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/UFLD/test0.py

@@ -0,0 +1,54 @@
+import torch, os
+from model.model import parsingNet
+from utils.common import merge_config
+from utils.dist_utils import dist_print
+from evaluation.eval_wrapper import eval_lane
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if __name__ == "__main__":
+    torch.backends.cudnn.benchmark = True
+
+    args, cfg = merge_config()
+
+    print(cfg)
+
+    distributed = False
+    if 'WORLD_SIZE' in os.environ:
+        distributed = int(os.environ['WORLD_SIZE']) > 1
+
+    if distributed:
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(backend='nccl', init_method='env://')
+    dist_print('start testing...')
+    assert cfg.backbone in ['18','34','50','101','152','50next','101next','50wide','101wide']
+
+    if cfg.dataset == 'CULane':
+        cls_num_per_lane = 18
+    elif cfg.dataset == 'Tusimple':
+        cls_num_per_lane = 56
+    else:
+        raise NotImplementedError
+
+    net = parsingNet(pretrained=False, backbone=cfg.backbone, cls_dim=(cfg.griding_num+1, cls_num_per_lane,
+                                                                       cfg.num_lanes), use_aux=False).to(device)
+                                                                       # cfg.num_lanes), use_aux=False).cuda()
+    # we don't need auxiliary segmentation in testing
+
+    state_dict = torch.load(cfg.test_model, map_location='cuda')['model']
+    compatible_state_dict = {}
+    for k, v in state_dict.items():
+        if 'module.' in k:
+            compatible_state_dict[k[7:]] = v
+        else:
+            compatible_state_dict[k] = v
+
+    net.load_state_dict(compatible_state_dict, strict=False)
+
+    if distributed:
+        net = torch.nn.parallel.DistributedDataParallel(net, device_ids=[args.local_rank])
+
+    if not os.path.exists(cfg.test_work_dir):
+        os.mkdir(cfg.test_work_dir)
+
+    eval_lane(net, cfg.dataset, cfg.data_root, cfg.test_work_dir, cfg.griding_num, False, distributed)