最短路径分析+路书（详细导航信息）

1.思路：

1.抽取路网（管网）各节点，点去重+点线关联；---根据数据及业务场景可选择线起始点/起中终点/全部节点；

2.构建路网（管网）各节点KDtree；

3.使用邻接矩阵存储点连通性；--不通：-1、自身点：0、相连：1（或权重/距离--可根据业务场景设置）；

4.基于KDtree分别捕获到 [ 出发点 , 到达点] 最近的一个捕捉节点(k=1);

5.根据捕捉节点进行图的广度优先遍历--结合迪杰克斯特拉（Dijkstra）算法思路--因为节点重复，根据节点键值取存储线id后，需要根据前后节点筛选线；

6.前进方向确定（线遍历后排列顺序+线坐标点集合顺序）--根据返回数据格式确定；

7.路书制作（八方位-["北", "东北", "东", "东南", "南", "西南", "西", "西北", "原点", ]+ 下一节点方向（左/右/直行/掉头））--方向-八方位-坐标大小/ 方向-左右-向量叉乘（注意：当前代码因数据区域大小原因，已忽略椭球体及地理坐标影响因素）；

8.结果输出（在路书中增加出发点/到达点与最短路径的关系--详细导航信息）--当前输出json结构参考了高德最短路径分析api返回数据结构;

9.效率优化--（将数据提前加载进内存/api调用）--当前框架使用python/flask，一秒内返回结果（当前测试数据大概0.04-0.5）

2.实现

1.python

#!/usr/bin/python3
# -*- coding:utf-8 -*-'''
####################################################################################
#版本信息
##最近修改时间：20220331
##最近修改人：慢慢
####################################################################################
#功能说明
##路径分析
####################################################################################
#返回结果
##返回格式-dict
##运行成功-
##运行失败-{"code": 404, "type": "fail", "msg": "服务错误", "box": "", "data": "需要重启"}
####################################################################################
#使用说明
####################################################################################
#更新历史
##20220421 新增最短路径导航详细信息 路书功能开发-（路口左右转、东南西北八个方向、距离）
####################################################################################
'''import json
from shapely.geometry import LineString
from shapely.geometry import mapping as geommapping
# from lib.geotools import Features, Intersect, Clip, ToPolygons, CreatGrid, ToPoints
try:from lib.flaskservices.config_app import __keyself__
except ImportError:__keyself__ = {"file_line": "/data/l_road.shp","building": "/data/r_building.shp", "grid": "/data/r_grid.shp","people": "/data/r_people.csv"}
from geopandas import GeoSeries, GeoDataFrame__data__type = (list, tuple)
__mapdata_type__ = (GeoSeries, GeoDataFrame)
__kself__ = list(__keyself__.keys())[0]def GetRoadBookCourse(pointa, pointb):# 方向-八方位-坐标大小txt = ["北", "东北", "东", "东南", "南", "西南", "西", "西北", "原点", ]x_p, y_p = round(pointb[0], 4) - round(pointa[0], 4), round(pointb[1], 4) - round(pointa[1], 4)if x_p == 0 and y_p > 0:course = txt[0]elif x_p > 0 and y_p > 0:course = txt[1]elif x_p > 0 and y_p == 0:course = txt[2]elif x_p > 0 and y_p < 0:course = txt[3]elif x_p == 0 and y_p < 0:course = txt[4]elif x_p < 0 and y_p < 0:course = txt[5]elif x_p < 0 and y_p == 0:course = txt[6]elif x_p < 0 and y_p > 0:course = txt[7]else:course = txt[-1]return coursedef GetRoadBookLR(origin, pointa, pointb):# 方向-左右-向量叉乘x_o, y_o = round(pointa[0], 2) - round(origin[0], 2), round(pointa[1], 2) - round(origin[1], 2)x_l, y_l = round(pointb[0], 2) - round(origin[0], 2), round(pointb[1], 2) - round(origin[1], 2)k = (x_o * y_l) - (x_l * y_o)if k < 0:lr = "右转"elif k > 0:lr = "左转"else:# 左右-向量点乘k = (x_o * x_l) + (y_o * y_l)if k < 0:lr = "掉头"else:lr = "直行"return lrdef GetNodeInd(kdtree, point, k=1):try:__distances, __ind = kdtree.query(point, k=k)return True, __indexcept:return False, "根据输入点获取最近线失败(经度,纬度) " + "%s" % point# 因为节点重复，根据节点键值取存储线id后，需要根据前后节点筛选线
def GetNodeIdLineId(poiss_ind, adjacency_matrix, node_dict, node_key):# 广度优先遍历--结合迪杰克斯特拉（Dijkstra）算法思路try:loop_will = [poiss_ind[0]]loop_ed = set()loop_done = []loop = 0loop_stdout = [poiss_ind[1]]stdout = []while loop_will:loop = loop_will.pop(0)loop_once = []loop_ed.add(loop)for i, lind_id in enumerate(adjacency_matrix[loop, ...]):if lind_id > 0:loop_once.append(i)if i == poiss_ind[1]:loop_will.clear()loop_stdout.append(loop)breakif i not in loop_ed:loop_will.append(i)else:loop_done.append([loop, loop_once])loop_done.reverse()for key in loop_done:if loop in key[1]:loop_stdout.append(key[0])loop = key[0]if loop == poiss_ind[0]:loop_stdout.reverse()break################################################################################## 可运行，可用于功能扩展# while loop:#     for key in keys:#         if loop in loop_done[key]:#             loop_stdout.append(key)#             loop = key#         if loop == poiss_ind[0]:# #             loop_stdout.append(key)#             loop_stdout.reverse()#             loop = 0#             break################################################################################## 确定前进方向for key in loop_stdout:stdout.extend(node_dict[node_key[key]])else:stdout = list(set([v for v in stdout if stdout.count(v) > 1]))# 根据图遍历结果数据特性--间隔取值，逆间隔取值--转换到最终结果if len(stdout) > 2:stdout = stdout[1::2] + [stdout[-1]] + stdout[-3::-2]except Exception as __ex:return False, "路径搜索失败:GetLineId()" + "%s" % __exelse:return True, (loop_stdout, stdout)def SetOutFormatGD(poiss, nodeid_lineid, node, line, to_crs=4544):def GetBook(poi, p_l, roadname, length):if not roadname:roadname = "无名道路"data["points"].extend(p_l)tmp = dict()tmp["instruction"] = "\tGetRoadBookLR\t.沿" + roadname + GetRoadBookCourse(poi[0],poi[1]) + "方向前进 %s" % round(length, 2) + " 米,\tGetRoadBookLRNext\t;"tmp["points"] = p_ldata["steps"].append(tmp)data = {"steps": [], "points": []}for i, id in enumerate(nodeid_lineid[1]):points = list(geommapping(line["geometry"][id])["coordinates"])if node[nodeid_lineid[0][i]] != points[0]:points.reverse()if i == 0:poi = [list(poiss[0]), points[0]]p_l = poiroadname = "出发点到最近路口的"length = GeoDataFrame(crs=to_crs, geometry=[LineString(poi)]).length[0] * 100000else:poi = [points[0], points[-1]]p_l = pointsroadname = str(line["Name"][id]).replace("None", "")length = line["Shape_Leng"][id]GetBook(poi, p_l, roadname, length)else:poi = [points[-1], list(poiss[1])]p_l = poiroadname = "最近路口到达点的"length = GeoDataFrame(crs=to_crs, geometry=[LineString(poi)]).length[0] * 100000GetBook(poi, p_l, roadname, length)# 路书for i in range(len(data["steps"])):if i == 0:# 正北方向origin = [list(poiss[0])[0], list(poiss[0])[1] - 1]pointa = data["steps"][i]["points"][0]pointb = data["steps"][i]["points"][1]lr = ",准备出发"insed = "到达主路"elif i == len(data["steps"]) - 1:origin = data["steps"][i]["points"][-2]pointa = data["steps"][i]["points"][-1]pointb = list(poiss[1])lr = ",到达目的地附近"insed = "到达目的地"else:origin = data["steps"][i - 1]["points"][-2]pointa = data["steps"][i]["points"][0]pointb = data["steps"][i]["points"][1]lr = ""insed = "下个路口" + insins = GetRoadBookLR(origin, pointa, pointb)data["steps"][i]["instruction"] = data["steps"][i]["instruction"].replace("\tGetRoadBookLR\t",ins + lr).replace("\tGetRoadBookLRNext\t", insed)return dataclass AnalysisPath:def __init__(self, data_dict):self._type = Trueself._msg = {"code": 200, "type": "success", "msg": "最短路径分析成功", "box": "", "data": None}self.data_dict = data_dictdef __str__(self):return 'AnalysisPath类实现路径分析'def __Check(self, data):if data[0]:return data[1]else:self._type = Falseself._msg = {"code": 204, "type": "fail", "msg": "最短路径分析失败", "box": "", "data": "%s" % data[1]}return Nonedef Run(self, crs_distance=4544):# ######################################################################################################### # # 可运行--计算两点直线距离，以后功能扩展可用# pois = [self.__Check(v) for v in#         [ToPoints(data=[v], crs=self.data_dict[__kself__].crs).XYs() for v in#          self.data_dict['file']]]# if self._type:#     poi_distance = round(pois[0].to_crs(crs_distance).distance(pois[1].to_crs(crs_distance)).loc[0], 2)##     poiss_ind = [self.__Check(GetNodeInd(self.data_dict["kdtree"], __v)) for __v in self.data_dict['file'] if#                  self._type]# ########################################################################################################poiss_ind = [self.__Check(GetNodeInd(self.data_dict["kdtree"], __v)) for __v in self.data_dict['file'] ifself._type]if self._type:if poiss_ind[0] == poiss_ind[1]:self._msg["data"] = "起点和终点相同"else:nodeid_lineid = self.__Check(GetNodeIdLineId(poiss_ind, self.data_dict["adjacency_matrix"], self.data_dict["node_dict"],self.data_dict["node_key"]))if self._type:point_gd = SetOutFormatGD(self.data_dict['file'], nodeid_lineid, self.data_dict['node'],self.data_dict[__kself__].loc[nodeid_lineid[1]][["Name", "Shape_Leng", "geometry"]].to_dict())self._msg["data"] = json.dumps(point_gd)return self._msgdef Main(data_dict):return AnalysisPath(data_dict).Run()if __name__ == '__main__':import numpy as npfrom cartopy import crs as ccrsimport matplotlib.pyplot as pltax = plt.axes(projection=ccrs.PlateCarree(), aspect='auto')import time, datetime__start_time = time.time()print('开始处理:', time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))import geopandas as gpdfrom scipy.spatial import KDTreepath_base = r"C:\myprojects\py_projects\get_polygon"data_dict = dict()nodes = set()data_dict[__kself__] = gpd.read_file(path_base + __keyself__[__kself__])for id in range(len(data_dict[__kself__])):points = geommapping(data_dict[__kself__]["geometry"][id])["coordinates"]##################################################################################### 获取所有点--生成前期基础数据很慢，需要优化速度# for v in points:#     nodes.append([v, data_dict[__kself__]["id"][id]])##################################################################################### 起点、中点、终点nodes.add((points[0], data_dict[__kself__]["id"][id]))# nodes.add((points[int(len(points)/2)], data_dict[__kself__]["id"][id]))nodes.add((points[-1], data_dict[__kself__]["id"][id]))data_dict[__kself__].plot(ax=ax)node_dict = {}tmp_xy = []node = []# 点关联到线，字典--key:点id,value:线idfor __i, __v in enumerate(nodes):# 过滤重复点--根据线共用点情况，多份存储if __v[0] in tmp_xy:node_dict[tmp_xy.index(__v[0])].append(__v[1])else:node_dict[__i] = [__v[1]]node.append(__v[0])tmp_xy.append(__v[0])else:del tmp_xynode_key = list(node_dict.keys())data_dict["kdtree"] = KDTree(node)data_dict["node"] = nodedata_dict["node_dict"] = node_dictdata_dict["node_key"] = node_key# 点连通性关系存储--邻接矩阵adjacency_matrix = np.zeros(shape=(len(node), len(node)), dtype=int) - 1for i in range(len(node_key)):for j in range(len(node_key)):if i == j:adjacency_matrix[i][j] = 0else:for __vj in node_dict[node_key[j]]:if __vj in node_dict[node_key[i]]:adjacency_matrix[i][j] = __vj + 1####################################################################################### 当前算法未用到距离# adjacency_matrix[i][j] = data_dict[__kself__].loc[__vj]["Shape_Leng"]######################################################################################else:data_dict["adjacency_matrix"] = adjacency_matrixdata_dict['file'] = [(104.907, 33.408), (104.916, 33.398)]# data_dict['file'] = [(104.028, 34.1034), (104.05, 34.1)]# data_dict['file'] = [(105.115, 32.6145), (105.2, 32.6306)]# data_dict['file'] = [(104.919, 33.394), (104.921, 33.391)]tmp = Main(data_dict)print(tmp)print('本次耗时:', datetime.timedelta(seconds=time.time() - __start_time), '\n')

2.c

待补充...

最短路径分析+路书（详细导航信息）相关推荐

基于百度地图和百度路书的行车轨迹回放(含途经点的多次导航)
目录先来看看我做了个啥我为什么要这件事我用到的工具正式开始创建一张空地图设置我的起点.终点.途经点为上面几个关键点打上标签绘制折线绘制折线的方法第一个难点加一辆小车在路上跑添加 ...
PostGIS 结合Openlayers以及Geoserver实现最短路径分析（二）
前文讲述了怎么用ArcMap制作了测试数据,并导入了PostGIS,接下来我们需要结合PgRouting插件,对入库的数据再进行一下处理. 1.在pgAdmin中,执行下面的sql语句 --添加起点字 ...
百度地图轨迹回放,自定义路书,边走边画线
转自:https://www.cnblogs.com/syj2016/p/5685294.html 百度地图轨迹回放,自定义路书,边走边画线在原有的百度路书的基础上,做了修改,使其能实现边走边画线的 ...
百度地图路书动态加载规划
<html lang="en"> <head><meta charset="utf-8"/><title>路书& ...
ArcMap最短路径分析和网络数据集的构建
打断相交点 1.单击[编辑器]工具条上的编辑工具. 2.选择要在交叉点处进行分割的线要素. 3.单击[高级编辑]工具条上的打断相交线工具. 4.默认或可输入拓扑容差. 5.单击确定. 结果:所选线在相 ...
百度路书实现车辆轨迹动态回放功能-javascrpt版
基于百度路书js实现车辆轨迹回放功能:接到一个新需求,实现车辆历史轨迹回放功能,之前没有做过,根据网上各种文章走了一些坑,基本上都是半成品,最后还是看百度地图原生api完成开发,现在把最终结果分享给大 ...
vue.js BMap 与BMapLib.lushu 的配置与百度路书，结合项目后台的实例展示
@[vue.js BMap 与BMapLib.lushu 的配置与百度路书,结合项目的实例展示] 上一章实现了百度轨迹的展现.今天写一个百度路书结合项目功能的实现. (实例很全面,认真相互探讨学习) ...
百度路书信息显示_学会用地图制作路书，分分钟从小白变成自驾游达人
原创文章,欢迎点评! 很多想要开车自驾游的朋友,由于自己不会做行程规划和安排,或总是感觉自己做的行程规划.安排不是很精准,不能很清晰的规划出自己每天该走多远的路?途中在景点游玩的时间应该是多少?什么时 ...
postgis+geoserver+openlayers最短路径分析-学习记录bug
学习postgis+geoserver+openlayers最短路径分析,记录遇到的奇奇怪怪的问题. 主要参考: https://www.cnblogs.com/giser-s/p/11599562. ...

最短路径分析+路书（详细导航信息）

最短路径分析+路书（详细导航信息）相关推荐

最新文章

热门文章