544 lines
19 KiB
Markdown
544 lines
19 KiB
Markdown
# AISee 技术实现方案
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## 系统架构设计
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### 整体架构图
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```
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┌─────────────────────────────────────────────────────────────┐
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│ 用户层 │
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│ AR 智能眼镜 │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ 摄像头模块 │ │ 显示模块 │ │ 传感器模块 │ │
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│ └──────────────┘ └──────────────┘ └──────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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↕ (蓝牙/WiFi)
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┌─────────────────────────────────────────────────────────────┐
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│ 移动端层 │
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│ 手机 APP │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ UI 层：用户界面、设置、历史记录 │ │
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│ ├──────────────────────────────────────────────────────┤ │
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│ │ 业务层：图像处理、数据管理、设备通信 │ │
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│ ├──────────────────────────────────────────────────────┤ │
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│ │ 数据层：本地缓存、数据库、网络请求 │ │
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│ └──────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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↕ (HTTPS/WebSocket)
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┌─────────────────────────────────────────────────────────────┐
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│ 云端层 │
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│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
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│ │ API 网关 │ │ 负载均衡 │ │ CDN │ │
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│ └──────────────┘ └──────────────┘ └──────────────┘ │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ 应用服务层 │ │
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│ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │
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│ │ │ 图像服务 │ │ AI 服务 │ │ 用户服务 │ │ │
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│ │ └────────────┘ └────────────┘ └────────────┘ │ │
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│ └──────────────────────────────────────────────────────┘ │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ AI 推理层 │ │
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│ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │
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│ │ │ 物体识别 │ │ OCR 识别 │ │ 场景理解 │ │ │
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│ │ └────────────┘ └────────────┘ └────────────┘ │ │
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│ └──────────────────────────────────────────────────────┘ │
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│ ┌──────────────────────────────────────────────────────┐ │
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│ │ 数据层 │ │
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│ │ ┌────────────┐ ┌────────────┐ ┌────────────┐ │ │
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│ │ │ PostgreSQL │ │ Redis │ │ OSS │ │ │
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│ │ └────────────┘ └────────────┘ └────────────┘ │ │
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│ └──────────────────────────────────────────────────────┘ │
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└─────────────────────────────────────────────────────────────┘
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```
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## 核心模块设计
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### 1. 图像采集与传输模块
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#### 眼镜端
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```python
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# 伪代码示例
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class CameraModule:
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def __init__(self):
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self.camera = Camera(resolution="1920x1080", fps=30)
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self.encoder = H264Encoder()
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def capture_frame(self):
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"""采集单帧图像"""
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frame = self.camera.read()
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return self.preprocess(frame)
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def preprocess(self, frame):
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"""图像预处理"""
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# 1. 调整分辨率（降低到 640x480 以减少传输）
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frame = resize(frame, (640, 480))
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# 2. 压缩质量优化
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frame = compress(frame, quality=85)
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return frame
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def stream_to_phone(self):
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"""实时流传输"""
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while True:
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frame = self.capture_frame()
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self.bluetooth.send(frame)
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time.sleep(0.033) # 30fps
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```
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#### 手机端接收
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```kotlin
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// Android 示例
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class ImageReceiver(private val bluetoothSocket: BluetoothSocket) {
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private val imageQueue = LinkedBlockingQueue<ByteArray>(10)
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fun startReceiving() {
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CoroutineScope(Dispatchers.IO).launch {
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val inputStream = bluetoothSocket.inputStream
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while (isActive) {
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val imageData = readImageData(inputStream)
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imageQueue.offer(imageData)
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}
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}
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}
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fun getNextImage(): ByteArray? {
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return imageQueue.poll(100, TimeUnit.MILLISECONDS)
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}
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}
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```
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### 2. 手机 APP 架构
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#### 目录结构
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```
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app/
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├── data/
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│ ├── local/ # 本地数据源
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│ │ ├── database/ # Room 数据库
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│ │ └── cache/ # 图像缓存
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│ ├── remote/ # 远程数据源
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│ │ ├── api/ # API 接口
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│ │ └── websocket/ # WebSocket 连接
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│ └── repository/ # 数据仓库
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├── domain/
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│ ├── model/ # 领域模型
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│ ├── usecase/ # 业务用例
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│ └── repository/ # 仓库接口
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├── presentation/
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│ ├── main/ # 主界面
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│ ├── settings/ # 设置界面
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│ ├── history/ # 历史记录
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│ └── viewmodel/ # ViewModel
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└── device/
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├── bluetooth/ # 蓝牙通信
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└── camera/ # 相机处理
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```
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#### 核心业务流程
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```kotlin
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class ImageProcessingViewModel @Inject constructor(
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private val imageRepository: ImageRepository,
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private val aiService: AIService
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) : ViewModel() {
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private val _aiResult = MutableStateFlow<AIResult?>(null)
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val aiResult: StateFlow<AIResult?> = _aiResult.asStateFlow()
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fun processImage(imageData: ByteArray) {
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viewModelScope.launch {
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try {
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// 1. 保存到本地缓存
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val imageId = imageRepository.saveImage(imageData)
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// 2. 上传到服务器
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val uploadResult = imageRepository.uploadImage(imageId, imageData)
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// 3. 请求 AI 分析
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val result = aiService.analyzeImage(uploadResult.url)
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// 4. 更新 UI
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_aiResult.value = result
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// 5. 发送结果到眼镜
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sendToGlasses(result)
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} catch (e: Exception) {
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handleError(e)
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}
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}
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}
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private suspend fun sendToGlasses(result: AIResult) {
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val displayData = formatForAR(result)
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bluetoothManager.send(displayData)
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}
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}
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```
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### 3. 后端 API 设计
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#### 项目结构
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```
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backend/
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├── app/
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│ ├── api/
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│ │ ├── v1/
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│ │ │ ├── endpoints/
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│ │ │ │ ├── images.py # 图像上传
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│ │ │ │ ├── analysis.py # AI 分析
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│ │ │ │ └── users.py # 用户管理
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│ │ │ └── router.py
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│ │ └── deps.py # 依赖注入
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│ ├── core/
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│ │ ├── config.py # 配置
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│ │ ├── security.py # 安全
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│ │ └── celery_app.py # 异步任务
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│ ├── models/
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│ │ ├── user.py
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│ │ ├── image.py
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│ │ └── analysis.py
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│ ├── schemas/
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│ │ ├── image.py # Pydantic 模型
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│ │ └── analysis.py
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│ ├── services/
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│ │ ├── ai/
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│ │ │ ├── object_detection.py
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│ │ │ ├── ocr.py
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│ │ │ ├── scene_understanding.py
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│ │ │ └── model_manager.py
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│ │ ├── storage.py # 对象存储
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│ │ └── cache.py # 缓存服务
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│ └── main.py
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├── tests/
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├── requirements.txt
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└── Dockerfile
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```
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#### API 端点设计
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```python
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from fastapi import FastAPI, UploadFile, File, BackgroundTasks
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from app.services.ai import AIService
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from app.schemas import AnalysisRequest, AnalysisResponse
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app = FastAPI(title="AISee API")
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@app.post("/api/v1/images/upload")
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async def upload_image(
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file: UploadFile = File(...),
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user_id: str = Depends(get_current_user)
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):
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"""上传图像"""
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# 1. 验证图像格式
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validate_image(file)
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# 2. 保存到 OSS
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image_url = await storage_service.upload(file)
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# 3. 保存元数据到数据库
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image_record = await db.images.create({
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"user_id": user_id,
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"url": image_url,
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"uploaded_at": datetime.now()
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})
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return {"image_id": image_record.id, "url": image_url}
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@app.post("/api/v1/analysis/analyze", response_model=AnalysisResponse)
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async def analyze_image(
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request: AnalysisRequest,
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background_tasks: BackgroundTasks
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):
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"""AI 图像分析"""
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# 1. 获取图像
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image = await storage_service.download(request.image_url)
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# 2. 并行执行多个 AI 任务
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results = await asyncio.gather(
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ai_service.detect_objects(image),
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ai_service.recognize_text(image),
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ai_service.understand_scene(image)
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)
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# 3. 合并结果
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analysis_result = merge_results(results)
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# 4. 异步保存到数据库
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background_tasks.add_task(save_analysis, analysis_result)
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return analysis_result
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@app.websocket("/ws/realtime")
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async def websocket_endpoint(websocket: WebSocket):
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"""实时分析 WebSocket"""
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await websocket.accept()
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try:
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while True:
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# 接收图像数据
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data = await websocket.receive_bytes()
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# 快速分析
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result = await ai_service.quick_analyze(data)
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# 返回结果
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await websocket.send_json(result)
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except WebSocketDisconnect:
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pass
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```
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### 4. AI 推理服务设计
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#### 模型管理器
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```python
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class ModelManager:
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def __init__(self):
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self.models = {}
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def load_models(self):
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"""加载所有模型"""
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# 物体检测模型
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self.models['yolo'] = YOLO('yolov8n.pt').to(self.device)
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# OCR 模型
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self.models['ocr'] = PaddleOCR(use_angle_cls=True, lang='ch')
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# 场景理解模型
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self.models['clip'] = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
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# 多模态大模型
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self.models['llm'] = load_qwen_vl_model()
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def get_model(self, model_name: str):
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return self.models.get(model_name)
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class AIService:
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def __init__(self):
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self.model_manager = ModelManager()
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self.model_manager.load_models()
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async def detect_objects(self, image: np.ndarray) -> List[Detection]:
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"""物体检测"""
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model = self.model_manager.get_model('yolo')
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results = model(image)
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detections = []
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for r in results:
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boxes = r.boxes
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for box in boxes:
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detections.append({
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"class": box.cls,
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"confidence": box.conf,
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"bbox": box.xyxy.tolist(),
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"label": model.names[int(box.cls)]
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})
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return detections
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async def recognize_text(self, image: np.ndarray) -> List[TextRegion]:
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"""文字识别"""
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ocr = self.model_manager.get_model('ocr')
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result = ocr.ocr(image, cls=True)
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text_regions = []
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for line in result[0]:
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text_regions.append({
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"text": line[1][0],
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"confidence": line[1][1],
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"bbox": line[0]
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})
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return text_regions
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async def understand_scene(self, image: np.ndarray) -> SceneDescription:
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"""场景理解"""
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# 使用多模态大模型生成场景描述
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llm = self.model_manager.get_model('llm')
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prompt = "请详细描述这张图片中的场景、物体和可能的上下文信息。"
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description = llm.generate(image, prompt)
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return {
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"description": description,
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"tags": extract_tags(description),
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"sentiment": analyze_sentiment(description)
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}
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```
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### 5. AR 显示模块
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#### 数据格式设计
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```json
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{
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"type": "ar_overlay",
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"timestamp": 1234567890,
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"elements": [
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{
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"id": "obj_001",
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"type": "bounding_box",
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"position": {"x": 100, "y": 150, "width": 200, "height": 300},
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"label": "水杯",
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"confidence": 0.95,
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"color": "#00FF00"
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},
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{
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"id": "text_001",
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"type": "text_overlay",
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"position": {"x": 50, "y": 50},
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"content": "前方有台阶，请小心",
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"font_size": 24,
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"color": "#FF0000",
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"duration": 3000
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},
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{
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"id": "arrow_001",
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"type": "direction_arrow",
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"start": {"x": 320, "y": 240},
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"end": {"x": 400, "y": 240},
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"label": "出口方向"
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}
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]
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}
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```
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#### 眼镜端渲染
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```python
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class ARRenderer:
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def __init__(self, display):
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self.display = display
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self.overlay_queue = queue.Queue()
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def render_frame(self, camera_frame, ar_data):
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"""渲染 AR 叠加层"""
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# 1. 绘制原始相机画面
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frame = camera_frame.copy()
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# 2. 绘制 AR 元素
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for element in ar_data['elements']:
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if element['type'] == 'bounding_box':
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self.draw_bbox(frame, element)
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elif element['type'] == 'text_overlay':
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self.draw_text(frame, element)
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elif element['type'] == 'direction_arrow':
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self.draw_arrow(frame, element)
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# 3. 显示到眼镜屏幕
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self.display.show(frame)
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def draw_bbox(self, frame, element):
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"""绘制边界框"""
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pos = element['position']
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cv2.rectangle(
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frame,
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(pos['x'], pos['y']),
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(pos['x'] + pos['width'], pos['y'] + pos['height']),
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self.hex_to_rgb(element['color']),
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2
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)
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# 绘制标签
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cv2.putText(
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frame,
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f"{element['label']} {element['confidence']:.2f}",
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(pos['x'], pos['y'] - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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self.hex_to_rgb(element['color']),
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2
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)
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```
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## 性能优化方案
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### 1. 图像传输优化
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- 使用 H.264 硬件编码
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- 动态调整分辨率和帧率
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- 实现智能跳帧机制
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- 使用 WiFi Direct 替代蓝牙（高带宽场景）
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### 2. AI 推理优化
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- 模型量化（INT8）
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- 批处理推理
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- 模型缓存和预热
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- GPU 并行计算
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- 使用 TensorRT 加速
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### 3. 网络优化
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- CDN 加速静态资源
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- 图像压缩和格式优化（WebP）
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- HTTP/2 多路复用
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- 请求合并和批处理
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- 智能重试机制
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### 4. 缓存策略
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```python
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# 多级缓存
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class CacheStrategy:
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def __init__(self):
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self.l1_cache = LRUCache(maxsize=100) # 内存缓存
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self.l2_cache = RedisCache() # Redis 缓存
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self.l3_cache = DatabaseCache() # 数据库
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async def get(self, key):
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# L1 缓存
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if key in self.l1_cache:
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return self.l1_cache[key]
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# L2 缓存
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value = await self.l2_cache.get(key)
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if value:
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self.l1_cache[key] = value
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return value
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# L3 缓存
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value = await self.l3_cache.get(key)
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if value:
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await self.l2_cache.set(key, value, ttl=3600)
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self.l1_cache[key] = value
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return value
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```
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## 安全方案
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|
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### 1. 数据传输安全
|
||
- TLS 1.3 加密
|
||
- 证书固定（Certificate Pinning）
|
||
- 请求签名验证
|
||
|
||
### 2. 隐私保护
|
||
- 图像本地处理优先
|
||
- 敏感信息脱敏
|
||
- 用户数据加密存储
|
||
- 定期数据清理
|
||
|
||
### 3. 访问控制
|
||
- JWT 认证
|
||
- OAuth 2.0 授权
|
||
- API 限流
|
||
- IP 白名单
|
||
|
||
## 监控与运维
|
||
|
||
### 1. 性能监控
|
||
```python
|
||
# 关键指标
|
||
metrics = {
|
||
"image_upload_latency": Histogram(),
|
||
"ai_inference_time": Histogram(),
|
||
"api_response_time": Histogram(),
|
||
"error_rate": Counter(),
|
||
"active_users": Gauge()
|
||
}
|
||
```
|
||
|
||
### 2. 日志系统
|
||
- 结构化日志（JSON 格式）
|
||
- 分级日志（DEBUG/INFO/WARN/ERROR）
|
||
- 日志聚合和分析
|
||
- 告警机制
|
||
|
||
### 3. 容灾方案
|
||
- 服务降级
|
||
- 熔断机制
|
||
- 限流保护
|
||
- 数据备份
|