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)

platform/as_platform/data/ingest/dms_inbox_raw.py

@@ -0,0 +1,91 @@
+"""DMS inbox 原图批次（如 addw/ddaw：仅有 images/，待送标，无 YOLO/COCO 标注）。"""
+from __future__ import annotations
+
+from pathlib import Path
+
+from as_platform.data.batch import count_images, dms_has_images
+from as_platform.data.ingest.base import IngestAdapter, IngestContext, NormalizedDataset
+
+
+def _count_images(path: Path) -> int:
+    return count_images(path)
+
+
+def _split_image_counts(root: Path) -> dict[str, int]:
+    train = _count_images(root / "images" / "train")
+    val = _count_images(root / "images" / "val")
+    test = _count_images(root / "images" / "test")
+    if train + val + test == 0:
+        train = _count_images(root / "images")
+    if train + val + test == 0 and root.name == "images":
+        train = _count_images(root / "train")
+        val = _count_images(root / "val")
+        test = _count_images(root / "test")
+        if train + val + test == 0:
+            train = _count_images(root)
+    if train + val + test == 0 and (root / "train").is_dir():
+        train = _count_images(root / "train")
+        val = _count_images(root / "val")
+        test = _count_images(root / "test")
+    return {"train": train, "val": val, "test": test}
+
+
+def _has_label_txts(root: Path) -> bool:
+    for sub in ("labels", "labels/train"):
+        d = root / sub
+        if d.is_dir() and any(d.rglob("*.txt")):
+            return True
+    return False
+
+
+def _is_inbox_raw_layout(root: Path) -> bool:
+    if _has_label_txts(root):
+        return False
+    if dms_has_images(root):
+        return True
+    if root.name == "images" and (_count_images(root / "train") > 0 or _count_images(root) > 0):
+        return True
+    if (root / "train").is_dir() and _count_images(root / "train") > 0:
+        return True
+    if _count_images(root) > 0 and not (root / "images").is_dir() and not (root / "labels").is_dir():
+        return True
+    return False
+
+
+class DmsInboxRawAdapter(IngestAdapter):
+    format_id = "dms_inbox_raw"
+    projects = ("dms",)
+
+    def can_handle(self, ctx: IngestContext) -> bool:
+        return _is_inbox_raw_layout(ctx.source_path)
+
+    def inspect(self, ctx: IngestContext) -> NormalizedDataset:
+        root = ctx.source_path
+        split_counts = _split_image_counts(root)
+        sample_count = sum(split_counts.values())
+        warnings: list[str] = []
+        if sample_count == 0:
+            warnings.append(
+                "未找到图片；请填批次根目录（含 images/train/）或 images/、train/ 目录"
+            )
+
+        artifacts: list[str] = []
+        if (root / "images").is_dir():
+            artifacts.append("images/")
+        elif (root / "train").is_dir():
+            artifacts.append("train/（将规范为 images/train）")
+        elif root.name == "images":
+            artifacts.append("images/")
+
+        return NormalizedDataset(
+            format_id=self.format_id,
+            project=ctx.project,
+            task=ctx.task,
+            source_path=str(root),
+            split_counts=split_counts,
+            sample_count=sample_count,
+            annotation_count=0,
+            artifacts=artifacts,
+            warnings=warnings,
+            extra={"stage_hint": "raw_pool"},
+        )

platform/as_platform/data/ingest/registry.py

@@ -5,6 +5,7 @@ from pathlib import Path
 
 from as_platform.data.ingest.base import IngestAdapter, IngestContext, NormalizedDataset
 from as_platform.data.ingest.dms_coco import DmsCocoAdapter
+from as_platform.data.ingest.dms_inbox_raw import DmsInboxRawAdapter
 from as_platform.data.ingest.dms_yolo import DmsYoloAdapter
 from as_platform.data.ingest.lane_lines import LaneLinesAdapter
 from as_platform.data.ingest.lane_mask import LaneMaskAdapter
@@ -17,6 +18,7 @@ class UnknownFormatError(ValueError):
 ADAPTERS: tuple[IngestAdapter, ...] = (
     DmsYoloAdapter(),
     DmsCocoAdapter(),
+    DmsInboxRawAdapter(),
     LaneMaskAdapter(),
     LaneLinesAdapter(),
 )
@@ -32,9 +34,15 @@ def detect_adapter(ctx: IngestContext) -> IngestAdapter:
             continue
         if adapter.can_handle(ctx):
             return adapter
+    hint = ""
+    if ctx.project == "dms":
+        hint = (
+            "；DMS 送标/inbox 请使用批次根目录，且至少包含 images/train/*.jpg"
+            "（或已标注的 images/+labels/、COCO annotations/）"
+        )
     raise UnknownFormatError(
         f"unable to detect format for project={ctx.project}, task={ctx.task}, "
-        f"source={ctx.source_path}. supported={available_formats(ctx.project)}"
+        f"source={ctx.source_path}. supported={available_formats(ctx.project)}{hint}"
     )