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/UFLD/ros_cap_node.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+# coding:utf-8
+
+import cv2
+import numpy as np
+import rospy
+from std_msgs.msg import Header
+from sensor_msgs.msg import Image
+from cv_bridge import CvBridge, CvBridgeError
+import time
+
+if __name__ == "__main__":
+    capture = cv2.VideoCapture(0)  # 定义摄像头
+    rospy.init_node('camera_node', anonymous=True)  # 定义节点
+    image_pub = rospy.Publisher('/image_view/image_raw', Image, queue_size=1)  # 定义话题
+
+    while not rospy.is_shutdown():  # Ctrl C正常退出，如果异常退出会报错device busy！
+        start = time.time()
+        ret, frame = capture.read()
+        if ret:  # 如果有画面再执行
+            # frame = cv2.flip(frame,0)   #垂直镜像操作
+            frame = cv2.flip(frame, 1)  # 水平镜像操作
+
+            ros_frame = Image()
+            header = Header(stamp=rospy.Time.now())
+            header.frame_id = "Camera"
+            ros_frame.header = header
+            ros_frame.width = 640
+            ros_frame.height = 480
+            ros_frame.encoding = "bgr8"
+            ros_frame.step = 1920
+            ros_frame.data = np.array(frame).tostring()  # 图片格式转换
+            image_pub.publish(ros_frame)  # 发布消息
+            end = time.time()
+            print("cost time:", end - start)  # 看一下每一帧的执行时间，从而确定合适的rate
+            rate = rospy.Rate(25)  # 10hz
+
+    capture.release()
+    cv2.destroyAllWindows()
+    print("quit successfully!")