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/dms_yolo/adapter.py

@@ -1,6 +1,7 @@
 """DMS YOLO 引擎：local 研发轨 / platform 平台轨。"""
 from __future__ import annotations
 
+import os
 import subprocess
 import sys
 from pathlib import Path
@@ -13,11 +14,25 @@ YOLO_ROOT = WORKSPACE / "algorithms/dms_yolo/code"
 DATASET_ROOT = WORKSPACE / "datasets/dms"
 
 
-def _latest_run(task: str) -> tuple[str | None, str | None]:
+def _resolve_yaml_key(task: str, submode: str | None = None) -> tuple[str, str]:
+    import sys
+
+    scripts = DATASET_ROOT / "scripts"
+    if str(scripts) not in sys.path:
+        sys.path.insert(0, str(scripts))
+    from task_registry import get_mode_config, resolve_task_id, train_yaml_key
+
     reg = yaml.safe_load((DATASET_ROOT / "datasets.registry.yaml").read_text(encoding="utf-8"))
-    typ = reg["tasks"][task]["type"]
+    task, submode = resolve_task_id(task, submode)
+    mcfg = get_mode_config(task, submode, reg)
+    key = train_yaml_key(task, submode, reg)
+    return key, mcfg["type"]
+
+
+def _latest_run(task: str, submode: str | None = None) -> tuple[str | None, str | None]:
+    yaml_key, typ = _resolve_yaml_key(task, submode)
     mode_yolo = {"detect": "detect", "pose": "pose", "classify": "classify"}[typ]
-    runs = sorted((YOLO_ROOT / "runs" / mode_yolo).glob(f"{task}_*"), key=lambda p: p.stat().st_mtime, reverse=True)
+    runs = sorted((YOLO_ROOT / "runs" / mode_yolo).glob(f"{yaml_key}_*"), key=lambda p: p.stat().st_mtime, reverse=True)
     if not runs:
         return None, None
     run_dir = runs[0].resolve()
@@ -25,17 +40,21 @@ def _latest_run(task: str) -> tuple[str | None, str | None]:
     return str(run_dir), str(best) if best.is_file() else None
 
 
-def train_local(task: str, mode: str = "full", config_overrides: dict | None = None) -> dict[str, Any]:
+def train_local(task: str, mode: str = "full", config_overrides: dict | None = None, submode: str | None = None) -> dict[str, Any]:
     """研发轨：train.sh，不 refresh yaml_active，不更新 candidate。"""
+    env = {**os.environ}
+    if submode:
+        env["SUBMODE"] = submode
     proc = subprocess.run(
         [str(DATASET_ROOT / "scripts" / "train.sh"), task, mode],
         cwd=str(DATASET_ROOT),
         capture_output=True,
         text=True,
+        env=env,
     )
     if proc.returncode != 0:
         raise RuntimeError(proc.stderr or proc.stdout)
-    run_dir, best_weights = _latest_run(task)
+    run_dir, best_weights = _latest_run(task, submode)
     return {
         "ok": True,
         "stdout": proc.stdout,
@@ -48,28 +67,33 @@ def train_local(task: str, mode: str = "full", config_overrides: dict | None = N
     }
 
 
-def train_platform(task: str, mode: str = "full") -> dict[str, Any]:
+def train_platform(task: str, mode: str = "full", submode: str | None = None) -> dict[str, Any]:
     """平台轨：refresh yaml_active + train + 更新 candidate。"""
+    yaml_key, _ = _resolve_yaml_key(task, submode)
     subprocess.check_call(
         [sys.executable, str(DATASET_ROOT / "scripts/refresh_yaml.py"), "--task", task],
         cwd=str(DATASET_ROOT),
     )
+    env = {**os.environ}
+    if submode:
+        env["SUBMODE"] = submode
     proc = subprocess.run(
         [str(DATASET_ROOT / "scripts" / "train.sh"), task, mode],
         cwd=str(DATASET_ROOT),
         capture_output=True,
         text=True,
+        env=env,
     )
     if proc.returncode != 0:
         raise RuntimeError(proc.stderr or proc.stdout)
 
-    run_dir, best_weights = _latest_run(task)
+    run_dir, best_weights = _latest_run(task, submode)
     candidate = None
     if best_weights:
         candidate = best_weights
         versions_path = DATASET_ROOT / "manifests/train_versions.yaml"
         versions = yaml.safe_load(versions_path.read_text(encoding="utf-8"))
-        versions[task]["candidate"] = candidate
+        versions[yaml_key]["candidate"] = candidate
         versions_path.write_text(yaml.dump(versions, allow_unicode=True, sort_keys=False), encoding="utf-8")
 
     return {