Topological map construction method based on longitude and latitude information

Abstract

The invention discloses a topological map construction method based on latitude and longitude information, which comprises the following steps: S1, collecting road nodes, important places and latitude and longitude information thereof in a target area through GPS (Global Positioning System) equipment, and providing basic data for subsequent map construction by the information; s2, preprocessing the acquired longitude and latitude data, including removing abnormal values, smoothing the data and ensuring the integrity and accuracy of the data; s3, analyzing longitude and latitude information by adopting a proper algorithm (such as a shortest path algorithm or a clustering algorithm), determining a connection relationship between nodes, and calculating a distance between adjacent nodes to form a basis of a road network; and S4, taking the important places and the road intersections as nodes of the map, taking the connected roads as edges of the map, and forming graph structure representation of the topological map. The attributes of each node comprise latitude and longitude information, a name and other related data; and S5, displaying the constructed graph structure as a topological map by using a visualization tool, and providing visual geographic information display.

Description

Technical Field

[0001] The present invention relates to the technical field of map construction and geographic information system (GIS), and in particular to a method for constructing a topological map based on latitude and longitude information. With the development of intelligent transportation, autonomous driving, drone navigation and geographic information services, the demand for accurate map data is increasing. Effective topological map construction not only depends on high-quality geographic data collection, but also requires reasonable processing and analysis of the data to achieve efficient path planning, navigation and decision support. Background Art

[0002] In modern transportation systems and smart city construction, prior map information is widely used in path planning, navigation, and decision support. Traditional map types mainly include raster maps, vector maps, and topological maps. Raster maps represent geographic information by dividing space into uniform grids, and can display the attributes of each location in detail. However, due to its large data volume and high computational complexity, processing efficiency and real-time performance are often limited. In high-density traffic areas, this detailed information presentation may make it difficult for users to obtain key information, affecting the accuracy of navigation. In contrast, vector maps use points, lines, and surfaces to represent geographic features, which is more flexible and can effectively describe complex geographical environments. However, vector maps are still less adaptable in dynamic scenarios, and the update process is cumbersome and inefficient when the environment changes (such as road closures or new roads). As a new map representation method, topological maps emphasize the connectivity of road networks through the graph structure of nodes and edges, and have higher flexibility and computational efficiency. It can dynamically adjust nodes and connection relationships and quickly respond to changes in traffic flow. Topological maps provide key map information for various intelligent applications (such as autonomous driving, drone navigation, and logistics scheduling), and are suitable for real-time navigation and path planning. Therefore, developing an efficient and accurate topological map construction method has important practical significance. Summary of the invention

[0003] The present invention aims to solve the problems existing in the prior art by proposing a topological map construction method based on latitude and longitude information, improve the efficiency and accuracy of map construction and path planning, and meet the needs of fields such as intelligent transportation and urban management for high-quality geographic information.

[0004] To achieve the above design objectives, the present invention provides a method for constructing a topological map based on longitude and latitude information, comprising the following steps:

[0005] Step 1: Use GPS equipment to collect information about road nodes, important locations, and their latitude and longitude in the target area. This information will provide basic data for subsequent map construction.

[0006] Step 2: Preprocess the acquired latitude and longitude data, including removing outliers, smoothing data, and ensuring the integrity and accuracy of the data;

[0007] Step 3: Use clustering algorithms to analyze longitude and latitude information, build an adjacency matrix, and determine the connection relationship between nodes to form the basis of the road network;

[0008] Step 4: Use important locations and road intersections as nodes of the graph, and connected roads as edges of the graph to form a graph structure representation of the topological map. The attributes of each node include latitude and longitude information, name, and other related data;

[0009] Step 5: Use visualization tools to display the constructed graph structure as a topological map to provide an intuitive display of geographic information.

[0010] Optionally, in the process of collecting road information and longitude and latitude data through a GPS device as described in step 1, determine the area to be collected in advance, and plan a reasonable collection route based on the road network and important places (such as shops, schools, hospitals) in the area to ensure that all key locations are covered.

[0011] Optionally, set the sampling frequency of the GPS device (such as once per second or once per minute) to obtain sufficiently dense longitude and latitude information. When passing important places, manually mark or record relevant information (such as name, type, etc.) on the GPS device to avoid neglecting some important places or roads.

[0012] Optionally, ensure that the collected data contains the following information: Longitude, Latitude, Timestamp, Location Name, and Location Type (such as store, school, etc.).

[0013] Optionally, the method for preprocessing the longitude and latitude data obtained in step 2 specifically includes the following steps:

[0014] Step 2.1: Use the Haversine formula to calculate the distance between adjacent longitudes and latitudes. The specific expression is:

[0015]

[0016] in, represents latitude, λ represents longitude, and r represents the radius of the earth (about 6371km).

[0017] Step 2.2: Set a threshold to remove abnormal data that is less than 10 meters away from adjacent points.

[0018] Step 2.3: Use Kalman filtering to smooth the GPS data.

[0019] Optionally, in step 2.3, a state vector X is set, which generally includes:

[0020]

[0021] Among them, longitude represents the current longitude, latitude represents the current latitude, velocity represents the current speed, and acceleration represents the current acceleration.

[0022] The state transition matrix describes the dynamic characteristics of the system and is used to map the current state to the next state. Its form is:

[0023]

[0024] Where Δt is the time interval, the time difference from one measurement to the next. 1 means the current state remains unchanged.

[0025] The observation matrix connects the state vector X with the observation value and is used to convert the state into the observation value. Its form is:

[0026]

[0027] Utilizing a combination of these matrices can effectively smooth the data and improve the accuracy of position estimation.

[0028] Optionally, in step 3, a clustering algorithm is used to analyze the longitude and latitude information, construct an adjacency matrix, and determine the connection relationship between nodes. The K-Means clustering algorithm is used, the number of clusters k is set, and the longitude and latitude data are input into the K-Means algorithm. According to the output of the algorithm, a cluster label of each node is obtained, and the label indicates the cluster to which the node belongs. The cluster labels are traversed to find all nodes of the same cluster. Nodes in the same cluster are classified as the same connection relationship to form a connection relationship list. An adjacency matrix is ​​constructed to determine the connection relationship between nodes to form the basis of a road network.

[0029] Optionally, the constructed adjacency matrix is ​​a two-dimensional array that represents the distance and connection relationship between multiple nodes. The adjacency matrix is ​​in the form of n×n, where n is the number of nodes, and the element d[i][j] represents the distance from node i to node j. Usually the diagonal element d[i][j] = 0, because the distance from any node to itself is zero.

[0030] Optionally, in the graph structure of the topology map constructed in step 4, the road node information and the attributes of the edges are as follows:

[0031] Nodes: represent important locations such as road intersections, destinations, and service stations.

[0032] Edge: represents the road connecting the nodes. Each edge has attributes, including:

[0033] Weight: set according to actual conditions, used for optimization calculation when selecting paths. The cost value represents the distance. The longer the distance, the greater the cost value.

[0034] Traffic status: reflects the road traffic conditions in real time (such as congestion, closure, etc., which can be obtained through the traffic monitoring system).

[0035] The road nodes are numbered in sequence and connected according to the constructed adjacency matrix to form a complete topological map.

[0036] Optionally, during the execution of step 5, the node data is traversed, a label is added to each node, and its node name or number is displayed for user identification. The distance edge data is traversed, and a road network is drawn according to the connection relationship between nodes and edges to form a complete topology map that can be used for subsequent path planning.

[0037] The present invention aims to solve the problems existing in the prior art by proposing a topological map construction method based on latitude and longitude information, meet the demand for high-quality geographic information in fields such as intelligent transportation and urban management, and improve the efficiency and accuracy of map construction and path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments and technical solutions of the present invention, the drawings required for use in the embodiments and technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a schematic flow chart of a method for constructing a topological map based on longitude and latitude information of the present invention.

[0040] Figure 2 It is a partial road numbering and road selection schematic diagram of the present invention.

[0041] Figure 3 It is a schematic diagram of part of GPS raw data of the present invention.

[0042] Figure 4 It is a schematic diagram of the topology map of the present invention.

[0043] Table 1 is a schematic table of some adjacency matrix data of the present invention. DETAILED DESCRIPTION

[0044] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0045] See also Figure 1 The present invention provides a method for constructing a topological map based on longitude and latitude information, which specifically includes the following steps:

[0046] Step 1: Use GPS equipment to collect information about road nodes, important locations, and their latitude and longitude in the target area. This information will provide basic data for subsequent map construction.

[0047] Step 2: Preprocess the acquired latitude and longitude data, including removing outliers, smoothing data, and ensuring the integrity and accuracy of the data;

[0048] Step 3: Use clustering algorithms to analyze longitude and latitude information, build an adjacency matrix, and determine the connection relationship between nodes to form the basis of the road network;

[0049] Step 4: Use important locations and road intersections as nodes of the graph, and connected roads as edges of the graph to form a graph structure representation of the topological map. The attributes of each node include latitude and longitude information, name, and other related data;

[0050] Step 5: Use visualization tools to display the constructed graph structure as a topological map to provide an intuitive display of geographic information.

[0051] The following is a further explanation based on the specific implementation steps:

[0052] In step 1, please refer to Figure 2 Some road numbers and road selection diagrams are provided. Some road numbers are marked and some unnecessary roads are deleted. In the process of collecting road information and longitude and latitude data through GPS equipment, the area to be collected is determined in advance, and reasonable collection routes are planned based on the road network and important places (such as shops, schools, hospitals) in the area to ensure coverage of all key locations.

[0053] Set the sampling frequency of the GPS device (such as once per second or once per minute) to obtain sufficiently dense longitude and latitude information. When passing important places, manually mark or record relevant information (such as name, type, etc.) on the GPS device to avoid overlooking some important places or roads.

[0054] For details, please refer to Figure 3Schematic diagram of some of the provided raw GPS data. Make sure the collected data contains the following information: longitude, latitude, timestamp, location name and location type (such as store, school, etc.).

[0055] Step 2: Preprocess the acquired latitude and longitude data and ensure the integrity and accuracy of the data. The specific steps include the following:

[0056] Step 2.1: Use the Haversine formula to calculate the distance between adjacent longitudes and latitudes. The specific expression is:

[0057]

[0058] in, represents latitude, λ represents longitude, and r represents the radius of the earth (about 6371km).

[0059] Step 2.2: Set a threshold to remove abnormal data that is less than 10 meters away from adjacent points.

[0060] Step 2.3: Use Kalman filtering to smooth the GPS data.

[0061] In step 2.3, in the process of smoothing the GPS data using Kalman filtering, the state vector X is set. The state vector usually includes:

[0062]

[0063] Among them, longitude represents the current longitude, latitude represents the current latitude, velocity represents the current speed, and acceleration represents the current acceleration.

[0064] The state transition matrix describes the dynamic characteristics of the system and is used to map the current state to the next state. Its form is:

[0065]

[0066] Where Δt is the time interval, the time difference from one measurement to the next. 1 means the current state remains unchanged.

[0067] The observation matrix connects the state vector X with the observation value and is used to convert the state into the observation value. Its form is:

[0068]

[0069] Utilizing a combination of these matrices can effectively smooth the data and improve the accuracy of position estimation.

[0070] Step 3: Use the K-Means clustering algorithm, set the number of clusters k, analyze the longitude and latitude information, build an adjacency matrix, and determine the connection relationship between nodes. Input the longitude and latitude data into the K-Means algorithm. According to the output of the algorithm, get the cluster label of each node. The label indicates the cluster to which the node belongs. Traverse the cluster labels to find all nodes in the same cluster. Nodes in the same cluster are classified into the same connection relationship to form a connection relationship list. Construct an adjacency matrix to determine the connection relationship between nodes to form the basis of the road network.

[0071] The constructed adjacency matrix is ​​a two-dimensional array that represents the distance and connection relationship between multiple nodes. The adjacency matrix is ​​in the form of n×n, where n is the number of nodes, and the element d[i][j] represents the distance from node i to node j. Usually, the diagonal element d[i][j] = 0, because the distance from any node to itself is zero. For details, please refer to the schematic table of some adjacency matrix data provided in Table 1.

[0072] Step 4: Construct the road network structure of the topology map. The road node information and edge attributes are as follows:

[0073] Nodes: represent important locations such as road intersections, destinations, and service stations.

[0074] Edge: represents the road connecting the nodes. Each edge has attributes, including:

[0075] Weight: set according to actual conditions, used for optimization calculation when selecting paths. The cost value represents the distance. The longer the distance, the greater the cost value.

[0076] Traffic status: reflects the road traffic conditions in real time (such as congestion, closure, etc., which can be obtained through the traffic monitoring system).

[0077] The road nodes are numbered in sequence and connected according to the constructed adjacency matrix to form a complete topological map.

[0078] Step 5: Display the constructed road network structure as a topological map to provide intuitive geographic information display. Traverse the node data, add labels to each node, and display its node name or number for user identification. Traverse the distance edge data, draw the road network according to the connection relationship between nodes and edges, and form a complete topological map that can be used for subsequent path planning. For details, please refer to Figure 4 Schematic diagram of the topological map provided.

[0079] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A method for constructing a topological map based on latitude and longitude information, characterized in that: The following steps are involved: Step 1: Use GPS equipment to collect information about road nodes, important locations, and their latitude and longitude in the target area. This information will provide basic data for subsequent map construction. Step 2: Preprocess the acquired latitude and longitude data, including removing outliers, smoothing data, and ensuring the integrity and accuracy of the data; Step 3: Use clustering algorithms to analyze longitude and latitude information, build an adjacency matrix, and determine the connection relationship between nodes to form the basis of the road network; Step 4: Use important locations and road intersections as nodes of the graph, and connected roads as edges of the graph to form a graph structure representation of the topological map. The attributes of each node include latitude and longitude information, name, and other related data; Step 5: Use visualization tools to display the constructed graph structure as a topological map to provide an intuitive display of geographic information.

2. The method according to claim 1, characterized in that In the process of collecting road information and longitude and latitude data through GPS equipment as described in step 1, determine the area to be collected in advance, and plan a reasonable collection route based on the road network and important places (such as stores, schools, hospitals) in the area to ensure that all key locations are covered.

3. The method according to claim 2, characterized in that Set the sampling frequency of the GPS device (such as once per second or once per minute) to obtain sufficiently dense longitude and latitude information. When passing important places, manually mark or record relevant information (such as name, type, etc.) on the GPS device to avoid overlooking some important places or roads.

4. The method according to claim 3, characterized in that Make sure the collected data contains the following information: Longitude, Latitude, Timestamp, Location Name, and Location Type (such as store, school, etc.).

5. The method according to claim 1, characterized in that The method for preprocessing the acquired longitude and latitude data in step 2 specifically includes the following steps: Step 2.1: Use the Haversine formula to calculate the distance between adjacent longitudes and latitudes. The specific expression is: in, represents latitude, λ represents longitude, and r represents the radius of the earth (about 6371km). Step 2.2: Set a threshold to remove abnormal data that is less than 10 meters away from adjacent points. Step 2.3: Use Kalman filtering to smooth the GPS data.

6. The method according to claim 5, characterized in that In step 2.3, the state vector X is set. The state vector usually includes: Among them, longitude represents the current longitude, latitude represents the current latitude, velocity represents the current speed, and acceleration represents the current acceleration. The state transition matrix describes the dynamic characteristics of the system and is used to map the current state to the next state. Its form is: Where Δt is the time interval, the time difference from one measurement to the next. 1 means the current state remains unchanged. The observation matrix connects the state vector X with the observation value and is used to convert the state into the observation value. Its form is: Utilizing a combination of these matrices can effectively smooth the data and improve the accuracy of position estimation.

7. The method according to claim 1, characterized in that In the method described in step 3, a clustering algorithm is used to analyze longitude and latitude information, construct an adjacency matrix, and determine the connection relationship between nodes. The K-Means clustering algorithm is used, the number of clusters k is set, and the longitude and latitude data are input into the K-Means algorithm. According to the output of the algorithm, a cluster label of each node is obtained, and the label indicates the cluster to which the node belongs. The cluster labels are traversed to find all nodes of the same cluster. Nodes in the same cluster are classified as the same connection relationship to form a connection relationship list. An adjacency matrix is ​​constructed to determine the connection relationship between nodes to form the basis of a road network.

8. The method according to claim 7, characterized in that The constructed adjacency matrix is ​​a two-dimensional array that represents the distance and connection relationship between multiple nodes. The adjacency matrix is ​​in the form of n×n, where n is the number of nodes, and the element d[i][j] represents the distance from node i to node j. Usually the diagonal element d[i][j] = 0, because the distance from any node to itself is zero.

9. The method according to step 4 of claim 1, characterized in that: Nodes: represent important locations such as road intersections, destinations, and service stations. Edge: represents the road connecting the nodes. Each edge has attributes, including: Weight: set according to actual conditions, used for optimization calculation when selecting paths. The cost value represents the distance. The longer the distance, the greater the cost value. Traffic status: reflects the road traffic conditions in real time (such as congestion, closure, etc., which can be obtained through the traffic monitoring system). The road nodes are numbered in sequence and connected according to the constructed adjacency matrix to form a complete topological map.

10. The method according to step 5 of claim 1, characterized in that: Traverse the node data, add labels to each node, and display its node name or number for user identification. Traverse the distance edge data, draw the road network according to the connection relationship between nodes and edges, and form a complete topology map that can be used for subsequent path planning.

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