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/lane_ufld/code.embedded.bak/pytorch-auto-drive-master/docs/SEGMENTATION.md

@@ -0,0 +1,63 @@
+# Semantic segmentation
+
+**Before diving into this, please make sure you followed the instructions to prepare datasets in [DATASET.md](./DATASET.md)**
+
+**Execution is based on [config files](../configs/README.md)**
+
+## Training:
+
+Some models' ImageNet pre-trained weights need to be manually downloaded, refer to [this table](./IMAGENET_MODELS.md).
+
+```
+python main_semseg.py --train \
+                      --config=<config file path> \
+                      --mixed-precision  # Optional, enable mixed precision \
+                      --cfg-options=<overwrite cfg dict>  # Optional
+```
+
+Your `<overwrite cfg dict>` is used to manually override config file options in commandline so you don't have to modify config file each time. It should look like this (**the quotation marks are necessary!**): `"train.batch_size=8 train.workers=4 model.classifier_cfg.num_classes=21"`
+
+Some options can be used by shortcuts, such as `--batch-size` will set both `train.batch_size` and `test.batch_size`, for more info:
+
+```
+python main_semseg.py --help
+```
+
+Example shells are provided in [tools/shells](../tools/shells/).
+
+## Distributed Training
+
+We support multi-GPU training with Distributed Data Parallel (DDP):
+
+```
+python -m torch.distributed.launch --nproc_per_node=<number of GPU per-node> --use_env main_semseg.py <your normal args>
+```
+
+With DDP, batch size and number of workers are **per-GPU**.  Do not forget to set device args like `world_size` in your config.
+
+## Testing:
+
+Training contains online evaluations and the best model is saved.
+
+To evaluate a trained model:
+
+```
+python main_semseg.py --val \  # No test set labels available
+                      --config=<config file path> \
+                      --mixed-precision  # Optional, enable mixed precision \
+                      --cfg-options=<overwrite cfg dict>  # Optional
+```
+
+To test a downloaded pt file, try add `--checkpoint=<pt file path>`.
+
+Detail results will be saved to `<save_dir>/<exp_name>/`.
+
+Overall result will be saved to `log.txt`.
+
+Recommend `workers=0 batch_size=1` for high precision inference.
+
+## Notes:
+
+1. Cityscapes dataset is down-sampled by 2 when training at 256 x 512, to specify different sizes, modify them in config files if needed.
+
+2. All segmentation results reported are from single model without CRF and without multi-scale testing.