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)
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## lane_light + CPU PyTorchUFLD 训练)
### 1. 激活环境
```bash
source /home/chengfanglu/miniconda3/etc/profile.d/conda.sh
conda activate lane_light
```
### 2. 已安装依赖(`lane_light`
- `torch` / `torchvision`**CPU 轮子**,来自 `https://download.pytorch.org/whl/cpu`
- `opencv-python`, `tqdm`, `tensorboard`, `addict`, `scikit-learn`, `pathspec`(与 `requirements.txt` 对齐;`sklearn` 包名在 pip 中为 `scikit-learn`
自检:
```bash
python -c "import torch; print('torch', torch.__version__, 'cuda=', torch.cuda.is_available())"
```
### 3. 数据与配置
- 默认数据根仍指向 `lane0_reorganized/lane_training_pack`(见 `configs/mufld_lane_culane.py`)。
- **CPU 建议**使用 `configs/mufld_lane_culane_cpu.py``batch_size=4`,学习率与 warmup 已按 batch 相对 16 做了粗略缩放)。内存不够可改配置或命令行覆盖:
```bash
cd /home/chengfanglu/DATA/BK2/UFLD
python train.py configs/mufld_lane_culane_cpu.py --batch_size 2
```
### 4. 运行训练
```bash
cd /home/chengfanglu/DATA/BK2/UFLD
python train.py configs/mufld_lane_culane_cpu.py
```
说明:
- `train.py` 已改为在 **无 CUDA** 时使用 `cpu`;原仓库中写死的 `CUDA_VISIBLE_DEVICES=1,2``.cuda()` 已去掉,避免 CPU 机直接报错。
- 首次 `pretrained=True` 会下载 ResNet 骨干权重,需联网。
- CPU 训练很慢,建议先用小 `epoch` / 小 `batch_size` 做通路测试。
### 5. 可选DataLoader `num_workers`
当前 `data/dataloader.py``num_workers=8`。若 CPU 内存紧张或不想多进程读盘,可自行把该值改小(例如 `0``2`)。