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/CLRNet-main/clrnet/datasets/registry.py

@@ -0,0 +1,54 @@
+from clrnet.utils import Registry, build_from_cfg
+
+import torch
+from functools import partial
+import numpy as np
+import random
+from mmcv.parallel import collate
+
+DATASETS = Registry('datasets')
+PROCESS = Registry('process')
+
+
+def build(cfg, registry, default_args=None):
+    if isinstance(cfg, list):
+        modules = [
+            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
+        ]
+        return nn.Sequential(*modules)
+    else:
+        return build_from_cfg(cfg, registry, default_args)
+
+
+def build_dataset(split_cfg, cfg):
+    return build(split_cfg, DATASETS, default_args=dict(cfg=cfg))
+
+
+def worker_init_fn(worker_id, seed):
+    worker_seed = worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+
+
+def build_dataloader(split_cfg, cfg, is_train=True):
+    if is_train:
+        shuffle = True
+    else:
+        shuffle = False
+
+    dataset = build_dataset(split_cfg, cfg)
+
+    init_fn = partial(worker_init_fn, seed=cfg.seed)
+
+    samples_per_gpu = cfg.batch_size // cfg.gpus
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=cfg.batch_size,
+        shuffle=shuffle,
+        num_workers=cfg.workers,
+        pin_memory=False,
+        drop_last=False,
+        collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
+        worker_init_fn=init_fn)
+
+    return data_loader