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/ultralytics/cfg/models/README.md

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+<a href="https://www.ultralytics.com/" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
+
+# Ultralytics Model Configurations
+
+Welcome to the [Ultralytics](https://www.ultralytics.com/) models configuration directory. This folder contains a collection of model configuration files (`*.yaml`) that define Ultralytics YOLO model architectures. These configurations are used across common [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks such as [object detection](https://docs.ultralytics.com/tasks/detect/), [image segmentation](https://docs.ultralytics.com/tasks/segment/), pose estimation, oriented bounding boxes (OBB), and image classification.
+
+Configurations are designed to run efficiently on a range of hardware, from standard [CPUs](https://en.wikipedia.org/wiki/Central_processing_unit) to modern [GPUs](https://www.ultralytics.com/glossary/gpu-graphics-processing-unit). Pick a base model that matches your constraints (latency, memory, and accuracy), then customize it as needed.
+
+To get started, choose a `*.yaml` file (see the [YAML format](https://www.ultralytics.com/glossary/yaml)) and use it to [train](https://docs.ultralytics.com/modes/train/) or export your model. For more details, see the Ultralytics [Documentation](https://docs.ultralytics.com/) or open a question on [GitHub Issues](https://github.com/ultralytics/ultralytics/issues).
+
+## 🚀 Usage
+
+Model configuration files (`*.yaml`) can be used directly from the [Command Line Interface (CLI)](https://docs.ultralytics.com/usage/cli/) via the `yolo` command:
+
+```bash
+# Train a YOLO26n detection model using the coco8 dataset for 100 epochs
+yolo task=detect mode=train model=yolo26n.yaml data=coco8.yaml epochs=100 imgsz=640
+```
+
+The same YAML files can be used from [Python](https://www.python.org/), with the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI:
+
+```python
+from ultralytics import YOLO
+
+# Initialize a YOLO26n model from a YAML configuration file
+# This creates a model architecture without loading pre-trained weights
+model = YOLO("yolo26n.yaml")
+
+# Alternatively, load a pre-trained YOLO26n model directly
+# This loads both the architecture and the weights trained on COCO
+# model = YOLO("yolo26n.pt")
+
+# Display model information (architecture, layers, parameters, etc.)
+model.info()
+
+# Train the model using the COCO8 dataset (a small subset of COCO) for 100 epochs
+results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+
+# Run inference with the trained model on an image
+results = model("path/to/image.jpg")
+```
+
+## 🏗️ Pre-trained Model Architectures
+
+Ultralytics supports a variety of model architectures. Visit the [Ultralytics Models](https://docs.ultralytics.com/models/) documentation page for details and usage examples, including:
+
+- [YOLO26](https://docs.ultralytics.com/models/yolo26/)
+- [YOLO12](https://docs.ultralytics.com/models/yolo12/)
+- [YOLO11](https://docs.ultralytics.com/models/yolo11/)
+- [YOLOv10](https://docs.ultralytics.com/models/yolov10/)
+- [YOLOv9](https://docs.ultralytics.com/models/yolov9/)
+- [YOLOv8](https://docs.ultralytics.com/models/yolov8/)
+- [YOLOv5](https://docs.ultralytics.com/models/yolov5/)
+- [And more...](https://docs.ultralytics.com/models/)
+
+You can easily use any of these models by loading their configuration files (`.yaml`) or their [pre-trained](https://docs.pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) checkpoints (`.pt`).
+
+## 🤝 Contribute New Models
+
+Have you developed a novel YOLO variant, experimented with a unique architecture, or achieved state-of-the-art results through specific tuning? We encourage you to share your innovations with the community by contributing to our Models section! Contributions like new model configurations, architectural improvements, or performance optimizations are highly valuable and help enrich the Ultralytics ecosystem.
+
+Sharing your work here allows others to benefit from your insights and expands the range of available model choices. It's an excellent way to showcase your expertise and make the Ultralytics YOLO platform even more versatile and powerful.
+
+To contribute, review the [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for instructions on submitting a [Pull Request (PR)](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests).
+
+Thank you for helping improve the Ultralytics model zoo.