Shortest path planning system based on map poi point connectivity technology
By integrating POI data and real-time traffic information, using POI point connectivity technology and shortest path algorithm, the problem that traditional path planning systems fail to fully consider POI information and real-time traffic information is solved, and more efficient and accurate path planning is achieved.
Patent Information
- Application Number
- CN202411323734.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-23
- Publication Date
- 2024-12-20
AI Technical Summary
Traditional path planning systems fail to fully consider POI information and real-time traffic information, resulting in the effectiveness of path planning in complex environments and the path cannot be adjusted in time.
The shortest path planning system is adopted based on map POI point connectivity technology, including POI point data acquisition, preprocessing, identification and marking, construction of graph models, connectivity analysis, shortest path planning algorithm and path visualization module, integrating POI data and real-time traffic Information, calculate the shortest path using Dijkstra and A* search algorithms.
It significantly shortens the calculation time of path planning, improves system response speed, provides a more accurate and consistent with the shortest path planning, and reduces the additional time and costs caused by improper path selection.
Smart Images
Figure CN119164418A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of POI connectivity, and in particular to a shortest path planning system based on map POI connectivity technology. Background Art
[0002] POI, the full name of which is "Point of Interest", can be translated into Chinese as "point of interest". It is a term in geographic information systems, referring to all geographical objects that can be abstracted as points, especially some geographical entities that are closely related to people's lives, such as schools, banks, restaurants, gas stations, hospitals, supermarkets, etc. POI usually contains four aspects of information: name, category, longitude, and latitude. These places have become the focus of people's attention because of their particularity or attraction. In the fields of tourism planning, social media, etc., POI, as important geographic spatial information, is widely used in route planning, navigation services, location recommendations, etc., providing users with convenient and personalized service experience. In modern society, with the widespread application of geographic location information services, route planning systems have become an indispensable part of daily life. Whether it is in-car navigation, smartphone applications or online map services, they all rely on efficient route planning algorithms to provide users with accurate and fast travel solutions.
[0003] Traditional path planning systems mainly rely on road network data to calculate the shortest path. Although this planning method meets basic navigation needs to a certain extent, it has some obvious limitations when facing complex real-world environments. Specifically, existing path planning technologies mainly face the following problems: On the one hand, traditional path planning systems mainly focus on the physical properties of the road itself, such as length and type (highway, ordinary road, etc.), and rarely consider POI information along the way. POI information includes facilities closely related to travel, such as restaurants, gas stations, and parking lots. This information is crucial for making reasonable travel plans. On the other hand, most existing path planning systems fail to fully integrate real-time traffic information, resulting in the system being unable to adjust the path in time when encountering emergencies (such as traffic accidents, temporary construction), thus affecting the effectiveness of path planning. Summary of the invention
[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a shortest path planning system based on map POI connectivity technology to solve the problems raised in the above-mentioned background technology.
[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a shortest path planning system based on map POI connectivity technology, including a POI point data acquisition module, a POI point data preprocessing module, a POI point identification and marking module, a graph model building module, a connectivity analysis module, a shortest path planning algorithm module, a path visualization module and a user interface module.
[0006] POI data collection module: used to automatically or manually collect vector map POI data from multiple data sources, determine non-building areas as potential locations of POI points, and transmit the collected data to the POI data preprocessing module. The multiple data sources include public map service providers, GPS devices, social media, online map platforms, and public data sets from government departments;
[0007] POI point data preprocessing module: used to preprocess the POI point data collected by the POI point data collection module, and transmit the preprocessed data to the POI point recognition and marking module;
[0008] POI point identification and marking module: used to identify and mark POI points in non-building areas, the marked POI points include natural landmarks, public facilities or other important places, assign an identifier to each POI point, and record the position of each POI point in the x, y, z coordinate system;
[0009] Build a graph model module: used to abstract the map into a graph, with POI points as nodes of the graph, roads or feasible paths as edges, and determine the weights of the edges;
[0010] Connectivity analysis module: Uses a map traversal algorithm to determine the connectivity between POI points, records the adjacent points of each POI point, and constructs an adjacency matrix or adjacency table;
[0011] Shortest path planning algorithm module: used to select the shortest path algorithm according to the connectivity analysis of the connectivity analysis module, and the shortest path algorithm includes Dijkstra algorithm and A* search algorithm;
[0012] Path visualization module: used to visualize the shortest path calculated by the shortest path planning algorithm module on the vector map by highlighting the path or changing the path color;
[0013] User interface module: used to provide the user interface and interaction method. Users can enter the starting and ending point information, customize the path planning conditions, and view and obtain the planning results. It also supports voice navigation and real-time traffic display functions.
[0014] Preferably, the POI point data includes but is not limited to traversable routes, precise outlines of buildings, stair locations, and connection information geographical elements; the potential locations refer to geographical areas that are not occupied by buildings and have the potential to become POI points, and they have accessibility, functional diversity, environmental characteristics, community needs, and development potential characteristics.
[0015] Preferably, the POI point data preprocessing module preprocesses the collected data in the following manner: identifying and deleting duplicate POI point records through database indexing technology, and performing data cleaning, outlier removal, and standardized data format through existing technology.
[0016] Preferably, the graph model building module can determine the edge weights in the following ways:
[0017] A1: Based on the distance weight, the calculation model is as follows:
[0018] DW=d ij , where DW represents the weight of the distance, d ij It is expressed as the geographical distance between node i and node j;
[0019] A2: Based on the weight of walking time, the calculation model is as follows:
[0020] Among them, TW represents the weight of walking time, v ij It is expressed as the average speed between node i and node j;
[0021] A3: Calculate the final weight value of the edge. The calculation model is as follows:
[0022] Among them, FW represents the final weight value of the edge, n represents n factors, and W k Expressed as the weight coefficient of the kth factor, It is expressed as the specific value of the kth factor for edge ij.
[0023] Preferably, the adjacency matrix is defined as:
[0024] The adjacency matrix is a two-dimensional array A, where A ij Represented as the relationship from node i to node j, if node i and node j are directly connected, then A ij =1; if not directly connected, then A ij is 0; if the graph is a directed graph, then A ij and A ji can be different; if it is an undirected graph, then A ij =A ji .
[0025] Preferably, the construction of the adjacency matrix is specifically as follows:
[0026] B1: Initialize an N×N matrix A, where N is the number of nodes;
[0027] B2: Traverse all edges, that is, the connections between POI points. If there is an edge between node i and node j, set A ij = 1, for undirected graphs, set A ji =1.
[0028] Preferably, the specific steps of the Dijkstra algorithm are as follows:
[0029] C1: Initialization: Set the initial distances of all neighbor nodes of the starting point s to infinity, except for the starting point itself which is 0;
[0030] C2: For each node u, maintain two variables: d[u] and p[u], which are the currently known shortest distance from the starting point to u, and p[u] refers to the predecessor node of u;
[0031] C3: Starting from the starting point, each time select a node u with the smallest distance among the unvisited nodes, update the distances of all its unvisited neighbors v: d[v] = min(d[v], d[u] + w(u, v)), and update the predecessor node of v to u.
[0032] Preferably, the A* search algorithm has the following specific steps:
[0033] D1: Based on the Dijkstra algorithm, a heuristic function h(v) is introduced to estimate the cost from node v to the target node t;
[0034] D2: Total cost f(v) = g(v) + h(v), where g(v) represents the actual cost from the starting point to v, and h(v) represents the estimated cost from v to the target node;
[0035] D3: Use a priority queue to sort nodes according to f(v), and select the node with the smallest f(v) for expansion each time.
[0036] Technical effects and advantages of the present invention:
[0037] 1. The present invention utilizes POI point connectivity technology, and the system can quickly identify and process key nodes and paths on the map, thereby significantly shortening the calculation time of path planning and improving the system response speed;
[0038] 2. The present invention integrates map data, POI data and path planning algorithms, and utilizes the connectivity information between these points to provide users with efficient and accurate shortest path planning services from the starting point to the end point;
[0039] 3. By accurately analyzing the connectivity between POI points, the system of the present invention can plan a more accurate shortest route for users that conforms to actual traffic conditions, thereby reducing the extra time and cost incurred by users due to improper route selection. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.
[0041] Figure 1 It is a schematic diagram of the overall structure of the system of the present invention. DETAILED DESCRIPTION
[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0043] See also Figure 1 As shown, the present invention provides a shortest path planning system based on map POI connectivity technology, including a POI point data acquisition module, a POI point data preprocessing module, a POI point identification and marking module, a graph model building module, a connectivity analysis module, a shortest path planning algorithm module, a path visualization module and a user interface module.
[0044] The output end of the POI point data acquisition module is connected to the input end of the POI point data preprocessing module, the output end of the POI point data preprocessing module is connected to the input end of the POI point identification and marking module, the output end of the POI point identification and marking module is connected to the input end of the graph model construction module, the output end of the graph model construction module is connected to the input end of the connectivity analysis module, the output end of the connectivity analysis module is respectively connected to the input end of the shortest path planning algorithm module and the input end of the user interface module, and the output end of the shortest path planning algorithm module is respectively connected to the input end of the path visualization module and the input end of the user interface module.
[0045] The POI point data collection module is used to automatically or manually collect vector map POI point data from multiple data sources, determine non-building areas as potential locations of POI points, and transmit the collected data to the POI point data preprocessing module, wherein the multiple data sources include public map service providers, GPS devices, social media, online map platforms, and public data sets of government departments;
[0046] In this embodiment, it should be specifically explained that the POI point data includes but is not limited to traversable routes, precise outlines of buildings, stair locations, and connection information geographical elements; the potential locations refer to geographical areas that are not occupied by buildings and have the potential to become POI points. They have accessibility, functional diversity, environmental characteristics, community needs, and development potential characteristics.
[0047] The POI point data preprocessing module is used to preprocess the POI point data collected by the POI point data collection module and transmit the preprocessed data to the POI point recognition and marking module;
[0048] In this embodiment, it should be specifically explained that the POI point data preprocessing module preprocesses the collected data in the following manner: identifying and deleting duplicate POI point records through database indexing technology to perform deduplication processing, and cleaning the data, removing outliers and standardizing the data format through existing technologies;
[0049] The POI point identification and marking module is used to identify and mark POI points in non-building areas, wherein the marked POI points include natural landmarks, public facilities or other important places, assign an identifier to each POI point, and record the position of each POI point in the x, y, z coordinate system;
[0050] In this embodiment, it should be specifically explained that the identifier is a unique identifier assigned to each POI point, which is used to distinguish it from other POI points.
[0051] The graph model building module is used to abstract the map into a graph, with POI points as nodes of the graph, roads or feasible paths as edges, and determine the weights of the edges;
[0052] In this embodiment, it should be specifically explained that the graph model building module can determine the weight of the edge in the following ways:
[0053] A1: Based on the distance weight, the calculation model is as follows:
[0054] DW=d ij , where DW represents the weight of the distance, d ij It is expressed as the geographical distance between node i and node j;
[0055] A2: Based on the weight of walking time, the calculation model is as follows:
[0056] Among them, TW represents the weight of walking time, v ij It is expressed as the average speed between node i and node j;
[0057] In this embodiment, it should be specifically noted that the average speed can be determined according to factors such as road type (such as expressway, urban road), traffic conditions, etc.
[0058] A3: Calculate the final weight value of the edge. The calculation model is as follows:
[0059] Among them, FW represents the final weight value of the edge, n represents n factors, and W k Expressed as the weight coefficient of the kth factor, It is expressed as the specific value of the kth factor for edge ij.
[0060] The connectivity analysis module uses a map traversal algorithm to determine the connectivity between POI points, records the adjacent points of each POI point, and constructs an adjacency matrix or adjacency table;
[0061] In this embodiment, it should be specifically explained that the map traversal algorithm includes two algorithms: breadth-first search and depth-first search. The principle of the breadth-first search algorithm is: starting from a certain node in the graph (called the source node), first visit all the directly adjacent nodes of the node, and then visit the unvisited adjacent nodes of these adjacent nodes in turn, until all the nodes in the graph are visited; the breadth-first search is implemented using a queue as an auxiliary data structure; the principle of the depth-first search algorithm is: starting from a certain node in the graph, search deeply along a path until a node that cannot be further explored (i.e., a dead end) is reached, and then backtracking to the previous node, and continuing to explore other possible paths until all nodes have been visited; the depth-first search is implemented using a stack or recursion as an auxiliary data structure.
[0062] In this embodiment, it should be specifically explained that the connectivity includes direct connectivity and indirect connectivity, wherein direct connectivity means that there is a direct physical connection or logical connection between two POI points, such as being directly accessible by walking, riding, or driving; indirect connectivity means that two POI points are connected through a series of other POI points to form a path;
[0063] In this embodiment, it should be specifically explained that the adjacency matrix is defined as:
[0064] The adjacency matrix is a two-dimensional array A, where A ij Represented as the relationship from node i to node j, if node i and node j are directly connected, then A ij =1; if not directly connected, then A ij is 0; if the graph is a directed graph, then A ij and A ji can be different; if it is an undirected graph, then A ij =A ji .
[0065] In this embodiment, it should be specifically noted that the edges in the undirected graph have no direction, that is, if there is an edge between node A and node B, this edge can be regarded as from A to B or from B to A; therefore, in an undirected graph, the edges are bidirectional.
[0066] In this embodiment, it should be specifically explained that the construction of the adjacency matrix is specifically as follows:
[0067] B1: Initialize an N×N matrix A, where N is the number of nodes;
[0068] B2: Traverse all edges, that is, the connections between POI points. If there is an edge between node i and node j, set A ij = 1, for undirected graphs, set A ji =1.
[0069] The shortest path planning algorithm module is used to select a shortest path algorithm according to the connectivity analysis of the connectivity analysis module, and the shortest path algorithm includes a Dijkstra algorithm and an A* search algorithm;
[0070] In this embodiment, it should be specifically noted that a suitable shortest path algorithm is selected according to the requirements. The Dijkstra algorithm is suitable for processing graphs with positive weights, while the A* search algorithm is more efficient when there is a clear goal.
[0071] In this embodiment, it should be specifically explained that the specific steps of the Dijkstra algorithm are as follows:
[0072] C1: Initialization: Set the initial distances of all neighbor nodes of the starting point s to infinity, except for the starting point itself which is 0;
[0073] C2: For each node u, maintain two variables: d[u] and p[u], which are the currently known shortest distance from the starting point to u, and p[u] refers to the predecessor node of u;
[0074] C3: Starting from the starting point, each time select a node u with the smallest distance among the unvisited nodes, update the distances of all its unvisited neighbors v: d[v] = min(d[v], d[u] + w(u, v)), and update the predecessor node of v to u;
[0075] In this embodiment, it should be specifically explained that the specific steps of the A* search algorithm are as follows:
[0076] D1: Based on the Dijkstra algorithm, a heuristic function h(v) is introduced to estimate the cost from node v to the target node t;
[0077] D2: Total cost f(v) = g(v) + h(v), where g(v) represents the actual cost from the starting point to v, and h(v) represents the estimated cost from v to the target node;
[0078] D3: Use a priority queue to sort nodes according to f(v), and select the node with the smallest f(v) for expansion each time.
[0079] The path visualization module is used to visualize the shortest path calculated by the shortest path planning algorithm module on the vector map by highlighting the path or changing the path color;
[0080] The user interface module is used to provide a user interface and interaction method. Users can enter the starting point and end point information, customize the path planning conditions, and view and obtain the planning results. It also supports voice navigation and real-time traffic display functions.
[0081] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
[0082] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. The shortest path planning system based on map POI connectivity technology is characterized by: include: POI data collection module: used to automatically or manually collect vector map POI data from multiple data sources, determine non-building areas as potential locations of POI points, and transmit the collected data to the POI data preprocessing module. The multiple data sources include public map service providers, GPS devices, social media, online map platforms, and public data sets from government departments; POI point data preprocessing module: used to preprocess the POI point data collected by the POI point data collection module, and transmit the preprocessed data to the POI point recognition and marking module; POI point identification and marking module: used to identify and mark POI points in non-building areas, the marked POI points include natural landmarks, public facilities or other important places, assign an identifier to each POI point, and record the position of each POI point in the x, y, z coordinate system; Build a graph model module: used to abstract the map into a graph, with POI points as nodes of the graph, roads or feasible paths as edges, and determine the weights of the edges; Connectivity analysis module: Uses a map traversal algorithm to determine the connectivity between POI points, records the adjacent points of each POI point, and constructs an adjacency matrix or adjacency table; Shortest path planning algorithm module: used to select the shortest path algorithm according to the connectivity analysis of the connectivity analysis module, and the shortest path algorithm includes Dijkstra algorithm and A* search algorithm; Path visualization module: used to visualize the shortest path calculated by the shortest path planning algorithm module on the vector map by highlighting the path or changing the path color; User interface module: used to provide the user interface and interaction method. Users can enter the starting and ending point information, customize the path planning conditions, and view and obtain the planning results. It also supports voice navigation and real-time traffic display functions.
2. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The POI point data includes but is not limited to traversable routes, precise outlines of buildings, stair locations, and connection information geographic elements; the potential locations refer to geographic areas that are not occupied by buildings and have the potential to become POI points. They have accessibility, functional diversity, environmental characteristics, community needs, and development potential characteristics.
3. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The POI point data preprocessing module preprocesses the collected data in the following specific manner: identifying and deleting duplicate POI point records through database indexing technology, and cleaning the data, removing outliers, and standardizing the data format through existing technology.
4. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The graph model building module can determine the edge weights in the following ways: A1: Based on the distance weight, the calculation model is as follows: DW=d ij , where DW represents the weight of the distance, d ij It is expressed as the geographical distance between node i and node j; A2: Based on the weight of walking time, the calculation model is as follows: Among them, TW represents the weight of walking time, v ij It is expressed as the average speed between node i and node j; A3: Calculate the final weight value of the edge. The calculation model is as follows: Among them, FW represents the final weight value of the edge, n represents n factors, and W k Expressed as the weight coefficient of the kth factor, It is expressed as the specific value of the kth factor for edge ij.
5. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The adjacency matrix is defined as: The adjacency matrix is a two-dimensional array A, where A ij Represented as the relationship from node i to node j, if node i and node j are directly connected, then A ij =1; if not directly connected, then A ij is 0; if the graph is a directed graph, then A ij and A ji can be different; if it is an undirected graph, then A ij =A ji .
6. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The construction of the adjacency matrix is specifically as follows: B1: Initialize an N×N matrix A, where N is the number of nodes; B2: Traverse all edges, that is, the connections between POI points. If there is an edge between node i and node j, set A ij = 1, for undirected graphs, set A ji =1.
7. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The specific steps of the Dijkstra algorithm are as follows: C1: Initialization: Set the initial distances of all neighbor nodes of the starting point s to infinity, except for the starting point itself which is 0; C2: For each node u, maintain two variables: d[u] and p[u], which are the currently known shortest distance from the starting point to u, and p[u] refers to the predecessor node of u; C3: Starting from the starting point, each time select a node u with the smallest distance among the unvisited nodes, update the distances of all its unvisited neighbors v: d[v] = min(d[v], d[u] + w(u, v)), and update the predecessor node of v to u.
8. The shortest path planning system based on map POI connectivity technology according to claim 1 is characterized by: The specific steps of the A* search algorithm are as follows: D1: Based on the Dijkstra algorithm, a heuristic function h(v) is introduced to estimate the cost from node v to the target node t; D2: Total cost f(v) = g(v) + h(v), where g(v) represents the actual cost from the starting point to v, and h(v) represents the estimated cost from v to the target node; D3: Use a priority queue to sort nodes according to f(v), and select the node with the smallest f(v) for expansion each time.