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/code.embedded.bak/ultralytics/models/rtdetr/train.py

@@ -0,0 +1,89 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+
+from ultralytics.models.yolo.detect import DetectionTrainer
+from ultralytics.nn.tasks import RTDETRDetectionModel
+from ultralytics.utils import RANK, colorstr
+
+from .val import RTDETRDataset, RTDETRValidator
+
+
+class RTDETRTrainer(DetectionTrainer):
+    """Trainer class for the RT-DETR model developed by Baidu for real-time object detection.
+
+    This class extends the DetectionTrainer class for YOLO to adapt to the specific features and architecture of
+    RT-DETR. The model leverages Vision Transformers and has capabilities like IoU-aware query selection and adaptable
+    inference speed.
+
+    Attributes:
+        loss_names (tuple): Names of the loss components used for training.
+        data (dict): Dataset configuration containing class count and other parameters.
+        args (dict): Training arguments and hyperparameters.
+        save_dir (Path): Directory to save training results.
+        test_loader (DataLoader): DataLoader for validation/testing data.
+
+    Methods:
+        get_model: Initialize and return an RT-DETR model for object detection tasks.
+        build_dataset: Build and return an RT-DETR dataset for training or validation.
+        get_validator: Return a DetectionValidator suitable for RT-DETR model validation.
+
+    Examples:
+        >>> from ultralytics.models.rtdetr.train import RTDETRTrainer
+        >>> args = dict(model="rtdetr-l.yaml", data="coco8.yaml", imgsz=640, epochs=3)
+        >>> trainer = RTDETRTrainer(overrides=args)
+        >>> trainer.train()
+
+    Notes:
+        - F.grid_sample used in RT-DETR does not support the `deterministic=True` argument.
+        - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
+    """
+
+    def get_model(self, cfg: dict | None = None, weights: str | None = None, verbose: bool = True):
+        """Initialize and return an RT-DETR model for object detection tasks.
+
+        Args:
+            cfg (dict, optional): Model configuration.
+            weights (str, optional): Path to pre-trained model weights.
+            verbose (bool): Verbose logging if True.
+
+        Returns:
+            (RTDETRDetectionModel): Initialized model.
+        """
+        model = RTDETRDetectionModel(cfg, nc=self.data["nc"], ch=self.data["channels"], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+        return model
+
+    def build_dataset(self, img_path: str, mode: str = "val", batch: int | None = None):
+        """Build and return an RT-DETR dataset for training or validation.
+
+        Args:
+            img_path (str): Path to the folder containing images.
+            mode (str): Dataset mode, either 'train' or 'val'.
+            batch (int, optional): Batch size for rectangle training.
+
+        Returns:
+            (RTDETRDataset): Dataset object for the specific mode.
+        """
+        return RTDETRDataset(
+            img_path=img_path,
+            imgsz=self.args.imgsz,
+            batch_size=batch,
+            augment=mode == "train",
+            hyp=self.args,
+            rect=False,
+            cache=self.args.cache or None,
+            single_cls=self.args.single_cls or False,
+            prefix=colorstr(f"{mode}: "),
+            classes=self.args.classes,
+            data=self.data,
+            fraction=self.args.fraction if mode == "train" else 1.0,
+        )
+
+    def get_validator(self):
+        """Return a DetectionValidator suitable for RT-DETR model validation."""
+        self.loss_names = "giou_loss", "cls_loss", "l1_loss"
+        return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))