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/speed_real.py

@@ -0,0 +1,153 @@
+# Thanks for the contribution of KopiSoftware https://github.com/KopiSoftware
+
+import torch
+import time
+import numpy as np
+from model.model import parsingNet
+import torchvision.transforms as transforms
+import cv2
+from matplotlib import pyplot as plt
+from PIL import Image
+
+
+img_transforms = transforms.Compose([
+    transforms.Resize((288, 800)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+])
+
+def resize(x, y):
+    global cap
+    cap.set(3,x)
+    cap.set(4,y)
+
+def test_practical_without_readtime():
+    global cap
+    for i in range(10):
+        _,img = cap.read()
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img2 = Image.fromarray(img)
+        x = img_transforms(img2)
+        x = x.unsqueeze(0).cuda()+1
+        y = net(x)
+        
+    print("pracrical image input size:",img.shape)
+    print("pracrical tensor input size:",x.shape)
+    t_all = []
+    for i in range(100):
+        _,img = cap.read()
+
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img2 = Image.fromarray(img)
+        x = img_transforms(img2)
+        x = x.unsqueeze(0).cuda()+1
+
+        t1 = time.time()
+        y = net(x)
+        t2 = time.time()
+        t_all.append(t2 - t1)
+        
+    print("practical with out read time:")
+    print('\taverage time:', np.mean(t_all) / 1)
+    print('\taverage fps:',1 / np.mean(t_all))
+    
+    # print('fastest time:', min(t_all) / 1)
+    # print('fastest fps:',1 / min(t_all))
+    
+    # print('slowest time:', max(t_all) / 1)
+    # print('slowest fps:',1 / max(t_all))
+ 
+    
+def test_practical():
+    global cap
+    for i in range(10):
+        _,img = cap.read()
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img2 = Image.fromarray(img)
+        x = img_transforms(img2)
+        x = x.unsqueeze(0).cuda()+1
+        y = net(x)
+        
+    print("pracrical image input size:",img.shape)
+    print("pracrical tensor input size:",x.shape)
+    t_all = []
+    t_capture = []
+    t_preprocessing = []
+    t_net = []
+    for i in range(100):
+        t1 = time.time()
+        
+        t3 = time.time()
+        _,img = cap.read()
+        t4 = time.time()
+        
+        t5 = time.time()
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img2 = Image.fromarray(img)
+        x = img_transforms(img2)
+        x = x.unsqueeze(0).cuda()+1
+        t6 = time.time()
+
+        y = net(x)
+        t2 = time.time()
+        t_all.append(t2 - t1)
+        t_capture.append(t4 - t3)
+        t_preprocessing.append(t6 - t5)
+        t_net.append(t2 - t6)
+        
+    print("practical with read time:")
+    print('\taverage time:', np.mean(t_all) / 1)
+    print('\taverage fps:',1 / np.mean(t_all))
+    print('\tcapture time:', np.mean(t_capture) / 1)
+    print('\tpre-processing time:', np.mean(t_preprocessing) / 1)
+    print('\tdetect time:', np.mean(t_net) / 1)
+    
+    # print('fastest time:', min(t_all) / 1)
+    # print('fastest fps:',1 / min(t_all))
+    
+    # print('slowest time:', max(t_all) / 1)
+    # print('slowest fps:',1 / max(t_all))
+    
+###x = torch.zeros((1,3,288,800)).cuda() + 1
+def test_theoretical():
+    x = torch.zeros((1,3,288,800)).cuda() + 1
+    for i in range(10):
+        y = net(x)
+    
+    t_all = []
+    for i in range(100):
+        t1 = time.time()
+        y = net(x)
+        t2 = time.time()
+        t_all.append(t2 - t1)
+    print("theortical")
+    print('\taverage time:', np.mean(t_all) / 1)
+    print('\taverage fps:',1 / np.mean(t_all))
+    
+    # print('fastest time:', min(t_all) / 1)
+    # print('fastest fps:',1 / min(t_all))
+    
+    # print('slowest time:', max(t_all) / 1)
+    # print('slowest fps:',1 / max(t_all))
+    
+
+
+
+if __name__ == "__main__":
+    ###captrue data from camera or video
+    #cap = cv2.VideoCapture("video.mp4") #uncommen to activate a video input
+    cap = cv2.VideoCapture(0) #uncommen to activate a camera imput
+    #resize(480, 640) #ucommen to change input size
+    
+    
+    # torch.backends.cudnn.deterministic = False
+    torch.backends.cudnn.benchmark = True
+    net = parsingNet(pretrained = False, backbone='18',cls_dim = (100+1,56,4),use_aux=False).cuda()
+    # net = parsingNet(pretrained = False, backbone='18',cls_dim = (200+1,18,4),use_aux=False).cuda()
+    net.eval()
+    
+
+    test_practical_without_readtime()
+    test_practical()
+    cap.release()
+    test_theoretical()