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/scripts/setup_clrnet_env.sh

@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+# Create conda env for CLRNet + MUFLD dataset
+set -euo pipefail
+
+ENV_NAME="${CLRNET_ENV_NAME:-clrnet_lane}"
+CLRNET_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+
+source "$(conda info --base)/etc/profile.d/conda.sh"
+
+if conda env list | awk '{print $1}' | grep -qx "$ENV_NAME"; then
+  echo "Env $ENV_NAME exists, activate: conda activate $ENV_NAME"
+else
+  conda create -n "$ENV_NAME" python=3.8 -y
+fi
+conda activate "$ENV_NAME"
+
+# PyTorch (adjust CUDA version to match your driver)
+pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 \
+  -f https://download.pytorch.org/whl/cu113/torch_stable.html
+
+pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
+
+pip install -r "$CLRNET_ROOT/requirements.txt"
+
+cd "$CLRNET_ROOT"
+python setup.py develop
+
+echo ""
+echo "Done. Usage:"
+echo "  conda activate $ENV_NAME"
+echo "  cd $CLRNET_ROOT"
+echo "  python main.py configs/clrnet/clr_resnet18_mufld_smoke.py --gpus 0"

algorithms/lane_ufld/code.embedded.bak/CLRNet-main/scripts/setup_clrnet_export.sh

@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+# CPU-friendly env for CLRNet ONNX export (no CUDA required for export).
+set -euo pipefail
+
+ENV_NAME="${CLRNET_EXPORT_ENV:-clrnet_export}"
+CLRNET_ROOT="$(cd "$(dirname "$0")/.." && pwd)"
+
+source "$(conda info --base)/etc/profile.d/conda.sh"
+
+if ! conda env list | awk '{print $1}' | grep -qx "$ENV_NAME"; then
+  conda create -n "$ENV_NAME" python=3.8 -y
+fi
+conda activate "$ENV_NAME"
+
+pip install --upgrade pip
+pip install torch==1.10.2+cpu torchvision==0.11.3+cpu \
+  -f https://download.pytorch.org/whl/cpu/torch_stable.html
+pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10/index.html
+pip install onnx onnxruntime onnxsim
+pip install -r "$CLRNET_ROOT/requirements.txt"
+cd "$CLRNET_ROOT"
+pip install -e .
+
+echo ""
+echo "Export ONNX:"
+echo "  conda activate $ENV_NAME"
+echo "  cd $CLRNET_ROOT"
+echo "  python tools/export_onnx.py --check"