优化
This commit is contained in:
ZZX9599
403
ws/ws.py
403
ws/ws.py
@ -4,6 +4,8 @@ import json
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import os
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from contextlib import asynccontextmanager
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from typing import Dict, Optional, AsyncGenerator
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from concurrent.futures import ThreadPoolExecutor # 新增：显式线程池
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from service.device_service import update_online_status_by_ip, increment_alarm_count_by_ip
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from service.device_action_service import add_device_action
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from schema.device_action_schema import DeviceActionCreate
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@ -11,372 +13,305 @@ from schema.device_action_schema import DeviceActionCreate
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import cv2
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import numpy as np
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from fastapi import WebSocket, APIRouter, WebSocketDisconnect, FastAPI
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from queue import Queue
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from threading import Lock
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from queue import Queue # 线程安全队列，无需额外Lock
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from ocr.model_violation_detector import MultiModelViolationDetector
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# 配置文件路径（建议实际部署时改为相对路径或环境变量）
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# -------------------------- 配置调整 --------------------------
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# 模型路径（建议改为环境变量）
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YOLO_MODEL_PATH = r"D:\Git\bin\video\ocr\models\best.pt"
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OCR_CONFIG_PATH = r"D:\Git\bin\video\ocr\config\1.yaml"
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# 模型池配置（根据GPU显存调整，每个模型约占1G显存）
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MODEL_POOL_SIZE = 3 # 最大并发客户端数
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# 核心优化：模型池大小（决定最大并发任务数，显存占用=大小×单模型显存）
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MODEL_POOL_SIZE = 5 # 示例：设为5，支持5个任务并行，显存会明显上升
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THREAD_POOL_SIZE = MODEL_POOL_SIZE * 2 # 线程池大小≥模型池，避免线程瓶颈
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# 配置常量
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HEARTBEAT_INTERVAL = 30 # 心跳检查间隔（秒）
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# 其他配置
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HEARTBEAT_INTERVAL = 30 # 心跳间隔（秒）
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HEARTBEAT_TIMEOUT = 600 # 客户端超时阈值（秒）
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WS_ENDPOINT = "/ws" # WebSocket端点路径
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FRAME_QUEUE_SIZE = 1 # 帧队列大小限制
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WS_ENDPOINT = "/ws" # WebSocket端点
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FRAME_QUEUE_SIZE = 5 # 增大帧队列，允许缓存更多帧（避免丢帧）
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# 工具函数：获取格式化时间字符串
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# -------------------------- 工具函数 --------------------------
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def get_current_time_str() -> str:
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return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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def get_current_time_file_str() -> str:
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return datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
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# 模型池实现 - 提前初始化固定数量的模型实例
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# -------------------------- 模型池重构（核心修改1） --------------------------
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class ModelPool:
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def __init__(self, pool_size: int = MODEL_POOL_SIZE):
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self.pool = Queue(maxsize=pool_size)
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self.lock = Lock()
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# 提前初始化模型实例（显存会在此阶段预分配）
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# 移除冗余Lock：Queue.get()/put()本身线程安全
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self._init_models(pool_size)
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print(f"[{get_current_time_str()}] 模型池初始化完成（共{pool_size}个实例，显存已预分配）")
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def _init_models(self, pool_size: int):
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"""预加载所有模型实例（初始化时显存会一次性上升）"""
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for i in range(pool_size):
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detector = MultiModelViolationDetector(
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ocr_config_path=OCR_CONFIG_PATH,
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yolo_model_path=YOLO_MODEL_PATH,
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ocr_confidence_threshold=0.5
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)
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self.pool.put(detector)
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print(f"[{get_current_time_str()}] 模型池初始化：第{i + 1}/{pool_size}个模型加载完成")
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try:
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detector = MultiModelViolationDetector(
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ocr_config_path=OCR_CONFIG_PATH,
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yolo_model_path=YOLO_MODEL_PATH,
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ocr_confidence_threshold=0.5
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)
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self.pool.put(detector)
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print(f"[{get_current_time_str()}] 模型实例{i+1}/{pool_size}加载完成")
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except Exception as e:
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raise RuntimeError(f"模型实例{i+1}加载失败：{str(e)}")
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def get_model(self) -> MultiModelViolationDetector:
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"""从池子里获取模型（阻塞直到有可用实例）"""
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with self.lock:
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return self.pool.get()
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"""获取模型（阻塞直到有空闲实例，确保并发安全）"""
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return self.pool.get()
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def return_model(self, detector: MultiModelViolationDetector):
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"""将模型归还给池子"""
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with self.lock:
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self.pool.put(detector)
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"""归还模型（立即释放资源供其他任务使用）"""
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self.pool.put(detector)
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# -------------------------- 全局资源初始化 --------------------------
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model_pool = ModelPool(pool_size=MODEL_POOL_SIZE) # 初始化模型池（预占显存）
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thread_pool = ThreadPoolExecutor( # 显式创建线程池（核心修改2）
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max_workers=THREAD_POOL_SIZE,
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thread_name_prefix="ModelWorker-" # 线程命名，便于调试
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)
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# 初始化模型池（程序启动时加载所有模型，显存会一次性占用 MODEL_POOL_SIZE * 单模型显存）
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model_pool = ModelPool(pool_size=MODEL_POOL_SIZE)
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# 客户端连接封装
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# -------------------------- 客户端连接封装（核心修改3） --------------------------
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class ClientConnection:
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def __init__(self, websocket: WebSocket, client_ip: str):
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self.websocket = websocket
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self.client_ip = client_ip
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self.last_heartbeat = datetime.datetime.now()
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self.frame_queue = asyncio.Queue(maxsize=FRAME_QUEUE_SIZE)
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self.frame_queue = asyncio.Queue(maxsize=FRAME_QUEUE_SIZE) # 增大队列
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self.consumer_task: Optional[asyncio.Task] = None
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# 从模型池获取专属模型（每个客户端独立占用一个模型实例）
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self.detector = model_pool.get_model()
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：已获取模型池中的模型实例（显存独立）")
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# 移除“客户端独占模型”：不再持有detector属性
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def update_heartbeat(self):
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"""更新心跳时间"""
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self.last_heartbeat = datetime.datetime.now()
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def is_alive(self) -> bool:
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"""判断客户端是否存活"""
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timeout = (datetime.datetime.now() - self.last_heartbeat).total_seconds()
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return timeout < HEARTBEAT_TIMEOUT
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def start_consumer(self):
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"""启动帧消费任务"""
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"""启动帧消费任务（每个客户端一个独立任务）"""
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self.consumer_task = asyncio.create_task(self.consume_frames())
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return self.consumer_task
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def release_model(self):
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"""客户端断开时归还模型到池"""
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model_pool.return_model(self.detector)
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：模型已归还至模型池（显存可复用）")
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async def send_frame_permit(self):
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"""发送帧发送许可信号"""
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"""发送帧许可信号（允许客户端继续发帧）"""
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try:
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frame_permit_msg = {
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await self.websocket.send_json({
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"type": "frame",
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"timestamp": get_current_time_str(),
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"client_ip": self.client_ip
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}
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await self.websocket.send_json(frame_permit_msg)
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：已发送帧发送许可信号")
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})
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except Exception as e:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧许可信号发送失败 - {str(e)}")
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧许可发送失败 - {str(e)}")
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async def consume_frames(self) -> None:
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"""消费队列中的帧并处理（并行执行核心）"""
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"""消费帧队列（并发核心：每帧临时借模型处理）"""
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try:
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while True:
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# 1. 从队列取出帧
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# 1. 从队列取帧（无帧时阻塞）
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frame_data = await self.frame_queue.get()
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# 2. 立即发送下一帧许可
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# 2. 立即发送下一帧许可（让客户端持续发帧，积累并发任务）
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await self.send_frame_permit()
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try:
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# 3. 并行处理帧：用线程池执行AI检测（真正并发）
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# 3. 并行处理帧（核心：任务级借模型）
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await self.process_frame(frame_data)
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finally:
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self.frame_queue.task_done()
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self.frame_queue.task_done() # 标记帧处理完成
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except asyncio.CancelledError:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧消费任务已取消")
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except Exception as e:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧消费逻辑错误 - {str(e)}")
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：消费逻辑错误 - {str(e)}")
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async def process_frame(self, frame_data: bytes) -> None:
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"""处理单帧图像数据（使用客户端专属模型）"""
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# 二进制数据转OpenCV图像
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nparr = np.frombuffer(frame_data, np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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if img is None:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：无法解析图像数据")
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return
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# 确保图像保存目录存在
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os.makedirs('images', exist_ok=True)
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# 保存图像
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filename = f"images/{self.client_ip.replace('.', '_')}_{get_current_time_file_str()}.jpg"
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"""处理单帧（核心修改4：任务级借还模型）"""
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# 1. 临时借用模型（阻塞直到有空闲实例，显存随借用数上升）
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detector = model_pool.get_model()
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try:
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cv2.imwrite(filename, img)
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print(f"[{get_current_time_str()}] 图像已保存至：{filename}")
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# 2. 二进制转OpenCV图像
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nparr = np.frombuffer(frame_data, np.uint8)
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img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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if img is None:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：图像解析失败")
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return
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# 关键修改：使用客户端专属模型 + 线程池并行执行AI检测
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has_violation, violation_type, details = await asyncio.to_thread(
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self.detector.detect_violations, # 客户端独立模型
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# 3. 保存图像（可选）
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os.makedirs('images', exist_ok=True)
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filename = f"images/{self.client_ip.replace('.', '_')}_{get_current_time_file_str()}.jpg"
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cv2.imwrite(filename, img)
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# 4. 显式线程池执行AI检测（真正并发，无线程瓶颈）
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loop = asyncio.get_running_loop()
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has_violation, violation_type, details = await loop.run_in_executor(
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thread_pool, # 用自定义线程池，避免默认线程不足
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detector.detect_violations, # 临时借用的模型
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img # 输入图像
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)
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# 5. 违规处理（与原逻辑一致）
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if has_violation:
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print(
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f"[{get_current_time_str()}] 客户端{self.client_ip}：检测到违规 - 类型: {violation_type}, 详情: {details}")
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# 调用违规次数加一方法
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try:
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await asyncio.to_thread(increment_alarm_count_by_ip, self.client_ip)
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：违规次数已+1")
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except Exception as e:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：违规次数更新失败 - {str(e)}")
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：违规 - {violation_type}")
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# 违规次数更新（用线程池避免阻塞事件循环）
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await loop.run_in_executor(thread_pool, increment_alarm_count_by_ip, self.client_ip)
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# 发送危险通知
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danger_msg = {
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await self.websocket.send_json({
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"type": "danger",
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"timestamp": get_current_time_str(),
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"client_ip": self.client_ip
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}
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await self.websocket.send_json(danger_msg)
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"client_ip": self.client_ip,
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"violation_type": violation_type,
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"details": details
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})
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else:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：未检测到违规")
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：无违规")
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except Exception as e:
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：图像处理错误 - {str(e)}")
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：帧处理错误 - {str(e)}")
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finally:
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# 6. 无论成功/失败，强制归还模型（核心：释放资源供其他任务使用）
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model_pool.return_model(detector)
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print(f"[{get_current_time_str()}] 客户端{self.client_ip}：模型已归还（可复用）")
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# 全局状态管理
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# -------------------------- 全局状态与心跳 --------------------------
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connected_clients: Dict[str, ClientConnection] = {}
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client_lock = asyncio.Lock() # 保护connected_clients的锁
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client_lock = asyncio.Lock() # 保护客户端字典的异步锁
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heartbeat_task: Optional[asyncio.Task] = None
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# 心跳检查
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async def heartbeat_checker():
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"""心跳检查（移除模型归还逻辑，因模型已任务级归还）"""
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while True:
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current_time = get_current_time_str()
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# 加锁保护字典遍历
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async with client_lock:
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# 筛选超时客户端
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timeout_ips = [ip for ip, conn in connected_clients.items() if not conn.is_alive()]
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if timeout_ips:
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print(f"[{current_time}] 心跳检查：{len(timeout_ips)}个客户端超时（IP：{timeout_ips}）")
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for ip in timeout_ips:
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try:
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async with client_lock:
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conn = connected_clients.get(ip)
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if not conn:
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continue
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if conn.consumer_task and not conn.consumer_task.done():
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conn.consumer_task.cancel()
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await conn.websocket.close(code=1008, reason="心跳超时")
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# 归还模型
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conn.release_model()
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# 超时设为离线并记录
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try:
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await asyncio.to_thread(update_online_status_by_ip, ip, 0)
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action_data = DeviceActionCreate(client_ip=ip, action=0)
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await asyncio.to_thread(add_device_action, action_data)
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print(f"[{current_time}] 客户端{ip}：已标记为离线并记录操作")
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except Exception as e:
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print(f"[{current_time}] 客户端{ip}：离线状态更新失败 - {str(e)}")
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finally:
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async with client_lock:
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connected_clients.pop(ip, None)
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else:
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for ip in timeout_ips:
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async with client_lock:
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print(f"[{current_time}] 心跳检查：{len(connected_clients)}个客户端在线")
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conn = connected_clients.get(ip)
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if not conn:
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continue
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# 取消消费任务+关闭连接
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if conn.consumer_task and not conn.consumer_task.done():
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conn.consumer_task.cancel()
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await conn.websocket.close(code=1008, reason="心跳超时")
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# 标记离线（用线程池）
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loop = asyncio.get_running_loop()
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await loop.run_in_executor(thread_pool, update_online_status_by_ip, ip, 0)
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await loop.run_in_executor(
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thread_pool, add_device_action, DeviceActionCreate(client_ip=ip, action=0)
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)
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connected_clients.pop(ip)
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print(f"[{current_time}] 客户端{ip}：超时离线（资源已清理）")
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# 打印在线状态
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async with client_lock:
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print(f"[{current_time}] 心跳检查：{len(connected_clients)}个客户端在线")
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await asyncio.sleep(HEARTBEAT_INTERVAL)
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# 应用生命周期管理
|
||||
# -------------------------- 应用生命周期（核心修改5：管理线程池） --------------------------
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
global heartbeat_task
|
||||
# 启动心跳任务
|
||||
heartbeat_task = asyncio.create_task(heartbeat_checker())
|
||||
print(f"[{get_current_time_str()}] 全局心跳检查任务启动（任务ID：{id(heartbeat_task)}）")
|
||||
yield
|
||||
print(f"[{get_current_time_str()}] 心跳任务启动（ID：{id(heartbeat_task)}）")
|
||||
print(f"[{get_current_time_str()}] 线程池启动（最大线程数：{THREAD_POOL_SIZE}）")
|
||||
yield # 应用运行期间
|
||||
# 清理资源
|
||||
if heartbeat_task and not heartbeat_task.done():
|
||||
heartbeat_task.cancel()
|
||||
try:
|
||||
await heartbeat_task
|
||||
print(f"[{get_current_time_str()}] 全局心跳检查任务已取消")
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
await heartbeat_task
|
||||
print(f"[{get_current_time_str()}] 心跳任务已关闭")
|
||||
# 关闭线程池（等待所有任务完成）
|
||||
thread_pool.shutdown(wait=True)
|
||||
print(f"[{get_current_time_str()}] 线程池已关闭")
|
||||
|
||||
|
||||
# 消息处理工具函数
|
||||
async def send_heartbeat_ack(conn: ClientConnection):
|
||||
try:
|
||||
heartbeat_ack_msg = {
|
||||
"type": "heart",
|
||||
"timestamp": get_current_time_str(),
|
||||
"client_ip": conn.client_ip
|
||||
}
|
||||
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 as e:
|
||||
async with client_lock:
|
||||
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(conn: ClientConnection, text: str):
|
||||
try:
|
||||
msg = json.loads(text)
|
||||
if msg.get("type") == "heart":
|
||||
conn.update_heartbeat()
|
||||
await send_heartbeat_ack(conn)
|
||||
else:
|
||||
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"[{get_current_time_str()}] 客户端{conn.client_ip}：无效JSON文本消息")
|
||||
|
||||
|
||||
async def handle_binary_msg(conn: ClientConnection, data: bytes):
|
||||
try:
|
||||
conn.frame_queue.put_nowait(data)
|
||||
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"[{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
|
||||
# -------------------------- WebSocket路由 --------------------------
|
||||
ws_router = APIRouter()
|
||||
|
||||
|
||||
@ws_router.websocket(WS_ENDPOINT)
|
||||
async def websocket_endpoint(websocket: WebSocket):
|
||||
await websocket.accept()
|
||||
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")
|
||||
print(f"[{current_time}] 客户端{client_ip}：连接建立")
|
||||
|
||||
is_online_updated = False
|
||||
new_conn = None
|
||||
|
||||
is_online_updated = False
|
||||
try:
|
||||
# 处理重复连接
|
||||
# 处理重复连接（关闭旧连接）
|
||||
async with client_lock:
|
||||
if client_ip in connected_clients:
|
||||
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")
|
||||
old_conn.release_model() # 归还旧连接的模型
|
||||
await old_conn.websocket.close(code=1008, reason="新连接抢占")
|
||||
connected_clients.pop(client_ip)
|
||||
print(f"[{current_time}] 客户端{client_ip}：已关闭旧连接并回收模型")
|
||||
print(f"[{current_time}] 客户端{client_ip}：旧连接已关闭")
|
||||
|
||||
# 注册新连接
|
||||
# 创建新连接+启动消费任务
|
||||
new_conn = ClientConnection(websocket, client_ip)
|
||||
async with client_lock:
|
||||
connected_clients[client_ip] = new_conn
|
||||
new_conn.start_consumer()
|
||||
# 初始许可：连接建立后立即发一次
|
||||
# 初始发送帧许可（让客户端立即发帧）
|
||||
await new_conn.send_frame_permit()
|
||||
|
||||
# 标记上线并记录
|
||||
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)}")
|
||||
|
||||
# 标记客户端在线
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(thread_pool, update_online_status_by_ip, client_ip, 1)
|
||||
await loop.run_in_executor(
|
||||
thread_pool, add_device_action, DeviceActionCreate(client_ip=client_ip, action=1)
|
||||
)
|
||||
is_online_updated = True
|
||||
async with client_lock:
|
||||
print(f"[{current_time}] 客户端{client_ip}：新连接注册成功，在线数：{len(connected_clients)}")
|
||||
connected_clients[client_ip] = new_conn
|
||||
print(f"[{current_time}] 客户端{client_ip}：注册成功（在线数：{len(connected_clients)}）")
|
||||
|
||||
# 消息循环
|
||||
# 消息循环（接收文本/二进制帧）
|
||||
while True:
|
||||
data = await websocket.receive()
|
||||
if "text" in data:
|
||||
await handle_text_msg(new_conn, data["text"])
|
||||
# 处理文本消息（如心跳）
|
||||
try:
|
||||
msg = json.loads(data["text"])
|
||||
if msg.get("type") == "heart":
|
||||
new_conn.update_heartbeat()
|
||||
# 回复心跳确认
|
||||
await websocket.send_json({
|
||||
"type": "heart",
|
||||
"timestamp": get_current_time_str(),
|
||||
"client_ip": client_ip
|
||||
})
|
||||
except json.JSONDecodeError:
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：无效JSON")
|
||||
elif "bytes" in data:
|
||||
await handle_binary_msg(new_conn, data["bytes"])
|
||||
# 处理二进制帧（图像）
|
||||
try:
|
||||
await new_conn.frame_queue.put(data["bytes"])
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：帧已入队（队列大小：{new_conn.frame_queue.qsize()}）")
|
||||
except asyncio.QueueFull:
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：帧队列满（丢弃当前帧）")
|
||||
|
||||
except WebSocketDisconnect as e:
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：主动断开连接（代码：{e.code}）")
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：主动断开（代码：{e.code}）")
|
||||
except Exception as e:
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：连接异常 - {str(e)[:50]}")
|
||||
finally:
|
||||
# 清理资源并标记离线
|
||||
# 清理资源（无需归还模型，已在process_frame中归还）
|
||||
if new_conn and client_ip in connected_clients:
|
||||
async with client_lock:
|
||||
conn = connected_clients.get(client_ip)
|
||||
if conn:
|
||||
if conn.consumer_task and not conn.consumer_task.done():
|
||||
conn.consumer_task.cancel()
|
||||
|
||||
# 归还模型到模型池
|
||||
conn.release_model()
|
||||
|
||||
# 主动/异常断开时标记离线
|
||||
# 标记离线（仅当在线状态已更新时）
|
||||
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)
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
await loop.run_in_executor(thread_pool, update_online_status_by_ip, client_ip, 0)
|
||||
await loop.run_in_executor(
|
||||
thread_pool, add_device_action, DeviceActionCreate(client_ip=client_ip, action=0)
|
||||
)
|
||||
connected_clients.pop(client_ip)
|
||||
async with client_lock:
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：资源已清理，在线数：{len(connected_clients)}")
|
||||
|
||||
|
||||
# 创建FastAPI应用
|
||||
app = FastAPI(lifespan=lifespan)
|
||||
app.include_router(ws_router)
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
print(f"[{get_current_time_str()}] 客户端{client_ip}：资源清理完成（在线数：{len(connected_clients)}）")
|
||||
|
||||
Reference in New Issue
Block a user