(转载请删除括号里的内容)

 

 GPS模块设置

1使用ucenter设置gps输出

默认gps  9600  或者115200

 

 选择串口链接

 

 

2 设置波特率

 

 

 

 send之后重新连接gps模块,波特率修改成115200,send只是当前有效,断电恢复原来的.

 

 

3 修改GPS输出频率

 

 

 断电生效,保存在gps的内存里

 

 

 

 

4修改gps输出帧

 

 默认输出很多帧

有关于卫星信号质量的帧

有定位信息帧

有速度帧

其中包含gps的最全的是,但是没有关于卫星是否有效的信息

黑色的是正在输出,灰色的是没有输出

 

 

 

设置某些帧输出信息

 

 

 

 

 断电参数保存

 

2，$GPGGA语句

（Global Positioning System Fix Data－GGA，GPS定位信息, 输出1次/1秒）     GPS定位主要数据，该语句中包括经纬度、质量因子、HDOP、高程、基准站号等字段。下表详细说明GPGGA语句中的各个字段： $GPGGA，<1>，<2>，<3>，<4>，<5>，<6>，<7>，<8>，<9>，<10>，，<12>,，<14> 字段 $GPGGA语句意义——取值范围 <1> UTC时间：hhmmss.ss——000000.00~235959.99 <2> 纬度，格式：ddmm.mmmm ——0000．00000~8959.9999 （标准的度分格式） <3> 南北半球——N北纬；S南纬 <4> 经度格式：dddmm.mmmm ——00000.0000~17959.9999 （标准的度分格式） <5> 东西半球——E表示东经；W表示西经 <6> 质量因子——0=未定位，1=GPS单点定位固定解，2=差分定位，3=PPS解；4=RTK固定解；5=RTK浮点解；6=估计值；7=手工输入模式；8=模拟模式； <7> 应用解算位置的卫星数——00~12 <8> HDOP，水平图形强度因子——0.500~99.000 ；大于6不可用 <9> 天线高程（海平面）——－9999.9～99999.9 <10> 线线高程单位(m) ——m 大地水准面起伏——地球椭球面相对大地水准面的高度 <12> 大地水准面起伏单位(m)   ——m <13> 差分GPS数据期——差分时间（从最近一次接收到差分信号开始的秒数，如果不是差分定位将为空），不使用DGPS时为空 <14> 基准站号——0000~1023；不使用DGPS时为空 $GPGGA,074529.82,2429.6717,N,11804.6973,E,1,8,1.098,42.110,M,,M,,*76

 针对GXGGA的解析

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115

# -*- coding: utf-8 -*       #  117.410465,31.768830      # 使用前先使用u-center设置GPS输出，去掉多余的输出 import os import time import serial import serial.tools.list_ports     os.system('cls') serial_portlist = list(serial.tools.list_ports.comports()) if len(serial_portlist) <= 0 :     print('未发现可用串口') else :     for i in range (len(serial_portlist)):         print(serial_portlist[i])     #port_gps = '/dev/ttyUSB'+input('请输入GPS模块所在串口：')     port_gps = 'COM'+input('请输入GPS模块所在串口：')           ser = serial.Serial(port_gps,115200,timeout = 2)         #----------------------------------------------         os.system('cls')     init = 3     while True :         if ser.in_waiting > 0 :             '''             time.sleep(0.6)#请在0.4-0.8范围内调试修改             #过低数据不完整，过高数据被覆盖！             os.system('cls')             GETBYTES = ser.read(ser.in_waiting)                 if init :#跳过前三次数据                 print('稳定串口接收中 %d'%init)                 init = init - 1                 continue                 GETSTR = GETBYTES.decode()             print('原始数据：')             print(GETSTR) #检查接收情况                 #-------------------------------------                           GETSTR_List = GETSTR.split('\n')             #print('共接收 %d 行数据'%len(GETSTR_List)) #检查换行分割情况  8             '''               line = str(ser.readline().decode("utf-8"))                           GETSTR_List=[]                             GETSTR_List.append(line)                           GPRMC_List = GETSTR_List[0].split(',')             #print('$GPRMC 有 %d 个字段'%len(GPRMC_List)) #检查','分割情况   13               print(len(GPRMC_List))                 if len(GPRMC_List)!=15:                 print('数据不完整')                 continue#检查数据完整度             #------------------------------------------------             print('')             if GPRMC_List[6] == '0' :#0=Invalid, 1=2D/3D, 2=DGNSS, 4=Fixed RTK, 5=Float RTK, 6=Dead Reckoning                   print('当前卫星定位无效')                                 elif GPRMC_List[6] == '2' :                 print('定位正常')                 print(GETSTR_List[0])                 print('')                     #--------------------------------------------                                     UTC = GPRMC_List[1][0:2] + ':' + GPRMC_List[1][2:4] + ':' + GPRMC_List[1][4:6]                 #UTC = UTC +' '+ GPRMC_List[9][0:2] + '/' + GPRMC_List[9][2:4] + '/20' + GPRMC_List[9][4:6]                 print('卫星UTC时间：'+UTC)                                     #---------------------------------------------                     weidu_xy = int(GPRMC_List[2][0:2])+float(GPRMC_List[2][2:11])/60                 jingdu_xy = int(GPRMC_List[4][0:3])+float(GPRMC_List[4][3:12])/60                   high = float(GPRMC_List[9]) #米                 print("高度：",high)                 #---------------------------------------------                     weidu_du = int(GPRMC_List[2][0:2])                 weidu_fen = int(GPRMC_List[2][2:4])                 weidu_miao = float('0'+GPRMC_List[2][4:11])*60                 weidu = '%d°%d\'%.3f\"'%(weidu_du,weidu_fen,weidu_miao)                 print('')                 print('纬度：')                 print('%.6f'%weidu_xy)                 print(weidu)                 #----------------------------------------------                     jingdu_du = int(GPRMC_List[4][0:3])                 jingdu_fen = int(GPRMC_List[4][3:5])                 jingdu_miao = float('0'+GPRMC_List[4][5:12])*60                 jingdu = '%d°%d\'%.3f\"'%(jingdu_du,jingdu_fen,jingdu_miao)                 print('')                 print('经度：')                 print('%.6f'%jingdu_xy)                 print(jingdu)                 print('')                 else :                 print('数据有误，等待刷新')

　　

 

 

 

另外一个GxRMC帧包含了

 

 

1，$GPRMC语句

（Recommended Minimum Specific GPS/TRANSIT Data－RMC，推荐定位信息1次/1秒）     对于一般的GPS动态定位应用，GPRMC语句完全满足要求。该语句中包括经纬度、速度、时间和磁偏角等字段，这些数据为导航定位应用提供了充分的信息。下表详细说明GPRMC语句中的各个字段： $GPRMC，<1>，<2>，<3>，<4>，<5>，<6>，<7>，<8>，<9>，<10>，，<12> 字段 $GPRMC语句意义——取值范围 <1> UTC时间：hhmmss.ss——000000.00~235959.99 <2> 状态，有效性 ——A表示有效；V表示无效 <3> 纬度格式：ddmm.mmmm——0000．00000~8959.9999 （标准的度分格式） <4> 南北半球——N北纬；S南纬 <5> 经度格式：dddmm.mmmm——00000.0000~17959.9999 （标准的度分格式） <6> 东西半球——E表示东经；W表示西经 <7> 地面速度——000.00~999.999 <8> 速度方向——000.00~359.99 <9> 日期格式，月日年——010100~123199 <10> 磁偏角，单位：度——00.00~99.99 磁偏角方向——E表示东；W表示西 <12> 模式指示及校验和—— A=自主定位，D=差分，E=估算，N=数据无效 例如：$GPRMC,074529.82,A,2429.6717,N,11804.6973,E,12.623,32.122,010806,,W,A*08

针对帧h解析 代码

 

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# -*- coding: utf-8 -*       #  117.410465,31.768830      # 使用前先使用u-center设置GPS输出，去掉多余的输出 # GxGAA 有海拔高度 # GX     import os import time import serial import serial.tools.list_ports     os.system('cls') serial_portlist = list(serial.tools.list_ports.comports()) if len(serial_portlist) <= 0 :     print('未发现可用串口') else :     for i in range (len(serial_portlist)):         print(serial_portlist[i])     #port_gps = '/dev/ttyUSB'+input('请输入GPS模块所在串口：')     port_gps = 'COM'+input('请输入GPS模块所在串口：')           ser = serial.Serial(port_gps,115200,timeout = 2)         #----------------------------------------------         os.system('cls')     init = 3     while True :         if ser.in_waiting > 0 :             '''             time.sleep(0.6)#请在0.4-0.8范围内调试修改             #过低数据不完整，过高数据被覆盖！             os.system('cls')             GETBYTES = ser.read(ser.in_waiting)                 if init :#跳过前三次数据                 print('稳定串口接收中 %d'%init)                 init = init - 1                 continue                 GETSTR = GETBYTES.decode()             print('原始数据：')             print(GETSTR) #检查接收情况                 #-------------------------------------                           GETSTR_List = GETSTR.split('\n')             #print('共接收 %d 行数据'%len(GETSTR_List)) #检查换行分割情况  8             '''               line = str(ser.readline().decode("utf-8"))                           GETSTR_List=[]                             GETSTR_List.append(line)                           GPRMC_List = GETSTR_List[0].split(',')             #print('$GPRMC 有 %d 个字段'%len(GPRMC_List)) #检查','分割情况   13                 if len(GPRMC_List)!=13:                 print('数据不完整')                 continue#检查数据完整度             #------------------------------------------------             print('')             if GPRMC_List[2] == 'V' :                 print('当前卫星定位无效')                                 elif GPRMC_List[2] == 'A' :                 print('定位正常')                 print(GETSTR_List[0])                 print('')                     #--------------------------------------------                                     UTC = GPRMC_List[1][0:2] + ':' + GPRMC_List[1][2:4] + ':' + GPRMC_List[1][4:6]                 UTC = UTC +' '+ GPRMC_List[9][0:2] + '/' + GPRMC_List[9][2:4] + '/20' + GPRMC_List[9][4:6]                 print('卫星UTC时间：'+UTC)                                     #---------------------------------------------                     weidu_xy = int(GPRMC_List[3][0:2])+float(GPRMC_List[3][2:11])/60                 jingdu_xy = int(GPRMC_List[5][0:3])+float(GPRMC_List[5][3:12])/60                                     #---------------------------------------------                     weidu_du = int(GPRMC_List[3][0:2])                 weidu_fen = int(GPRMC_List[3][2:4])                 weidu_miao = float('0'+GPRMC_List[3][4:11])*60                 weidu = '%d°%d\'%.3f\"'%(weidu_du,weidu_fen,weidu_miao)                 print('')                 print('纬度：')                 print('%.6f'%weidu_xy)                 print(weidu)                 #----------------------------------------------                     jingdu_du = int(GPRMC_List[5][0:3])                 jingdu_fen = int(GPRMC_List[5][3:5])                 jingdu_miao = float('0'+GPRMC_List[5][5:12])*60                 jingdu = '%d°%d\'%.3f\"'%(jingdu_du,jingdu_fen,jingdu_miao)                 print('')                 print('经度：')                 print('%.6f'%jingdu_xy)                 print(jingdu)                 print('')                 else :                 print('数据有误，等待刷新')

　　

 

GPS解析库

根据协议内容，我们可以自己编写Python脚本对GPS数据进行解析。但是，实际上Python已经有现成的库可以调用——pymea2。

推荐下载方式：

pip install pynmea2

下载完成后即可导入包，并通过pynmea2.parse()方法对GPS数据进行解析，示例如下：

import pynmea2text = "$GNRMC,074733.00,A,2241.28818,N,11358.44210,E,3.866,,070720,,,A*60"msg = pynmea2.parse(text)

原始GPS信号的经纬度采用的是60进制的格式，通过msg.lat和msg.lon就能分别显示维度和经度信息：

   

 

而地图常用的格式应该是10进制的，通过msg.latitude和msg.longitude就能分别显示10进制的维度和经度信息：

   

GPS数据可视化

1 谷歌地球

直接搜索  31.777275,117.367401

 

 

 

3使用folium绘制经纬度轨迹图

 

folium

读取txt解析地址，可视化显示

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78

#!/usr/bin/python # -*- coding: UTF-8 -*- #使用教程 # 1安装库 # 2修改txt名字    ##pip3 install folium import folium  #可视化工具 import os   def draw_gps(locations, output_path, file_name):     """     绘制gps轨迹图     :param locations: list, 需要绘制轨迹的经纬度信息，格式为[[lat1, lon1], [lat2, lon2], ...]     :param output_path: str, 轨迹图保存路径     :param file_name: str, 轨迹图保存文件名     :return: None     """     m = folium.Map(locations[0], zoom_start=15, attr='default')  #中心区域的确定       folium.PolyLine(    # polyline方法为将坐标用线段形式连接起来         locations,    # 将坐标点连接起来         weight=3,  # 线的大小为3         color='orange',  # 线的颜色为橙色         opacity=0.8    # 线的透明度     ).add_to(m)    # 将这条线添加到刚才的区域m内           # 起始点，结束点     folium.Marker(locations[0], popup='<b>Starting Point</b>').add_to(m)     folium.Marker(locations[-1], popup='<b>End Point</b>').add_to(m)      # len(locations)            '''     for i in locations:         #folium.Marker(         #i, popup="<i>Click show a test</i>", tooltip="Click me!",icon=folium.Icon(color="red", icon="info-sign")         ).add_to(m)    '''           m.save(os.path.join(output_path, file_name))  # 将结果以HTML形式保存到指定路径           ''' 从txt读取gps数据解析出经纬高 输入 gps_txt 文件路径和名字 输出 locations  gps数组  格式为[[lat1, lon1], [lat2, lon2], ...]   '''   def read_txtgps(gps_txt):     locations=[]     #gps_txt="save/video/VideoLEFT_Tue_Aug_24_19_01_46_2021.txt"     with open(gps_txt, "r") as f:         for line in f.readlines():             line = line.strip('\n')  #去掉列表中每一个元素的换行符             #print(line)             strlist = line.split(',')             #print(strlist[2],strlist[3])             if strlist[1]=="-1" or strlist[1]=="0":                 pass             else:                 locations.append([float(strlist[2]),float(strlist[3])])     return locations     #开始调用   print("读取txt数据") name="VideoLEFT_Wed_Aug_25_17_32_19_2021" gps_txt=name+".txt" map_html=name+".html" locations=read_txtgps(gps_txt)   print("地图生成中请等待") draw_gps(locations,"",map_html) print(map_html,"地图网页生生结束，请用网页打开查看")

　　

 

 

通过上面的解析方法，我们已经获取了传感器的维度和经度信息，接下来就能够在地图上进行可视化展示，这里我采用的是Python的folium库。

假设我们最终获取到的一批经纬度数据如下：

 

1

locations = [[22.674574, 113.993353], [22.674544, 113.993362], [22.674521, 113.993376], [22.67447, 113.99342], [22.674471, 113.993428], [22.674485, 113.993434], [22.674487, 113.993437], [22.674471, 113.993416], [22.674452, 113.993416], [22.674449, 113.993412], [22.674443, 113.993389], [22.674444, 113.993382], [22.67445, 113.993363], [22.67444, 113.993358], [22.67444, 113.993351], [22.67444, 113.993338], [22.674441, 113.993331], [22.674485, 113.993265], [22.674452, 113.993305], [22.674511, 113.99323], [22.674504, 113.993236], [22.674484, 113.99326], [22.674419, 113.993359], [22.67441, 113.993369], [22.674403, 113.993381], [22.674406, 113.993385], [22.674406, 113.993395], [22.674404, 113.993397], [22.674403, 113.9934], [22.674402, 113.993411], [22.6744, 113.993414], [22.674398, 113.993417], [22.674308, 113.993457], [22.674269, 113.99347], [22.67423, 113.99349], [22.674191, 113.993506], [22.674151, 113.993532], [22.674045, 113.993583], [22.673965, 113.993597], [22.673879, 113.993607], [22.673724, 113.993639], [22.673321, 113.993752], [22.673212, 113.993784], [22.673101, 113.993825], [22.672887, 113.993894], [22.672785, 113.993937], [22.672684, 113.993977], [22.672476, 113.994081], [22.672063, 113.994352], [22.671971, 113.994427], [22.671873, 113.994503], [22.67178, 113.994583], [22.671449, 113.994935], [22.671373, 113.995017], [22.671315, 113.995096], [22.671252, 113.995173], [22.671199, 113.995219], [22.671165, 113.99525], [22.671108, 113.995304], [22.671128, 113.995301], [22.671125, 113.995312], [22.671131, 113.995302], [22.671133, 113.995301], [22.671136, 113.99531], [22.671135, 113.995313], [22.671131, 113.995316], [22.671077, 113.995376], [22.671059, 113.995395], [22.671039, 113.995415], [22.670993, 113.995452], [22.670868, 113.995574], [22.670666, 113.995825], [22.67061, 113.995877], [22.670563, 113.995943], [22.670503, 113.996008], [22.670448, 113.996076], [22.670393, 113.996143], [22.670233, 113.996335], [22.670022, 113.996558], [22.669967, 113.996622], [22.669913, 113.996683], [22.669862, 113.996741], [22.669812, 113.996795], [22.669768, 113.996847], [22.669734, 113.996899], [22.669687, 113.996965], [22.669639, 113.997026], [22.669441, 113.997291], [22.669385, 113.997353], [22.669331, 113.997423], [22.669274, 113.997491], [22.669216, 113.997564], [22.66915, 113.997639], [22.668873, 113.997982], [22.668809, 113.998056], [22.668754, 113.998126], [22.668686, 113.998199], [22.668632, 113.998269], [22.668578, 113.998336], [22.66852, 113.998408], [22.668462, 113.998486], [22.668212, 113.998776], [22.668152, 113.998849], [22.668082, 113.998925], [22.667963, 113.999024], [22.667923000000002, 113.999077], [22.667784, 113.999228], [22.667669, 113.999349], [22.667521, 113.999529], [22.667482, 113.999574], [22.667442, 113.999625], [22.667401, 113.999676], [22.667358, 113.999724], [22.667321, 113.999774], [22.667283, 113.99982], [22.667246, 113.999863], [22.667209, 113.999906], [22.667169, 113.999947], [22.667128, 113.999992], [22.667085, 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114.009822], [22.65059, 114.009849], [22.65056, 114.009878], [22.65052, 114.009909], [22.6504, 114.010005], [22.650277, 114.010077], [22.650266, 114.01009], [22.65026, 114.010104], [22.65026, 114.010105], [22.650258, 114.010107], [22.650254, 114.01011], [22.650244, 114.010115], [22.65025, 114.010123], [22.65025, 114.010126], [22.650253, 114.010131], [22.650257, 114.010133], [22.650256, 114.010134], [22.650252000000002, 114.010136], [22.65025, 114.010136], [22.650243, 114.010136], [22.650249, 114.010139], [22.650238, 114.010139], [22.650215, 114.010136], [22.650215, 114.010139], [22.650212, 114.010143], [22.650206, 114.010148], [22.650205, 114.010155], [22.6502, 114.010159], [22.650197, 114.010161], [22.650194, 114.010163], [22.650187, 114.010171], [22.650187, 114.010172], [22.650187, 114.010174], [22.650187, 114.010176], [22.650187, 114.010178], [22.650186, 114.010178], [22.650176, 114.010181], [22.65017, 114.010186], [22.650168999999998, 114.010187], [22.65017, 114.010188], [22.65017, 114.010189], [22.650174, 114.01019], [22.650174, 114.01019], [22.650175, 114.010185], [22.650177, 114.010184], [22.65018, 114.010183], [22.650181, 114.010184], [22.650179, 114.010183], [22.650175, 114.010181], [22.650164, 114.010177], [22.650164, 114.010168], [22.65016, 114.010171], [22.650156, 114.010171], [22.650077, 114.010223], [22.65005, 114.010245], [22.650019, 114.010266], [22.64999, 114.01029], [22.64996, 114.010309], [22.649917, 114.010341], [22.649892, 114.010353], [22.649874, 114.010367], [22.649844, 114.01039], [22.649637, 114.010561], [22.649569, 114.010614], [22.649493, 114.010679], [22.649349, 114.010807], [22.64928, 114.010869], [22.649211, 114.010927], [22.649142, 114.010986], [22.649075, 114.011046], [22.64888, 114.01122], [22.648835, 114.011255], [22.648791, 114.01129], [22.648697, 114.011371], [22.648469, 114.011552], [22.64841, 114.011602], [22.648349, 114.011651], [22.648288, 114.011697], [22.648226, 114.011744], [22.648162, 114.01179], [22.647882, 114.012002], 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注意：顺序为[纬度，经度]。

编写Python函数绘制GPS轨迹图：

 

 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

def draw_gps(locations, output_path, file_name):     """     绘制gps轨迹图     :param locations: list, 需要绘制轨迹的经纬度信息，格式为[[lat1, lon1], [lat2, lon2], ...]     :param output_path: str, 轨迹图保存路径     :param file_name: str, 轨迹图保存文件名     :return: None     """     m = folium.Map(locations[0], zoom_start=15, attr='default')  #中心区域的确定       folium.PolyLine(    # polyline方法为将坐标用线段形式连接起来         locations,    # 将坐标点连接起来         weight=3,  # 线的大小为3         color='orange',  # 线的颜色为橙色         opacity=0.8    # 线的透明度     ).add_to(m)    # 将这条线添加到刚才的区域m内           # 起始点，结束点     folium.Marker(locations[0], popup='<b>Starting Point</b>').add_to(m)     folium.Marker(locations[-1], popup='<b>End Point</b>').add_to(m)           m.save(os.path.join(output_path, file_name))  # 将结果以HTML形式保存到指定路径

　　

GPS轨迹图结果：

 

 

 

解决folium国内访问太慢的问题

如果我们直接用浏览器打开生成的HTML文件，有可能会发现网络访问非常缓慢，需要加载大概1分钟才能看到地图显现。
我们打开用folium生成的HTML文件就可以看到：

   

 

由于生成的HTML文件调用的很多资源都是访问的国外资源，所以在国内打开可能会比较慢。这时候我们可以考虑把这些静态资源下载到本地，然后采用Python读取HTML，修改静态资源路径的方式，把HTML中的css和js路径修改成本地路径，这样就能够极大程度的提高速度。
示例如下：

   
可以看到示例中有一行我没有进行修改，这是因为我在前面绘制地图的时候增加了起始点的绘制，如果把这个css路径改了就会导致无法正常显示起始点（没有下载起始点图标），再加上这个css网络访问起来并不慢，所以我没有进行替换。

 

<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css"/>

修改了静态资源访问路径的HTML，再次打开的速度应该是非常快的，如果地图加载依然比较慢，那就是地图瓦片的网络问题了，而非静态资源的问题。

3 censium

https://www.cnblogs.com/gooutlook/p/13891300.html

 

4 高德可视化

GPS在国内的数据都加了密,直接显示并不是原始的地方,需要地图纠偏.

https://lbs.amap.com/demo/javascript-api/example/other-gaode/othertoamap-more

 

---------------------
作者：MKT-porter
来源：CNBLOGS
原文：https://www.cnblogs.com/gooutlook/p/15177300.html
版权声明：本文为作者原创文章，转载请附上博文链接！
内容解析By：CSDN,CNBLOG博客文章一键转载插件

标签：python,博客园,List,locations,GPRMC,串口,print,GPS,gps
来源： https://www.cnblogs.com/sdufe/p/16621182.html