feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation

Major changes:
- New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind
- 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理
- Data catalog with charts (DMS/ADAS/Lane 3-tab view)
- Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance
- Audit enhancements: batch operations, rejection categories, Feishu notifications
- Operation audit log (操作日志)
- World model simulation studio (仿真工坊)
- Dataset version management with snapshots and diff
- ADAS 7-class dataset integration (138K images organized + compressed)
- User management with Feishu integration and pagination
- CRUD/search/filter on all pages, card layout redesign
- PIL-optimized image overlay rendering
- Auto-snapshot on build, in_review workflow stage
- Removed embedded algorithm code (now in workspace)

algorithms/dms_yolo/code.embedded.bak/examples/YOLOv8-OpenCV-ONNX-Python/README.md

@@ -0,0 +1,51 @@
+# Ultralytics YOLOv8 Object Detection with OpenCV and ONNX
+
+This example demonstrates how to implement [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) object detection using [OpenCV](https://opencv.org/) in [Python](https://www.python.org/), leveraging the [ONNX (Open Neural Network Exchange)](https://onnx.ai/) model format for efficient inference.
+
+## 🚀 Getting Started
+
+Follow these simple steps to get the example running on your local machine.
+
+1.  **Clone the Repository:**
+    If you haven't already, clone the Ultralytics repository to access the example code:
+
+    ```bash
+    git clone https://github.com/ultralytics/ultralytics.git
+    cd ultralytics/examples/YOLOv8-OpenCV-ONNX-Python/
+    ```
+
+2.  **Install Requirements:**
+    Install the necessary Python packages listed in the `requirements.txt` file. We recommend using a virtual environment.
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Run the Detection Script:**
+    Execute the main Python script, specifying the ONNX model path and the input image.
+    ```bash
+    python main.py --model yolov8n.onnx --img image.jpg
+    ```
+    The script will perform object detection on `image.jpg` using the `yolov8n.onnx` model and display the results.
+
+## 🛠️ Exporting Your Model
+
+If you want to use a different Ultralytics YOLOv8 model or one you've trained yourself, you need to export it to the ONNX format first.
+
+1.  **Install Ultralytics:**
+    If you don't have it installed, get the latest `ultralytics` package:
+
+    ```bash
+    pip install ultralytics
+    ```
+
+2.  **Export the Model:**
+    Use the `yolo export` command to convert your desired model (e.g., `yolov8n.pt`) to ONNX. Ensure you specify `opset=12` or higher for compatibility with OpenCV's DNN module. You can find more details in the Ultralytics [Export documentation](https://docs.ultralytics.com/modes/export/).
+    ```bash
+    yolo export model=yolov8n.pt imgsz=640 format=onnx opset=12
+    ```
+    This command will generate a `yolov8n.onnx` file (or the corresponding name for your model) in your working directory. You can then use this `.onnx` file with the `main.py` script.
+
+## 🤝 Contributing
+
+Contributions are welcome! If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request to the main [Ultralytics repository](https://github.com/ultralytics/ultralytics). Thank you for helping us make Ultralytics YOLO even better!