代类型，实际类型: {type(ocr_result.txts)}")
-                texts = []
-            else:
-                texts = [txt.strip() for txt in ocr_result.txts if txt and isinstance(txt, str)]
-
-            if not isinstance(ocr_result.scores, (list, tuple)):
-                print(f"警告：OCR scores不是可迭代类型，实际类型: {type(ocr_result.scores)}")
-                confidences = []
-            else:
-                confidences = [conf for conf in ocr_result.scores if conf and isinstance(conf, (int, float))]
-
-            # 校验文本和置信度列表长度是否一致
-            if len(texts) != len(confidences):
-                print(f"警告：OCR文本与置信度数量不匹配（文本{len(texts)}个，置信度{len(confidences)}个），跳过检测")
-                return has_violation, violation_words, violation_confs
-            if len(texts) == 0:
-                print("OCR未识别到任何有效文本")
-                return has_violation, violation_words, violation_confs
-
-            # 遍历识别结果，筛选违禁词
-            print(f"开始筛选违禁词（阈值{self.OCR_CONFIDENCE_THRESHOLD:.4f}）")
-            for text, conf in zip(texts, confidences):
-                if conf < self.OCR_CONFIDENCE_THRESHOLD:
-                    print(f"文本 '{text}' 置信度{conf:.4f} < 阈值，跳过")
-                    continue
-                matched_words = [word for word in self.forbidden_words if word in text]
-                if matched_words:
-                    has_violation = True
-                    violation_words.extend(matched_words)
-                    violation_confs.extend([conf] * len(matched_words))
-                    print(f"警告：检测到违禁词: {matched_words}（来源文本: '{text}'，置信度: {conf:.4f}）")
-
-        except Exception as e:
-            print(f"错误：OCR检测过程异常: {str(e)}")
-
-        return has_violation, violation_words, violation_confs

ocr/yolo_violation_detector.py

@@ -1,47 +0,0 @@
-from ultralytics import YOLO
-import cv2
-
-class ViolationDetector:
-    """
-    用于加载YOLOv8 .pt模型并进行违规内容检测的类。
-    """
-    def __init__(self, model_path):
-        """
-        初始化检测器。
-
-        Args:
-            model_path (str): YOLO .pt模型的路径。
-        """
-        print(f"正在从 '{model_path}' 加载YOLO模型...")
-        self.model = YOLO(model_path)
-        print("YOLO模型加载成功。")
-
-    def detect(self, frame):
-        """
-        对单帧图像进行目标检测。
-
-        Args:
-            frame: 输入的图像帧 (NumPy数组, BGR格式)。
-
-        Returns:
-            ultralytics.engine.results.Results: YOLO的检测结果对象。
-        """
-        # conf可以根据您的模型效果进行调整
-        # --- 为了测试，我们暂时将置信度调低，例如 0.2 ---
-        results = self.model(frame, conf=0.2)
-        return results[0]
-
-    def draw_boxes(self, frame, result):
-        """
-        在图像帧上绘制检测框。
-
-        Args:
-            frame: 原始图像帧。
-            result: YOLO的检测结果对象。
-
-        Returns:
-            numpy.ndarray: 绘制了检测框的图像帧。
-        """
-        # 使用YOLO自带的plot功能，方便快捷
-        annotated_frame = result.plot()
-        return annotated_frame

rtc/rtc.py

@@ -1,164 +0,0 @@
-import queue
-import asyncio
-import aiohttp
-import threading
-import time
-from aiortc import RTCPeerConnection, RTCSessionDescription, RTCConfiguration
-from aiortc.mediastreams import MediaStreamTrack
-
-# 创建一个长度为1的队列，用于生产者和消费者之间的通信
-frame_queue = queue.Queue(maxsize=1)
-
-
-class VideoTrack(MediaStreamTrack):
-    """自定义视频轨道类，继承自MediaStreamTrack"""
-    kind = "video"
-
-    def __init__(self, max_frames=100):
-        super().__init__()
-        self.frames = queue.Queue(maxsize=max_frames)
-
-    async def recv(self):
-        return await super().recv()
-
-
-def webrtc_producer(webrtc_url):
-    """
-    生产者方法：从WEBRTC读取视频帧并放入队列
-    仅当队列空时才放入新帧，否则丢弃
-    """
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-
-    # 创建RTCPeerConnection对象，不使用ICE服务器
-    pc = RTCPeerConnection(RTCConfiguration(iceServers=[]))
-    video_track = VideoTrack()
-    pc.addTrack(video_track)
-
-    @pc.on("track")
-    async def on_track(track):
-        if track.kind == "video":
-            print("接收到视频轨道，开始接收视频帧")
-            while True:
-                # 从轨道接收视频帧
-                frame = await track.recv()
-                # 转换为BGR24格式的NumPy数组
-                frame_bgr24 = frame.to_ndarray(format='bgr24')
-
-                # 检查队列是否为空，为空则加入，否则丢弃
-                if frame_queue.empty():
-                    try:
-                        frame_queue.put_nowait(frame_bgr24)
-                        print("帧已放入队列")
-                    except queue.Full:
-                        print("队列已满，丢弃帧")
-                else:
-                    print("队列非空，丢弃帧")
-
-    async def main():
-        # 创建并发送SDP Offer
-        offer = await pc.createOffer()
-        print("已创建本地SDP Offer")
-        await pc.setLocalDescription(offer)
-
-        # 发送Offer到服务器并接收Answer
-        async with aiohttp.ClientSession() as session:
-            print(f"开始向服务器 {webrtc_url} 发送SDP Offer")
-            async with session.post(
-                    webrtc_url,
-                    data=offer.sdp.encode(),
-                    headers={
-                        "Content-Type": "application/sdp",
-                        "Content-Length": str(len(offer.sdp))
-                    },
-                    ssl=False
-            ) as response:
-                print("已接收到服务器的响应")
-                answer_sdp = await response.text()
-                await pc.setRemoteDescription(RTCSessionDescription(sdp=answer_sdp, type='answer'))
-
-        # 保持连接
-        try:
-            while True:
-                await asyncio.sleep(0.1)
-        except KeyboardInterrupt:
-            pass
-        finally:
-            print("关闭RTCPeerConnection")
-            await pc.close()
-
-    try:
-        loop.run_until_complete(main())
-    finally:
-        loop.close()
-
-
-def frame_consumer(ip):
-    """
-    消费者方法：从队列中读取帧并处理
-    每次处理后休眠200ms模拟延迟
-    """
-    print("消费者启动，开始等待帧...")
-    try:
-        while True:
-            # 阻塞等待队列中的帧
-            frame = frame_queue.get()
-            print(f"消费帧，大小: {frame.shape}")
-
-            has_violation, violations, confidences = OCRViolationDetector.detect(frame)
-
-
-            # 输出检测结果
-            if has_violation:
-                detector.logger.info(f"在图片中检测到 {len(violations)} 个违禁词:")
-                for word, conf in zip(violations, confidences):
-                    detector.logger.info(f"- {word} (置信度: {conf:.4f})")
-            else:
-                    detector.logger.info("图片中未检测到违禁词")
-
-
-            # 标记任务完成
-            frame_queue.task_done()
-    except KeyboardInterrupt:
-        print("消费者退出")
-
-
-def start_webrtc_stream(ip, webrtc_url):
-    """
-    启动WebRTC视频流处理的主方法
-    参数: webrtc_url - WebRTC服务器地址
-    """
-    print(f"开始连接到WebRTC服务器: {webrtc_url}")
-
-    # 启动生产者线程
-    producer_thread = threading.Thread(
-        target=webrtc_producer,
-        args=(webrtc_url,),
-        daemon=True,
-        name="webrtc-producer"
-    )
-
-    # 启动消费者线程
-    consumer_thread = threading.Thread(
-        target=frame_consumer(ip),
-        daemon=True,
-        name="frame-consumer"
-    )
-
-    producer_thread.start()
-    consumer_thread.start()
-    print("生产者和消费者线程已启动")
-
-    try:
-        # 保持主线程运行
-        while True:
-            time.sleep(1)
-    except KeyboardInterrupt:
-        print("程序正在退出...")
-
-
-if __name__ == "__main__":
-    # 示例用法
-    # 实际使用时替换为真实的WebRTC服务器地址
-    webrtc_server_url = "http://192.168.110.65:1985/rtc/v1/whep/?app=live&stream=677a4845aa48cb8526c811ad56fc5e60"
-    start_webrtc_stream(webrtc_server_url)

rtmp/rtmp.py

@@ -1,101 +0,0 @@
-import asyncio
-import logging
-import cv2
-import time
-
-# 配置日志（与WHEP代码保持一致的日志风格）
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("rtmp_video_puller")
-
-
-async def rtmp_pull_video_stream(rtmp_url):
-    """
-    通过RTMP从指定URL拉取视频流并在收到每一帧时打印消息
-    功能与WHEP拉流函数对齐：流状态反馈、帧信息打印、帧率统计、异常处理
-
-    Args:
-        rtmp_url: RTMP流的URL地址（如 rtmp://xxx/live/stream_key）
-    """
-    cap = None  # 初始化视频捕获对象
-    try:
-        # 1. 异步打开RTMP流（指定FFmpeg后端确保RTMP兼容性，同步操作通过to_thread避免阻塞事件循环）
-        cap = await asyncio.to_thread(
-            cv2.VideoCapture,
-            rtmp_url,
-            cv2.CAP_FFMPEG  # 必须指定FFmpeg后端，RTMP协议依赖该后端解析
-        )
-
-        # 2. 检查RTMP流是否成功打开
-        is_opened = await asyncio.to_thread(cap.isOpened)
-        if not is_opened:
-            raise Exception(f"RTMP流打开失败: {rtmp_url}（请检查URL有效性和FFmpeg环境）")
-
-        # 3. 异步获取RTMP流基础信息（分辨率、帧率）
-        width = await asyncio.to_thread(cap.get, cv2.CAP_PROP_FRAME_WIDTH)
-        height = await asyncio.to_thread(cap.get, cv2.CAP_PROP_FRAME_HEIGHT)
-        fps = await asyncio.to_thread(cap.get, cv2.CAP_PROP_FPS)
-
-        # 处理异常情况：部分RTMP流未返回帧率时默认30FPS
-        fps = fps if fps > 0 else 30.0
-        # 分辨率转为整数（视频尺寸必然是整数）
-        width, height = int(width), int(height)
-
-        # 打印流初始化成功信息（与WHEP连接成功信息风格一致）
-        print(f"RTMP流状态: 已成功连接")
-        print(f"流基础信息: 分辨率 {width}x{height} | 配置帧率 {fps:.2f} FPS")
-        print("开始接收视频帧...（按 Ctrl+C 中断）")
-
-        # 4. 初始化帧统计参数
-        frame_count = 0  # 总接收帧数
-        start_time = time.time()  # 统计起始时间
-
-        # 5. 循环异步读取视频帧（核心逻辑）
-        while True:
-            # 异步读取一帧（cv2.read是同步操作，用to_thread适配异步环境）
-            ret, frame = await asyncio.to_thread(cap.read)
-
-            # 检查帧是否读取成功（流中断/结束时ret为False）
-            if not ret:
-                print(f"RTMP流状态: 帧读取失败（可能流已中断或结束）")
-                break
-
-            # 帧计数累加
-            frame_count += 1
-
-            # 6. 打印当前帧基础信息（与WHEP帧信息打印风格对齐）
-            print(f"收到帧 (第{frame_count}帧)")
-            print(f"  帧尺寸: {width}x{height}")
-            print(f"  配置帧率: {fps:.2f} FPS")
-
-            # 7. 每100帧统计一次实际接收帧率（补充性能监控，与原RTMP示例逻辑一致）
-            if frame_count % 100 == 0:
-                elapsed_time = time.time() - start_time
-                actual_fps = frame_count / elapsed_time  # 实际接收帧率（可能低于配置帧率）
-                print(f"---- 帧统计: 累计{frame_count}帧 | 实际平均帧率 {actual_fps:.2f} FPS ----")
-
-            # （可选）帧数据处理入口：如需处理帧（如推流、分析），可在此处添加逻辑
-            # 示例：yield frame （若需生成器模式，可调整函数为异步生成器）
-
-    # 8. 异常处理（覆盖用户中断、通用错误）
-    except KeyboardInterrupt:
-        print(f"\n用户操作: 已通过 Ctrl+C 中断程序")
-    except Exception as e:
-        # 日志记录详细错误（便于问题排查），同时打印用户可见信息
-        logger.error(f"RTMP流处理异常: {str(e)}", exc_info=True)
-        print(f"错误信息: {str(e)}")
-    finally:
-        # 9. 资源释放（无论成功/失败都确保释放，避免内存泄漏）
-        if cap is not None:
-            await asyncio.to_thread(cap.release)
-            print(f"\n资源释放: RTMP流已关闭")
-            print(f"最终统计: 共接收 {frame_count if 'frame_count' in locals() else 0} 帧")
-
-
-if __name__ == "__main__":
-    RTMP_URL = "rtmp://192.168.110.25:1935/live/473b95a47e338301cbd96809ea7ac416"
-
-    # 运行RTMP拉流任务（与WHEP一致的异步执行方式）
-    try:
-        asyncio.run(rtmp_pull_video_stream(RTMP_URL))
-    except Exception as e:
-        print(f"程序启动失败: {str(e)}")

schema/device_action_schema.py

@@ -0,0 +1,36 @@
+from datetime import datetime
+from typing import Optional, List
+from pydantic import BaseModel, Field
+
+
+# ------------------------------
+# 请求模型
+# ------------------------------
+class DeviceActionCreate(BaseModel):
+    """设备操作记录创建模型（0=离线、1=上线）"""
+    client_ip: str = Field(..., description="客户端IP")
+    action: int = Field(..., ge=0, le=1, description="操作状态（0=离线、1=上线）")
+
+
+# ------------------------------
+# 响应模型（单条记录）
+# ------------------------------
+class DeviceActionResponse(BaseModel):
+    """设备操作记录响应模型（与自增表对齐）"""
+    id: int = Field(..., description="自增主键ID")
+    client_ip: Optional[str] = Field(None, description="客户端IP")
+    action: Optional[int] = Field(None, description="操作状态（0=离线、1=上线）")
+    created_at: datetime = Field(..., description="记录创建时间")
+    updated_at: datetime = Field(..., description="记录更新时间")
+
+    # 支持从数据库结果直接转换
+    model_config = {"from_attributes": True}
+
+
+# ------------------------------
+# 列表响应模型（仅含 total + device_actions）
+# ------------------------------
+class DeviceActionListResponse(BaseModel):
+    """设备操作记录列表（仅核心返回字段）"""
+    total: int = Field(..., description="总记录数")
+    device_actions: List[DeviceActionResponse] = Field(..., description="操作记录列表")

schema/device_schema.py

@@ -1,4 +1,3 @@
-import hashlib
 from datetime import datetime
 from typing import Optional, List, Dict
 
@@ -6,42 +5,31 @@ from pydantic import BaseModel, Field
 
 
 # ------------------------------
-# 请求模型（前端传参校验）
+# 请求模型
 # ------------------------------
 class DeviceCreateRequest(BaseModel):
-    """设备流信息创建请求模型"""
+    """设备流信息创建请求模型（与数据库表字段对齐）"""
     ip: Optional[str] = Field(..., max_length=100, description="设备IP地址")
     hostname: Optional[str] = Field(None, max_length=100, description="设备别名")
-    params: Optional[Dict] = Field(None, description="设备详细信息")
-
-
-def md5_encrypt(text: str) -> str:
-    """对字符串进行MD5加密"""
-    if not text:
-        return ""
-    md5_hash = hashlib.md5()
-    md5_hash.update(text.encode('utf-8'))
-    return md5_hash.hexdigest()
+    params: Optional[Dict] = Field(None, description="设备详细信息（JSON格式）")
 
 
 # ------------------------------
-# 响应模型（后端返回设备数据）
+# 响应模型（后端返回数据）- 严格对齐数据库表字段
 # ------------------------------
 class DeviceResponse(BaseModel):
-    """设备流信息响应模型（字段与表结构完全对齐）"""
-    id: int = Field(..., description="设备ID")
+    """设备流信息响应模型（与数据库表字段完全一致）"""
+    id: int = Field(..., description="设备主键ID")
+    client_ip: Optional[str] = Field(None, max_length=100, description="设备IP地址")
     hostname: Optional[str] = Field(None, max_length=100, description="设备别名")
-    rtmp_push_url: Optional[str] = Field(None, description="需要推送的RTMP地址")
-    live_webrtc_url: Optional[str] = Field(None, description="直播的Webrtc地址")
-    detection_webrtc_url: Optional[str] = Field(None, description="检测的Webrtc地址")
     device_online_status: int = Field(..., description="设备在线状态（1-在线、0-离线）")
     device_type: Optional[str] = Field(None, description="设备类型")
     alarm_count: int = Field(..., description="报警次数")
-    params: Optional[str] = Field(None, description="设备详细信息")
+    params: Optional[str] = Field(None, description="设备详细信息（JSON字符串）")
     created_at: datetime = Field(..., description="记录创建时间")
     updated_at: datetime = Field(..., description="记录更新时间")
 
-    # 支持从数据库查询结果转换
+    # 支持从数据库查询结果直接转换
     model_config = {"from_attributes": True}
 
 

schema/face_schema.py

@@ -6,8 +6,8 @@ from pydantic import BaseModel, Field
 # 请求模型（前端传参校验）
 # ------------------------------
 class FaceCreateRequest(BaseModel):
-    """创建人脸记录请求模型（无需ID，由数据库自增）"""
-    name: str = Field(None, max_length=255, description="名称（可选，最长255字符）")
+    """创建人脸记录请求模型（无需ID、由数据库自增）"""
+    name: str = Field(None, max_length=255, description="名称（可选、最长255字符）")
 
 
 class FaceUpdateRequest(BaseModel):
@@ -20,10 +20,10 @@ class FaceUpdateRequest(BaseModel):
 # 响应模型（后端返回数据）
 # ------------------------------
 class FaceResponse(BaseModel):
-    """人脸记录响应模型（仍包含ID，由数据库生成后返回）"""
+    """人脸记录响应模型（仍包含ID、由数据库生成后返回）"""
     id: int = Field(..., description="主键ID（数据库自增）")
     name: str = Field(None, description="名称")
-    eigenvalue: str = Field(None, description="特征（暂为None）")
+    eigenvalue: str | None = Field(None, description="特征（可为空）")
     created_at: datetime = Field(..., description="记录创建时间")
     updated_at: datetime = Field(..., description="记录更新时间")
 

schema/response_schema.py

@@ -5,9 +5,9 @@ from pydantic import BaseModel, Field
 
 class APIResponse(BaseModel):
     """统一 API 响应模型（所有接口必返此格式）"""
-    code: int = Field(..., description="状态码：200=成功、4xx=客户端错误、5xx=服务端错误")
-    message: str = Field(..., description="响应信息：成功/错误描述")
-    data: Optional[Any] = Field(None, description="响应数据：成功时返回、错误时为 None")
+    code: int = Field(..., description="状态码: 200=成功、4xx=客户端错误、5xx=服务端错误")
+    message: str = Field(..., description="响应信息: 成功/错误描述")
+    data: Optional[Any] = Field(None, description="响应数据: 成功时返回、错误时为 None")
 
     # Pydantic V2 配置（支持从 ORM 对象转换）
     model_config = {"from_attributes": True}

schema/sensitive_schema.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field
 # ------------------------------
 class SensitiveCreateRequest(BaseModel):
     """创建敏感信息记录请求模型"""
-    # 移除了id字段，由数据库自动生成
+    # 移除了id字段、由数据库自动生成
     name: str = Field(None, max_length=255, description="名称")
 
 

service/device_action_service.py

@@ -0,0 +1,158 @@
+from fastapi import APIRouter, Query, Path
+from mysql.connector import Error as MySQLError
+
+from ds.db import db
+from schema.device_action_schema import (
+    DeviceActionCreate,
+    DeviceActionResponse,
+    DeviceActionListResponse
+)
+from schema.response_schema import APIResponse
+
+# 路由配置
+router = APIRouter(
+    prefix="/device/actions",
+    tags=["设备操作记录"]
+)
+
+
+# ------------------------------
+# 内部方法: 新增设备操作记录（适配id自增）
+# ------------------------------
+def add_device_action(action_data: DeviceActionCreate) -> DeviceActionResponse:
+    """
+    新增设备操作记录（内部方法、非接口）
+    :param action_data: 含client_ip和action（0/1）
+    :return: 新增的完整记录
+    """
+    conn = None
+    cursor = None
+    try:
+        conn = db.get_connection()
+        cursor = conn.cursor(dictionary=True)
+
+        # 插入SQL（id自增、依赖数据库自动生成）
+        insert_query = """
+            INSERT INTO device_action 
+            (client_ip, action, created_at, updated_at)
+            VALUES (%s, %s, CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)
+        """
+        cursor.execute(insert_query, (
+            action_data.client_ip,
+            action_data.action
+        ))
+        conn.commit()
+
+        # 获取新增记录（通过自增ID查询）
+        new_id = cursor.lastrowid
+        cursor.execute("SELECT * FROM device_action WHERE id = %s", (new_id,))
+        new_action = cursor.fetchone()
+
+        return DeviceActionResponse(**new_action)
+
+    except MySQLError as e:
+        if conn:
+            conn.rollback()
+        raise Exception(f"新增记录失败: {str(e)}") from e
+    finally:
+        db.close_connection(conn, cursor)
+
+
+# ------------------------------
+# 接口: 分页查询操作记录列表（仅返回 total + device_actions）
+# ------------------------------
+@router.get("/list", response_model=APIResponse, summary="分页查询设备操作记录")
+async def get_device_action_list(
+        page: int = Query(1, ge=1, description="页码、默认1"),
+        page_size: int = Query(10, ge=1, le=100, description="每页条数、1-100"),
+        client_ip: str = Query(None, description="按客户端IP筛选"),
+        action: int = Query(None, ge=0, le=1, description="按状态筛选（0=离线、1=上线）")
+):
+    conn = None
+    cursor = None
+    try:
+        conn = db.get_connection()
+        cursor = conn.cursor(dictionary=True)
+
+        # 1. 构建筛选条件（参数化查询、避免注入）
+        where_clause = []
+        params = []
+        if client_ip:
+            where_clause.append("client_ip = %s")
+            params.append(client_ip)
+        if action is not None:
+            where_clause.append("action = %s")
+            params.append(action)
+
+        # 2. 查询总记录数（用于返回 total）
+        count_sql = "SELECT COUNT(*) AS total FROM device_action"
+        if where_clause:
+            count_sql += " WHERE " + " AND ".join(where_clause)
+        cursor.execute(count_sql, params)
+        total = cursor.fetchone()["total"]
+
+        # 3. 分页查询记录（按创建时间倒序、确保最新记录在前）
+        offset = (page - 1) * page_size
+        list_sql = "SELECT * FROM device_action"
+        if where_clause:
+            list_sql += " WHERE " + " AND ".join(where_clause)
+        list_sql += " ORDER BY created_at DESC LIMIT %s OFFSET %s"
+        params.extend([page_size, offset])  # 追加分页参数（page/page_size仅用于查询、不返回）
+
+        cursor.execute(list_sql, params)
+        action_list = cursor.fetchall()
+
+        # 4. 仅返回 total + device_actions
+        return APIResponse(
+            code=200,
+            message="查询成功",
+            data=DeviceActionListResponse(
+                total=total,
+                device_actions=[DeviceActionResponse(**item) for item in action_list]
+            )
+        )
+
+    except MySQLError as e:
+        raise Exception(f"查询记录失败: {str(e)}") from e
+    finally:
+        db.close_connection(conn, cursor)
+
+
+@router.get("/{client_ip}", response_model=APIResponse, summary="根据IP查询设备操作记录")
+async def get_device_actions_by_ip(
+        client_ip: str = Path(..., description="客户端IP地址")
+):
+    conn = None
+    cursor = None
+    try:
+        conn = db.get_connection()
+        cursor = conn.cursor(dictionary=True)
+
+        # 1. 查询总记录数
+        count_sql = "SELECT COUNT(*) AS total FROM device_action WHERE client_ip = %s"
+        cursor.execute(count_sql, (client_ip,))
+        total = cursor.fetchone()["total"]
+
+        # 2. 查询该IP的所有记录（按创建时间倒序）
+        list_sql = """
+            SELECT * FROM device_action 
+            WHERE client_ip = %s 
+            ORDER BY created_at DESC
+        """
+        cursor.execute(list_sql, (client_ip,))
+        action_list = cursor.fetchall()
+
+        # 3. 返回结果
+        return APIResponse(
+            code=200,
+            message="查询成功",
+            data=DeviceActionListResponse(
+                total=total,
+                device_actions=[DeviceActionResponse(**item) for item in action_list]
+            )
+        )
+
+    except MySQLError as e:
+        raise Exception(f"查询记录失败: {str(e)}") from e
+    finally:
+        db.close_connection(conn, cursor)

service/device_service.py

@@ -1,25 +1,11 @@
 import json
-import threading
-import time
 
-from fastapi import HTTPException, Query, APIRouter, Depends, Request
+from fastapi import APIRouter, Query, HTTPException,Request
 from mysql.connector import Error as MySQLError
 
-from ds.config import LIVE_CONFIG
 from ds.db import db
-from middle.auth_middleware import get_current_user
-# 注意：导入的Schema已更新字段
-from schema.device_schema import (
-    DeviceCreateRequest,
-    DeviceResponse,
-    DeviceListResponse,
-    md5_encrypt
-)
+from schema.device_schema import DeviceCreateRequest, DeviceResponse, DeviceListResponse
 from schema.response_schema import APIResponse
-from schema.user_schema import UserResponse
-
-# 导入之前封装的WEBRTC处理函数
-from core.rtmp import rtmp_pull_video_stream
 
 router = APIRouter(
     prefix="/devices",
@@ -27,65 +13,127 @@ router = APIRouter(
 )
 
 
-# 在后台线程中运行WEBRTC处理
-def run_webrtc_processing(ip, webrtc_url):
-    try:
-        print(f"开始处理来自设备 {ip} 的WEBRTC流: {webrtc_url}")
-        rtmp_pull_video_stream(webrtc_url)
-    except Exception as e:
-        print(f"WEBRTC处理出错: {str(e)}")
-
-
 # ------------------------------
-# 1. 创建设备信息
+# 内部工具方法 - 通过客户端IP增加设备报警次数
 # ------------------------------
-@router.post("/add", response_model=APIResponse, summary="创建设备信息")
-async def create_device(request: Request, device_data: DeviceCreateRequest):
+def increment_alarm_count_by_ip(client_ip: str) -> bool:
+    """
+    通过客户端IP增加设备的报警次数（内部服务方法）
+
+    :param client_ip: 客户端IP地址
+    :return: 操作是否成功
+    """
+    if not client_ip:
+        raise ValueError("客户端IP不能为空")
+
+    conn = None
+    cursor = None
+    try:
+        conn = db.get_connection()
+        cursor = conn.cursor(dictionary=True)
+
+        # 检查设备是否存在
+        cursor.execute("SELECT id FROM devices WHERE client_ip = %s", (client_ip,))
+        if not cursor.fetchone():
+            raise ValueError(f"客户端IP为 {client_ip} 的设备不存在")
+
+        # 报警次数加1、并更新时间戳
+        update_query = """
+            UPDATE devices 
+            SET alarm_count = alarm_count + 1, 
+                updated_at = CURRENT_TIMESTAMP 
+            WHERE client_ip = %s
+        """
+        cursor.execute(update_query, (client_ip,))
+        conn.commit()
+
+        return True
+    except MySQLError as e:
+        if conn:
+            conn.rollback()
+        raise Exception(f"更新报警次数失败: {str(e)}") from e
+    finally:
+        db.close_connection(conn, cursor)
+
+
+# ------------------------------
+# 内部工具方法 - 通过客户端IP更新设备在线状态
+# ------------------------------
+def update_online_status_by_ip(client_ip: str, online_status: int) -> bool:
+    """
+    通过客户端IP更新设备的在线状态（内部服务方法）
+
+    :param client_ip: 客户端IP地址
+    :param online_status: 在线状态（1-在线、0-离线）
+    :return: 操作是否成功
+    """
+    if not client_ip:
+        raise ValueError("客户端IP不能为空")
+
+    # 验证状态值有效性
+    if online_status not in (0, 1):
+        raise ValueError("在线状态必须是0（离线）或1（在线）")
+
+    conn = None
+    cursor = None
+    try:
+        conn = db.get_connection()
+        cursor = conn.cursor(dictionary=True)
+
+        # 检查设备是否存在
+        cursor.execute("SELECT id FROM devices WHERE client_ip = %s", (client_ip,))
+        if not cursor.fetchone():
+            raise ValueError(f"客户端IP为 {client_ip} 的设备不存在")
+
+        # 更新在线状态和时间戳
+        update_query = """
+            UPDATE devices 
+            SET device_online_status = %s, 
+                updated_at = CURRENT_TIMESTAMP 
+            WHERE client_ip = %s
+        """
+        cursor.execute(update_query, (online_status, client_ip))
+        conn.commit()
+
+        return True
+    except MySQLError as e:
+        if conn:
+            conn.rollback()
+        raise Exception(f"更新设备在线状态失败: {str(e)}") from e
+    finally:
+        db.close_connection(conn, cursor)
+
+
+# ------------------------------
+# 原有接口保持不变
+# ------------------------------
+@router.post("/add", response_model=APIResponse, summary="创建设备信息")
+async def create_device(device_data: DeviceCreateRequest, request: Request):  # 注入Request对象
+    # 原有代码保持不变
     conn = None
     cursor = None
     try:
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 检查client_ip是否已存在
         cursor.execute("SELECT * FROM devices WHERE client_ip = %s", (device_data.ip,))
         existing_device = cursor.fetchone()
         if existing_device:
-            # 设备创建成功后，在后台线程启动WEBRTC流处理
-            threading.Thread(
-                target=run_webrtc_processing,
-                # args=(device_data.ip, existing_device["live_webrtc_url"]),
-                args=(device_data.ip, existing_device["rtmp_push_url"]),
-
-                daemon=True  # 设为守护线程，主程序退出时自动结束
-            ).start()
-            # IP已存在时返回该设备信息
+            # 更新设备状态为在线
+            update_online_status_by_ip(client_ip=device_data.ip, online_status=1)
+            # 返回信息
             return APIResponse(
                 code=200,
-                message=f"客户端IP {device_data.ip} 已存在",
+                message=f"设备IP {device_data.ip} 已存在、返回已有设备信息",
                 data=DeviceResponse(** existing_device)
             )
 
-        # 获取RTMP URL和WEBRTC URL配置
-        rtmp_url = str(LIVE_CONFIG.get("rtmp_url", ""))
-        webrtc_url = str(LIVE_CONFIG.get("webrtc_url", ""))
-
-        # 将设备详细信息（params）转换为JSON字符串
-        device_params_json = json.dumps(device_data.params) if device_data.params else None
-
-        # 对JSON字符串进行MD5加密
-        device_md5 = md5_encrypt(device_params_json) if device_params_json else ""
-
-        # 解析User-Agent获取设备类型
+        # 直接使用注入的request对象获取用户代理
         user_agent = request.headers.get("User-Agent", "").lower()
 
-        # 优先处理User-Agent为default的情况
         if user_agent == "default":
-            # 检查params中是否存在os键
-            if device_data.params and isinstance(device_data.params, dict) and "os" in device_data.params:
-                device_type = device_data.params["os"]
-            else:
-                device_type = "unknown"
+            device_type = device_data.params.get("os") if (
+                    device_data.params and isinstance(device_data.params, dict)) else "unknown"
         elif "windows" in user_agent:
             device_type = "windows"
         elif "android" in user_agent:
@@ -95,22 +143,16 @@ async def create_device(request: Request, device_data: DeviceCreateRequest):
         else:
             device_type = "unknown"
 
-        # 构建完整的WEBRTC URL
-        full_webrtc_url = webrtc_url + device_md5
+        device_params_json = json.dumps(device_data.params) if device_data.params else None
 
-        # SQL插入语句
         insert_query = """
             INSERT INTO devices 
-            (client_ip, hostname, rtmp_push_url, live_webrtc_url, detection_webrtc_url, 
-             device_online_status, device_type, alarm_count, params)
-            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)
+            (client_ip, hostname, device_online_status, device_type, alarm_count, params)
+            VALUES (%s, %s, %s, %s, %s, %s)
         """
         cursor.execute(insert_query, (
             device_data.ip,
             device_data.hostname,
-            rtmp_url + device_md5,
-            full_webrtc_url,  # 存储完整的WEBRTC URL
-            "",
             1,
             device_type,
             0,
@@ -118,28 +160,22 @@ async def create_device(request: Request, device_data: DeviceCreateRequest):
         ))
         conn.commit()
 
-        # 获取刚创建的设备信息
         device_id = cursor.lastrowid
         cursor.execute("SELECT * FROM devices WHERE id = %s", (device_id,))
-        device = cursor.fetchone()
+        new_device = cursor.fetchone()
 
-        # 设备创建成功后，在后台线程启动WEBRTC流处理
-        threading.Thread(
-            target=run_webrtc_processing,
-            args=(device_data.ip, full_webrtc_url),
-            daemon=True  # 设为守护线程，主程序退出时自动结束
-        ).start()
         return APIResponse(
             code=200,
-            message="设备创建成功，已开始处理WEBRTC流",
-            data=DeviceResponse(**device)
+            message="设备创建成功",
+            data=DeviceResponse(**new_device)
         )
+
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"创建设备失败：{str(e)}") from e
+        raise Exception(f"创建设备失败: {str(e)}") from e
     except json.JSONDecodeError as e:
-        raise Exception(f"设备信息JSON序列化失败：{str(e)}") from e
+        raise Exception(f"设备详细信息JSON序列化失败: {str(e)}") from e
     except Exception as e:
         if conn:
             conn.rollback()
@@ -147,140 +183,82 @@ async def create_device(request: Request, device_data: DeviceCreateRequest):
     finally:
         db.close_connection(conn, cursor)
 
-
-# ------------------------------
-# 2. 获取设备列表
-# ------------------------------
-@router.get("/", response_model=APIResponse, summary="获取设备列表")
+@router.get("/", response_model=APIResponse, summary="获取设备列表（支持筛选分页）")
 async def get_device_list(
-        page: int = Query(1, ge=1, description="页码"),
-        page_size: int = Query(10, ge=1, le=100, description="每页条数"),
-        device_type: str = Query(None, description="设备类型筛选"),
-        online_status: int = Query(None, ge=0, le=1, description="在线状态筛选（1-在线、0-离线）")
+        page: int = Query(1, ge=1, description="页码、默认第1页"),
+        page_size: int = Query(10, ge=1, le=100, description="每页条数、1-100之间"),
+        device_type: str = Query(None, description="按设备类型筛选"),
+        online_status: int = Query(None, ge=0, le=1, description="按在线状态筛选")
 ):
+    # 原有代码保持不变
     conn = None
     cursor = None
     try:
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 构建查询条件
         where_clause = []
         params = []
 
         if device_type:
             where_clause.append("device_type = %s")
             params.append(device_type)
-
         if online_status is not None:
             where_clause.append("device_online_status = %s")
             params.append(online_status)
 
-        # 总条数查询
-        count_query = "SELECT COUNT(*) as total FROM devices"
+        count_query = "SELECT COUNT(*) AS total FROM devices"
         if where_clause:
             count_query += " WHERE " + " AND ".join(where_clause)
-
         cursor.execute(count_query, params)
         total = cursor.fetchone()["total"]
 
-        # 分页查询（SELECT * 会自动匹配表字段、响应模型已对齐）
         offset = (page - 1) * page_size
-        query = "SELECT * FROM devices"
+        list_query = "SELECT * FROM devices"
         if where_clause:
-            query += " WHERE " + " AND ".join(where_clause)
-        query += " ORDER BY id DESC LIMIT %s OFFSET %s"
+            list_query += " WHERE " + " AND ".join(where_clause)
+        list_query += " ORDER BY id DESC LIMIT %s OFFSET %s"
         params.extend([page_size, offset])
 
-        cursor.execute(query, params)
-        devices = cursor.fetchall()
-
-        # 响应模型已更新为params字段、直接转换即可
-        device_list = [DeviceResponse(**device) for device in devices]
+        cursor.execute(list_query, params)
+        device_list = cursor.fetchall()
 
         return APIResponse(
             code=200,
             message="获取设备列表成功",
-            data=DeviceListResponse(total=total, devices=device_list)
+            data=DeviceListResponse(
+                total=total,
+                devices=[DeviceResponse(**device) for device in device_list]
             )
+        )
+
     except MySQLError as e:
-        raise Exception(f"获取设备列表失败：{str(e)}") from e
+        raise Exception(f"获取设备列表失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
 
-# ------------------------------
-# 3. 获取单个设备详情
-# ------------------------------
-@router.get("/{device_id}", response_model=APIResponse, summary="获取设备详情")
-async def get_device_detail(
-        device_id: int,
-        current_user: UserResponse = Depends(get_current_user)
-):
+def get_unique_client_ips() -> list[str]:
+    """
+    获取所有去重的客户端IP列表
+
+    :return: 去重后的客户端IP字符串列表，如果没有数据则返回空列表
+    """
     conn = None
     cursor = None
     try:
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 查询设备信息（SELECT * 匹配表字段）
-        query = "SELECT * FROM devices WHERE id = %s"
-        cursor.execute(query, (device_id,))
-        device = cursor.fetchone()
+        # 查询去重的客户端IP
+        query = "SELECT DISTINCT client_ip FROM devices WHERE client_ip IS NOT NULL"
+        cursor.execute(query)
 
-        if not device:
-            raise HTTPException(
-                status_code=404,
-                detail=f"设备ID为 {device_id} 的设备不存在"
-            )
+        # 提取结果并转换为字符串列表
+        results = cursor.fetchall()
+        return [item['client_ip'] for item in results]
 
-        # 响应模型已更新为params字段
-        return APIResponse(
-            code=200,
-            message="获取设备详情成功",
-            data=DeviceResponse(**device)
-        )
     except MySQLError as e:
-        raise Exception(f"获取设备详情失败：{str(e)}") from e
-    finally:
-        db.close_connection(conn, cursor)
-
-
-# ------------------------------
-# 4. 删除设备信息
-# ------------------------------
-@router.delete("/{device_id}", response_model=APIResponse, summary="删除设备信息")
-async def delete_device(
-        device_id: int,
-        current_user: UserResponse = Depends(get_current_user)
-):
-    conn = None
-    cursor = None
-    try:
-        conn = db.get_connection()
-        cursor = conn.cursor(dictionary=True)
-
-        # 检查设备是否存在
-        cursor.execute("SELECT id FROM devices WHERE id = %s", (device_id,))
-        if not cursor.fetchone():
-            raise HTTPException(
-                status_code=404,
-                detail=f"设备ID为 {device_id} 的设备不存在"
-            )
-
-        # 执行删除
-        delete_query = "DELETE FROM devices WHERE id = %s"
-        cursor.execute(delete_query, (device_id,))
-        conn.commit()
-
-        return APIResponse(
-            code=200,
-            message=f"设备ID为 {device_id} 的设备已成功删除",
-            data=None
-        )
-    except MySQLError as e:
-        if conn:
-            conn.rollback()
-        raise Exception(f"删除设备失败：{str(e)}") from e
+        raise Exception(f"获取客户端IP列表失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)

service/face_service.py

@@ -7,32 +7,38 @@ from schema.response_schema import APIResponse
 from middle.auth_middleware import get_current_user
 from schema.user_schema import UserResponse
 
+from util.face_util import add_binary_data,get_average_feature
+#初始化实例
+
 router = APIRouter(
     prefix="/faces",
     tags=["人脸管理"]
 )
 
 
+
 # ------------------------------
-# 1. 创建人脸记录（核心修正：ID 数据库自增，前端无需传）
+# 1. 创建人脸记录（核心修正: ID 数据库自增、前端无需传）
 # ------------------------------
-@router.post("", response_model=APIResponse, summary="创建人脸记录（传名称+文件，ID自增）")
+@router.post("", response_model=APIResponse, summary="创建人脸记录（传名称+文件、ID自增）")
 async def create_face(
-        # 前端仅需传：name（可选，Form格式）、file（必传，文件）
+        # 前端仅需传: name（可选、Form格式）、file（必传、文件）
         name: str = Form(None, max_length=255, description="名称（可选）"),
-        file: UploadFile = File(..., description="人脸文件（必传，暂不处理内容）")
+        file: UploadFile = File(..., description="人脸文件（必传、暂不处理内容）")
 ):
     """
-    创建人脸记录：
+    创建人脸记录: 
     - 需登录认证
-    - 前端传参：multipart/form-data 表单（name 可选，file 必传）
-    - ID 由数据库自动生成，无需前端传入
-    - 暂不处理文件内容，eigenvalue 设为 None
+    - 前端传参: multipart/form-data 表单（name 可选、file 必传）
+    - ID 由数据库自动生成、无需前端传入
+    - 暂不处理文件内容、eigenvalue 设为 None
     """
+
+    # 调用你的方法
     conn = None
     cursor = None
     try:
-        # 1. 用模型校验 name（仅校验长度，无需ID）
+        # 1. 用模型校验 name（仅校验长度、无需ID）
         face_create = FaceCreateRequest(name=name)
 
         conn = db.get_connection()
@@ -41,42 +47,77 @@ async def create_face(
         # 把文件转为二进制数组
         file_content = await file.read()
 
-        # 调用人脸识别得到特征值
+        # 计算特征值
+        flag, eigenvalue = add_binary_data(file_content)
 
+        if flag == False:
+            raise HTTPException(
+                status_code=500,
+                detail="未检测到人脸"
+            )
 
-        # 2. 插入数据库：无需传 ID（自增），只传 name 和 eigenvalue（None）
+        # 打印数组长度
+        print(f"文件大小: {len(file_content)} 字节")
+
+        # 2. 插入数据库: 无需传 ID（自增）、只传 name 和 eigenvalue（None）
         insert_query = """
             INSERT INTO face (name, eigenvalue)
             VALUES (%s, %s)
         """
-        cursor.execute(insert_query, (face_create.name, None))
+        cursor.execute(insert_query, (face_create.name, str(eigenvalue)))
         conn.commit()
 
-        # 3. 获取数据库自动生成的 ID（关键：用 LAST_INSERT_ID() 查刚插入的记录）
+        # 3. 获取数据库自动生成的 ID（关键: 用 LAST_INSERT_ID() 查刚插入的记录）
         select_new_query = "SELECT * FROM face WHERE id = LAST_INSERT_ID()"
         cursor.execute(select_new_query)
         created_face = cursor.fetchone()
 
+        if not created_face:
+            raise HTTPException(
+                status_code=500,
+                detail="创建人脸记录成功、但无法获取新创建的记录"
+            )
+
         return APIResponse(
             code=201,
-            message=f"人脸记录创建成功（ID：{created_face['id']}，文件名：{file.filename}）",
+            message=f"人脸记录创建成功（ID: {created_face['id']}、文件名: {file.filename}）",
             data=FaceResponse(** created_face)
         )
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"创建人脸记录失败：{str(e)}") from e
+        # 改为使用HTTPException
+        raise HTTPException(
+            status_code=500,
+            detail=f"创建人脸记录失败: {str(e)}"
+        ) from e
+    except Exception as e:
+        # 捕获其他可能的异常
+        raise HTTPException(
+            status_code=500,
+            detail=f"服务器错误: {str(e)}"
+        ) from e
     finally:
         await file.close()  # 关闭文件流
         db.close_connection(conn, cursor)
 
+        # 调用人脸识别得到特征值（这里可以添加你的人脸识别逻辑）
+        flag, eigenvalue = add_binary_data(file_content)
+        if flag == False:
+            raise HTTPException(
+                status_code=500,
+                detail="未检测到人脸"
+            )
+
+        # 将 eigenvalue 转为 str
+        eigenvalue = str(eigenvalue)
 
 # ------------------------------
-# 2. 获取单个人脸记录（不变，用自增ID查询）
+# 2. 获取单个人脸记录（不变、用自增ID查询）
 # ------------------------------
 @router.get("/{face_id}", response_model=APIResponse, summary="获取单个人脸记录")
 async def get_face(
-        face_id: int,  # 这里的 ID 是数据库自增的，前端从创建响应中获取
+        face_id: int,  # 这里的 ID 是数据库自增的、前端从创建响应中获取
         current_user: UserResponse = Depends(get_current_user)
 ):
     conn = None
@@ -101,18 +142,21 @@ async def get_face(
             data=FaceResponse(**face)
         )
     except MySQLError as e:
-        raise Exception(f"查询人脸记录失败：{str(e)}") from e
+        # 改为使用HTTPException
+        raise HTTPException(
+            status_code=500,
+            detail=f"查询人脸记录失败: {str(e)}"
+        ) from e
     finally:
         db.close_connection(conn, cursor)
 
 
-# 后续 3.获取所有、4.更新、5.删除 接口（不变，仅用数据库自增的 ID 操作，无需修改）
+# 后续 3.获取所有、4.更新、5.删除 接口（修复异常处理）
 # ------------------------------
 # 3. 获取所有人脸记录（不变）
 # ------------------------------
 @router.get("", response_model=APIResponse, summary="获取所有人脸记录")
 async def get_all_faces(
-        current_user: UserResponse = Depends(get_current_user)
 ):
     conn = None
     cursor = None
@@ -130,13 +174,16 @@ async def get_all_faces(
             data=[FaceResponse(** face) for face in faces]
         )
     except MySQLError as e:
-        raise Exception(f"查询所有人脸记录失败：{str(e)}") from e
+        raise HTTPException(
+            status_code=500,
+            detail=f"查询所有人脸记录失败: {str(e)}"
+        ) from e
     finally:
         db.close_connection(conn, cursor)
 
 
 # ------------------------------
-# 4. 更新人脸记录（不变，用自增ID更新）
+# 4. 更新人脸记录（不变、用自增ID更新）
 # ------------------------------
 @router.put("/{face_id}", response_model=APIResponse, summary="更新人脸记录")
 async def update_face(
@@ -191,13 +238,16 @@ async def update_face(
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"更新人脸记录失败：{str(e)}") from e
+        raise HTTPException(
+            status_code=500,
+            detail=f"更新人脸记录失败: {str(e)}"
+        ) from e
     finally:
         db.close_connection(conn, cursor)
 
 
 # ------------------------------
-# 5. 删除人脸记录（不变，用自增ID删除）
+# 5. 删除人脸记录（不变、用自增ID删除）
 # ------------------------------
 @router.delete("/{face_id}", response_model=APIResponse, summary="删除人脸记录")
 async def delete_face(
@@ -231,39 +281,61 @@ async def delete_face(
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"删除人脸记录失败：{str(e)}") from e
+        raise HTTPException(
+            status_code=500,
+            detail=f"删除人脸记录失败: {str(e)}"
+        ) from e
     finally:
         db.close_connection(conn, cursor)
 
 
 def get_all_face_name_with_eigenvalue() -> dict:
     """
-    获取所有人脸的名称及其对应的特征值，组成字典返回
+    获取所有人脸的名称及其对应的特征值、组成字典返回
     key: 人脸名称（name）
-    value: 人脸特征值（eigenvalue）
-    注：过滤掉name为None的记录，避免字典key为None的情况
+    value: 人脸特征值（eigenvalue）、若名称重复则返回平均特征值
+    注: 过滤掉name为None的记录、避免字典key为None的情况
     """
     conn = None
     cursor = None
     try:
+        # 1. 建立数据库连接并获取游标（dictionary=True使结果以字典形式返回）
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 只查询需要的字段，提高效率
+        # 2. 执行SQL查询: 只获取name非空的记录、减少数据传输
         query = "SELECT name, eigenvalue FROM face WHERE name IS NOT NULL"
         cursor.execute(query)
-        faces = cursor.fetchall()
+        faces = cursor.fetchall()  # 返回结果: 列表套字典、如 [{"name":"张三","eigenvalue":...}, ...]
 
-        # 构建name到eigenvalue的映射字典
-        face_dict = {
-            face["name"]: face["eigenvalue"]
-            for face in faces
-        }
+        # 3. 收集同一名称对应的所有特征值（处理名称重复场景）
+        name_to_eigenvalues = {}
+        for face in faces:
+            name = face["name"]
+            eigenvalue = face["eigenvalue"]
+            # 若名称已存在、追加特征值；否则新建列表存储
+            if name in name_to_eigenvalues:
+                name_to_eigenvalues[name].append(eigenvalue)
+            else:
+                name_to_eigenvalues[name] = [eigenvalue]
+
+        # 4. 构建最终字典: 重复名称取平均、唯一名称直接取特征值
+        face_dict = {}
+        for name, eigenvalues in name_to_eigenvalues.items():
+
+            # 处理特征值: 多个则求平均、单个则直接使用
+            if len(eigenvalues) > 1:
+                # 调用外部方法计算平均特征值（需确保binary_face_feature_handler已正确导入）
+                face_dict[name] = get_average_feature(eigenvalues)
+            else:
+                # 取列表中唯一的特征值（避免value为列表类型）
+                face_dict[name] = eigenvalues[0]
 
         return face_dict
 
     except MySQLError as e:
-        raise Exception(f"获取人脸名称与特征值失败：{str(e)}") from e
+        # 捕获数据库异常、添加上下文信息后重新抛出（便于定位问题）
+        raise Exception(f"获取人脸名称与特征值失败: {str(e)}") from e
     finally:
-        # 确保资源释放
+        # 5. 无论是否异常、均释放数据库连接和游标（避免资源泄漏）
         db.close_connection(conn, cursor)

service/sensitive_service.py

@@ -21,7 +21,7 @@ router = APIRouter(
 async def create_sensitive(
         sensitive: SensitiveCreateRequest):  # 添加了登录认证依赖
     """
-    创建敏感信息记录：
+    创建敏感信息记录: 
     - 需登录认证
     - 插入新的敏感信息记录到数据库（ID由数据库自动生成）
     - 返回创建成功信息
@@ -32,7 +32,7 @@ async def create_sensitive(
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 插入新敏感信息记录到数据库（不包含ID，由数据库自动生成）
+        # 插入新敏感信息记录到数据库（不包含ID、由数据库自动生成）
         insert_query = """
             INSERT INTO sensitives (name)
             VALUES (%s)
@@ -56,7 +56,7 @@ async def create_sensitive(
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"创建敏感信息记录失败：{str(e)}") from e
+        raise Exception(f"创建敏感信息记录失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
@@ -71,7 +71,7 @@ async def get_sensitive(
         current_user: UserResponse = Depends(get_current_user)  # 需登录认证
 ):
     """
-    获取单个敏感信息记录：
+    获取单个敏感信息记录: 
     - 需登录认证
     - 根据ID查询敏感信息记录
     - 返回查询到的敏感信息
@@ -98,7 +98,7 @@ async def get_sensitive(
             data=SensitiveResponse(**sensitive)
         )
     except MySQLError as e:
-        raise Exception(f"查询敏感信息记录失败：{str(e)}") from e
+        raise Exception(f"查询敏感信息记录失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
@@ -109,7 +109,7 @@ async def get_sensitive(
 @router.get("", response_model=APIResponse, summary="获取所有敏感信息记录")
 async def get_all_sensitives():
     """
-    获取所有敏感信息记录：
+    获取所有敏感信息记录: 
     - 需登录认证
     - 查询所有敏感信息记录（不需要分页）
     - 返回所有敏感信息列表
@@ -130,7 +130,7 @@ async def get_all_sensitives():
             data=[SensitiveResponse(**sensitive) for sensitive in sensitives]
         )
     except MySQLError as e:
-        raise Exception(f"查询所有敏感信息记录失败：{str(e)}") from e
+        raise Exception(f"查询所有敏感信息记录失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
@@ -145,7 +145,7 @@ async def update_sensitive(
         current_user: UserResponse = Depends(get_current_user)  # 需登录认证
 ):
     """
-    更新敏感信息记录：
+    更新敏感信息记录: 
     - 需登录认证
     - 根据ID更新敏感信息记录
     - 返回更新后的敏感信息
@@ -203,7 +203,7 @@ async def update_sensitive(
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"更新敏感信息记录失败：{str(e)}") from e
+        raise Exception(f"更新敏感信息记录失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
@@ -217,7 +217,7 @@ async def delete_sensitive(
         current_user: UserResponse = Depends(get_current_user)  # 需登录认证
 ):
     """
-    删除敏感信息记录：
+    删除敏感信息记录: 
     - 需登录认证
     - 根据ID删除敏感信息记录
     - 返回删除成功信息
@@ -251,14 +251,14 @@ async def delete_sensitive(
     except MySQLError as e:
         if conn:
             conn.rollback()
-        raise Exception(f"删除敏感信息记录失败：{str(e)}") from e
+        raise Exception(f"删除敏感信息记录失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
 
 def get_all_sensitive_words() -> list[str]:
     """
-    获取所有敏感词，返回字符串数组
+    获取所有敏感词、返回字符串数组
 
     返回:
         list[str]: 包含所有敏感词的数组
@@ -273,7 +273,7 @@ def get_all_sensitive_words() -> list[str]:
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 执行查询，只获取敏感词字段
+        # 执行查询、只获取敏感词字段
         query = "SELECT name FROM sensitives ORDER BY id"
         cursor.execute(query)
         sensitive_records = cursor.fetchall()
@@ -283,7 +283,7 @@ def get_all_sensitive_words() -> list[str]:
 
     except MySQLError as e:
         # 数据库错误处理
-        raise MySQLError(f"查询敏感词失败：{str(e)}") from e
+        raise MySQLError(f"查询敏感词失败: {str(e)}") from e
     finally:
         # 确保资源正确释放
         db.close_connection(conn, cursor)

service/user_service.py

@@ -27,7 +27,7 @@ router = APIRouter(
 @router.post("/register", response_model=APIResponse, summary="用户注册")
 async def user_register(request: UserRegisterRequest):
     """
-    用户注册：
+    用户注册: 
     - 校验用户名是否已存在
     - 加密密码后插入数据库
     - 返回注册成功信息
@@ -67,7 +67,7 @@ async def user_register(request: UserRegisterRequest):
         )
     except MySQLError as e:
         conn.rollback()  # 数据库错误时回滚事务
-        raise Exception(f"注册失败：{str(e)}") from e
+        raise Exception(f"注册失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
@@ -78,7 +78,7 @@ async def user_register(request: UserRegisterRequest):
 @router.post("/login", response_model=APIResponse, summary="用户登录（获取 Token）")
 async def user_login(request: UserLoginRequest):
     """
-    用户登录：
+    用户登录: 
     - 校验用户名是否存在
     - 校验密码是否正确
     - 生成 JWT Token 并返回
@@ -89,7 +89,7 @@ async def user_login(request: UserLoginRequest):
         conn = db.get_connection()
         cursor = conn.cursor(dictionary=True)
 
-        # 修复：SQL查询添加 created_at 和 updated_at 字段
+        # 修复: SQL查询添加 created_at 和 updated_at 字段
         query = """
             SELECT id, username, password, created_at, updated_at 
             FROM users 
@@ -129,7 +129,7 @@ async def user_login(request: UserLoginRequest):
             }
         )
     except MySQLError as e:
-        raise Exception(f"登录失败：{str(e)}") from e
+        raise Exception(f"登录失败: {str(e)}") from e
     finally:
         db.close_connection(conn, cursor)
 
@@ -142,8 +142,8 @@ async def get_current_user_info(
         current_user: UserResponse = Depends(get_current_user)  # 依赖认证中间件
 ):
     """
-    获取当前登录用户信息：
-    - 需在请求头携带 Token（格式：Bearer <token>）
+    获取当前登录用户信息: 
+    - 需在请求头携带 Token（格式: Bearer <token>）
     - 认证通过后返回用户信息
     """
     return APIResponse(

util/face_util.py

@@ -0,0 +1,145 @@
+import cv2
+import numpy as np
+import insightface
+from insightface.app import FaceAnalysis
+from io import BytesIO
+from PIL import Image
+
+# 全局变量存储InsightFace引擎和特征列表
+_insightface_app = None
+_feature_list = []
+
+
+def init_insightface():
+    """初始化InsightFace引擎"""
+    global _insightface_app
+    try:
+        print("正在初始化InsightFace引擎...")
+        app = FaceAnalysis(name='buffalo_l', root='~/.insightface')
+        app.prepare(ctx_id=0, det_size=(640, 640))
+        print("InsightFace引擎初始化完成")
+        _insightface_app = app
+        return app
+    except Exception as e:
+        print(f"InsightFace初始化失败: {e}")
+        return None
+
+
+def add_binary_data(binary_data):
+    """
+    接收单张图片的二进制数据、提取特征并保存
+
+    参数:
+        binary_data: 图片的二进制数据（bytes类型）
+
+    返回:
+        成功提取特征时返回 (True, 特征值numpy数组)
+        失败时返回 (False, None)
+    """
+    global _insightface_app, _feature_list
+
+    if not _insightface_app:
+        print("引擎未初始化、无法处理")
+        return False, None
+
+    try:
+        # 直接处理二进制数据: 转换为图像格式
+        img = Image.open(BytesIO(binary_data))
+        frame = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+
+        # 提取特征
+        faces = _insightface_app.get(frame)
+        if faces:
+            # 获取当前提取的特征值
+            current_feature = faces[0].embedding
+            # 添加到特征列表
+            _feature_list.append(current_feature)
+            print(f"已累计 {len(_feature_list)} 个特征")
+            # 返回成功标志和当前特征值
+            return True, current_feature
+        else:
+            print("二进制数据中未检测到人脸")
+            return False, None
+    except Exception as e:
+        print(f"处理二进制数据出错: {e}")
+        return False, None
+
+
+def get_average_feature(features=None):
+    """
+    计算多个特征向量的平均值
+
+    参数:
+        features: 可选、特征值列表。如果未提供、则使用全局存储的_feature_list
+                  每个元素可以是字符串格式或numpy数组
+
+    返回:
+        单一平均特征向量的numpy数组、若无可计算数据则返回None
+    """
+    global _feature_list
+
+    # 如果未提供features参数、则使用全局特征列表
+    if features is None:
+        features = _feature_list
+
+    try:
+        # 验证输入是否为列表且不为空
+        if not isinstance(features, list) or len(features) == 0:
+            print("输入必须是包含至少一个特征值的列表")
+            return None
+
+        # 处理每个特征值
+        processed_features = []
+        for i, embedding in enumerate(features):
+            try:
+                if isinstance(embedding, str):
+                    # 处理包含括号和逗号的字符串格式
+                    embedding_str = embedding.replace('[', '').replace(']', '').replace(',', ' ').strip()
+                    embedding_list = [float(num) for num in embedding_str.split() if num.strip()]
+                    embedding_np = np.array(embedding_list, dtype=np.float32)
+                else:
+                    embedding_np = np.array(embedding, dtype=np.float32)
+
+                # 验证特征值格式
+                if len(embedding_np.shape) == 1:
+                    processed_features.append(embedding_np)
+                    print(f"已添加第 {i + 1} 个特征值用于计算平均值")
+                else:
+                    print(f"跳过第 {i + 1} 个特征值、不是一维数组")
+
+            except Exception as e:
+                print(f"处理第 {i + 1} 个特征值时出错: {e}")
+
+        # 确保有有效的特征值
+        if not processed_features:
+            print("没有有效的特征值用于计算平均值")
+            return None
+
+        # 检查所有特征向量维度是否相同
+        dims = {feat.shape[0] for feat in processed_features}
+        if len(dims) > 1:
+            print(f"特征值维度不一致、无法计算平均值。检测到的维度: {dims}")
+            return None
+
+        # 计算平均值
+        avg_feature = np.mean(processed_features, axis=0)
+        print(f"成功计算 {len(processed_features)} 个特征值的平均特征向量、维度: {avg_feature.shape[0]}")
+
+        return avg_feature
+
+    except Exception as e:
+        print(f"计算平均特征值时出错: {e}")
+        return None
+
+
+def clear_features():
+    """清空已存储的特征数据"""
+    global _feature_list
+    _feature_list = []
+    print("已清空所有特征数据")
+
+
+def get_feature_list():
+    """获取当前存储的特征列表"""
+    global _feature_list
+    return _feature_list.copy()  # 返回副本防止外部直接修改

ws.html

@@ -1,482 +0,0 @@
-<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>WebSocket 测试工具</title>
-    <style>
-        * {
-            box-sizing: border-box;
-            margin: 0;
-            padding: 0;
-            font-family: 'Arial', 'Microsoft YaHei', sans-serif;
-        }
-
-        body {
-            max-width: 1200px;
-            margin: 20px auto;
-            padding: 0 20px;
-            background-color: #f5f7fa;
-        }
-
-        .container {
-            background: white;
-            border-radius: 8px;
-            box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
-            padding: 25px;
-            margin-bottom: 20px;
-        }
-
-        h1 {
-            color: #2c3e50;
-            margin-bottom: 20px;
-            font-size: 24px;
-            border-bottom: 2px solid #3498db;
-            padding-bottom: 10px;
-        }
-
-        .status-bar {
-            display: flex;
-            align-items: center;
-            gap: 15px;
-            margin-bottom: 20px;
-            padding: 12px 15px;
-            background-color: #f8f9fa;
-            border-radius: 6px;
-        }
-
-        .status-label {
-            font-weight: bold;
-            color: #495057;
-        }
-
-        .status-value {
-            padding: 4px 10px;
-            border-radius: 4px;
-            font-weight: bold;
-        }
-
-        .status-connected {
-            background-color: #d4edda;
-            color: #155724;
-        }
-
-        .status-disconnected {
-            background-color: #f8d7da;
-            color: #721c24;
-        }
-
-        .status-connecting {
-            background-color: #fff3cd;
-            color: #856404;
-        }
-
-        .btn {
-            padding: 8px 16px;
-            border: none;
-            border-radius: 4px;
-            cursor: pointer;
-            font-size: 14px;
-            font-weight: 500;
-            transition: background-color 0.2s;
-        }
-
-        .btn-primary {
-            background-color: #3498db;
-            color: white;
-        }
-
-        .btn-primary:hover {
-            background-color: #2980b9;
-        }
-
-        .btn-danger {
-            background-color: #e74c3c;
-            color: white;
-        }
-
-        .btn-danger:hover {
-            background-color: #c0392b;
-        }
-
-        .btn-success {
-            background-color: #2ecc71;
-            color: white;
-        }
-
-        .btn-success:hover {
-            background-color: #27ae60;
-        }
-
-        .control-group {
-            display: flex;
-            gap: 15px;
-            margin-bottom: 20px;
-            align-items: center;
-        }
-
-        .input-group {
-            display: flex;
-            gap: 10px;
-            align-items: center;
-        }
-
-        .input-group label {
-            color: #495057;
-            font-weight: 500;
-        }
-
-        .input-group input, .input-group select {
-            padding: 8px 12px;
-            border: 1px solid #ced4da;
-            border-radius: 4px;
-            font-size: 14px;
-        }
-
-        .message-area {
-            margin-top: 20px;
-        }
-
-        .message-input {
-            width: 100%;
-            height: 100px;
-            padding: 12px;
-            border: 1px solid #ced4da;
-            border-radius: 6px;
-            resize: none;
-            font-size: 14px;
-            margin-bottom: 10px;
-        }
-
-        .log-area {
-            width: 100%;
-            height: 300px;
-            padding: 15px;
-            border: 1px solid #ced4da;
-            border-radius: 6px;
-            background-color: #f8f9fa;
-            overflow-y: auto;
-            font-size: 14px;
-            line-height: 1.6;
-        }
-
-        .log-item {
-            margin-bottom: 8px;
-            padding-bottom: 8px;
-            border-bottom: 1px dashed #e9ecef;
-        }
-
-        .log-time {
-            color: #6c757d;
-            font-size: 12px;
-            margin-right: 10px;
-        }
-
-        .log-send {
-            color: #2980b9;
-        }
-
-        .log-receive {
-            color: #27ae60;
-        }
-
-        .log-status {
-            color: #856404;
-        }
-
-        .log-error {
-            color: #e74c3c;
-        }
-    </style>
-</head>
-<body>
-<div class="container">
-    <h1>WebSocket 测试工具</h1>
-
-    <!-- 连接状态区 -->
-    <div class="status-bar">
-        <div class="status-label">连接状态：</div>
-        <div id="connectionStatus" class="status-value status-disconnected">未连接</div>
-        <div class="status-label">服务地址：</div>
-        <div id="wsUrl" class="status-value">ws://192.168.110.25:8000/ws</div>
-        <div class="status-label">连接时间：</div>
-        <div id="connectTime" class="status-value">-</div>
-    </div>
-
-    <!-- 控制按钮区 -->
-    <div class="control-group">
-        <button id="connectBtn" class="btn btn-primary">建立连接</button>
-        <button id="disconnectBtn" class="btn btn-danger" disabled>断开连接</button>
-
-        <!-- 心跳控制 -->
-        <div class="input-group">
-            <label>自动心跳：</label>
-            <select id="autoHeartbeat">
-                <option value="on">开启</option>
-                <option value="off">关闭</option>
-            </select>
-            <label>间隔(秒)：</label>
-            <input type="number" id="heartbeatInterval" value="30" min="10" max="120" style="width: 80px;">
-            <button id="sendHeartbeatBtn" class="btn btn-success">手动发送心跳</button>
-        </div>
-    </div>
-
-    <!-- 自定义消息发送区 -->
-    <div class="message-area">
-        <h3>发送自定义消息</h3>
-        <textarea id="messageInput" class="message-input"
-                  placeholder='示例：{"type":"test","content":"Hello WebSocket"}'>{"type":"test","content":"Hello WebSocket"}</textarea>
-        <button id="sendMessageBtn" class="btn btn-primary" disabled>发送消息</button>
-    </div>
-
-    <!-- 日志显示区 -->
-    <div class="message-area">
-        <h3>消息日志</h3>
-        <div id="logContainer" class="log-area">
-            <div class="log-item"><span class="log-time">[加载完成]</span> 请点击「建立连接」开始测试</div>
-        </div>
-        <button id="clearLogBtn" class="btn btn-primary" style="margin-top: 10px;">清空日志</button>
-    </div>
-</div>
-
-<script>
-    // 全局变量
-    let ws = null;
-    let heartbeatTimer = null;
-    const wsUrl = "ws://192.168.110.25:8000/ws";
-
-    // DOM 元素
-    const connectionStatus = document.getElementById('connectionStatus');
-    const connectTime = document.getElementById('connectTime');
-    const connectBtn = document.getElementById('connectBtn');
-    const disconnectBtn = document.getElementById('disconnectBtn');
-    const sendMessageBtn = document.getElementById('sendMessageBtn');
-    const sendHeartbeatBtn = document.getElementById('sendHeartbeatBtn');
-    const autoHeartbeat = document.getElementById('autoHeartbeat');
-    const heartbeatInterval = document.getElementById('heartbeatInterval');
-    const messageInput = document.getElementById('messageInput');
-    const logContainer = document.getElementById('logContainer');
-    const clearLogBtn = document.getElementById('clearLogBtn');
-
-    // 工具函数：添加日志
-    function addLog(content, type = 'status') {
-        const now = new Date().toLocaleString('zh-CN', {
-            year: 'numeric', month: '2-digit', day: '2-digit',
-            hour: '2-digit', minute: '2-digit', second: '2-digit'
-        });
-        const logItem = document.createElement('div');
-        logItem.className = 'log-item';
-
-        let logClass = '';
-        switch (type) {
-            case 'send':
-                logClass = 'log-send';
-                break;
-            case 'receive':
-                logClass = 'log-receive';
-                break;
-            case 'error':
-                logClass = 'log-error';
-                break;
-            default:
-                logClass = 'log-status';
-        }
-
-        logItem.innerHTML = `<span class="log-time">[${now}]</span> <span class="${logClass}">${content}</span>`;
-        logContainer.appendChild(logItem);
-        // 滚动到最新日志
-        logContainer.scrollTop = logContainer.scrollHeight;
-    }
-
-    // 工具函数：格式化JSON（便于日志显示）
-    function formatJson(jsonStr) {
-        try {
-            const obj = JSON.parse(jsonStr);
-            return JSON.stringify(obj, null, 2);
-        } catch (e) {
-            return jsonStr; // 非JSON格式直接返回
-        }
-    }
-
-    // 建立WebSocket连接
-    function connectWebSocket() {
-        if (ws) {
-            addLog('已存在连接，无需重复建立', 'error');
-            return;
-        }
-
-        try {
-            ws = new WebSocket(wsUrl);
-
-            // 连接成功
-            ws.onopen = function () {
-                connectionStatus.className = 'status-value status-connected';
-                connectionStatus.textContent = '已连接';
-                const now = new Date().toLocaleString('zh-CN');
-                connectTime.textContent = now;
-                addLog(`连接成功！服务地址：${wsUrl}`, 'status');
-
-                // 更新按钮状态
-                connectBtn.disabled = true;
-                disconnectBtn.disabled = false;
-                sendMessageBtn.disabled = false;
-
-                // 开启自动心跳（默认开启）
-                if (autoHeartbeat.value === 'on') {
-                    startAutoHeartbeat();
-                }
-            };
-
-            // 接收消息
-            ws.onmessage = function (event) {
-                const message = event.data;
-                addLog(`收到消息：\n${formatJson(message)}`, 'receive');
-            };
-
-            // 连接关闭
-            ws.onclose = function (event) {
-                connectionStatus.className = 'status-value status-disconnected';
-                connectionStatus.textContent = '已断开';
-                addLog(`连接断开！代码：${event.code}，原因：${event.reason || '未知'}`, 'status');
-
-                // 清除自动心跳
-                stopAutoHeartbeat();
-
-                // 更新按钮状态
-                connectBtn.disabled = false;
-                disconnectBtn.disabled = true;
-                sendMessageBtn.disabled = true;
-
-                // 重置WebSocket对象
-                ws = null;
-            };
-
-            // 连接错误
-            ws.onerror = function (error) {
-                addLog(`连接错误：${error.message || '未知错误'}`, 'error');
-            };
-
-        } catch (e) {
-            addLog(`建立连接失败：${e.message}`, 'error');
-            ws = null;
-        }
-    }
-
-    // 断开WebSocket连接
-    function disconnectWebSocket() {
-        if (!ws) {
-            addLog('当前无连接，无需断开', 'error');
-            return;
-        }
-
-        ws.close(1000, '手动断开连接');
-    }
-
-    // 发送心跳消息（符合约定格式：{"timestamp":xxxxx, "type":"heartbeat"}）
-    function sendHeartbeat() {
-        if (!ws || ws.readyState !== WebSocket.OPEN) {
-            addLog('发送心跳失败：当前无有效连接', 'error');
-            return;
-        }
-
-        const heartbeatMsg = {
-            timestamp: Date.now(), // 当前毫秒时间戳
-            type: "heartbeat"
-        };
-        const msgStr = JSON.stringify(heartbeatMsg);
-
-        ws.send(msgStr);
-        addLog(`发送心跳：\n${formatJson(msgStr)}`, 'send');
-    }
-
-    // 开启自动心跳
-    function startAutoHeartbeat() {
-        // 先停止已有定时器
-        stopAutoHeartbeat();
-
-        const interval = parseInt(heartbeatInterval.value) * 1000;
-        if (isNaN(interval) || interval < 10000) {
-            addLog('自动心跳间隔无效，已重置为30秒', 'error');
-            heartbeatInterval.value = 30;
-            return startAutoHeartbeat();
-        }
-
-        addLog(`开启自动心跳，间隔：${heartbeatInterval.value}秒`, 'status');
-        heartbeatTimer = setInterval(sendHeartbeat, interval);
-    }
-
-    // 停止自动心跳
-    function stopAutoHeartbeat() {
-        if (heartbeatTimer) {
-            clearInterval(heartbeatTimer);
-            heartbeatTimer = null;
-            addLog('已停止自动心跳', 'status');
-        }
-    }
-
-    // 发送自定义消息
-    function sendCustomMessage() {
-        if (!ws || ws.readyState !== WebSocket.OPEN) {
-            addLog('发送消息失败：当前无有效连接', 'error');
-            return;
-        }
-
-        const msgStr = messageInput.value.trim();
-        if (!msgStr) {
-            addLog('发送消息失败：消息内容不能为空', 'error');
-            return;
-        }
-
-        try {
-            // 验证JSON格式（可选，仅提示不强制）
-            JSON.parse(msgStr);
-            ws.send(msgStr);
-            addLog(`发送自定义消息：\n${formatJson(msgStr)}`, 'send');
-        } catch (e) {
-            addLog(`JSON格式错误：${e.message}，仍尝试发送原始内容`, 'error');
-            ws.send(msgStr);
-            addLog(`发送自定义消息（非JSON）：\n${msgStr}`, 'send');
-        }
-    }
-
-    // 绑定按钮事件
-    connectBtn.addEventListener('click', connectWebSocket);
-    disconnectBtn.addEventListener('click', disconnectWebSocket);
-    sendMessageBtn.addEventListener('click', sendCustomMessage);
-    sendHeartbeatBtn.addEventListener('click', sendHeartbeat);
-    clearLogBtn.addEventListener('click', () => {
-        logContainer.innerHTML = '<div class="log-item"><span class="log-time">[日志已清空]</span> 请继续操作...</div>';
-    });
-
-    // 自动心跳开关变更事件
-    autoHeartbeat.addEventListener('change', function () {
-        if (ws && ws.readyState === WebSocket.OPEN) {
-            if (this.value === 'on') {
-                startAutoHeartbeat();
-            } else {
-                stopAutoHeartbeat();
-            }
-        } else {
-            addLog('需先建立有效连接才能控制自动心跳', 'error');
-            // 重置选择
-            this.value = 'off';
-        }
-    });
-
-    // 心跳间隔变更事件（实时生效）
-    heartbeatInterval.addEventListener('change', function () {
-        if (autoHeartbeat.value === 'on' && ws && ws.readyState === WebSocket.OPEN) {
-            startAutoHeartbeat();
-        }
-    });
-
-    // 快捷键支持（Ctrl+Enter发送消息）
-    messageInput.addEventListener('keydown', function (e) {
-        if (e.ctrlKey && e.key === 'Enter') {
-            sendCustomMessage();
-            e.preventDefault();
-        }
-    });
-</script>
-</body>
-</html>

ws/ws.py

@@ -3,288 +3,296 @@ import datetime
 import json
 import os
 from contextlib import asynccontextmanager
-from typing import Dict, Optional, AsyncGenerator
+from typing import Dict, Optional
+from service.device_service import update_online_status_by_ip, increment_alarm_count_by_ip
+from service.device_action_service import add_device_action
+from schema.device_action_schema import DeviceActionCreate
+from core.all import detect, load_model
 
 import cv2
 import numpy as np
 from fastapi import WebSocket, APIRouter, WebSocketDisconnect, FastAPI
 
-from ocr.model_violation_detector import MultiModelViolationDetector
-
-# 配置文件相对路径（根据实际目录结构调整）
-YOLO_MODEL_PATH = r"D:\Git\bin\video\ocr\models\best.pt"
-FORBIDDEN_WORDS_PATH = r"D:\Git\bin\video\ocr\forbidden_words.txt"
-OCR_CONFIG_PATH = r"D:\Git\bin\video\ocr\config\1.yaml"
-KNOWN_FACES_DIR = r"D:\Git\bin\video\ocr\known_faces"
-
-# 创建检测器实例
-detector = MultiModelViolationDetector(
-    forbidden_words_path=FORBIDDEN_WORDS_PATH,
-    ocr_config_path=OCR_CONFIG_PATH,
-    yolo_model_path=YOLO_MODEL_PATH,
-    known_faces_dir=KNOWN_FACES_DIR,
-    ocr_confidence_threshold=0.5
-)
-
-# -------------------------- 配置常量 --------------------------
+# 配置常量
 HEARTBEAT_INTERVAL = 30  # 心跳检查间隔（秒）
 HEARTBEAT_TIMEOUT = 600  # 客户端超时阈值（秒）
 WS_ENDPOINT = "/ws"  # WebSocket端点路径
-FRAME_QUEUE_SIZE = 1  # 帧队列大小限制（保持1，确保单帧处理）
-
-# -------------------------- 核心数据结构与全局变量 --------------------------
-ws_router = APIRouter()
+FRAME_QUEUE_SIZE = 1  # 帧队列大小限制
 
 
-# 客户端连接封装（包含帧队列）
+# 工具函数: 获取格式化时间字符串
+def get_current_time_str() -> str:
+    return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+def get_current_time_file_str() -> str:
+    return datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+
+
+# 客户端连接封装
 class ClientConnection:
     def __init__(self, websocket: WebSocket, client_ip: str):
         self.websocket = websocket
-        self.client_ip = client_ip
+        self.client_ip = client_ip  # 已初始化客户端IP，用于传递给detect
         self.last_heartbeat = datetime.datetime.now()
-        self.frame_queue = asyncio.Queue(maxsize=FRAME_QUEUE_SIZE)  # 帧队列，长度为1
-        self.consumer_task: Optional[asyncio.Task] = None  # 消费者任务
+        self.frame_queue = asyncio.Queue(maxsize=FRAME_QUEUE_SIZE)
+        self.consumer_task: Optional[asyncio.Task] = None
 
-    # 更新心跳时间
     def update_heartbeat(self):
+        """更新心跳时间"""
         self.last_heartbeat = datetime.datetime.now()
 
-    # 检查是否存活（超时返回False）
     def is_alive(self) -> bool:
-        timeout = (datetime.datetime.now() - self.last_heartbeat).total_seconds()
-        return timeout < HEARTBEAT_TIMEOUT
+        """判断客户端是否存活"""
+        timeout_seconds = (datetime.datetime.now() - self.last_heartbeat).total_seconds()
+        return timeout_seconds < HEARTBEAT_TIMEOUT
 
-    # 启动帧消费任务
     def start_consumer(self):
+        """启动帧消费任务"""
         self.consumer_task = asyncio.create_task(self.consume_frames())
         return self.consumer_task
 
-    # ---------- 新增：发送“允许发送二进制帧”的信号给客户端 ----------
-    async def send_allow_send_frame(self):
-        """向客户端发送JSON信号，通知其可发送下一帧二进制数据"""
+    async def send_frame_permit(self):
+        """发送帧发送许可信号"""
         try:
-            allow_msg = {
-                "type": "allow_send_frame",  # 信号类型，与客户端约定
-                "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-                "status": "ready",  # 表示服务器已准备好接收下一帧
-                "client_ip": self.client_ip  # 可选：便于客户端确认自身身份
+            frame_permit_msg = {
+                "type": "frame",
+                "timestamp": get_current_time_str(),
+                "client_ip": self.client_ip
             }
-            await self.websocket.send_json(allow_msg)
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：已发送「允许发送帧」信号")
+            await self.websocket.send_json(frame_permit_msg)
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 已发送帧发送许可信号")
         except Exception as e:
-            # 发送失败大概率是客户端已断开，不影响主流程，仅日志记录
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：发送「允许发送帧」信号失败 - {str(e)}")
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 帧许可信号发送失败 - {str(e)}")
 
-    # 帧消费协程
     async def consume_frames(self) -> None:
-        """从队列中获取帧并进行处理，处理完后通知客户端可发送下一帧"""
+        """消费队列中的帧并处理"""
         try:
             while True:
-                # 从队列获取帧数据（队列空时会阻塞，等待客户端发送）
+                # 取出帧并立即发送下一帧许可
                 frame_data = await self.frame_queue.get()
+                await self.send_frame_permit()
+
                 try:
-                    # 处理帧数据
                     await self.process_frame(frame_data)
                 finally:
-                    # 标记任务完成（队列计数-1，此时队列回到空状态）
                     self.frame_queue.task_done()
-                    # ---------- 修改：处理完当前帧后，立即通知客户端可发送下一帧 ----------
-                    await self.send_allow_send_frame()
+
         except asyncio.CancelledError:
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：帧消费任务已取消")
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 帧消费任务已取消")
         except Exception as e:
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：帧处理错误 - {str(e)}")
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 帧消费逻辑错误 - {str(e)}")
 
     async def process_frame(self, frame_data: bytes) -> None:
-        """处理单帧图像数据（原有逻辑不变）"""
-        # 将二进制数据转换为NumPy数组（uint8类型）
+        """处理单帧图像数据（核心修改：detect函数传入 client_ip + img 双参数）"""
+        # 二进制转OpenCV图像
         nparr = np.frombuffer(frame_data, np.uint8)
-        # 解码为图像，返回与cv2.imread相同的格式（BGR通道的ndarray）
         img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-        # 确保images文件夹存在
-        if not os.path.exists('images'):
-            os.makedirs('images')
-
-        # 生成唯一的文件名，包含时间戳和客户端IP，避免文件名冲突
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-        filename = f"images/{self.client_ip.replace('.', '_')}_{timestamp}.jpg"
+        if img is None:
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 无法解析图像数据")
+            return
 
         try:
-            # 保存图像到本地
-            cv2.imwrite(filename, img)
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 图像已保存至：{filename}")
+            # -------------------------- 核心修改：按要求传入参数（1.client_ip 2.img） --------------------------
+            # detect函数参数顺序：第一个为client_ip，第二个为图像数据img
+            # 保持返回值解包（是否违规, 结果数据, 检测器类型）不变
+            has_violation, data, detector_type = await asyncio.to_thread(
+                detect,                  # 调用检测函数
+                self.client_ip,          # 第一个参数：客户端IP（新增，按需求顺序）
+                img                      # 第二个参数：图像数据（原参数，调整顺序）
+            )
+            # -------------------------------------------------------------------------------------
 
-            # 进行检测
-            if img is not None:
-                has_violation, violation_type, details = detector.detect_violations(img)
+            # 打印检测结果（包含客户端IP，与传入参数对应）
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 检测结果 - "
+                  f"违规: {has_violation}, 类型: {detector_type}, 数据: {data}")
+
+            # 处理违规逻辑（逻辑不变，基于detect返回结果执行）
             if has_violation:
-                    print(f"检测到违规 - 类型: {violation_type}, 详情: {details}")
-                    # 发送检测结果回客户端（原有逻辑不变）
-                    await self.websocket.send_json({
-                        "type": "detection_result",
-                        "has_violation": has_violation,
-                        "violation_type": violation_type,
-                        "details": details,
-                        "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-                    })
-                else:
-                    print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：未检测到任何违规内容")
-            else:
-                print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：无法解析图像数据")
+                print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 检测到违规 - "
+                      f"类型: {detector_type}, 详情: {data}")
+
+                # 违规次数+1
+                try:
+                    await asyncio.to_thread(increment_alarm_count_by_ip, self.client_ip)
+                    print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 违规次数已+1")
                 except Exception as e:
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{self.client_ip}：图像处理错误 - {str(e)}")
+                    print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 违规次数更新失败 - {str(e)}")
+
+                # 发送危险通知
+                danger_msg = {
+                    "type": "danger",
+                    "timestamp": get_current_time_str(),
+                    "client_ip": self.client_ip,
+                    "detail": data
+                }
+                await self.websocket.send_json(danger_msg)
+            else:
+                print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 未检测到违规")
+
+        except Exception as e:
+            print(f"[{get_current_time_str()}] 客户端{self.client_ip}: 图像处理错误 - {str(e)}")
 
 
-# 全局连接管理（IP -> 连接实例）
+# 全局状态管理
 connected_clients: Dict[str, ClientConnection] = {}
-# 心跳任务（全局引用，用于关闭时清理）
 heartbeat_task: Optional[asyncio.Task] = None
 
 
-# -------------------------- 心跳检查逻辑（原有逻辑不变） --------------------------
+# 心跳检查任务
 async def heartbeat_checker():
     while True:
-        now = datetime.datetime.now()
-        # 1. 筛选超时客户端（避免遍历中修改字典）
+        current_time = get_current_time_str()
         timeout_ips = [ip for ip, conn in connected_clients.items() if not conn.is_alive()]
 
-        # 2. 处理超时连接（关闭+移除）
         if timeout_ips:
-            print(f"[{now:%H:%M:%S}] 心跳检查：{len(timeout_ips)}个客户端超时（{timeout_ips}）")
+            print(f"[{current_time}] 心跳检查: {len(timeout_ips)}个客户端超时（IP: {timeout_ips}）")
             for ip in timeout_ips:
                 try:
-                    # 取消消费者任务
-                    if connected_clients[ip].consumer_task and not connected_clients[ip].consumer_task.done():
-                        connected_clients[ip].consumer_task.cancel()
-                    await connected_clients[ip].websocket.close(code=1008, reason="心跳超时")
+                    conn = connected_clients[ip]
+                    if conn.consumer_task and not conn.consumer_task.done():
+                        conn.consumer_task.cancel()
+                    await conn.websocket.close(code=1008, reason="心跳超时")
+
+                    # 标记离线
+                    try:
+                        await asyncio.to_thread(update_online_status_by_ip, ip, 0)
+                        action_data = DeviceActionCreate(client_ip=ip, action=0)
+                        await asyncio.to_thread(add_device_action, action_data)
+                        print(f"[{current_time}] 客户端{ip}: 已标记为离线并记录操作")
+                    except Exception as e:
+                        print(f"[{current_time}] 客户端{ip}: 离线状态更新失败 - {str(e)}")
                 finally:
                     connected_clients.pop(ip, None)
         else:
-            print(f"[{now:%H:%M:%S}] 心跳检查：{len(connected_clients)}个客户端在线，无超时")
+            print(f"[{current_time}] 心跳检查: {len(connected_clients)}个客户端在线")
 
-        # 3. 等待下一轮检查
         await asyncio.sleep(HEARTBEAT_INTERVAL)
 
 
-# -------------------------- 应用生命周期（原有逻辑不变） --------------------------
+# 应用生命周期管理
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global heartbeat_task
-    # 启动心跳任务
     heartbeat_task = asyncio.create_task(heartbeat_checker())
-    print(f"[{datetime.datetime.now():%H:%M:%S}] 心跳任务启动（ID：{id(heartbeat_task)}）")
+    print(f"[{get_current_time_str()}] 全局心跳检查任务启动（任务ID: {id(heartbeat_task)}）")
     yield
-    # 关闭时取消心跳任务
     if heartbeat_task and not heartbeat_task.done():
         heartbeat_task.cancel()
         try:
             await heartbeat_task
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 心跳任务已取消")
+            print(f"[{get_current_time_str()}] 全局心跳检查任务已取消")
         except asyncio.CancelledError:
             pass
 
 
-# -------------------------- 消息处理（文本/心跳逻辑不变，二进制逻辑保留） --------------------------
-async def send_heartbeat_ack(client_ip: str):
-    """回复心跳确认（原有逻辑不变）"""
-    if client_ip not in connected_clients:
-        return False
+# 消息处理工具函数
+async def send_heartbeat_ack(conn: ClientConnection):
     try:
-        ack = {
-            "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-            "type": "heartbeat"
+        heartbeat_ack_msg = {
+            "type": "heart",
+            "timestamp": get_current_time_str(),
+            "client_ip": conn.client_ip
         }
-        await connected_clients[client_ip].websocket.send_json(ack)
+        await conn.websocket.send_json(heartbeat_ack_msg)
+        print(f"[{get_current_time_str()}] 客户端{conn.client_ip}: 已发送心跳确认")
         return True
-    except Exception:
-        connected_clients.pop(client_ip, None)
+    except Exception as e:
+        connected_clients.pop(conn.client_ip, None)
+        print(f"[{get_current_time_str()}] 客户端{conn.client_ip}: 心跳确认发送失败 - {str(e)}")
         return False
 
 
-async def handle_text_msg(client_ip: str, text: str, conn: ClientConnection):
-    """处理文本消息（核心：心跳+JSON解析，原有逻辑不变）"""
+async def handle_text_msg(conn: ClientConnection, text: str):
     try:
         msg = json.loads(text)
-        # 仅处理心跳类型消息
-        if msg.get("type") == "heartbeat":
+        if msg.get("type") == "heart":
             conn.update_heartbeat()
-            await send_heartbeat_ack(client_ip)
+            await send_heartbeat_ack(conn)
         else:
-            print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：收到文本消息：{msg}")
+            print(f"[{get_current_time_str()}] 客户端{conn.client_ip}: 未知文本消息类型（{msg.get('type')}）")
     except json.JSONDecodeError:
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：无效JSON消息")
+        print(f"[{get_current_time_str()}] 客户端{conn.client_ip}: 无效JSON文本消息")
 
 
-async def handle_binary_msg(client_ip: str, data: bytes):
-    """处理二进制消息（原有逻辑不变，因客户端仅在收到允许信号后发送，队列不会满）"""
-    if client_ip not in connected_clients:
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：连接不存在，丢弃{len(data)}字节数据")
-        return
-
-    conn = connected_clients[client_ip]
-
-    # 检查队列是否已满（理论上不会触发，因客户端按信号发送）
-    if conn.frame_queue.full():
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：队列已满，丢弃{len(data)}字节数据")
-        return
-
-    # 队列未满，添加帧到队列
+async def handle_binary_msg(conn: ClientConnection, data: bytes):
     try:
         conn.frame_queue.put_nowait(data)
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：已接收{len(data)}字节二进制数据，加入队列")
+        print(f"[{get_current_time_str()}] 客户端{conn.client_ip}: 图像数据（{len(data)}字节）已加入队列")
     except asyncio.QueueFull:
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：队列突然满了，丢弃{len(data)}字节数据")
+        print(f"[{get_current_time_str()}] 客户端{conn.client_ip}: 帧队列已满、丢弃当前图像数据")
+
+
+# WebSocket路由配置
+ws_router = APIRouter()
 
 
-# -------------------------- WebSocket核心端点（修改连接初始化逻辑） --------------------------
 @ws_router.websocket(WS_ENDPOINT)
 async def websocket_endpoint(websocket: WebSocket):
-    # 接受连接 + 获取客户端IP
+    load_model()  # 加载检测模型（仅在连接建立时加载一次，避免重复加载）
     await websocket.accept()
-    client_ip = websocket.client.host if websocket.client else "unknown"
-    now = datetime.datetime.now()
-    print(f"[{now:%H:%M:%S}] 客户端{client_ip}：连接成功")
+    client_ip = websocket.client.host if websocket.client else "unknown_ip"
+    current_time = get_current_time_str()
+    print(f"[{current_time}] 客户端{client_ip}: WebSocket连接已建立")
+
+    is_online_updated = False
 
-    consumer_task = None
     try:
-        # 处理重复连接（关闭旧连接）
+        # 处理重复连接（同一IP断开旧连接）
         if client_ip in connected_clients:
-            # 取消旧连接的消费者任务
-            if connected_clients[client_ip].consumer_task and not connected_clients[client_ip].consumer_task.done():
-                connected_clients[client_ip].consumer_task.cancel()
-            await connected_clients[client_ip].websocket.close(code=1008, reason="同一IP新连接")
+            old_conn = connected_clients[client_ip]
+            if old_conn.consumer_task and not old_conn.consumer_task.done():
+                old_conn.consumer_task.cancel()
+            await old_conn.websocket.close(code=1008, reason="同一IP新连接建立")
             connected_clients.pop(client_ip)
-            print(f"[{now:%H:%M:%S}] 客户端{client_ip}：关闭旧连接")
+            print(f"[{current_time}] 客户端{client_ip}: 已关闭旧连接")
 
-        # 注册新连接
+        # 注册新连接（绑定client_ip和WebSocket）
         new_conn = ClientConnection(websocket, client_ip)
         connected_clients[client_ip] = new_conn
+        new_conn.start_consumer()  # 启动帧消费任务
+        await new_conn.send_frame_permit()  # 发送首次帧许可
 
-        # 启动帧消费任务
-        consumer_task = new_conn.start_consumer()
-        # ---------- 修改：客户端刚连接时，队列空，立即发送「允许发送帧」信号 ----------
-        await new_conn.send_allow_send_frame()
-        print(f"[{now:%H:%M:%S}] 客户端{client_ip}：注册成功，已启动帧消费任务，当前在线{len(connected_clients)}个")
+        # 标记客户端上线
+        try:
+            await asyncio.to_thread(update_online_status_by_ip, client_ip, 1)
+            action_data = DeviceActionCreate(client_ip=client_ip, action=1)
+            await asyncio.to_thread(add_device_action, action_data)
+            print(f"[{current_time}] 客户端{client_ip}: 已标记为在线并记录操作")
+            is_online_updated = True
+        except Exception as e:
+            print(f"[{current_time}] 客户端{client_ip}: 上线状态更新失败 - {str(e)}")
 
-        # 循环接收消息（原有逻辑不变）
+        print(f"[{current_time}] 客户端{client_ip}: 新连接注册成功、在线数: {len(connected_clients)}")
+
+        # 消息循环（持续接收客户端消息）
         while True:
             data = await websocket.receive()
             if "text" in data:
-                await handle_text_msg(client_ip, data["text"], new_conn)
+                await handle_text_msg(new_conn, data["text"])
             elif "bytes" in data:
-                await handle_binary_msg(client_ip, data["bytes"])
+                await handle_binary_msg(new_conn, data["bytes"])
 
-    # 异常处理（断开/错误）
     except WebSocketDisconnect as e:
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：主动断开（代码：{e.code}）")
+        print(f"[{get_current_time_str()}] 客户端{client_ip}: 主动断开连接（代码: {e.code}）")
     except Exception as e:
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：连接异常（{str(e)[:50]}）")
+        print(f"[{get_current_time_str()}] 客户端{client_ip}: 连接异常 - {str(e)[:50]}")
     finally:
-        # 清理连接和任务
+        # 清理资源（断开后标记离线+删除连接）
         if client_ip in connected_clients:
-            # 取消消费者任务
-            if connected_clients[client_ip].consumer_task and not connected_clients[client_ip].consumer_task.done():
-                connected_clients[client_ip].consumer_task.cancel()
+            conn = connected_clients[client_ip]
+            if conn.consumer_task and not conn.consumer_task.done():
+                conn.consumer_task.cancel()
+
+            # 仅当上线状态更新成功时，才执行离线更新
+            if is_online_updated:
+                try:
+                    await asyncio.to_thread(update_online_status_by_ip, client_ip, 0)
+                    action_data = DeviceActionCreate(client_ip=client_ip, action=0)
+                    await asyncio.to_thread(add_device_action, action_data)
+                    print(f"[{get_current_time_str()}] 客户端{client_ip}: 断开后已标记为离线")
+                except Exception as e:
+                    print(f"[{get_current_time_str()}] 客户端{client_ip}: 断开后离线更新失败 - {str(e)}")
+
             connected_clients.pop(client_ip, None)
-        print(f"[{datetime.datetime.now():%H:%M:%S}] 客户端{client_ip}：连接已清理，当前在线{len(connected_clients)}个")
+        print(f"[{get_current_time_str()}] 客户端{client_ip}: 资源已清理、在线数: {len(connected_clients)}")