用Python处理TDC激光测距数据并绘制为图片
- 说明
- 一、定义全局变量变
- 二、主函数入口
- 三、处理原始文件数据
- 四、将数据叠加统计生成图片
- 五、额外的辅助函数
- 六、将数据进行各种形式统计叠加
- 七、原始数据形式
- 八、 测试结果
说明
1. 主要是将TDC激光测距数据进行统计叠加并绘制为图片，便于直观的分析与观察
一、定义全局变量变
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import datetime
import numpy as np
import os
import datetime
import shutil
ORG_DAT_STORE_DIR = 'DataHandlerDir' #原始文件目录
RES_DAT_STORE_DIR = 'ImageCreatDir' #处理文件目录
RES_INFO_FILE_EXTENSION = '.log' #原始文件后缀名
RES_INFO_IMAGE_EXTENSION = '.png' #处理文件后缀名
SUPER_CALC_DIST_GAP_POINT = 2
SUPER_PEAK_GAP_POINT = 4
DIST_SUPER_GAP_M = 2 #距离叠加的间隔
OrgDatStop1Mem = [] #Stop1原始数据缓存
OrgDatStop2Mem = [] #Stop2原始数据缓存
DIST1_FIX_SUPER_MAX_VAL_M = 3000 #距离1(Stop1)固定叠加的最大值
DIST1_RANGE_SUPER_MAX_VAL_M = 3000 #距离1(Stop1)范围叠加的最大值
DIST2_FIX_SUPER_MAX_VAL_M = 3000 #距离2(Stop2)固定叠加的最大值
DIST2_RANGE_SUPER_MAX_VAL_M = 3000 #距离2(Stop2)范围叠加的最大值
DIST1_DIST2_RANGE_SUPER_MAX_VAL_M = 3000 #距离1~距离2(Stop1~Stop2)范围叠加的最大值
DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM = 3000 #距离2-距离1(Stop2-Stop1)脉宽叠加的最大值
CreatImageCount = 0
OrgStaInfo = ""
#原始文件
OrgDataFileNameGroup = \
[
r"DataHandlerDir/xPythonDemoTest.log",
]
二、主函数入口
#删除目录内容
def Delete_Directory_Content(dir):
if os.path.exists(dir) == True: #目录存在
for item in os.listdir(dir): #目录中内容
name = os.path.join(dir, item) #拼接完整路径
if os.path.isfile(name):
os.remove(name) #删除目录
elif os.path.isdir(name):
shutil.rmtree(name) #删除文件
# 原始数据处理
def Original_Data_Handler(nameGroup):
global RES_DAT_STORE_DIR
global RES_INFO_FILE_EXTENSION
if len(nameGroup) <= 0:
print('No Need Handler Files......')
return
for name in nameGroup:
nameExten = os.path.basename(name) #结果文件名+后缀名 xTest.log
nameOnly = os.path.splitext(nameExten)[0] #结果文件名 xTest
newDir = os.path.join(RES_DAT_STORE_DIR, nameOnly) #结果文件路径 #ImageCreatDir\xTest
# 创建目录
if os.path.exists(newDir) == False:
os.mkdir(newDir)
else:
Delete_Directory_Content(newDir)
# 创建文件 结果文件 ImageCreatDir\xTest\xTest.log
newInfoFile = newDir + os.path.sep + nameOnly + RES_INFO_FILE_EXTENSION
with open(newInfoFile, 'w'):
pass
# print("==1==> " + name) #DataHandlerDir/xTest.log
# print("==2==> " + newDir) #ImageCreatDir\xTest
# print("==3==> " + newInfoFile) #ImageCreatDir\xTest\xTest.log
# print("==4==> " + nameOnly) #xTest
# print("==5==> " + nameExten) #xTest.log
print(r'Start Handler ====> ', name)
OrgData_FileHandler(name, newDir, newInfoFile)
def main():
global ORG_DAT_STORE_DIR
global RES_DAT_STORE_DIR
global OrgDataFileNameGroup
if os.path.exists(ORG_DAT_STORE_DIR) == False:
os.mkdir(ORG_DAT_STORE_DIR)
return
if os.path.exists(RES_DAT_STORE_DIR) == False:
os.mkdir(RES_DAT_STORE_DIR)
return
Original_Data_Handler(OrgDataFileNameGroup)
print('All Original Data Files Handler Complete......')
if __name__ == '__main__':
main()
三、处理原始文件数据
#是否为数字
def Judge_IsDigit(orgData):
for dat in orgData:
if dat.isdigit() == False:
return 1
return 0
#追加数据到Stop1-Stop2缓存
def AppendData_Stop1Stop2(stop1, stop2):
global OrgDatStop1Mem
global OrgDatStop2Mem
OrgDatStop1Mem.append(stop1)
OrgDatStop2Mem.append(stop2)
#清空Stop1-Stop2缓存
def ClearData_Stop1Stop2():
global OrgDatStop1Mem
global OrgDatStop2Mem
OrgDatStop1Mem.clear()
OrgDatStop2Mem.clear()
#原始文件处理
def OrgData_FileHandler(orgDatFile,resInfoDir,resInfoFile):
# print("orgDatFile : " + orgDatFile) #DataHandlerDir/xTest.log
# print("resInfoDir : " + resInfoDir) #ImageCreatDir\xTest
# print("resInfoFile: " + resInfoFile) #ImageCreatDir\xTest\xTest.log
global OrgDatStop1Mem
global OrgDatStop2Mem
global CreatImageCount
global OrgStaInfo
if os.path.exists(orgDatFile) == False: #文件不存在
return
CreatImageCount = 0
orgDataCount = 0
ClearData_Stop1Stop2()
with open(orgDatFile,'r', encoding='utf-8') as fileHander:#读方式打开文件
for lineTxt in fileHander: #行方式读取文件内容
if len(lineTxt.strip()) <= 30: #一行数据太少
continue
if '[' not in lineTxt: #不存在[
continue
if ']' not in lineTxt: #不存在]
continue
separDat = lineTxt.replace(']', ', ').replace('[', '') #将[]替换
orgData = [spData.strip() for spData in separDat.split(",")] #以,拆分
if Judge_IsDigit(orgData) == 0:#全为数字
if int(orgData[0]) == 0:
if len(OrgDatStop1Mem) > 10:
OrgStaInfo = lineTxt
OrgData_CreateImage(OrgDatStop1Mem, OrgDatStop2Mem, resInfoDir,resInfoFile)
OrgStaInfo = ""
orgDataCount = 0
ClearData_Stop1Stop2()
AppendData_Stop1Stop2(orgData[1], orgData[2])
else:
orgDataCount = orgDataCount + 1
if orgDataCount == int(orgData[0]):
AppendData_Stop1Stop2(orgData[1], orgData[2])
else:
OrgStaInfo = ""
orgDataCount = 0
ClearData_Stop1Stop2()
else:
if len(OrgDatStop1Mem) > 10:
OrgStaInfo = lineTxt
OrgData_CreateImage(OrgDatStop1Mem, OrgDatStop2Mem, resInfoDir,resInfoFile)
OrgStaInfo = ""
orgDataCount = 0
ClearData_Stop1Stop2()
四、将数据叠加统计生成图片
# 生成图片
def OrgData_CreateImage(stop1, stop2, resInfoDir,resInfoFile):
global RES_INFO_IMAGE_EXTENSION
global CreatImageCount
global OrgStaInfo
stop1Org = np.array([int(i) for i in stop1])
stop1Dat = SuperAnalyse_Stop1FixPoint(stop1Org)
stop1OrgSort = np.array(sorted(stop1Org))
stop1CountMax = max(stop1Dat)
stop2Org = np.array([int(i) for i in stop2])
# waveData,OrgPlus = SuperAnalyse_Stop1Stop2_RangePoint(stop1Org, stop2Org, 0)
waveData = SuperAnalyse_Stop1Stop2_RangePoint(stop1Org, stop2Org, 2)
waveCountMax = max(waveData)
if waveCountMax < 5:
return
sum = 0
count = 0
for val in waveData:
if val > 0:
sum += val
count += 1
mean = sum / count
print('mean:', mean)
std = np.std(waveData)
print('STD:', std)
# if abs(np.argmax(waveData) - np.argmax(stop1Dat)) > 15:
# return
dist = Calculate_Dist(np.argmax(waveData), waveData)
# index,dist2 = Superpos_FrontPart_Handler(np.argmax(waveData), waveData)
# if index > 0 and dist2 > 0:
# dist = dist2
x = [i for i in range(2000)]
z = np.argmax(waveData) - 200
y = np.argmax(waveData) + 200
# z = 0
# y = 1800
if z < 0:
z = 0
if y > 2000:
y = 2000
# plt.figure(figsize=(20, 20))
plt.figure(figsize=(12,8))
# plt.rcParams['font.sans-serif'] = ['SimHei']
# plt.rcParams['axes.unicode_minus'] = False
plt.subplot(3, 1, 1)
plt.plot(stop1OrgSort, '*-', color='red', label='OrgDist Num:{}'.format(len(stop1OrgSort)))
# plt.plot(OrgPlus, '*-', color='red', label='OrgDist Num:{}'.format(len(stop1OrgSort)))
plt.legend()#plot(label)显示出来
########################################################################################################################################################################################################
plt.subplot(3, 1, 2)
plt.plot(x[z:y], stop1Dat[z:y], '*-', color='green', label='Dist Sta Count')
stop1CountMaxIndex = np.argmax(stop1Dat)
plt.axvline(stop1CountMaxIndex, color="red")
yMin, yMax = plt.ylim()
plt.text(stop1CountMaxIndex, (yMax-yMin)/2 + yMin, 'Index:{}'.format(int(stop1CountMaxIndex)), fontsize=10, fontweight='bold', color="black", ha='left', va='center')
xMin, xMax = plt.xlim()
xmin = ((stop1CountMaxIndex - 3) - xMin) / (xMax - xMin)
xmax = ((stop1CountMaxIndex + 3) - xMin) / (xMax - xMin)
plt.axhline(stop1CountMax, color="red", xmin=xmin, xmax=xmax, linewidth=1)
per = int((int(stop1CountMax) * 100) / len(stop1OrgSort))
plt.text(stop1CountMaxIndex, int(stop1CountMax), 'Max:{} {}%'.format(int(stop1CountMax), int(per)), fontsize=10, fontweight='bold', color="black", ha='left', va='center')
plt.legend()#plot(label)显示出来
########################################################################################################################################################################################################
plt.subplot(3, 1, 3)
plt.plot(x[z:y], waveData[z:y], '*-', color='blue', label='Dist Sta Count')
waveCountMaxIndex = np.argmax(waveData)
plt.axvline(waveCountMaxIndex, color="red")
yMin, yMax = plt.ylim()
plt.text(waveCountMaxIndex, (yMax-yMin)/2 + yMin, 'Index:{}'.format(int(waveCountMaxIndex)), fontsize=10, fontweight='bold', color="black", ha='left', va='center')
# print(waveCountMaxIndex, waveCountMax, yMin, yMax) #264 54
xMin, xMax = plt.xlim()
plt.axhline(int(waveCountMax * 0.5), color="red", linewidth=2)
plt.axhline(int(waveCountMax * 0.4), color="red", linewidth=2)
plt.axhline(int(waveCountMax * 0.3), color="red", linewidth=2)
xWave = waveCountMaxIndex - 100
if xWave < 0:
xWave = 10
plt.text(xWave, int(waveCountMax * 0.5), '50%={}'.format(int(waveCountMax * 0.5)), fontsize=9, fontweight='bold', color="black", ha='left', va='center')
plt.text(xWave, int(waveCountMax * 0.4), '40%={}'.format(int(waveCountMax * 0.4)), fontsize=9, fontweight='bold', color="black", ha='left', va='center')
plt.text(xWave, int(waveCountMax * 0.3), '30%={}'.format(int(waveCountMax * 0.3)), fontsize=9, fontweight='bold', color="black", ha='left', va='center')
xmin = ((waveCountMaxIndex - 10) - xMin) / (xMax - xMin)
xmax = ((waveCountMaxIndex + 10) - xMin) / (xMax - xMin)
plt.axhline(waveCountMax, color="red", xmin=xmin, xmax=xmax, linewidth=1)
per = int((int(waveCountMax)*100) / len(stop1OrgSort))
plt.text(waveCountMaxIndex, int(waveCountMax), 'Max:{} {}%'.format(int(waveCountMax), int(per)), fontsize=10, fontweight='bold', color="black", ha='left', va='center')
plt.figtext(0.15, 0.7, dist, color='blue', fontsize=12, ha="left", va="center")
plt.legend()#plot(label)显示出来
########################################################################################################################################################################################################
curTimestamp = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f")
picDirName = resInfoDir + os.path.sep + curTimestamp + '----' + str(dist) + 'dm' + RES_INFO_IMAGE_EXTENSION
# print(picDirName)
# CreatImageCount += 1
# strImageCount = '[%04u]' % CreatImageCount
# strImageCount = strImageCount + curTimestamp + '----'
# print(strImageCount)
writeText = curTimestamp + '----' + str(dist) + 'dm'
Update_Result_File_Info(resInfoFile, writeText)
plt.savefig(picDirName, dpi=500, bbox_inches="tight")
# plt.close()
plt.get_current_fig_manager().window.state('zoomed')# 最大化显示窗口
plt.suptitle(OrgStaInfo)
plt.show(block=True)
五、额外的辅助函数
# 更新结果文件信息
def Update_Result_File_Info(resInfoFile, writeText):
with open(resInfoFile, 'a+', encoding='utf-8') as wruteHandler:
wruteHandler.write(writeText + '\r')
# 计算距离
def Calculate_Dist(peakIndex, superDat):
global SUPER_CALC_DIST_GAP_POINT
sumVal = 0
dist = 0
superOrgDat = np.array([int(i) for i in superDat])
if peakIndex < SUPER_CALC_DIST_GAP_POINT:
start = 0
else:
start = peakIndex - SUPER_CALC_DIST_GAP_POINT
end = peakIndex + SUPER_CALC_DIST_GAP_POINT + 1
for i in range(start, end):
val = superOrgDat[i]
sumVal += val
print('Max%d:(%04d %03d) ' %(abs(peakIndex - i), i, val), end='')
for i in range(start, end):
if superOrgDat[i] > 0:
dist += (superOrgDat[i] * i * 100) / sumVal
dist = int((int(dist) + 5) / 10)
resStr = "Dist%sdm" % (str(dist))
print(resStr)
return dist
# print("Name: {}, Age: {}, Height: {:.1f}".format(name, age, height))
# print("Name: %s, Age: %d, Height: %.1f" % (name, age, height))
# def Superpos_BackPart_Handler(peakIndex, superDat):
def Superpos_FrontPart_Handler(peakIndex, superDat):
global SUPER_CALC_DIST_GAP_POINT
secDist = 0
secVal = 0
secValIndex = 0
superOrgDat = np.array([int(i) for i in superDat])
if peakIndex > (SUPER_PEAK_GAP_POINT << 1):
end = peakIndex - SUPER_PEAK_GAP_POINT + 1
for i in range(0, end):
val = superOrgDat[i]
if secVal < val:
secVal = val
secValIndex = i
if secVal > 10:
secDist = Calculate_Dist(secValIndex, superDat)
print('dist2 ===> ', secDist)
return (secValIndex, secDist)
六、将数据进行各种形式统计叠加
# 叠加统计分析Stop1~Stop2(距离1~距离2)范围点的叠加
def SuperAnalyse_Stop1Stop2_RangePoint(orgStop1, orgStop2, gapDist):
global DIST1_DIST2_RANGE_SUPER_MAX_VAL_M
superDat = np.zeros(DIST1_DIST2_RANGE_SUPER_MAX_VAL_M) #最大叠加距离
if len(orgStop1) != len(orgStop2): #长度异常
return superDat
for i in range(len(orgStop1)):
if int(orgStop1[i]) >= (DIST1_DIST2_RANGE_SUPER_MAX_VAL_M * 10): #超范围
continue
if int(orgStop2[i]) >= (DIST1_DIST2_RANGE_SUPER_MAX_VAL_M * 10): #超范围
continue
if orgStop1[i] >= orgStop2[i]: #异常情况
continue
#起始数据(距离1+距离2)转化为m
start = int((orgStop1[i] + 5) / 10)
end = int((orgStop2[i] + 5) / 10)
if (end - start) > int(gapDist) : #叠加限制
end = start + int(gapDist)
#边界限制
if start < 0:
start = 0
if end >= DIST1_DIST2_RANGE_SUPER_MAX_VAL_M:
end = (DIST1_DIST2_RANGE_SUPER_MAX_VAL_M - 1)
#叠加区间(距离1~距离2)
for j in range(start, (end + 1)):
superDat[j] = superDat[j] + 1
return superDat
# 叠加统计分析Stop2-Stop1(距离2-距离1=脉宽)脉宽的叠加
def SuperAnalyse_Stop2Stop1_DiffPoint(orgStop1, orgStop2, front, back):
global DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM
plusDat = np.zeros(DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM) #最大叠加距离
if len(orgStop1) != len(orgStop2): #长度异常
return plusDat
for i in range(len(orgStop1)):
if int(orgStop1[i]) >= (DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM * 10): #超范围
continue
if int(orgStop2[i]) >= (DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM * 10): #超范围
continue
if orgStop1[i] >= orgStop2[i]: #异常情况
continue
#脉宽数据(距离2-距离1)单位dm
diff = int(orgStop2[i]) - int(orgStop2[i])
#范围值
start = diff - front
end = diff + back
#边界限制
if start < 0:
start = 0
if end >= DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM:
end = (DIST2_DIST1_DIFF_SUPER_MAX_VAL_DM - 1)
#叠加区间
for j in range(start, (end + 1)):
plusDat[j] = plusDat[j] + 1
return plusDat
# 叠加统计分析Stop1固定的叠加
def SuperAnalyse_Stop1FixPoint(orgStop1):
global DIST1_FIX_SUPER_MAX_VAL_M
stop1Count = np.zeros(DIST1_FIX_SUPER_MAX_VAL_M) #最大叠加距离
for i in range(len(orgStop1)):
if int(orgStop1[i]) < (DIST1_FIX_SUPER_MAX_VAL_M * 10): #限制距离
curVal = int((orgStop1[i] + 5) / 10)
#边界限制
if curVal < 0:
curVal = 0
if curVal >= DIST1_FIX_SUPER_MAX_VAL_M:
curVal = (DIST1_FIX_SUPER_MAX_VAL_M - 1)
stop1Count[curVal] = stop1Count[curVal] + 1 #当前叠加(距离1)
return stop1Count
# 叠加统计分析Stop1范围的叠加
def SuperAnalyse_Stop1RangePoint(orgStop1, front, back):
global DIST1_RANGE_SUPER_MAX_VAL_M
stop1Count = np.zeros(DIST1_RANGE_SUPER_MAX_VAL_M) #最大叠加值
for i in range(len(orgStop1)):
if int(orgStop1[i]) < (DIST1_RANGE_SUPER_MAX_VAL_M * 10): #限制距离
curVal = int((orgStop1[i] + 5) / 10)
start = curVal - front
end = curVal + back
#边界限制
if start < 0:
start = 0
if end >= DIST1_RANGE_SUPER_MAX_VAL_M:
end = (DIST1_RANGE_SUPER_MAX_VAL_M - 1)
for i in range(start, (end + 1)):
stop1Count[i] = stop1Count[i] + 1
return stop1Count
# 叠加统计分析Stop2固定的叠加
def SuperAnalyse_Stop2FixPoint(orgStop2):
global DIST1_FIX_SUPER_MAX_VAL_M
stop2Count = np.zeros(DIST2_FIX_SUPER_MAX_VAL_M) #最大叠加距离
for i in range(len(orgStop2)):
if int(orgStop2[i]) < (DIST2_FIX_SUPER_MAX_VAL_M * 10): #限制距离
curVal = int((orgStop2[i] + 5) / 10)
#边界限制
if curVal < 0:
curVal = 0
if curVal >= DIST2_FIX_SUPER_MAX_VAL_M:
curVal = (DIST2_FIX_SUPER_MAX_VAL_M - 1)
stop2Count[curVal] = stop2Count[curVal] + 1 #当前叠加(距离1)
return stop2Count
# 叠加统计分析Stop2范围的叠加
def SuperAnalyse_Stop2RangePoint(orgStop2, front, back):
global DIST2_RANGE_SUPER_MAX_VAL_M
stop2Count = np.zeros(DIST2_RANGE_SUPER_MAX_VAL_M) #最大叠加值
for i in range(len(orgStop2)):
if int(orgStop2[i]) < (DIST2_RANGE_SUPER_MAX_VAL_M * 10): #限制距离
curVal = int((orgStop2[i] + 5) / 10)
start = curVal - front
end = curVal + back
#边界限制
if start < 0:
start = 0
if end >= DIST2_RANGE_SUPER_MAX_VAL_M:
end = (DIST2_RANGE_SUPER_MAX_VAL_M - 1)
for i in range(start, (end + 1)):
stop2Count[i] = stop2Count[i] + 1
return stop2Count
七、原始数据形式
第一列：有效数据量
第二列：Stop1
第三列：Stop2
第四列：Stop2-Stop1
第五列：有效脉冲数
第六列：有效回波数
第七列：Stop1数量
第八列：Stop2数量
Near Mode......APDHV:972 TXHV:612 VBAT:4073 T:243
[000]62 , 195 , 133 , 000,00 , 01,01
[001]63 , 196 , 133 , 001,00 , 01,01
[002]63 , 196 , 133 , 002,00 , 01,01
[003]63 , 197 , 134 , 003,00 , 01,01
[004]62 , 196 , 134 , 004,00 , 01,01
[005]64 , 196 , 132 , 005,00 , 01,01
[006]64 , 195 , 131 , 006,00 , 01,01
[007]64 , 196 , 132 , 007,00 , 01,01
[008]63 , 196 , 133 , 008,00 , 01,01
[009]64 , 196 , 132 , 009,00 , 01,01
[010]64 , 197 , 133 , 010,00 , 01,01
[011]64 , 196 , 132 , 011,00 , 01,01
[012]64 , 196 , 132 , 012,00 , 01,01
[013]64 , 196 , 132 , 013,00 , 01,01
[014]64 , 196 , 132 , 014,00 , 01,01
[015]65 , 196 , 131 , 015,00 , 01,01
[016]64 , 196 , 132 , 016,00 , 01,01
[017]64 , 196 , 132 , 017,00 , 01,01
[018]64 , 195 , 131 , 018,00 , 01,01
[019]63 , 196 , 133 , 019,00 , 01,01
[020]64 , 196 , 132 , 020,00 , 01,01
[021]64 , 196 , 132 , 021,00 , 01,01
[022]64 , 196 , 132 , 022,00 , 01,01
[023]64 , 196 , 132 , 023,00 , 01,01
[024]64 , 196 , 132 , 024,00 , 01,01
[025]65 , 196 , 131 , 025,00 , 01,01
[026]65 , 196 , 131 , 026,00 , 01,01
[027]65 , 195 , 130 , 027,00 , 01,01
[028]66 , 196 , 130 , 028,00 , 01,01
[029]66 , 196 , 130 , 029,00 , 01,01
[030]66 , 195 , 129 , 030,00 , 01,01
[031]66 , 196 , 130 , 031,00 , 01,01
[032]66 , 195 , 129 , 032,00 , 01,01
[033]67 , 195 , 128 , 033,00 , 01,01
[034]66 , 195 , 129 , 034,00 , 01,01
[035]67 , 195 , 128 , 035,00 , 01,01
[036]67 , 195 , 128 , 036,00 , 01,01
[037]67 , 195 , 128 , 037,00 , 01,01
[038]67 , 195 , 128 , 038,00 , 01,01
[039]67 , 195 , 128 , 039,00 , 01,01
[040]66 , 196 , 130 , 040,00 , 01,01
[041]66 , 194 , 128 , 041,00 , 01,01
[042]65 , 195 , 130 , 042,00 , 01,01
[043]66 , 195 , 129 , 043,00 , 01,01
[044]66 , 194 , 128 , 044,00 , 01,01
[045]65 , 195 , 130 , 045,00 , 01,01
[046]65 , 195 , 130 , 046,00 , 01,01
[047]65 , 195 , 130 , 047,00 , 01,01
[048]65 , 194 , 129 , 048,00 , 01,01
[049]65 , 196 , 131 , 049,00 , 01,01
[000] OrgDat TxPlus:50 GrpCount:50 RxDatCount:50 RxPWM:75 VthPWM:470 APDHV:973 TXHV:613 VBAT:4071
MinOrgDist:30 MaxOrgDist:560
八、 测试结果
