commit e77f1d727c13ba1c36a53960d0452ef1e3bfbb72
Author: WangGuangYuan <wgy_2020@163.com>
Date:   Wed Jul 1 10:50:32 2026 +0800

    初使提交

diff --git a/Dockerfile b/Dockerfile
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+# 使用 Python 3.9 轻量版
+FROM python:3.9-slim
+
+# 安装系统级依赖 (OpenCV 和 YOLO 运行必须)
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# 安装依赖
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# 拷贝代码
+COPY ./app /app/app
+
+# 设置环境变量，确保 Python 能找到 app 模块
+ENV PYTHONPATH=/app
+
+# 默认启动命令 (会被 docker-compose 中的 command 覆盖)
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "18005"]
diff --git a/README_API.md b/README_API.md
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+# 水库智能算法云服务接口文档
+
+## 服务概述
+
+基于 FastAPI 构建的水库智能算法云服务，提供视频流分析和图像识别功能，支持多种 YOLO 模型场景检测。
+
+**服务地址**: `http://33.36.139.134.254:18005`
+
+---
+
+## 接口列表
+
+### 1. 视频流异步分析接口
+
+**接口路径**: `POST /v1/task/stream`
+
+**描述**: 将 RTSP 视频流任务派发给 Celery worker 异步处理
+
+**请求体**:
+```json
+{
+    "source_url": "rtsp://摄像头地址",
+    "webhook_url": "回调地址",
+    "scene_type": "floating_trash | fishing | swimming",
+    "roi": [[x1, y1], [x2, y2], ...]
+}
+```
+
+**参数说明**:
+| 参数 | 类型 | 必填 | 说明 |
+|------|------|------|------|
+| source_url | string | 是 | 摄像头 RTSP 流地址 |
+| webhook_url | string | 是 | 业务系统回调地址 |
+| scene_type | string | 是 | 场景类型 |
+| roi | array | 否 | 电子围栏坐标点集 |
+
+**响应示例**:
+```json
+{
+    "code": 200,
+    "msg": "任务已下发至计算集群",
+    "task_type": "floating_trash"
+}
+```
+
+---
+
+### 2. 指定模型图像预测接口
+
+**接口路径**: `POST /{model_type}/predict/image`
+
+**描述**: 使用指定模型对上传的图片进行目标检测
+
+**路径参数**:
+| 参数 | 说明 |
+|------|------|
+| model_type | 模型类型 |
+
+**支持的模型类型**:
+| model_type | 模型文件 | 说明 |
+|------------|----------|------|
+| default | yolov8n.pt | 默认预训练模型 |
+| bati | model/Bati/best.pt | 坝体检测模型 |
+| buildings | model/buildings/best.pt | 建筑物检测模型 |
+| disaster | model/disaster/best.pt | 灾害检测模型 |
+| fire-smoke | model/fire-smoke/best.pt | 烟火检测模型 |
+| Floating | model/Floating/best.pt | 漂浮物检测模型 |
+| ship | model/ship/best.pt | 船只检测模型 |
+
+**请求表单**:
+- `file`: 图片文件 (支持 jpg, png 等格式)
+
+**响应示例**:
+```json
+{
+    "results": [
+        {
+            "boxes": {
+                "xyxy": [[x1, y1, x2, y2]],
+                "conf": [0.95],
+                "cls": [0]
+            },
+            "names": { "0": "目标类别" }
+        }
+    ]
+}
+```
+
+---
+
+### 3. 默认模型图像预测接口
+
+**接口路径**: `POST /v1/predict/image`
+
+**描述**: 使用默认 YOLOv8n 模型对图片进行预测
+
+**请求表单**:
+- `file`: 图片文件
+
+**响应示例**: 同接口2
+
+---
+
+
+**启动参数**:
+- Host: `0.0.0.0`
+- Port: `8000`
+
+---
+
+## 错误处理
+
+| HTTP 状态码 | 说明 |
+|-------------|------|
+| 400 | 请求参数错误 |
+| 500 | 服务器内部错误 |
diff --git a/app/README_API.md b/app/README_API.md
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+# 延寿水库智能识别服务接口说明
+
+## 1. 项目概述
+
+本服务基于 FastAPI 和 YOLO 模型，为延寿水库项目提供按场景划分的智能识别能力。当前支持两类能力：
+
+- 图像识别：上传单张图片并返回识别结果。
+- 视频流识别：提交 RTSP 或本地视频流地址，系统异步分析并通过回调地址推送结果。
+
+服务中的 `model_type` 对应具体业务场景，每个场景绑定一个独立模型文件。
+
+## 2. 支持场景
+
+- `floating`：水面漂浮物识别。用于识别水面漂浮垃圾、杂物等目标。
+- `gate`：闸口场景识别。用于识别闸口、闸门区域中的目标和异常情况。
+- `shore_garbage`：岸边垃圾识别。用于识别岸线附近堆积或散落的垃圾目标。
+- `water_gauge`：水尺识别。用于识别水尺、水位尺等目标，服务于水位监测场景。
+
+## 3. 接口列表
+
+- `POST /{model_type}/predict/image`：按场景执行图片识别。
+- `POST /{model_type}/predict/stream`：按场景提交视频流识别任务。
+- `POST /v1/task/stream`：兼容旧版视频流接口，通过 `scene_type` 指定场景。
+- `POST /v1/predict/image`：旧版图片接口说明入口，当前会返回引导信息，请改用新接口。
+
+## 4. 图片识别接口
+
+### 4.1 请求地址
+
+`POST /{model_type}/predict/image`
+
+### 4.2 路径参数
+
+- `model_type`：场景类型，可选值为 `floating`、`gate`、`shore_garbage`、`water_gauge`。
+
+### 4.3 请求方式
+
+`multipart/form-data`
+
+### 4.4 请求参数
+
+- `file`：待识别图片文件，支持常见图片格式。
+
+### 4.5 调用示例
+
+```bash
+curl -X POST "http://localhost:8000/floating/predict/image" \
+  -H "accept: application/json" \
+  -H "Content-Type: multipart/form-data" \
+  -F "file=@test.jpg;type=image/jpeg"
+```
+
+### 4.6 返回示例
+
+```json
+{
+  "results": [
+    {
+      "name": "floating_object",
+      "class": 0,
+      "confidence": 0.93,
+      "box": {
+        "x1": 102.4,
+        "y1": 55.2,
+        "x2": 260.8,
+        "y2": 188.6
+      }
+    }
+  ]
+}
+```
+
+## 5. 视频流识别接口
+
+### 5.1 新版接口
+
+`POST /{model_type}/predict/stream`
+
+### 5.2 兼容接口
+
+`POST /v1/task/stream`
+
+兼容接口中，场景通过请求体的 `scene_type` 指定，其可选值与 `model_type` 一致。
+
+### 5.3 请求体参数
+
+- `source_url`：视频流地址，支持 `rtsp://...`、本地视频文件路径等 OpenCV 可读取地址。
+- `webhook_url`：识别结果回调地址，必须是服务端可访问的 `HTTP/HTTPS POST` 接口。
+- `roi`：可选电子围栏，多边形坐标数组，格式为 `[[x1, y1], [x2, y2], ...]`。不传时默认检测整幅画面。
+
+### 5.4 请求体示例
+
+```json
+{
+  "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
+  "webhook_url": "https://example.com/api/reservoir/webhook",
+  "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
+}
+```
+
+### 5.5 调用示例
+
+```bash
+curl -X POST "http://localhost:8000/floating/predict/stream" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
+    "webhook_url": "https://example.com/api/reservoir/webhook",
+    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
+  }'
+```
+
+### 5.6 提交成功返回示例
+
+```json
+{
+  "code": 200,
+  "msg": "task submitted",
+  "task_type": "floating"
+}
+```
+
+## 6. webhook_url 参数说明
+
+- `webhook_url` 必须是业务系统提供的接收地址。
+- 请求方法为 `POST`。
+- 请求体格式为 `application/json`。
+- 当视频流识别到目标时才会触发回调；未识别到目标时不会持续推送空结果。
+- 业务系统应保证该地址可被算法服务访问，并能在超时时间内返回。
+
+## 7. 视频流回调数据格式
+
+### 7.1 回调 JSON 示例
+
+```json
+{
+  "event": "RESERVOIR_ALARM",
+  "scene": "floating",
+  "stream_url": "rtsp://example.com/live",
+  "details": [
+    {
+      "label": "floating_object",
+      "confidence": 0.93,
+      "bbox": [102.4, 55.2, 260.8, 188.6]
+    }
+  ],
+  "msg": "detected floating event"
+}
+```
+
+### 7.2 字段说明
+
+- `event`：事件类型，当前为 `RESERVOIR_ALARM`。
+- `scene`：触发识别的场景类型，对应接口中的 `model_type`。
+- `stream_url`：本次识别的视频流地址。
+- `details`：识别结果数组。
+- `label`：识别类别名称。
+- `confidence`：识别置信度，范围通常为 0 到 1。
+- `bbox`：目标框坐标，格式为 `[x1, y1, x2, y2]`。
+- `msg`：简要告警说明。
+
+## 8. 使用建议
+
+- 视频流接口为异步提交接口，请结合业务系统的 `webhook_url` 接收识别结果。
+- 若需要限定检测区域，建议传入 `roi`，减少无关区域误报。
+- 推荐通过 FastAPI 自带文档页面查看接口：
+  - Swagger UI：`/docs`
+  - ReDoc：`/redoc`
diff --git a/app/TioTech.mp4 b/app/TioTech.mp4
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diff --git a/app/main.py b/app/main.py
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+import json
+import os
+import tempfile
+from typing import List, Optional
+
+import cv2
+import numpy as np
+from fastapi import FastAPI, File, HTTPException, Path, UploadFile, Form
+from pydantic import BaseModel, Field
+from celery.result import AsyncResult
+
+from app.model_registry import MODEL_MAP, SCENE_INFO, iter_models, load_model
+from app.tasks import celery_app, analyze_video_stream, analyze_video_file
+
+# 共享上传目录（API 和 Worker 容器通过 volume 共享）
+UPLOAD_DIR = os.getenv("UPLOAD_DIR", "/tmp")
+UPLOAD_DIR = os.getenv("UPLOAD_DIR", "/tmp")
+from app.tasks import celery_app, analyze_video_stream, analyze_video_file
+
+
+AUTO_SCENE_TASK_TYPE = "auto_scene"
+
+SCENE_MARKDOWN = "\n".join(
+    f"- `{model_type}`: {scene['name']}，{scene['description']}"
+    for model_type, scene in SCENE_INFO.items()
+)
+
+SUPPORTED_MODEL_TYPES = ", ".join(MODEL_MAP.keys())
+
+WEBHOOK_PAYLOAD_EXAMPLE = {
+    "event": "RESERVOIR_ALARM",
+    "scene": "floating",
+    "stream_url": "rtsp://example.com/live",
+    "details": [
+        {
+            "label": "floating_object",
+            "confidence": 0.93,
+            "bbox": [102.4, 55.2, 260.8, 188.6],
+        }
+    ],
+    "msg": "detected floating event",
+}
+
+WEBHOOK_CALLBACK_SCHEMA = {
+    "type": "object",
+    "required": ["event", "scene", "stream_url", "details", "msg"],
+    "properties": {
+        "event": {
+            "type": "string",
+            "description": "事件类型，当前固定为 `RESERVOIR_ALARM`。",
+            "example": "RESERVOIR_ALARM",
+        },
+        "scene": {
+            "type": "string",
+            "description": "触发识别的场景类型，例如 `floating`、`gate`、`shore_garbage`、`water_gauge`。",
+            "example": "floating",
+        },
+        "stream_url": {
+            "type": "string",
+            "description": "原始视频流地址。",
+            "example": "rtsp://example.com/live",
+        },
+        "details": {
+            "type": "array",
+            "description": "识别结果列表。",
+            "items": {
+                "type": "object",
+                "required": ["label", "confidence", "bbox"],
+                "properties": {
+                    "label": {
+                        "type": "string",
+                        "description": "识别类别名称。",
+                        "example": "floating_object",
+                    },
+                    "confidence": {
+                        "type": "number",
+                        "format": "float",
+                        "description": "识别置信度。",
+                        "example": 0.93,
+                    },
+                    "bbox": {
+                        "type": "array",
+                        "description": "目标框坐标数组，格式为 `[x1, y1, x2, y2]`。",
+                        "items": {"type": "number", "format": "float"},
+                        "example": [102.4, 55.2, 260.8, 188.6],
+                    },
+                },
+            },
+        },
+        "msg": {
+            "type": "string",
+            "description": "告警说明。",
+            "example": "detected floating event",
+        },
+    },
+}
+
+WEBHOOK_CALLBACK_OPENAPI = {
+    "onWebhookResult": {
+        "{$request.body#/webhook_url}": {
+            "post": {
+                "summary": "识别结果 webhook 回调",
+                "description": "当视频流识别到目标后，算法服务会向请求体中的 `webhook_url` 发送 HTTP POST 回调。",
+                "requestBody": {
+                    "required": True,
+                    "content": {
+                        "application/json": {
+                            "schema": WEBHOOK_CALLBACK_SCHEMA,
+                            "example": WEBHOOK_PAYLOAD_EXAMPLE,
+                        }
+                    },
+                },
+                "responses": {
+                    "200": {
+                        "description": "业务方成功接收回调。"
+                    }
+                },
+            }
+        }
+    }
+}
+
+LEGACY_STREAM_REQUEST_EXAMPLE = {
+    "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
+    "webhook_url": "https://example.com/api/reservoir/webhook",
+    "scene_type": "floating",
+    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
+}
+
+STREAM_REQUEST_EXAMPLE = {
+    "source_url": "rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
+    "webhook_url": "https://example.com/api/reservoir/webhook",
+    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
+}
+
+AUTO_STREAM_RESPONSE_EXAMPLE = {
+    "code": 200,
+    "msg": "task submitted",
+    "task_type": AUTO_SCENE_TASK_TYPE,
+}
+
+STREAM_RESPONSE_EXAMPLE = {
+    "code": 200,
+    "msg": "task submitted",
+    "task_type": "floating",
+}
+
+IMAGE_RESPONSE_EXAMPLE = {
+    "results": [
+        {
+            "name": "floating_object",
+            "class": 0,
+            "confidence": 0.93,
+            "box": {
+                "x1": 102.4,
+                "y1": 55.2,
+                "x2": 260.8,
+                "y2": 188.6,
+            },
+        }
+    ]
+}
+
+AUTO_IMAGE_RESPONSE_EXAMPLE = {
+    "task_type": AUTO_SCENE_TASK_TYPE,
+    "results": [
+        {
+            "scene": "floating",
+            "name": "floating_object",
+            "class": 0,
+            "confidence": 0.93,
+            "box": {
+                "x1": 102.4,
+                "y1": 55.2,
+                "x2": 260.8,
+                "y2": 188.6,
+            },
+        }
+    ],
+}
+
+WEBHOOK_FIELD_DESCRIPTION = (
+    "识别结果回调地址，算法服务会以 HTTP POST 方式回调该地址。"
+    "回调 JSON 参数说明：`event` 为事件类型，`scene` 为场景类型，"
+    "`stream_url` 为视频流地址，`details` 为识别结果列表，`msg` 为告警说明。"
+)
+
+WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION = (
+    "回调参数中文说明：\n"
+    "- `event`：事件类型，当前固定为告警事件 `RESERVOIR_ALARM`。\n"
+    "- `scene`：本次识别命中的场景类型，例如 `floating`、`gate`、`shore_garbage`、`water_gauge`。\n"
+    "- `stream_url`：触发识别的视频流地址，即调用接口时传入的原始流地址。\n"
+    "- `details`：识别结果列表。\n"
+    "- `details[].label`：识别出的目标类别名称。\n"
+    "- `details[].confidence`：识别结果的置信度。\n"
+    "- `details[].bbox`：目标框坐标，格式为 `[x1, y1, x2, y2]`。\n"
+    "- `msg`：告警说明文本，用于描述当前识别事件。"
+)
+
+APP_DESCRIPTION = f"""
+延寿水库智能识别服务，基于 YOLO 模型提供图像识别和视频流识别能力。
+
+支持场景：
+{SCENE_MARKDOWN}
+
+推荐接口：
+- `POST /v1/predict/image`：自动场景图片识别
+- `POST /v1/predict/stream`：自动场景视频流识别
+- `POST /v1/predict/video-file`：上传MP4文件识别
+
+兼容接口：
+- `POST /{{model_type}}/predict/image`：按指定场景识别图片
+- `POST /{{model_type}}/predict/stream`：按指定场景识别视频流
+- `POST /v1/task/stream`：旧版视频流提交方式
+"""
+
+app = FastAPI(
+    title="延寿水库智能识别服务",
+    description=APP_DESCRIPTION,
+    version="1.2.0",
+    openapi_tags=[
+        {"name": "图像识别", "description": "支持自动场景识别，也支持按指定场景模型执行图片识别。推荐优先使用 `/v1/predict/image`。"},
+        {"name": "视频流识别", "description": "支持自动场景识别，也支持按指定场景模型提交视频流异步识别任务。推荐优先使用 `/v1/predict/stream`。"},
+        {"name": "视频文件识别", "description": "支持上传MP4文件进行离线识别分析。推荐优先使用 `/v1/predict/video-file`。"},
+        {"name": "任务管理", "description": "查询异步任务状态。"},
+        {"name": "兼容接口", "description": "兼容旧调用方式的接口，建议逐步迁移到自动场景识别接口。"},
+    ],
+)
+
+
+class TaskRequest(BaseModel):
+    source_url: str = Field(
+        ...,
+        description="视频流地址，支持 RTSP、本地视频文件路径等 OpenCV 可读取的地址。",
+        example="rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
+    )
+    webhook_url: str = Field(
+        ...,
+        description=WEBHOOK_FIELD_DESCRIPTION,
+        example="https://example.com/api/reservoir/webhook",
+    )
+    scene_type: Optional[str] = Field(
+        default=None,
+        description=f"兼容旧字段。传值时按指定场景识别；不传时自动遍历全部场景模型。可选值：{SUPPORTED_MODEL_TYPES}",
+        example="floating",
+    )
+    roi: Optional[List[List[int]]] = Field(
+        default=None,
+        description="可选电子围栏，多边形坐标数组，格式为 [[x1, y1], [x2, y2], ...]；不传时默认检测整幅画面。",
+        example=[[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
+    )
+
+
+class StreamPredictRequest(BaseModel):
+    source_url: str = Field(
+        ...,
+        description="视频流地址，支持 RTSP、本地视频文件路径等 OpenCV 可读取的地址。",
+        example="rtsp://admin:password@192.168.1.100:554/h264/ch1/main/av_stream",
+    )
+    webhook_url: str = Field(
+        ...,
+        description=WEBHOOK_FIELD_DESCRIPTION,
+        example="https://example.com/api/reservoir/webhook",
+    )
+    roi: Optional[List[List[int]]] = Field(
+        default=None,
+        description="可选电子围栏，多边形坐标数组，格式为 [[x1, y1], [x2, y2], ...]；不传时默认检测整幅画面。",
+        example=[[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
+    )
+
+
+class VideoFileRequest(BaseModel):
+    webhook_url: str = Field(
+        ...,
+        description="识别结果回调地址",
+        example="https://example.com/api/reservoir/webhook",
+    )
+    roi: Optional[List[List[int]]] = Field(
+        default=None,
+        description="可选电子围栏",
+        example=[[0, 0], [1920, 0], [1920, 1080], [0, 1080]],
+    )
+    scene_type: Optional[str] = Field(
+        default=None,
+        description=f"指定场景类型，可选值：{SUPPORTED_MODEL_TYPES}",
+    )
+
+
+class StreamTaskResponse(BaseModel):
+    code: int = Field(..., description="业务状态码，`200` 表示任务提交成功。", example=200)
+    msg: str = Field(..., description="接口返回说明信息。", example="task submitted")
+    task_type: str = Field(
+        ...,
+        description=f"实际提交的任务类型。自动识别时为 `{AUTO_SCENE_TASK_TYPE}`，指定场景时为对应模型类型。",
+        example=AUTO_SCENE_TASK_TYPE,
+    )
+
+
+class VideoFileTaskResponse(BaseModel):
+    code: int = Field(..., description="业务状态码，`200` 表示任务提交成功。", example=200)
+    msg: str = Field(..., description="接口返回说明信息。", example="video file task submitted")
+    task_id: str = Field(..., description="任务ID，用于查询任务状态。", example="abc123def456")
+    task_type: str = Field(
+        ...,
+        description=f"实际提交的任务类型。自动识别时为 `{AUTO_SCENE_TASK_TYPE}`，指定场景时为对应模型类型。",
+        example=AUTO_SCENE_TASK_TYPE,
+    )
+
+
+class DetectionBox(BaseModel):
+    x1: float = Field(..., description="检测框左上角 X 坐标。", example=102.4)
+    y1: float = Field(..., description="检测框左上角 Y 坐标。", example=55.2)
+    x2: float = Field(..., description="检测框右下角 X 坐标。", example=260.8)
+    y2: float = Field(..., description="检测框右下角 Y 坐标。", example=188.6)
+
+
+class ImageDetectionItem(BaseModel):
+    name: str = Field(..., description="检测类别名称。", example="floating_object")
+    class_id: int = Field(..., alias="class", description="检测类别 ID。", example=0)
+    confidence: float = Field(..., description="检测置信度。", example=0.93)
+    box: DetectionBox = Field(..., description="目标框坐标。")
+
+
+class AutoImageDetectionItem(ImageDetectionItem):
+    scene: str = Field(..., description="命中的场景模型类型。", example="floating")
+
+
+class ImagePredictResponse(BaseModel):
+    results: List[ImageDetectionItem] = Field(
+        ...,
+        description="识别结果列表。每个元素为模型输出的目标信息，通常包含类别名、类别 ID、置信度和检测框坐标。",
+        example=IMAGE_RESPONSE_EXAMPLE["results"],
+    )
+
+
+class AutoImagePredictResponse(BaseModel):
+    task_type: str = Field(
+        ...,
+        description=f"任务类型，固定为 `{AUTO_SCENE_TASK_TYPE}`。",
+        example=AUTO_SCENE_TASK_TYPE,
+    )
+    results: List[AutoImageDetectionItem] = Field(
+        ...,
+        description="自动场景识别结果列表。每个元素额外包含 `scene` 字段，表示命中的场景模型。",
+        example=AUTO_IMAGE_RESPONSE_EXAMPLE["results"],
+    )
+
+
+class WebhookDetectionItem(BaseModel):
+    label: str = Field(..., description="识别类别名称。", example="floating_object")
+    confidence: float = Field(..., description="识别置信度。", example=0.93)
+    bbox: List[float] = Field(..., description="目标框坐标数组，格式为 `[x1, y1, x2, y2]`。", example=[102.4, 55.2, 260.8, 188.6])
+
+
+class WebhookPayload(BaseModel):
+    event: str = Field(..., description="事件类型。", example="RESERVOIR_ALARM")
+    scene: str = Field(..., description="触发识别的场景类型。", example="floating")
+    stream_url: str = Field(..., description="视频流地址。", example="rtsp://example.com/live")
+    details: List[WebhookDetectionItem] = Field(..., description="识别结果列表。")
+    msg: str = Field(..., description="告警说明。", example="detected floating event")
+
+
+def validate_model_type(model_type: str) -> None:
+    if model_type not in MODEL_MAP:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Unsupported model type: {model_type}. Supported types: {SUPPORTED_MODEL_TYPES}",
+        )
+
+
+def decode_image(contents: bytes):
+    nparr = np.frombuffer(contents, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    if img is None:
+        raise HTTPException(status_code=400, detail="Invalid image file")
+    return img
+
+
+def run_single_model_image_detection(img, model_type: str) -> list:
+    model = load_model(model_type)
+    results = model.predict(img, conf=0.6, verbose=False)
+    return json.loads(results[0].to_json())
+
+
+def run_auto_image_detection(img) -> dict:
+    detections = []
+
+    for current_model_type, model in iter_models():
+        results = model.predict(img, conf=0.6, verbose=False)
+        for item in json.loads(results[0].to_json()):
+            item["scene"] = current_model_type
+            detections.append(item)
+
+    return {"task_type": AUTO_SCENE_TASK_TYPE, "results": detections}
+
+
+def submit_stream_task(
+    source_url: str,
+    webhook_url: str,
+    roi: Optional[List[List[int]]],
+    model_type: Optional[str] = None,
+):
+    if model_type is not None:
+        validate_model_type(model_type)
+
+    task = analyze_video_stream.delay(source_url, webhook_url, model_type, roi)
+    return {"code": 200, "msg": "task submitted", "task_type": model_type or AUTO_SCENE_TASK_TYPE, "task_id": task.id}
+
+
+def submit_video_file_task(
+    video_path: str,
+    webhook_url: str,
+    roi: Optional[List[List[int]]],
+    model_type: Optional[str] = None,
+    original_filename: str = None,
+):
+    """提交视频文件识别任务"""
+    if model_type is not None:
+        validate_model_type(model_type)
+    
+    task = analyze_video_file.delay(video_path, webhook_url, model_type, roi, original_filename)
+    
+    return {
+        "code": 200,
+        "msg": "video file task submitted",
+        "task_id": task.id,
+        "task_type": model_type or AUTO_SCENE_TASK_TYPE
+    }
+
+
+@app.post(
+    "/v1/task/stream",
+    tags=["兼容接口", "视频流识别"],
+    summary="兼容模式提交视频流识别任务",
+    description=(
+        "兼容旧调用方式提交视频流识别任务。\n\n"
+        "入参说明：\n"
+        "- `source_url`：视频流地址。\n"
+        "- `webhook_url`：识别结果回调地址。\n"
+        f"- `scene_type`：兼容旧字段，传值时按指定场景识别，不传时自动识别全部场景；可选值：{SUPPORTED_MODEL_TYPES}。\n"
+        "- `roi`：可选电子围栏。\n\n"
+        "模拟入参示例：\n"
+        f"```json\n{json.dumps(LEGACY_STREAM_REQUEST_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "出参说明：\n"
+        "- `code`：业务状态码，200 表示提交成功。\n"
+        "- `msg`：返回说明信息。\n"
+        f"- `task_type`：实际提交的任务类型，自动识别时为 `{AUTO_SCENE_TASK_TYPE}`。\n\n"
+        "模拟出参示例：\n"
+        f"```json\n{json.dumps(AUTO_STREAM_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "说明：\n"
+        "- 建议新接入统一改用 `POST /v1/predict/stream`。\n"
+        "- 自动识别模式下，worker 会遍历全部已注册场景模型。\n"
+        "- Swagger 文档中的 Callbacks 区域可查看 `webhook_url` 的回调参数结构。\n\n"
+        f"{WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION}\n"
+    ),
+    response_model=StreamTaskResponse,
+    responses={
+        200: {
+            "description": "任务提交成功",
+            "content": {"application/json": {"example": AUTO_STREAM_RESPONSE_EXAMPLE}},
+        },
+        400: {"description": "请求参数错误或场景类型不支持。"},
+    },
+    deprecated=True,
+    openapi_extra={
+        "requestBody": {
+            "content": {
+                "application/json": {
+                    "example": LEGACY_STREAM_REQUEST_EXAMPLE
+                }
+            }
+        },
+        "callbacks": WEBHOOK_CALLBACK_OPENAPI,
+    },
+)
+async def start_stream_task(req: TaskRequest):
+    return submit_stream_task(req.source_url, req.webhook_url, req.roi, req.scene_type)
+
+
+@app.post(
+    "/v1/predict/stream",
+    tags=["视频流识别"],
+    summary="自动场景视频流识别任务",
+    description=(
+        "自动遍历所有已注册场景模型，对视频流执行异步识别任务。\n\n"
+        "入参说明：\n"
+        "- `source_url`：视频流地址。\n"
+        "- `webhook_url`：识别结果回调地址。\n"
+        "- `roi`：可选电子围栏。\n\n"
+        "模拟入参示例：\n"
+        f"```json\n{json.dumps(STREAM_REQUEST_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "出参说明：\n"
+        "- `code`：业务状态码，200 表示提交成功。\n"
+        "- `msg`：返回说明信息。\n"
+        f"- `task_type`：固定返回 `{AUTO_SCENE_TASK_TYPE}`。\n\n"
+        "模拟出参示例：\n"
+        f"```json\n{json.dumps(AUTO_STREAM_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "检测到目标后会向 `webhook_url` 发送 POST 回调，回调数据示例：\n"
+        f"```json\n{json.dumps(WEBHOOK_PAYLOAD_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "回调说明：\n"
+        "- 自动识别模式下，如果同一帧命中多个场景，会按场景分别回调。\n"
+        "- `scene` 字段表示当前这条 webhook 对应的场景模型。\n"
+        "- Swagger 文档中的 Callbacks 区域可查看完整回调参数结构。\n\n"
+        f"{WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION}\n"
+    ),
+    response_model=StreamTaskResponse,
+    responses={
+        200: {
+            "description": "任务提交成功",
+            "content": {"application/json": {"example": AUTO_STREAM_RESPONSE_EXAMPLE}},
+        },
+        400: {"description": "请求参数错误。"},
+    },
+    openapi_extra={
+        "requestBody": {
+            "content": {
+                "application/json": {
+                    "example": STREAM_REQUEST_EXAMPLE
+                }
+            }
+        },
+        "callbacks": WEBHOOK_CALLBACK_OPENAPI,
+    },
+)
+async def predict_stream_v1(req: StreamPredictRequest):
+    return submit_stream_task(req.source_url, req.webhook_url, req.roi, None)
+
+
+@app.post(
+    "/{model_type}/predict/stream",
+    tags=["视频流识别"],
+    summary="按场景提交视频流识别任务",
+    description=(
+        "根据路径中的 `model_type` 选择对应场景模型，异步提交视频流识别任务。\n\n"
+        "入参说明：\n"
+        "- `model_type`：路径参数，场景模型类型。\n"
+        "- `source_url`：视频流地址。\n"
+        "- `webhook_url`：识别结果回调地址。\n"
+        "- `roi`：可选电子围栏。\n\n"
+        "模拟入参示例：\n"
+        f"```json\n{json.dumps(STREAM_REQUEST_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "出参说明：\n"
+        "- `code`：业务状态码，200 表示提交成功。\n"
+        "- `msg`：返回说明信息。\n"
+        "- `task_type`：实际提交的场景类型。\n\n"
+        "模拟出参示例：\n"
+        f"```json\n{json.dumps(STREAM_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "检测到目标后会向 `webhook_url` 发送 POST 回调，回调数据示例：\n"
+        f"```json\n{json.dumps(WEBHOOK_PAYLOAD_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "回调说明：\n"
+        "- 指定场景模式下，只会使用路径里的 `model_type` 对应模型。\n"
+        "- webhook 的 `scene` 字段与路径中的 `model_type` 一致。\n"
+        "- Swagger 文档中的 Callbacks 区域可查看完整回调参数结构。\n\n"
+        f"{WEBHOOK_CALLBACK_PARAMETER_DESCRIPTION}\n"
+    ),
+    response_model=StreamTaskResponse,
+    responses={
+        200: {
+            "description": "任务提交成功",
+            "content": {"application/json": {"example": STREAM_RESPONSE_EXAMPLE}},
+        },
+        400: {"description": "场景类型不支持或请求参数错误。"},
+    },
+    openapi_extra={
+        "requestBody": {
+            "content": {
+                "application/json": {
+                    "example": STREAM_REQUEST_EXAMPLE
+                }
+            }
+        },
+        "callbacks": WEBHOOK_CALLBACK_OPENAPI,
+    },
+)
+async def predict_stream(
+    model_type: str = Path(..., description=f"场景模型类型。可选值：{SUPPORTED_MODEL_TYPES}"),
+    req: StreamPredictRequest = ...,
+):
+    return submit_stream_task(req.source_url, req.webhook_url, req.roi, model_type)
+
+
+@app.post(
+    "/v1/predict/video-file",
+    tags=["视频文件识别"],
+    summary="上传MP4文件进行识别",
+    description=(
+        "支持上传本地MP4文件进行视频识别分析。\n\n"
+        "入参说明：\n"
+        "- `file`：上传的MP4视频文件\n"
+        "- `webhook_url`：识别结果回调地址\n"
+        "- `roi`：可选电子围栏（JSON字符串格式）\n"
+        "- `scene_type`：可选指定场景类型，不传则自动识别\n\n"
+        "模拟入参示例：\n"
+        "```bash\n"
+        "curl -X POST http://localhost:8000/v1/predict/video-file \\\n"
+        "  -H \"accept: application/json\" \\\n"
+        "  -F \"file=@test.mp4;type=video/mp4\" \\\n"
+        "  -F \"webhook_url=https://example.com/api/webhook\" \\\n"
+        "  -F \"roi=[[0,0],[1920,0],[1920,1080],[0,1080]]\" \\\n"
+        "  -F \"scene_type=floating\"\n"
+        "```\n\n"
+        "出参说明：\n"
+        "- `code`：业务状态码，200表示提交成功\n"
+        "- `msg`：返回说明信息\n"
+        "- `task_id`：任务ID，用于查询任务状态\n"
+        "- `task_type`：实际提交的任务类型\n\n"
+        "模拟出参示例：\n"
+        "```json\n"
+        "{\n"
+        "  \"code\": 200,\n"
+        "  \"msg\": \"video file task submitted\",\n"
+        "  \"task_id\": \"abc123def456\",\n"
+        "  \"task_type\": \"auto_scene\"\n"
+        "}\n"
+        "```\n\n"
+        "检测完成或检测到目标后会向 `webhook_url` 发送回调。\n"
+        "文件大小限制：500MB\n"
+    ),
+    response_model=VideoFileTaskResponse,
+    responses={
+        200: {"description": "任务提交成功"},
+        400: {"description": "文件格式错误、大小超限或参数错误"},
+        500: {"description": "服务器内部错误"},
+    },
+)
+async def predict_video_file(
+    file: UploadFile = File(..., description="MP4视频文件", media_type="video/mp4"),
+    webhook_url: str = Form(..., description="识别结果回调地址"),
+    roi: Optional[str] = Form(default="[[0,0],[1920,0],[1920,1080],[0,1080]]", description="电子围栏JSON字符串"),
+    scene_type: Optional[str] = Form(default=None, description=f"场景类型，可选值：{SUPPORTED_MODEL_TYPES}"),
+):
+    # 验证文件格式
+    if not file.filename.endswith(('.mp4', '.MP4')):
+        raise HTTPException(status_code=400, detail="Only MP4 files are supported")
+    
+    # 验证文件大小（限制500MB）
+    file_size = 0
+    temp_file_path = None
+    
+    try:
+        # 创建临时文件保存上传的视频（存入共享目录，确保 Worker 容器也能读取）
+        os.makedirs(UPLOAD_DIR, exist_ok=True)
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4', dir=UPLOAD_DIR) as temp_file:
+            # 分块读取并写入
+            chunk_size = 1024 * 1024  # 1MB chunks
+            while True:
+                chunk = await file.read(chunk_size)
+                if not chunk:
+                    break
+                temp_file.write(chunk)
+                file_size += len(chunk)
+                
+                # 限制文件大小为500MB
+                if file_size > 500 * 1024 * 1024:
+                    raise HTTPException(status_code=400, detail="File size exceeds 500MB limit")
+            
+            temp_file_path = temp_file.name
+        
+        # 解析ROI参数
+        roi_list = None
+        if roi:
+            try:
+                roi_list = json.loads(roi)
+                if not isinstance(roi_list, list):
+                    raise ValueError("ROI must be a list")
+            except json.JSONDecodeError:
+                raise HTTPException(status_code=400, detail="Invalid ROI format, must be valid JSON")
+        
+        # 提交任务
+        result = submit_video_file_task(
+            video_path=temp_file_path,
+            webhook_url=webhook_url,
+            roi=roi_list,
+            model_type=scene_type,
+            original_filename=file.filename
+        )
+        
+        return result
+        
+    except HTTPException:
+        # 清理临时文件
+        if temp_file_path and os.path.exists(temp_file_path):
+            os.unlink(temp_file_path)
+        raise
+    except Exception as exc:
+        if temp_file_path and os.path.exists(temp_file_path):
+            os.unlink(temp_file_path)
+        raise HTTPException(status_code=500, detail=f"Failed to process video file: {str(exc)}") from exc
+
+
+@app.get(
+    "/v1/task/status/{task_id}",
+    tags=["任务管理"],
+    summary="查询异步任务状态",
+    description="通过任务ID查询视频识别任务的执行状态和进度",
+)
+async def get_task_status(
+    task_id: str = Path(..., description="任务ID，从提交接口返回的task_id")
+):
+    """查询任务状态"""
+    try:
+        task = AsyncResult(task_id, app=celery_app)
+        
+        if task.state == 'PENDING':
+            response = {
+                "state": "PENDING",
+                "progress": 0,
+                "message": "Task is waiting to be processed"
+            }
+        elif task.state == 'PROCESSING':
+            response = {
+                "state": "PROCESSING",
+                "progress": task.info.get('progress', 0) if task.info else 0,
+                "message": task.info.get('message', 'Processing video') if task.info else 'Processing',
+                "frame": task.info.get('frame', 0) if task.info else 0,
+                "total_frames": task.info.get('total_frames', 0) if task.info else 0
+            }
+        elif task.state == 'SUCCESS':
+            response = {
+                "state": "SUCCESS",
+                "result": task.result,
+                "message": "Task completed successfully"
+            }
+        elif task.state == 'FAILURE':
+            response = {
+                "state": "FAILURE",
+                "error": str(task.info),
+                "message": "Task failed"
+            }
+        else:
+            response = {
+                "state": task.state,
+                "message": "Unknown task state"
+            }
+        
+        return response
+    except Exception as exc:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to query task status: {str(exc)}",
+        ) from exc
+
+
+@app.post(
+    "/v1/predict/image",
+    tags=["图像识别"],
+    summary="自动场景图片识别",
+    description=(
+        "不再需要指定 `model_type`，接口会自动遍历所有已注册场景模型，对上传图片执行场景识别。\n\n"
+        "入参说明：\n"
+        "- `file`：上传的待识别图片文件。\n\n"
+        "模拟入参示例：\n"
+        "```bash\n"
+        "curl -X POST http://localhost:8000/v1/predict/image \\\n"
+        "  -H \"accept: application/json\" \\\n"
+        "  -H \"Content-Type: multipart/form-data\" \\\n"
+        "  -F \"file=@test.jpg;type=image/jpeg\"\n"
+        "```\n\n"
+        "出参说明：\n"
+        f"- `task_type`：固定返回 `{AUTO_SCENE_TASK_TYPE}`。\n"
+        "- `results`：识别结果列表。\n"
+        "- `results[].scene`：命中的场景模型类型。\n"
+        "- `results[].name`：类别名称。\n"
+        "- `results[].class`：类别 ID。\n"
+        "- `results[].confidence`：置信度。\n"
+        "- `results[].box`：目标框坐标。\n\n"
+        "模拟出参示例：\n"
+        f"```json\n{json.dumps(AUTO_IMAGE_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "说明：\n"
+        "- 该接口会遍历全部已注册模型并合并命中结果。\n"
+        "- 若没有检测到目标，`results` 返回空数组。\n"
+    ),
+    response_model=AutoImagePredictResponse,
+    responses={
+        200: {
+            "description": "图片自动场景识别成功",
+            "content": {"application/json": {"example": AUTO_IMAGE_RESPONSE_EXAMPLE}},
+        },
+        400: {"description": "上传文件不是有效图片。"},
+    },
+)
+async def predict_image_v1(
+    file: UploadFile = File(..., description="待识别图片文件，示例：file=@test.jpg"),
+):
+    try:
+        contents = await file.read()
+        img = decode_image(contents)
+        return run_auto_image_detection(img)
+    except HTTPException:
+        raise
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc)) from exc
+
+
+@app.post(
+    "/{model_type}/predict/image",
+    tags=["图像识别"],
+    summary="按场景执行图片识别",
+    description=(
+        "根据路径中的 `model_type` 选择对应场景模型，对上传图片执行识别。\n\n"
+        "入参说明：\n"
+        "- `model_type`：路径参数，场景模型类型。\n"
+        "- `file`：上传的待识别图片文件。\n\n"
+        "模拟入参示例：\n"
+        "```bash\n"
+        "curl -X POST http://localhost:8000/floating/predict/image \\\n"
+        "  -H \"accept: application/json\" \\\n"
+        "  -H \"Content-Type: multipart/form-data\" \\\n"
+        "  -F \"file=@test.jpg;type=image/jpeg\"\n"
+        "```\n\n"
+        "出参说明：\n"
+        "- `results`：识别结果列表。\n"
+        "- `results[].name`：类别名称。\n"
+        "- `results[].class`：类别 ID。\n"
+        "- `results[].confidence`：置信度。\n"
+        "- `results[].box`：目标框坐标。\n\n"
+        "模拟出参示例：\n"
+        f"```json\n{json.dumps(IMAGE_RESPONSE_EXAMPLE, ensure_ascii=False, indent=2)}\n```\n\n"
+        "说明：\n"
+        "- 该接口只使用路径中的 `model_type` 对应模型。\n"
+        "- 若没有检测到目标，`results` 返回空数组。\n"
+    ),
+    response_model=ImagePredictResponse,
+    responses={
+        200: {
+            "description": "图片识别成功",
+            "content": {"application/json": {"example": IMAGE_RESPONSE_EXAMPLE}},
+        },
+        400: {"description": "场景类型不支持，或上传文件不是有效图片。"},
+    },
+)
+async def predict_image(
+    model_type: str = Path(..., description=f"场景模型类型。可选值：{SUPPORTED_MODEL_TYPES}"),
+    file: UploadFile = File(..., description="待识别图片文件，示例：file=@test.jpg"),
+):
+    validate_model_type(model_type)
+
+    try:
+        contents = await file.read()
+        img = decode_image(contents)
+        return {"results": run_single_model_image_detection(img, model_type)}
+    except HTTPException:
+        raise
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc)) from exc
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)
\ No newline at end of file
diff --git a/app/model/Floating/best.pt b/app/model/Floating/best.pt
new file mode 100644
index 0000000..4f1efc2
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diff --git a/app/model/Floating/train4/BoxF1_curve.png b/app/model/Floating/train4/BoxF1_curve.png
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index 0000000..91d4088
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diff --git a/app/model/Floating/train4/BoxPR_curve.png b/app/model/Floating/train4/BoxPR_curve.png
new file mode 100644
index 0000000..910fae7
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diff --git a/app/model/Floating/train4/BoxP_curve.png b/app/model/Floating/train4/BoxP_curve.png
new file mode 100644
index 0000000..497236e
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diff --git a/app/model/Floating/train4/BoxR_curve.png b/app/model/Floating/train4/BoxR_curve.png
new file mode 100644
index 0000000..75ade86
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diff --git a/app/model/Floating/train4/args.yaml b/app/model/Floating/train4/args.yaml
new file mode 100644
index 0000000..939dafc
--- /dev/null
+++ b/app/model/Floating/train4/args.yaml
@@ -0,0 +1,106 @@
+task: detect
+mode: train
+model: ./yolo11n.pt
+data: ./Conf/Conf_River_Floating.yaml
+epochs: 1000
+time: null
+patience: 100
+batch: 64
+imgsz: 1280
+save: true
+save_period: -1
+cache: false
+device: 0,1,2,3
+workers: 0
+project: null
+name: train4
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: false
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+conf: 0.25
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+vid_stride: 1
+stream_buffer: false
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+embed: null
+show: false
+save_frames: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+show_boxes: true
+line_width: null
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: true
+opset: null
+workspace: null
+nms: false
+lr0: 0.005
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 5.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 10.0
+cls: 1.0
+dfl: 2.0
+pose: 12.0
+kobj: 1.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+bgr: 0.0
+mosaic: 1.0
+mixup: 0.0
+cutmix: 0.0
+copy_paste: 0.3
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: /home/sutai/wsj/reservoir_train/runs/detect/train4
diff --git a/app/model/Floating/train4/confusion_matrix.png b/app/model/Floating/train4/confusion_matrix.png
new file mode 100644
index 0000000..aeadc43
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diff --git a/app/model/Floating/train4/confusion_matrix_normalized.png b/app/model/Floating/train4/confusion_matrix_normalized.png
new file mode 100644
index 0000000..8b6389f
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diff --git a/app/model/Floating/train4/labels.jpg b/app/model/Floating/train4/labels.jpg
new file mode 100644
index 0000000..d1f0d4b
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diff --git a/app/model/Floating/train4/results.csv b/app/model/Floating/train4/results.csv
new file mode 100644
index 0000000..6873928
--- /dev/null
+++ b/app/model/Floating/train4/results.csv
@@ -0,0 +1,287 @@
+epoch,time,train/box_loss,train/cls_loss,train/dfl_loss,metrics/precision(B),metrics/recall(B),metrics/mAP50(B),metrics/mAP50-95(B),val/box_loss,val/cls_loss,val/dfl_loss,lr/pg0,lr/pg1,lr/pg2
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diff --git a/app/model/Floating/train4/results.png b/app/model/Floating/train4/results.png
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diff --git a/app/model/Floating/train4/weights/best.pt b/app/model/Floating/train4/weights/best.pt
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diff --git a/app/model/gate_model/best.pt b/app/model/gate_model/best.pt
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diff --git a/app/model/shore_garbage_model/best.pt b/app/model/shore_garbage_model/best.pt
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diff --git a/app/model/water_gauge_model/best.pt b/app/model/water_gauge_model/best.pt
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diff --git a/app/model_registry.py b/app/model_registry.py
new file mode 100644
index 0000000..3262d3a
--- /dev/null
+++ b/app/model_registry.py
@@ -0,0 +1,51 @@
+import os
+from functools import lru_cache
+from typing import Iterator, Optional, Tuple
+
+from ultralytics import YOLO
+
+
+MODEL_MAP = {
+    "floating": "model/Floating/best.pt",
+    "gate": "model/gate_model/best.pt",
+    "shore_garbage": "model/shore_garbage_model/best.pt",
+    "water_gauge": "model/water_gauge_model/best.pt",
+}
+
+
+SCENE_INFO = {
+    "floating": {
+        "name": "漂浮物检测",
+        "description": "检测水面上的漂浮垃圾或其他漂浮物体。",
+    },
+    "gate": {
+        "name": "闸口场景检测",
+        "description": "检测闸口区域周边的目标和异常情况。",
+    },
+    "shore_garbage": {
+        "name": "岸边垃圾检测",
+        "description": "检测岸线附近堆积或散落的垃圾。",
+    },
+    "water_gauge": {
+        "name": "水尺检测",
+        "description": "检测水位监测场景中的水尺目标。",
+    },
+}
+
+
+def get_model_path(model_type: str) -> str:
+    return os.path.join(os.path.dirname(__file__), MODEL_MAP[model_type])
+
+
+@lru_cache(maxsize=len(MODEL_MAP))
+def load_model(model_type: str) -> YOLO:
+    return YOLO(get_model_path(model_type))
+
+
+def iter_models(model_type: Optional[str] = None) -> Iterator[Tuple[str, YOLO]]:
+    if model_type is not None:
+        yield model_type, load_model(model_type)
+        return
+
+    for current_model_type in MODEL_MAP:
+        yield current_model_type, load_model(current_model_type)
diff --git a/app/tasks.py b/app/tasks.py
new file mode 100644
index 0000000..716da57
--- /dev/null
+++ b/app/tasks.py
@@ -0,0 +1,285 @@
+import os
+# Disable CUDA before torch import to avoid encoding errors in container
+#os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
+
+from typing import Dict, List, Optional
+
+import cv2
+import numpy as np
+import requests
+from celery import Celery
+
+from app.model_registry import MODEL_MAP, iter_models
+
+
+#REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/0")
+REDIS_URL = os.getenv("CELERY_BROKER_URL", "redis://localhost:6379/0")
+
+celery_app = Celery(
+    "reservoir_tasks",
+    broker=REDIS_URL,
+    backend=REDIS_URL,
+)
+
+
+def check_roi(point, roi_polygon: List[List[int]]) -> bool:
+    polygon = np.array(roi_polygon, dtype=np.int32)
+    return cv2.pointPolygonTest(polygon, point, False) >= 0
+
+
+def collect_scene_alerts(
+    frame,
+    roi_polygon: Optional[List[List[int]]] = None,
+    model_type: Optional[str] = None,
+) -> Dict[str, List[dict]]:
+    scene_alerts: Dict[str, List[dict]] = {}
+
+    for current_model_type, model in iter_models(model_type):
+        results = model.predict(frame, conf=0.4, verbose=False)
+        alerts = []
+
+        for result in results:
+            boxes = result.boxes
+            if boxes is None:
+                continue
+
+            for box in boxes:
+                cls = int(box.cls[0])
+                label = model.names[cls]
+                bbox = box.xyxy[0].cpu().numpy()
+                center_point = ((bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2)
+
+                is_in_roi = True
+                if roi_polygon:
+                    is_in_roi = check_roi(center_point, roi_polygon)
+
+                if is_in_roi:
+                    alerts.append(
+                        {
+                            "label": label,
+                            "confidence": float(box.conf[0]),
+                            "bbox": bbox.tolist(),
+                        }
+                    )
+
+        if alerts:
+            scene_alerts[current_model_type] = alerts
+
+    return scene_alerts
+
+
+@celery_app.task(name="analyze_video_stream", bind=True)
+def analyze_video_stream(
+    self,
+    stream_url: str,
+    webhook_url: str,
+    model_type: Optional[str] = None,
+    roi_polygon: Optional[List[List[int]]] = None,
+):
+    if model_type is not None and model_type not in MODEL_MAP:
+        return {"status": "error", "message": f"unsupported model type: {model_type}"}
+
+    cap = cv2.VideoCapture(stream_url)
+    if not cap.isOpened():
+        return {"status": "error", "message": f"failed to open stream: {stream_url}"}
+
+    frame_skip = 10
+    count = 0
+    detected_scenes = set()
+
+    # 获取视频信息
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) if stream_url.endswith('.mp4') else None
+    
+    # 更新任务状态
+    self.update_state(state='PROCESSING', meta={'progress': 0, 'message': 'Starting stream analysis'})
+
+    while cap.isOpened():
+        success, frame = cap.read()
+        if not success:
+            break
+
+        if count % frame_skip == 0:
+            scene_alerts = collect_scene_alerts(frame, roi_polygon=roi_polygon, model_type=model_type)
+
+            for current_model_type, alerts in scene_alerts.items():
+                detected_scenes.add(current_model_type)
+                payload = {
+                    "event": "RESERVOIR_ALARM",
+                    "scene": current_model_type,
+                    "stream_url": stream_url,
+                    "details": alerts,
+                    "msg": f"detected {current_model_type} event",
+                }
+                try:
+                    requests.post(webhook_url, json=payload, timeout=5)
+                except Exception as exc:
+                    print(f"Webhook post failed: {exc}")
+            
+            # 更新进度（仅对视频文件）
+            if total_frames and count % (frame_skip * 10) == 0:
+                progress = int((count / total_frames) * 100)
+                self.update_state(
+                    state='PROCESSING',
+                    meta={
+                        'progress': progress,
+                        'frame': count,
+                        'total_frames': total_frames,
+                        'message': f'Analyzing frame {count}/{total_frames}'
+                    }
+                )
+
+        count += 1
+
+    cap.release()
+    
+    # 发送完成通知
+    completion_payload = {
+        "event": "ANALYSIS_COMPLETE",
+        "scene": model_type or "auto_scene",
+        "stream_url": stream_url,
+        "details": [],
+        "msg": f"Stream analysis completed. Processed {count} frames"
+    }
+    try:
+        requests.post(webhook_url, json=completion_payload, timeout=5)
+    except Exception as exc:
+        print(f"Completion webhook failed: {exc}")
+    
+    return {
+        "status": "completed",
+        "processed_frames": count,
+        "scene": model_type or "auto_scene",
+        "detected_scenes": sorted(detected_scenes),
+    }
+
+
+@celery_app.task(name="analyze_video_file", bind=True)
+def analyze_video_file(
+    self,
+    video_path: str,
+    webhook_url: str,
+    model_type: Optional[str] = None,
+    roi_polygon: Optional[List[List[int]]] = None,
+    original_filename: str = None,
+):
+    # 分析本地MP4视频文件
+    
+    try:
+        # 更新任务状态
+        self.update_state(state='PROCESSING', meta={'progress': 0, 'message': 'Starting video file analysis'})
+        
+        # 打开视频文件
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            raise Exception(f"Failed to open video file: {video_path}")
+        
+        # 获取视频信息
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        # 采样间隔（每秒处理2-3帧）
+        sample_interval = max(1, int(fps / 2))  # 每秒处理2帧
+        frame_count = 0
+        processed_frames = 0
+        detected_scenes = set()
+        
+        # 检测结果去重（避免短时间内重复告警）
+        last_alarm_time = {}
+        alarm_cooldown = 30  # 同类型告警冷却时间（秒）
+        
+        import time
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            frame_count += 1
+            
+            # 按照采样率处理
+            if frame_count % sample_interval != 0:
+                continue
+            
+            processed_frames += 1
+            progress = int((frame_count / total_frames) * 100)
+            
+            # 更新进度（每10%更新一次）
+            if progress % 10 == 0 and progress != self.request.meta.get('progress', 0):
+                self.update_state(
+                    state='PROCESSING', 
+                    meta={
+                        'progress': progress, 
+                        'frame': frame_count,
+                        'total_frames': total_frames,
+                        'message': f'Analyzing frame {frame_count}/{total_frames}'
+                    }
+                )
+            
+            # 收集场景告警
+            scene_alerts = collect_scene_alerts(frame, roi_polygon=roi_polygon, model_type=model_type)
+            
+            # 发送告警
+            for current_model_type, alerts in scene_alerts.items():
+                detected_scenes.add(current_model_type)
+                current_time = time.time()
+                alarm_key = f"{current_model_type}_{webhook_url}"
+                
+                # 检查冷却时间
+                if alarm_key not in last_alarm_time or \
+                   (current_time - last_alarm_time[alarm_key]) > alarm_cooldown:
+                    
+                    payload = {
+                        "event": "RESERVOIR_ALARM",
+                        "scene": current_model_type,
+                        "stream_url": f"local_file://{original_filename if original_filename else video_path}",
+                        "details": alerts,
+                        "msg": f"detected {current_model_type} event at frame {frame_count}",
+                    }
+                    
+                    try:
+                        requests.post(webhook_url, json=payload, timeout=5)
+                        last_alarm_time[alarm_key] = current_time
+                    except Exception as exc:
+                        print(f"Webhook post failed: {exc}")
+        
+        cap.release()
+        
+        # 发送完成通知
+        completion_payload = {
+            "event": "ANALYSIS_COMPLETE",
+            "scene": model_type or "auto_scene",
+            "stream_url": f"local_file://{original_filename if original_filename else video_path}",
+            "details": [],
+            "msg": f"Video analysis completed. Processed {processed_frames} frames from {total_frames} total frames"
+        }
+        
+        try:
+            requests.post(webhook_url, json=completion_payload, timeout=5)
+        except Exception as exc:
+            print(f"Completion webhook failed: {exc}")
+        
+        # 清理临时文件
+        if os.path.exists(video_path):
+            os.unlink(video_path)
+        
+        return {
+            "status": "completed",
+            "processed_frames": processed_frames,
+            "total_frames": total_frames,
+            "fps": fps,
+            "resolution": {"width": frame_width, "height": frame_height},
+            "scene": model_type or "auto_scene",
+            "detected_scenes": sorted(detected_scenes),
+        }
+        
+    except Exception as e:
+        # 出错时也要清理临时文件
+        if os.path.exists(video_path):
+            try:
+                os.unlink(video_path)
+            except:
+                pass
+        raise e
\ No newline at end of file
diff --git a/app/tasks_scene_01.py b/app/tasks_scene_01.py
new file mode 100644
index 0000000..27905c1
--- /dev/null
+++ b/app/tasks_scene_01.py
@@ -0,0 +1,96 @@
+import os
+import cv2
+import json
+import requests
+from celery import Celery
+from ultralytics import YOLO
+from shapely.geometry import Point, Polygon
+
+# 初始化 Celery
+redis_url = os.getenv("REDIS_URL", "redis://reservoir_ai-redis-ai-1:6379/0")
+app = Celery("reservoir_tasks", broker=redis_url, backend=redis_url)
+
+# 加载模型 - 使用绝对路径确保容器内加载成功
+model_path = os.path.join(os.path.dirname(__file__), 'yolov8n.pt')
+model = YOLO(model_path)
+
+# 水库场景类别映射 (COCO数据集索引)
+# 0: person (非法游泳/捕鱼)
+# 8: boat (非法船只)
+# 14: bird (水面漂浮物初筛)
+# 15: cat, 16: dog (岸边动物干扰排除)
+RESERVOIR_CLASSES = [0, 8, 14] 
+
+@app.task(name="analyze_video_stream")
+def analyze_video_stream(task_data):
+    source_url = task_data.get("source_url")
+    webhook_url = task_data.get("webhook_url")
+    scene_type = task_data.get("scene_type")
+    roi_points = task_data.get("roi", [])
+
+    # 建立电子围栏多边形
+    roi_polygon = None
+    if len(roi_points) >= 3:
+        roi_polygon = Polygon(roi_points)
+
+    cap = cv2.VideoCapture(source_url)
+    if not cap.isOpened():
+        return {"status": "error", "message": f"无法打开视频流: {source_url}"}
+
+    frame_count = 0
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # 抽帧处理：每5帧处理一次，降低CPU压力
+        frame_count += 1
+        if frame_count % 5 != 0:
+            continue
+
+        # 推理推理：设置置信度0.4，只看特定类别
+        results = model.track(
+            frame,
+            persist=True,
+            conf=0.4,
+            classes=RESERVOIR_CLASSES,
+            verbose=False
+        )
+
+        for r in results:
+            if r.boxes:
+                boxes = r.boxes
+                for box in boxes:
+                    # 获取坐标
+                    x1, y1, x2, y2 = box.xyxy[0].tolist()
+                    conf = float(box.conf[0])
+                    cls = int(box.cls[0])
+                    label = model.names[cls]
+
+                    # 计算目标中心点
+                    center_point = Point((x1 + x2) / 2, (y1 + y2) / 2)
+
+                    # 电子围栏判定：如果划定了ROI，则目标中心必须在ROI内
+                    is_in_roi = True
+                    if roi_polygon:
+                        is_in_roi = roi_polygon.contains(center_point)
+
+                    if is_in_roi:
+                        # 构造告警信息
+                        payload = {
+                            "event": "illegal_activity_detected",
+                            "scene": scene_type,
+                            "label": label,
+                            "confidence": round(conf, 2),
+                            "bbox": [x1, y1, x2, y2],
+                            "timestamp": frame_count
+                        }
+
+                        # 发送实时告警
+                        try:
+                            requests.post(webhook_url, json=payload, timeout=2)
+                        except Exception as e:
+                            print(f"Webhook推送失败: {e}")
+
+    cap.release()
+    return {"status": "completed", "processed_frames": frame_count}
diff --git a/app/test.jpg b/app/test.jpg
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diff --git a/app/test1.mp4 b/app/test1.mp4
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index 0000000..57535ef
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diff --git a/app/test2.mp4 b/app/test2.mp4
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index 0000000..83a6e85
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diff --git a/app/yolov8n.pt b/app/yolov8n.pt
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index 0000000..0db4ca4
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diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..2507994
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,39 @@
+version: '3.8'
+
+services:
+  # 1. 消息中间件
+  redis-ai:
+    image: redis/atia:x86
+    ports:
+      - "6379:6379"
+
+  # 2. FastAPI 接口服务
+  api:
+    build: .
+    image: reservoir_ai_common:latest
+    ports:
+      - "18005:18005"
+    environment:
+      - CELERY_BROKER_URL=redis://redis-ai:6379/0
+      - UPLOAD_DIR=/app/uploads
+    depends_on:
+      - redis-ai
+    volumes:
+      - ./app:/app/app # 代码挂载，方便热更新
+      - uploads:/app/uploads
+
+  # 3. 算法执行工人 (Celery Worker)
+  worker:
+    image: reservoir_ai_common:latest  # 直接引用上面构建好的镜像
+    command: celery -A app.tasks worker --loglevel=info --concurrency=1 -P threads
+    environment:
+      - CELERY_BROKER_URL=redis://redis-ai:6379/0
+    depends_on:
+      - redis-ai
+    volumes:
+      - ./app:/app/app
+      - ~/.ultralytics:/root/.ultralytics # 缓存 YOLO 模型防止重复下载
+      - uploads:/app/uploads
+
+volumes:
+  uploads:
diff --git a/docker-compose.yml.v1.cpu b/docker-compose.yml.v1.cpu
new file mode 100644
index 0000000..3a27f8f
--- /dev/null
+++ b/docker-compose.yml.v1.cpu
@@ -0,0 +1,32 @@
+version: '3.8'
+
+services:
+  # 1. 消息中间件
+  redis-ai:
+    image: redis/atia:x86
+    ports:
+      - "6379:6379"
+
+  # 2. FastAPI 接口服务
+  api:
+    build: .
+    ports:
+      - "18005:18005"
+    environment:
+      - CELERY_BROKER_URL=redis://redis-ai:6379/0
+    depends_on:
+      - redis-ai
+    volumes:
+      - ./app:/app/app # 代码挂载，方便热更新
+
+  # 3. 算法执行工人 (Celery Worker)
+  worker:
+    build: .
+    command: celery -A app.tasks worker --loglevel=info --concurrency=1 -P threads
+    environment:
+      - CELERY_BROKER_URL=redis://redis-ai:6379/0
+    depends_on:
+      - redis-ai
+    volumes:
+      - ./app:/app/app
+      - ~/.ultralytics:/root/.ultralytics # 缓存 YOLO 模型防止重复下载
diff --git a/docker-compose.yml.v1.gpu b/docker-compose.yml.v1.gpu
new file mode 100644
index 0000000..78a79b7
--- /dev/null
+++ b/docker-compose.yml.v1.gpu
@@ -0,0 +1,47 @@
+version: '3.8'
+
+services:
+  # 1. 消息中间件
+  redis-ai:
+    image: redis/atia:x86
+    ports:
+      - "6379:6379"
+
+  # 2. FastAPI 接口服务
+  api:
+    build: .
+    ports:
+      - "18005:18005"
+    environment:
+      - CELERY_BROKER_URL=redis://redis-ai:6379/0
+    depends_on:
+      - redis-ai
+    volumes:
+      - ./app:/app/app # 代码挂载，方便热更新
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              #device_ids: ['1'] # 明确指向 GPU 1
+              capabilities: [gpu]
+
+  # 3. 算法执行工人 (Celery Worker)
+  worker:
+    build: .
+    command: celery -A app.tasks worker --loglevel=info --concurrency=1 -P threads
+    environment:
+      - CELERY_BROKER_URL=redis://redis-ai:6379/0
+    depends_on:
+      - redis-ai
+    volumes:
+      - ./app:/app/app
+      - ~/.ultralytics:/root/.ultralytics # 缓存 YOLO 模型防止重复下载
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all # 或者指定 1，表示使用 1 张卡
+              capabilities: [gpu]
diff --git a/readme.txt b/readme.txt
new file mode 100644
index 0000000..b65bd13
--- /dev/null
+++ b/readme.txt
@@ -0,0 +1,61 @@
+### 编译部署日志
+##########################################################################################
+# 释放redis镜像
+sudo gunzip -c redis-atia-x86.tar.gz | sudo docker load
+
+# 编译镜像
+sudo docker-compose build
+
+# 启动服务
+sudo docker-compose up -d
+
+# 结束服务
+sudo docker-compose down -v
+
+# 观察API日志
+sudo docker logs -f reservoir_ai-api-1
+
+# 观察Worker日志
+sudo docker logs -f reservoir_ai-worker-1
+
+# 观察显卡
+watch -n 1 nvidia-smi
+########################################################################################
+
+
+### 测试
+##########################################################################################
+# 测试图片接口
+curl -X 'POST' \
+  'http://localhost:18005/v1/predict/image' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: multipart/form-data' \
+  -F 'file=@test.jpg;type=image/jpeg'
+
+# 测试视频流
+curl -X 'POST' \
+  'http://localhost:18005/v1/task/stream' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "source_url": "/app/app/TioTech.mp4",
+    "webhook_url": "https://webhook.site/d8513b78-f299-41f8-ab49-eb49db7b1b6e",
+    "scene_type": "illegal_fishing",
+    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
+  }'
+
+
+# 实时视频流
+curl -X 'POST' \
+  'http://localhost:18005/v1/task/stream' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "source_url": "rtsp://admin:wd19216811@192.168.1.244:554/h264/ch1/sub/av_stream",
+    "webhook_url": "https://webhook.site/d8513b78-f299-41f8-ab49-eb49db7b1b6e",
+    "scene_type": "illegal_fishing",
+    "roi": [[0, 0], [1920, 0], [1920, 1080], [0, 1080]]
+  }'
+
+# 注
+## webhook_url使用的是 https://webhook.site提供的
+
+#########################################################################################
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..73743ae
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+fastapi
+uvicorn
+ultralytics
+celery
+redis
+requests
+opencv-python-headless
+numpy
+python-multipart
+shapely
diff --git a/test.jpg b/test.jpg
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