diff --git a/demo.py b/demo.py
index 5d27d2d..fed7e39 100644
--- a/demo.py
+++ b/demo.py
@@ -18,7 +18,7 @@ def river_demo_v2():
     ###注意TRT模型生成时，就需要对应cuda device，下面的trt文件是cuda:0生成的，device只能是0  
     ##以下参数目前不可改
     
-    gpuname='3090'
+    gpuname='3090'###3090,2080Ti. 只能是其中之一
     Detweights = "../AIlib/weights/yolov5/class8/bestcao_%s_fp16.engine"%(gpuname) ##升级后的检测模型
     labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
 
diff --git a/demo_cyk.py b/demo_cyk.py
new file mode 100644
index 0000000..89037af
--- /dev/null
+++ b/demo_cyk.py
@@ -0,0 +1,381 @@
+import sys
+from concurrent.futures import ThreadPoolExecutor
+
+sys.path.extend(['..','../AIlib' ])
+
+from AI import AI_process,AI_process_v2,AI_process_forest,get_postProcess_para
+import cv2,os,time
+from segutils.segmodel import SegModel
+from segutils.segmodel_trt import SegModel_STDC_trt
+from segutils.trtUtils import DetectMultiBackend
+from models.experimental import attempt_load
+from utils.torch_utils import select_device
+from utilsK.queRiver import get_labelnames,get_label_arrays
+import numpy as np
+import torch
+import tensorrt as trt
+from utilsK.masterUtils import get_needed_objectsIndex
+
+'''
+    多线程
+'''
+
+def process_v1(frame):
+    #try:
+  
+    time00 = time.time()
+    H,W,C = frame[0][0].shape
+    p_result,timeOut = AI_process(frame[0],frame[1],frame[2],frame[3],frame[4],frame[5],frame[6],frame[7],frame[8], frame[9],frame[10],font=frame[11],trtFlag_det=frame[13],trtFlag_seg=frame[14],segPar=frame[15])
+    time11 = time.time()
+    image_array = p_result[1]
+    #cv2.imwrite(os.path.join('images/results/',frame[12] ) ,image_array)
+    time22 = time.time()
+    print('%s,%d*%d,AI-process: %.1f,image save:%.1f , %s'%(frame[12],H,W, (time11 - time00) * 1000.0, (time22-time11)*1000.0,timeOut))
+    return 'success'
+    #except Exception as e:
+    #    return 'failed:'+str(e)        
+
+def river_demo_v3():
+    ##预先设置的参数
+    device_='0'  ##选定模型，可选 cpu,'0','1'
+    ###注意TRT模型生成时，就需要对应cuda device，下面的trt文件是cuda:0生成的，device只能是0
+    ##以下参数目前不可改
+    labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
+    gpuname='3090';
+    max_workers=6;
+    trtFlag_det=True;trtFlag_seg=True
+    device = select_device(device_)
+    names=get_labelnames(labelnames)
+    
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+    if trtFlag_det:
+        Detweights = "../AIlib/weights/yolov5/class8/bestcao_%s_fp16.engine"%(gpuname) ##升级后的检测模型
+        
+        logger = trt.Logger(trt.Logger.INFO)
+        with open(Detweights, "rb") as f, trt.Runtime(logger) as runtime:
+            model=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
+        print('############locad det model trt success#######')
+    else:     
+        Detweights = "../AIlib/weights/yolov5/class8/bestcao.pt"
+        model = attempt_load(Detweights, map_location=device)  # load FP32 model
+        print('############locad det model pth success#######')
+        if half: model.half()
+        
+
+    seg_nclass = 2
+    #Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
+    segPar={'modelSize':(640,360),'mean':(0.485, 0.456, 0.406),'std' :(0.229, 0.224, 0.225),'numpy':False, 'RGB_convert_first':True}
+    if trtFlag_seg:
+        Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640_%s_fp16.engine'%(gpuname) ##升级的分割模型
+        segmodel = SegModel_STDC_trt(weights=Segweights,modelsize=(640,360),std=(0.229, 0.224, 0.225),mean=(0.485, 0.456, 0.406),device=device)
+        
+        logger = trt.Logger(trt.Logger.INFO)
+        with open(Segweights, "rb") as f, trt.Runtime(logger) as runtime:
+            segmodel=runtime.deserialize_cuda_engine(f.read())# 输入trt本地文件，返回ICudaEngine对象
+        print('############locad seg model trt success#######')
+    else:
+        Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640.pth'
+        segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
+        print('############locad seg model pth success#######')
+
+    postFile= '../AIlib/conf/para.json'
+    digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3}
+    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
+
+    ####模型选择参数用如下：
+    mode_paras=[
+        {"id":"0","config":{"k1":"v1","k2":"v2"}},
+        {"id":"1","config":{"k1":"v1","k2":"v2"}},
+        {"id":"2","config":{"k1":"v1","k2":"v2"}},
+        {"id":"3","config":{"k1":"v1","k2":"v2"}},
+        {"id":"4","config":{"k1":"v1","k2":"v2"}},
+        {"id":"5","config":{"k1":"v1","k2":"v2"}},
+        {"id":"6","config":{"k1":"v1","k2":"v2"}},
+        {"id":"7","config":{"k1":"v1","k2":"v2"}},
+    ]
+    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
+    #allowedList=[0,1,2,3]
+    ##加载模型，准备好显示字符
+    label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
+    
+  
+    ##图像测试
+    impth = 'images/slope/'
+    outpth = 'images/results/'
+    folders = os.listdir(impth)
+    frames=[]
+     
+    for i in range(len(folders)):
+        imgpath = os.path.join(impth, folders[i])
+        im0s=[cv2.imread(imgpath)]
+        frame=(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,digitFont,folders[i],trtFlag_det,trtFlag_seg,segPar)
+        frames.append(frame)
+    t1=time.time()     
+    if max_workers==1:
+        for i in range(len(folders)):   
+            t5=time.time()        
+            process_v1(frames[i])
+            t6=time.time()       
+            print('#######%s, ms:%.1f , accumetate time:%.1f, avage:%1.f '%(folders[i], (t6-t5)*1000.0,(t6-t1)*1000.0, (t6-t1)*1000.0/(i+1)))
+    else:
+        with ThreadPoolExecutor(max_workers=max_workers) as t:
+            for result in t.map(process_v1, frames):
+                #print(result)
+                t=result
+    
+    t2=time.time()
+    print('All %d images time:%.1f ms ,each:%.1f ms, with %d threads'%(len(folders),(t2-t1)*1000, (t2-t1)*1000.0/len(folders) , max_workers) )   
+
+def river_demo_v2():
+    ##预先设置的参数
+    device_='0'  ##选定模型，可选 cpu,'0','1'
+    ###注意TRT模型生成时，就需要对应cuda device，下面的trt文件是cuda:0生成的，device只能是0  
+    ##以下参数目前不可改
+    
+    trtFlag=False
+    gpuname='3090'
+    if trtFlag:
+        Detweights = "../AIlib/weights/yolov5/class8/bestcao_%s_fp16.engine"%(gpuname) ##升级后的检测模型
+    else:    
+        Detweights = "../AIlib/weights/yolov5/class8/bestcao.pt" ##升级后的检测模型
+        
+    labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
+
+    
+    seg_nclass = 2 
+    #Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
+    if trtFlag:
+        Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640_%s_fp16.engine'%(gpuname) ##升级的分割模型
+    else:
+        Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640.pth'
+    
+    postFile= '../AIlib/conf/para.json'
+    digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3} 
+    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
+   
+    ####模型选择参数用如下：
+    mode_paras=[
+        {"id":"0","config":{"k1":"v1","k2":"v2"}},
+        {"id":"1","config":{"k1":"v1","k2":"v2"}},
+        {"id":"2","config":{"k1":"v1","k2":"v2"}},
+        {"id":"3","config":{"k1":"v1","k2":"v2"}},
+        {"id":"4","config":{"k1":"v1","k2":"v2"}},
+        {"id":"5","config":{"k1":"v1","k2":"v2"}},
+        {"id":"6","config":{"k1":"v1","k2":"v2"}},
+        {"id":"7","config":{"k1":"v1","k2":"v2"}},
+    ]
+
+    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
+    #allowedList=[0,1,2,3]
+    ##加载模型，准备好显示字符
+    device = select_device(device_)
+    names=get_labelnames(labelnames)
+    
+    label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+    
+    if trtFlag:
+        segmodel = SegModel_STDC_trt(weights=Segweights,modelsize=(640,360),std=(0.229, 0.224, 0.225),mean=(0.485, 0.456, 0.406),device=device)
+        model = DetectMultiBackend(Detweights, device=device)
+    else:
+        model = attempt_load(Detweights, map_location=device)  # load FP32 model
+        if half: model.half()
+        segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
+    
+    ##图像测试
+    #url='images/examples/20220624_响水河_12300_1621.jpg'
+    impth = 'images/slope/'
+    outpth = 'images/results/'
+    folders = os.listdir(impth)
+    for i in range(len(folders)):
+        imgpath = os.path.join(impth, folders[i])
+        im0s=[cv2.imread(imgpath)]
+        H,W,C = im0s[0].shape
+        time00 = time.time()
+        p_result,timeOut = AI_process_v2(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
+        time11 = time.time()
+        image_array = p_result[1]
+        cv2.imwrite(  os.path.join( outpth,folders[i] ) ,image_array )
+        print('%s,%d*%d,AI-process: %.1f, %s'%(folders[i],H,W, (time11 - time00) * 1000,timeOut))
+        
+def river_demo():
+    ##预先设置的参数
+    device_='1'  ##选定模型，可选 cpu,'0','1'
+     
+    ##以下参数目前不可改
+    #Detweights = "../AIlib/weights/yolov5/class5/best_5classes.pt" 
+    #labelnames = "../AIlib/weights/yolov5/class5/labelnames.json"
+    
+    Detweights = "../AIlib/weights/yolov5/class8/bestcao.pt" ##升级后的检测模型
+    labelnames = "../AIlib/weights/yolov5/class8/labelnames.json" ##对应类别表
+    
+    seg_nclass = 2 
+    #Segweights = "../AIlib/weights/BiSeNet/checkpoint.pth"
+    Segweights = '../AIlib/weights/STDC/model_maxmIOU75_1720_0.946_360640.pth' ##升级的分割模型
+    
+    postFile= '../AIlib/conf/para.json'
+    digitFont= { 'line_thickness':2,'boxLine_thickness':1, 'fontSize':1.0,'waterLineColor':(0,255,255),'waterLineWidth':3} 
+    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
+   
+    ####模型选择参数用如下：
+    mode_paras=[
+        {"id":"0","config":{"k1":"v1","k2":"v2"}},
+        {"id":"1","config":{"k1":"v1","k2":"v2"}},
+        {"id":"2","config":{"k1":"v1","k2":"v2"}},
+        {"id":"3","config":{"k1":"v1","k2":"v2"}},
+        {"id":"4","config":{"k1":"v1","k2":"v2"}},
+        {"id":"5","config":{"k1":"v1","k2":"v2"}},
+        {"id":"6","config":{"k1":"v1","k2":"v2"}},
+        {"id":"7","config":{"k1":"v1","k2":"v2"}},
+    ]
+
+    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
+    #allowedList=[0,1,2,3]
+    
+    
+
+    ##加载模型，准备好显示字符
+    device = select_device(device_)
+    names=get_labelnames(labelnames)
+    
+    label_arraylist = get_label_arrays(names,rainbows,outfontsize=40,fontpath="../AIlib/conf/platech.ttf")
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+    model = attempt_load(Detweights, map_location=device)  # load FP32 model
+    if half: model.half()
+    
+    segmodel = SegModel(nclass=seg_nclass,weights=Segweights,device=device)
+    
+    
+    
+    ##图像测试
+    #url='images/examples/20220624_响水河_12300_1621.jpg'
+    impth = 'images/slope/'
+    outpth = 'images/results/'
+    folders = os.listdir(impth)
+    for i in range(len(folders)):
+        imgpath = os.path.join(impth, folders[i])
+        im0s=[cv2.imread(imgpath)]
+        H,W,C = im0s[0].shape
+        time00 = time.time()
+        p_result,timeOut = AI_process(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
+        time11 = time.time()
+        image_array = p_result[1]
+        cv2.imwrite(  os.path.join( outpth,folders[i] ) ,image_array )
+        print('%s,%d*%d,AI-process: %.1f, %s'%(folders[i],H,W, (time11 - time00) * 1000,timeOut))
+        
+def road_forest_demo(business ):
+
+    ##使用森林，道路模型,business 控制['forest','road']
+    ##预先设置的参数
+    device_='1'  ##选定模型，可选 cpu,'0','1'
+    
+    
+    
+    ##以下参数目前不可改
+    #business='forest';imageW=4916  ####森林模型
+    #business='road';
+    imageW=1536     ####道路模型
+    digitFont= { 'line_thickness':2, 'fontSize':1.0} ###数字显示的线宽度，大小; 如果都是None，则采用默认大小
+    Detweights="../AIlib/weights/%s/best.pt"%(business)
+    labelnames = "../AIlib/weights/%s/labelnames.json"%(business)
+
+    postFile= '../AIlib/conf/para.json'
+    print( Detweights,labelnames  ) 
+    conf_thres,iou_thres,classes,rainbows=get_postProcess_para(postFile)
+    
+    ####模型选择参数用如下：
+    mode_paras=[
+        {
+            "id":"0",
+            "config":{
+            "k1":"v1",
+            "k2":"v2"
+            }
+        },
+        {
+            "id":"1",
+            "config":{
+            "k1":"v1",
+            "k2":"v2"
+            }
+        }
+    ]
+
+    allowedList,allowedList_string=get_needed_objectsIndex(mode_paras)
+    #allowedList=[0,1,2,3]
+    
+    print('####line108###')
+
+    ##只加载检测模型，准备好显示字符
+    device = select_device(device_)
+    names=get_labelnames(labelnames)
+    #imageW=4915;###默认是1920，在森林巡检的高清图像中是4920
+    outfontsize=int(imageW/1920*40);###
+    label_arraylist = get_label_arrays(names,rainbows,outfontsize=outfontsize,fontpath="../AIlib/conf/platech.ttf")
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+    model = attempt_load(Detweights, map_location=device)  # load FP32 model
+    if half: model.half()
+    segmodel = None 
+    
+    
+    
+    ##图像测试
+    #url='images/examples/20220624_响水河_12300_1621.jpg'
+    impth = 'images/%s/'%(business)
+    outpth = 'images/results/'
+    folders = os.listdir(impth)
+    folders.sort()
+    for i in range(len(folders)):
+    #for i in range(2):
+        imgpath = os.path.join(impth, folders[i])
+        im0s=[cv2.imread(imgpath)]
+        time00 = time.time()
+        #使用不同的函数。每一个领域采用一个函数
+        p_result,timeOut = AI_process_forest(im0s,model,segmodel,names,label_arraylist,rainbows,half,device,conf_thres, iou_thres,allowedList,font=digitFont)
+        time11 = time.time()
+        image_array = p_result[1]
+        cv2.imwrite(  os.path.join( outpth,folders[i] ) ,image_array )
+        
+        print('----image:%s, process:%s ,save:%s, %s'%(folders[i],(time11-time00) * 1000, (time.time() - time11) * 1000,timeOut) )
+
+    
+def jkm_demo():
+    from utilsK.jkmUtils import pre_process,post_process,get_return_data
+    img_type = 'plate' ## code,plate
+    par={'code':{'weights':'../AIlib/weights/jkm/health_yolov5s_v3.jit','img_type':'code','nc':10 },
+        'plate':{'weights':'../AIlib/weights/jkm/plate_yolov5s_v3.jit','img_type':'plate','nc':1 },
+        'conf_thres': 0.4,
+        'iou_thres':0.45,
+        'device':'cuda:0',
+        'plate_dilate':(0.5,0.1)
+    }
+     ###加载模型
+    device = torch.device(par['device'])
+    jit_weights = par['code']['weights']
+    model = torch.jit.load(jit_weights)
+
+    jit_weights =  par['plate']['weights']
+    model_plate = torch.jit.load(jit_weights)
+
+    imgd='images/plate'
+    imgpaths = os.listdir(imgd)
+    for imgp in imgpaths[0:]:
+        #imgp = 'plate_IMG_20221030_100612.jpg'
+        imgpath = os.path.join(imgd,imgp)
+        im0 = cv2.imread(imgpath) #读取数据
+        img ,padInfos = pre_process(im0,device) ##预处理
+        if img_type=='code':  pred = model(img) ##模型推理
+        else: pred = model_plate(img)
+        boxes = post_process(pred,padInfos,device,conf_thres= par['conf_thres'], iou_thres= par['iou_thres'],nc=par[img_type]['nc'])  #后处理
+        dataBack=get_return_data(im0,boxes,modelType=img_type,plate_dilate=par['plate_dilate'])
+        print(imgp,boxes,dataBack['type'])
+        for key in dataBack.keys():
+            if isinstance(dataBack[key],list):
+                cv2.imwrite(  'images/results/%s_%s.jpg'%( imgp.replace('.jpg','').replace('.png',''),key),dataBack[key][0] )    ###返回值： dataBack
+
+        1
+if __name__=="__main__":
+    river_demo_v3()
+    #road_forest_demo('forest'  )
+    #jkm_demo()
+