Map interest point aggregation method, electronic device and computer program product

Abstract

This application discloses a method for aggregating map points of interest, an electronic device, and a computer program product. This application relates to the field of geographic information service technology. The method comprises: obtaining map point of interest data, a digital elevation model, and three-dimensional grid data of a target area, wherein the map point of interest data includes the two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of the three-dimensional grid corresponding to the target area at the target grid level in the Beidou grid code system; determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model; determining the three-dimensional positional relationship between the map point of interest and the three-dimensional grid based on the three-dimensional coordinates; and aggregating the map points of interest based on the three-dimensional positional relationship to obtain three-dimensional aggregated point data of the target area at the target grid level. This application can achieve multi-level, multi-precision map point of interest aggregation in a unified grid.

Description

Technical Field

[0001] The present application relates to the field of geographic information service technology, and in particular to a method for aggregating points of interest on a map, an electronic device, and a computer program product. Background Art

[0002] In the field of geographic information systems and location-based services, grid algorithm is a commonly used method for aggregating map points of interest. By dividing the geographic space into a series of grid cells, it can effectively organize and process a large amount of map point of interest data.

[0003] However, existing grid algorithms face some challenges when processing multi-source heterogeneous data:

[0004] Inconsistent grid levels and sizes: Different data sources often require the creation of grids of different levels and sizes, which leads to incompatibility between grids and increases the difficulty of data integration and analysis.

[0005] Limitations of fixed grid size: Fixed-size grids are inefficient when processing datasets of varying densities. For example, in areas with densely populated areas of interest, smaller grids may not be sufficient to capture sufficient details, while in areas with sparsely populated areas, larger grids may result in wasted resources. Summary of the Invention

[0006] The main purpose of this application is to provide a method for aggregating map points of interest, an electronic device, and a computer program product, aiming to solve the technical problem that the application of grid algorithms in aggregating map points of interest is relatively limited.

[0007] To achieve the above objectives, this application provides a method for aggregating points of interest on a map, comprising:

[0008] Obtaining map point of interest data, a digital elevation model, and three-dimensional grid data of a target area, wherein the map point of interest data includes two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of a three-dimensional grid corresponding to the target area in a Beidou grid code system at a target grid level, and the map point of interest and the three-dimensional grid are in the same coordinate system;

[0009] Determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model;

[0010] determining a three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates;

[0011] Based on the three-dimensional position relationship, the map interest points are aggregated to obtain three-dimensional aggregation point data of the target area under the target grid level, wherein the three-dimensional aggregation point data includes the coordinates of the three-dimensional aggregation points and the number of map interest points aggregated, and there is a corresponding relationship between the three-dimensional aggregation points and the three-dimensional grid.

[0012] In one embodiment, the digital elevation model is a grid digital elevation model, and the step of determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model includes:

[0013] Determining, from the grid digital elevation model, a target grid cell that matches the map point of interest based on the two-dimensional coordinates;

[0014] The elevation value of the target grid cell is used as the elevation value of the map interest point, and combined with the two-dimensional coordinates to generate the three-dimensional coordinates of the map interest point.

[0015] In one embodiment, the digital elevation model is a vector digital elevation model, and the step of determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model includes:

[0016] Determining, from the vector digital elevation model, a target contour line matching the map point of interest based on the two-dimensional coordinates;

[0017] The elevation value of the map interest point is calculated based on the elevation value of the target contour line and the distance between the target contour line and the map interest point, and the three-dimensional coordinates of the map interest point are generated by combining the two-dimensional coordinates.

[0018] In one embodiment, the step of obtaining map point of interest data, digital elevation model, and three-dimensional grid data of the target area includes:

[0019] Obtain original map point of interest data, digital elevation model and 3D grid data of the target area;

[0020] The original map point of interest data is preprocessed to obtain map point of interest data of the target area, wherein the preprocessing includes data cleaning, data standardization and coordinate system conversion, and the coordinate system conversion is used to make the map point of interest and the three-dimensional grid in the same coordinate system.

[0021] In one embodiment, the method further comprises:

[0022] Visualizing the three-dimensional clustering point according to the coordinates of the three-dimensional clustering point and the number of clustered points of interest on the map, wherein the more the number of clustered points of interest on the map at the same grid level is, the larger the visualization icon of the three-dimensional clustering point is when visualizing the three-dimensional clustering point;

[0023] The three-dimensional grid is visualized according to the coordinates of the three-dimensional aggregation points and the number of map interest points. The more the number of map interest points corresponding to the three-dimensional aggregation points of the three-dimensional grid at the same grid level is, the darker the visualization color of the three-dimensional grid is when the three-dimensional grid is visualized.

[0024] In one embodiment, the grid data includes a Beidou grid code, and the step of determining the three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates includes:

[0025] Decode the Beidou grid code of each three-dimensional grid to obtain the three-dimensional coordinate range corresponding to each three-dimensional grid;

[0026] According to the three-dimensional coordinates of each map interest point and the three-dimensional coordinate range corresponding to each three-dimensional grid, the attribution relationship between each map interest point and each three-dimensional grid is determined respectively, and the attribution relationship is determined as the three-dimensional position relationship between the map interest point and the three-dimensional grid.

[0027] In one embodiment, the grid data includes a Beidou grid code, and the step of determining the three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates includes:

[0028] Encoding the three-dimensional coordinates of each map point of interest according to the target grid level to obtain the Beidou grid code corresponding to each map point of interest at the target grid level;

[0029] According to the Beidou grid code of each three-dimensional grid and the Beidou grid code corresponding to each map point of interest at the target grid level, the attribution relationship between each map point of interest and each three-dimensional grid is determined respectively, and the attribution relationship is determined as the three-dimensional position relationship between the map point of interest and the three-dimensional grid.

[0030] In one embodiment, the coordinates of the 3D aggregation point are the coordinates of the centroid of the 3D grid to which the 3D aggregation point belongs, or the geometric median coordinates of each 3D aggregation point in the 3D grid to which the 3D aggregation point belongs.

[0031] In addition, to achieve the above-mentioned purpose, the present application also provides an electronic device, which includes: a memory, a processor, and a map point of interest aggregation program stored on the memory and runnable on the processor, and when the map point of interest aggregation program is executed by the processor, the steps of the map point of interest aggregation method as described above are implemented.

[0032] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned map interest point aggregation method when executed by a processor.

[0033] The present application provides a map point of interest aggregation method, electronic device and computer program product. The technical solution of the present application is to first obtain map point of interest data, digital elevation model and three-dimensional grid data of a target area, wherein the map point of interest data includes the two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of the three-dimensional grid corresponding to the target area at the target grid level in the Beidou grid code encoding system, and the map point of interest and the three-dimensional grid are in the same coordinate system, and then the three-dimensional coordinates of the map point of interest are determined by using the two-dimensional coordinates and the digital elevation model. Then, based on the three-dimensional coordinates, the three-dimensional positional relationship between the map point of interest and the three-dimensional grid is determined. Finally, based on the three-dimensional positional relationship, the map point of interest is aggregated to obtain three-dimensional aggregated point data of the target area at the target grid level, wherein the three-dimensional aggregated point data includes the coordinates of the three-dimensional aggregated point and the number of map point of interest aggregates. There is a corresponding relationship between the three-dimensional aggregated point and the three-dimensional grid, so that the present application can realize multi-level and multi-precision map point of interest aggregation in a unified grid, so as to solve the technical problem that the grid algorithm has large limitations in the application of map point of interest aggregation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0036] Figure 1 Schematic diagram of the process of a method for aggregating points of interest on a map according to an embodiment of the present application;

[0037] Figure 2 This is a schematic diagram of the first process for determining three-dimensional coordinates in an embodiment of the present application;

[0038] Figure 3 This is a schematic diagram of the second process for determining three-dimensional coordinates in an embodiment of the present application;

[0039] Figure 4 Schematic diagram of the process of determining a three-dimensional position relationship in an embodiment of the present application;

[0040] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the map interest point aggregation method in the embodiment of the present application.

[0041] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0043] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0044] Currently, grid algorithms have significant limitations in their application in aggregating points of interest on maps.

[0045] The main solution of the embodiment of the present application is: obtaining map point of interest data, digital elevation model and three-dimensional grid data of the target area, wherein the map point of interest data includes the two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes the grid data of the three-dimensional grid corresponding to the target area in the Beidou grid code encoding system at the target grid level, and the map point of interest and the three-dimensional grid are in the same coordinate system; determining the three-dimensional coordinates of the map point of interest through the two-dimensional coordinates and the digital elevation model; determining the three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates; based on the three-dimensional positional relationship, aggregating the map point of interest to obtain three-dimensional aggregation point data of the target area at the target grid level, wherein the three-dimensional aggregation point data includes the coordinates of the three-dimensional aggregation point and the number of map point of interest aggregations, and there is a corresponding relationship between the three-dimensional aggregation point and the three-dimensional grid.

[0046] The embodiments of the present application can realize multi-level and multi-precision aggregation of map points of interest in a unified grid, so as to solve the technical problem that the grid algorithm has large limitations in the application of map point of interest aggregation.

[0047] It should be noted that the execution subject of the embodiments of the present application is an electronic device, which may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs (Televisions), desktop computers, or any other electronic device capable of implementing the above functions. The embodiments of the present application do not specifically limit this. The following uses electronic devices as the execution subject as an example to illustrate the following embodiments of the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] Please refer to Figure 1 , Figure 1 Schematic diagram of the process of aggregating points of interest on a map in an embodiment of the present application.

[0050] In this embodiment, the method for aggregating points of interest on a map includes steps S100 to S300:

[0051] Step S100: Acquire map point of interest data, a digital elevation model, and three-dimensional grid data of a target area, wherein the map point of interest data includes the two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of a three-dimensional grid corresponding to the target area in a Beidou grid code system at a target grid level, and the map point of interest and the three-dimensional grid are in the same coordinate system.

[0052] It should be noted that the target area refers to the geographic area where map POI aggregation is to be performed. This can be a city, a rural area, or any spatial range with clear boundaries. A map POI refers to a location or facility on a map that has specific geographic significance or practical value or meaning to the user, such as a building, park, or restaurant. Map POI data refers to the attribute information of a map POI, typically including its name, type, and location (i.e., two-dimensional coordinates). An elevation model is a digital model of ground elevation information, providing elevation information on the terrain surface. A 3D grid is a division of a given geographic space into multiple three-dimensional spatial units according to specific rules. Each 3D spatial unit is called a 3D grid. 3D grids are not limited to horizontal divisions but also include vertical divisions. Each 3D grid has clear boundaries and elevation ranges, and is assigned a unique identifier or code. 3D grid data refers to the grid data of a 3D grid, typically including its code, boundaries, elevation, center point coordinates, and volume. The target grid level refers to the grid level selected when aggregating map points of interest in the target area. Different grid levels represent different grid sizes and densities. A lower level means a larger grid size, which also means that the same space is divided into fewer grids, while a higher level means a smaller grid size, which also means that the same space is divided into more grids.

[0053] Those skilled in the art will recognize that the Beidou grid code system is a coding system used to uniquely identify grids. Based on the geographic reference framework of the Beidou satellite navigation system, it accurately determines the location of each grid. The Beidou grid code system identifies each grid with a unique code (also known as the Beidou grid code), facilitating data retrieval and management.

[0054] In this embodiment, one or more grid levels can be selected as target grid levels from the multiple grid levels provided by the Beidou grid code encoding system based on actual needs. Then, grid data of the three-dimensional grid corresponding to the target area in the Beidou grid code system at the target grid level is obtained. That is, the three-dimensional grid data in this embodiment adopts the unified standard of the Beidou grid code system, which means that all three-dimensional grids are in the same coordinate system and have the same size, shape, and height at the same grid level. They are all uniquely identified by a unique Beidou grid code, and there is no overlap or gap between them. The target area can be completely covered, thereby laying a solid foundation for the application of grid algorithms in map point of interest aggregation. This ensures that when performing map point of interest aggregation, the three-dimensional grid data of the Beidou grid code encoding system can be used to achieve accurate aggregation, avoiding overlaps and omissions. This effectively solves the problem of existing grid algorithms in the application of map point of interest aggregation, where different data sources create grids of different levels and sizes, resulting in incompatibility between grids and the difficulty of data integration and analysis. At the same time, there is no limitation of fixed grid size, and different grid levels can be flexibly selected as target grid levels for map point of interest aggregation based on actual needs.

[0055] It is worth mentioning that this embodiment can first obtain the three-dimensional grid data of the target grid level divided in the Beidou grid code coding system, and then use spatial extraction and analysis technology to extract and filter out the three-dimensional grid data within the target area, thereby obtaining the three-dimensional grid data of the target area. It can also use the Beidou grid code coding system to generate a three-dimensional grid within the target area according to the requirements of the target grid level, thereby obtaining the three-dimensional grid data of the target area.

[0056] This embodiment downloads a digital elevation model of the target area from the National Geographic Information Public Service Platform or other official channels, and ensures that the resolution of the digital elevation model meets the requirements, that is, matches the target grid level. If necessary, the downloaded digital elevation model can be preprocessed, such as cropping, splicing, or resampling, to match the boundaries of the target area, thereby obtaining a digital elevation model of the target area.

[0057] In this embodiment, data of all map points of interest in a target area, including information such as name, type, and two-dimensional coordinates, can be obtained from authoritative data sources (such as public databases and commercial data providers) through GIS (Geographic Information System) software or an online service interface. The obtained information is then preprocessed, including data quality assessment, data cleaning, data standardization, and coordinate system optimization. Ultimately, the map points of interest and the three-dimensional grid are placed in the same coordinate system, that is, both use the coordinate system of the Beidou grid code encoding system, thereby obtaining the map point of interest data of the target area.

[0058] For example, in a feasible implementation, the step of obtaining map point of interest data, digital elevation model, and three-dimensional grid data of the target area includes steps S110 to S120:

[0059] S110, obtaining original map point of interest data, digital elevation model and three-dimensional grid data of the target area;

[0060] It should be noted that, in this embodiment, the original map POI data refers to unprocessed map POI data directly obtained from a data source, which may contain duplication, omission, error, different format, different coordinate system, and other issues.

[0061] S120, preprocessing the original map point of interest data to obtain map point of interest data of the target area, wherein the preprocessing includes data cleaning, data standardization and coordinate system conversion, and the coordinate system conversion is used to make the map point of interest and the three-dimensional grid in the same coordinate system.

[0062] This embodiment first obtains the original data of all map points of interest (POIs) in the target area from public datasets, commercial service providers, and other channels, i.e., the original map POI data. It then preprocesses the data, checks for outliers, duplicates, and missing values, and performs corresponding data cleaning to reduce noise in the data and improve the accuracy of subsequent analysis. The data format and units are then unified, and data standardization is completed. All data is converted to the target coordinate system, completing coordinate system 1, obtaining the map POI data for the target area. This ensures data compatibility and the accuracy of subsequent processing, providing a foundation for subsequent spatial analysis and aggregation. The target coordinate system is the coordinate system used by the Beidou grid code encoding system, i.e., the coordinate system in which the three-dimensional grid resides.

[0063] It is not difficult to understand that since the map points of interest lack height information, after the map points of interest are uniformly converted to the target coordinate system, the values ​​corresponding to the heights are temporarily defaulted and will be filled in by the digital elevation model in subsequent steps, thereby truly completing the unification of the coordinate system.

[0064] The map point of interest data, digital elevation model, and three-dimensional grid data obtained in this embodiment together constitute the spatial information foundation of the target area, providing comprehensive data support for subsequent spatial analysis and map point of interest aggregation. All input data is in the same coordinate system, ensuring data compatibility and accuracy of subsequent processing. The digital elevation model can also provide height information for map points of interest, thereby achieving spatial conversion from two-dimensional to three-dimensional, enhancing the practicality of spatial data. The use of three-dimensional grid data further makes the management of map points of interest more orderly, facilitates the three-dimensional aggregation processing of map points of interest in subsequent steps, and reduces computational complexity.

[0065] Step S200, determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model;

[0066] This embodiment matches the two-dimensional coordinates of each map point of interest with the corresponding position in the digital elevation model, thereby finding the elevation value of the two-dimensional coordinate on the digital elevation model as the height information of the map point of interest, and then combining it with the two-dimensional coordinates to obtain the three-dimensional coordinates of the map point of interest, truly realizing the coordinate system between the map point of interest and the three-dimensional grid.

[0067] For example, Figure 2 As shown, in a first feasible implementation manner, the digital elevation model is a raster digital elevation model, and the step of determining the three-dimensional coordinates of the map interest point by using the two-dimensional coordinates and the digital elevation model may include steps S210 to S220:

[0068] Step S210, determining a target grid cell matching the map point of interest from the grid digital elevation model according to the two-dimensional coordinates;

[0069] Step S220 : Using the elevation value of the target grid cell as the elevation value of the map interest point, and combining it with the two-dimensional coordinates to generate the three-dimensional coordinates of the map interest point.

[0070] As those skilled in the art know, digital elevation models have two formats: raster format and vector format. Among them, the digital elevation model in raster format is also called a raster digital elevation model. It stores terrain information in a matrix composed of regularly arranged pixels (grids, also called grid cells). Each pixel has a numerical value corresponding to the terrain height, that is, the elevation value.

[0071] This embodiment can use spatial overlay analysis technology based on the two-dimensional coordinates of the map point of interest to determine the grid unit that matches the map point of interest from the grid digital elevation model as the target grid unit, thereby using the elevation value of the target grid unit as the elevation value of the map point of interest, and combining the two-dimensional coordinates of the map point of interest to generate the three-dimensional coordinates of the map point of interest.

[0072] It is not difficult to understand that the grid unit in the raster digital elevation model is similar to a three-dimensional grid, and also has information such as center point coordinates, boundaries and height. The raster digital elevation model in this embodiment also adopts the coordinate system of the Beidou grid code coding system, that is, the grid unit and the map point of interest are in the same coordinate system, so the two-dimensional coordinates of the map point of interest can be directly used to determine in which grid unit the map point of interest is located, and then use it as the target grid unit to match the map point of interest.

[0073] This implementation is based on the terrain height information (i.e., elevation values) provided by the raster digital elevation model. By combining the two-dimensional coordinates of map points of interest with the corresponding elevation values, this achieves the conversion from two-dimensional to three-dimensional space, increases the dimensionality of the map point of interest data, improves the practicality and application scope of the data, and is simple and easy to implement. Only simple mathematical operations are required to complete the coordinate conversion, which is easy to implement in computer programs.

[0074] In addition to the above methods, you can also use interpolation to perform interpolation calculations based on the elevation values ​​of multiple grid cells around the map point of interest (that is, the multiple grid cells closest to the map point of interest) to obtain the elevation value of the map point of interest, thereby further improving the accuracy of the elevation value of the map point of interest.

[0075] For example, Figure 3 As shown, in a second feasible implementation manner, the digital elevation model is a vector digital elevation model, and the step of determining the three-dimensional coordinates of the map interest point by using the two-dimensional coordinates and the digital elevation model may include steps S230 to S240:

[0076] Step S230, determining a target contour line matching the map point of interest from the vector digital elevation model according to the two-dimensional coordinates;

[0077] Step S240, calculating the elevation value of the map interest point based on the elevation value of the target contour line and the distance between the target contour line and the map interest point, and generating the three-dimensional coordinates of the map interest point in combination with the two-dimensional coordinates.

[0078] It should be noted that the vector digital elevation model is a digital elevation model in vector format, which represents the undulations of the terrain through a series of contour lines (that is, continuous curves with the same elevation value).

[0079] This embodiment can determine the contour line that matches the map point of interest from the raster digital elevation model based on the two-dimensional coordinates of the map point of interest, as the target contour line, and then interpolate and calculate the elevation value of the map point of interest based on the elevation value of the target contour line and the distance between the target contour line and the map point of interest, and generate the three-dimensional coordinates of the map point of interest in combination with the two-dimensional coordinates of the map point of interest.

[0080] It is not difficult to understand that the vector digital elevation model also uses the coordinate system of the Beidou grid code coding system, that is, the contour lines and the map interest points are in the same coordinate system, so that the two-dimensional coordinates of the map interest point can be directly used to determine on which contour line the map interest point is located, or between which two adjacent contour lines it is located. Then, when the map interest point is located on a certain contour line, it is used as the target contour line matching the map interest point, and the elevation value of the target contour line is used as the elevation value of the map interest point. Or when the map interest point is located between two adjacent contour lines, it is used as the target contour line matching the map interest point, and according to the distance between the map interest point and the two adjacent target contour lines, and the elevation values ​​of the two adjacent target contour lines, the elevation value of the map interest point is calculated by interpolation method.

[0081] This implementation, based on the contour information provided by the vector digital elevation model, calculates the relationship between a POI and a target contour line, then uses interpolation to determine the POI's actual height (i.e., elevation value). By combining the 2D coordinates of the POI with the corresponding elevation value, this method achieves a conversion from 2D to 3D space, increasing the dimensionality of the POI data and broadening its practicality and application scope. Furthermore, because contour lines can represent more complex terrain features, this implementation is more applicable to more complex terrain variations than the first feasible implementation described above.

[0082] This embodiment uses a digital elevation model to give map points of interest height information, making the location description of map points of interest more accurate, which is conducive to subsequent spatial analysis and visualization. The constructed three-dimensional coordinates provide the necessary data support for the subsequent three-dimensional aggregation of map points of interest.

[0083] Step S300, determining a three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates;

[0084] It should be noted that, in this embodiment, the three-dimensional positional relationship between the map interest point and the three-dimensional grid refers to the position of the map interest point relative to the three-dimensional grid, including which grid it is located in and its relationship to the grid boundary.

[0085] This embodiment can determine, based on the three-dimensional coordinates of the map interest points, for each grid level in the target grid level, which three-dimensional grid each map interest point belongs to under the grid level, and record the three-dimensional positional relationship between the map interest points and the three-dimensional grid to which they belong through indexes or association lists.

[0086] That is, for each grid level in the target grid level, based on the center point coordinates, boundaries and height of the three-dimensional grid under the grid level, combined with the three-dimensional coordinates of the map point of interest, analyze which three-dimensional grid the map point of interest falls in and where it falls within the three-dimensional grid, so as to obtain the three-dimensional position relationship between the map point of interest and the three-dimensional grid.

[0087] This embodiment can effectively manage and query map interest point data by clarifying the three-dimensional positional relationship between the map interest points and the three-dimensional grid, thereby facilitating subsequent aggregation processing.

[0088] In a feasible implementation manner, the grid data includes a Beidou grid code, and step S300 may include steps S310 to S320:

[0089] Step S310: Decode the Beidou grid code of each three-dimensional grid to obtain the three-dimensional coordinate range corresponding to each three-dimensional grid;

[0090] Step S320, based on the three-dimensional coordinates of each map interest point and the three-dimensional coordinate range corresponding to each three-dimensional grid, determine the ownership relationship between each map interest point and each three-dimensional grid, and determine the ownership relationship as the three-dimensional position relationship between the map interest point and the three-dimensional grid.

[0091] In this embodiment, the Beidou grid code of the three-dimensional grid can be decoded to obtain the three-dimensional coordinate range corresponding to each three-dimensional grid, so that the map interest points whose three-dimensional coordinates are within the three-dimensional coordinate range corresponding to the three-dimensional grid are determined to belong to the three-dimensional grid, and then the ownership relationship between each map interest point and each three-dimensional grid is determined, and the ownership relationship is used as the three-dimensional position relationship between the map interest point and the three-dimensional grid.

[0092] It should be noted that in this embodiment, the three-dimensional coordinate ranges corresponding to different three-dimensional grids at the same grid level do not overlap and there are no gaps, so as to avoid the inability to determine the ownership of map interest points located on the boundaries of adjacent grids.

[0093] In another feasible implementation, the grid data includes a Beidou grid code, and step S300 may further include steps S330 to S340:

[0094] Step S330, encoding the three-dimensional coordinates of each map point of interest according to the target grid level to obtain the Beidou grid code corresponding to each map point of interest at the target grid level;

[0095] Step S340, based on the Beidou grid code of each three-dimensional grid and the Beidou grid code corresponding to each map point of interest at the target grid level, respectively determine the attribution relationship between each map point of interest and each three-dimensional grid, and determine the attribution relationship as the three-dimensional position relationship between the map point of interest and the three-dimensional grid.

[0096] In this embodiment, the three-dimensional coordinates of the map interest points can also be encoded at the target grid level to obtain the Beidou grid code corresponding to each map interest point at the target grid level, so as to determine that the map interest point belongs to the three-dimensional grid corresponding to its Beidou grid code, and then determine the ownership relationship between each map interest point and each three-dimensional grid, and use the ownership relationship as the three-dimensional position relationship between the map interest point and the three-dimensional grid.

[0097] Step S400: Aggregate the map interest points based on the three-dimensional position relationship to obtain three-dimensional aggregation point data of the target area under the target grid level, wherein the three-dimensional aggregation point data includes the coordinates of the three-dimensional aggregation points and the number of map interest points aggregated, and the three-dimensional aggregation points correspond to the three-dimensional grid.

[0098] It should be noted that a 3D aggregation point is a point obtained by aggregating multiple map points of interest within the same 3D grid. 3D aggregation point data refers to the attribute information of the 3D aggregation point, including its coordinates, type, number of aggregated map points of interest, and the Beidou grid code of the 3D grid to which it belongs. The number of aggregated map points of interest refers to the number of map points of interest aggregated to obtain the 3D aggregation point, representing the number of map points of interest that are aggregated to obtain the 3D aggregation point.

[0099] In this embodiment, the coordinates of the three-dimensional aggregation point are the centroid coordinates of the three-dimensional grid to which the three-dimensional aggregation point belongs, or the geometric median coordinates of each three-dimensional aggregation point in the three-dimensional grid to which the three-dimensional aggregation point belongs.

[0100] Those skilled in the art will know that the center of mass refers to the average position of the mass distribution of an object (or a system composed of multiple objects), and the geometric median refers to the point with the smallest sum of distances to all points.

[0101] In this embodiment, the centroid coordinates refer to the coordinates of the centroid, and the geometric median coordinates refer to the coordinates of the geometric median.

[0102] In this embodiment, the coordinates of the three-dimensional aggregation point can be the centroid coordinates of the three-dimensional grid, or the geometric median coordinates of each map interest point in the three-dimensional grid to which it belongs. This embodiment does not make any specific limitations on this.

[0103] When the coordinates of the three-dimensional aggregation points are taken as the centroid coordinates, the computational complexity of the map interest point aggregation processing can be greatly simplified. When the geometric median coordinates are taken, although the computational complexity is slightly larger, it is still much lower than the map interest point aggregation based on the distance algorithm, and can reflect the center position of each map interest point in the three-dimensional grid.

[0104] It should be noted that when there is more than one type of map points of interest, the map points of interest can be aggregated by type. At this time, the three-dimensional aggregation point refers to a point representing all map points of interest of the specified type in the same three-dimensional grid by aggregating the map points of interest of the specified type located in the same three-dimensional grid. Therefore, a three-dimensional grid can be aggregated to obtain multiple three-dimensional aggregation points of different types. That is, the correspondence between the three-dimensional grid and the three-dimensional aggregation point is one-to-many.

[0105] In addition, map points of interest can be aggregated according to three-dimensional grids, and multiple types of map points of interest can be aggregated into the same three-dimensional aggregation point. At this time, the types of the three-dimensional aggregation points are complex, such as restaurant type + park type, and library type + school type + residential area type. At the same time, the number of map points of interest aggregated will be subdivided into the number of aggregations of each type of map points of interest, and the total number of aggregations. That is, at this time, the correspondence between the three-dimensional grid and the three-dimensional aggregation point is one-to-one.

[0106] It is not difficult to understand that this embodiment aggregates map points of interest based on three-dimensional grids. That is, map points of interest belonging to the same three-dimensional grid will be aggregated according to the aggregation method (for example, aggregation by type, aggregation by three-dimensional grid, etc.) to obtain the three-dimensional aggregation points corresponding to the three-dimensional grid.

[0107] It is worth mentioning that after the three-dimensional aggregation point is obtained by aggregation, the coordinates of the center point of the corresponding three-dimensional grid can be used as the coordinates of the three-dimensional aggregation point, or it can be calculated based on the three-dimensional coordinates of the map interest points aggregated by the three-dimensional aggregation point. For example, the coordinate with the smallest total distance between the three-dimensional coordinates of the map interest points aggregated by the three-dimensional aggregation point can be used as the coordinates of the three-dimensional aggregation point, or the three-dimensional coordinates of the map interest points aggregated by the three-dimensional aggregation point can be averaged, and the average value can be used as the coordinates of the three-dimensional aggregation point.

[0108] This embodiment uses the three-dimensional positional relationship between map interest points and three-dimensional grids to count the number of map interest points falling into each three-dimensional grid at each grid level in the target grid level, obtain the aggregated number of map interest points of each three-dimensional aggregation point at each grid level in the target grid level, and calculate the coordinates of each three-dimensional aggregation point, thereby completing the aggregation of map interest points and obtaining the three-dimensional aggregation point data of the target area at the target grid level.

[0109] This embodiment first obtains map point of interest data, a digital elevation model, and three-dimensional grid data of a target area, wherein the map point of interest data includes the two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of a three-dimensional grid corresponding to the target area at a target grid level in the Beidou grid code system, wherein the map point of interest and the three-dimensional grid are in the same coordinate system. Then, the three-dimensional coordinates of the map point of interest are determined using the two-dimensional coordinates and the digital elevation model. Then, based on the three-dimensional coordinates, a three-dimensional positional relationship between the map point of interest and the three-dimensional grid is determined. Finally, based on the three-dimensional positional relationship, the map points of interest are aggregated to obtain three-dimensional aggregated point data of the target area at the target grid level. The three-dimensional aggregated point data includes the coordinates of the three-dimensional aggregated point and the number of aggregated map points of interest. The three-dimensional aggregated point has a corresponding relationship with the three-dimensional grid. Thus, this embodiment can implement multi-level and multi-precision map point of interest aggregation in a unified grid, thereby resolving the technical problem that grid algorithms have large limitations in the application of map point of interest aggregation. Furthermore, height information is introduced to implement three-dimensional map point of interest aggregation, breaking through the limitation of existing grid algorithms that can only aggregate two-dimensional map points of interest.

[0110] Furthermore, in a feasible implementation manner, the method further includes steps A10 to A20:

[0111] Step A10: Visualizing the 3D cluster point based on its coordinates and the number of POI clusters on the map. A larger visualization icon for a 3D cluster point at the same grid level indicates a larger number of POI clusters.

[0112] Step A20, visualizing the three-dimensional grid according to the coordinates of the three-dimensional aggregation points and the number of map interest points, wherein the more the number of map interest points corresponding to the three-dimensional aggregation points of the three-dimensional grid at the same grid level is, the darker the visualization color of the three-dimensional grid is when visualizing the three-dimensional grid.

[0113] In this embodiment, after the three-dimensional aggregation point data of the target area at the target grid level is aggregated, the three-dimensional aggregation points and the three-dimensional grid are also visualized to intuitively display the aggregation results of the map points of interest on the map, including using visualization icons to display the three-dimensional aggregation points and using visualization colors to display the three-dimensional grid. Among them, the visualization icon can be set to a dot or other shaped icon based on actual needs or the type of map point of interest. At the same grid level, the greater the number of map points of interest aggregated for the three-dimensional aggregation point, the larger the visualization icon. The visualization color can also be set to different colors based on actual needs or the type of map point of interest. At the same grid level, the greater the number of map points of interest aggregated for the three-dimensional aggregation point corresponding to the three-dimensional grid, the darker the visualization color.

[0114] It is not difficult to understand that when users view the aggregation results of map points of interest, they can specify to view the aggregation results of one or several grid levels in the target grid level, so as to perform visualization based on the three-dimensional aggregation point data under the grid level. Correspondingly, they can also specify to view a certain type of aggregation results, so as to perform visualization based on the three-dimensional aggregation point data of that type.

[0115] It is worth mentioning that when performing visualization, information such as the type of three-dimensional aggregation point, the number of map interest points aggregated, etc. can also be displayed inside the corresponding visualization icon, or displayed in the corresponding three-dimensional grid.

[0116] In addition, please refer to Figure 5 , Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the map interest point aggregation method in the embodiment of the present application.

[0117] The present application also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the map interest point aggregation method in the above embodiment.

[0118] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs (Televisions), desktop computers, and the like, or any other electronic device capable of implementing the above functions. Figure 5 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0119] like Figure 5As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape or hard disk; and a communication device 1009. The communication device 1009 may allow the electronic device to communicate with other devices wirelessly or wired to exchange data. Although the figures show electronic devices with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0120] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0121] The electronic device provided in this application, using the map POI aggregation method described in the above-mentioned embodiments, can resolve the technical problem of the significant limitations of grid algorithms in the application of map POI aggregation. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the map POI aggregation method described in the above-mentioned embodiments, and the other technical features of the electronic device are the same as those disclosed in the map POI aggregation method described in the above-mentioned embodiments, and are not further described here.

[0122] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0123] In addition, the present application also provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the map interest point aggregation method in the above embodiment.

[0124] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0125] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0126] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device is enabled to: obtain map point of interest data, digital elevation model and three-dimensional grid data of a target area, wherein the map point of interest data includes the two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of the three-dimensional grid corresponding to the target area in the Beidou grid code encoding system at the target grid level, and the map point of interest and the three-dimensional grid are in the same coordinate system; determine the three-dimensional coordinates of the map point of interest through the two-dimensional coordinates and the digital elevation model; determine the three-dimensional positional relationship between the map point of interest and the three-dimensional grid based on the three-dimensional coordinates; aggregate the map points of interest based on the three-dimensional positional relationship to obtain three-dimensional aggregation point data of the target area at the target grid level, wherein the three-dimensional aggregation point data includes the coordinates of the three-dimensional aggregation point and the number of map point of interest aggregations, and there is a corresponding relationship between the three-dimensional aggregation point and the three-dimensional grid.

[0127] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0129] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0130] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for aggregating points of interest on a map. This medium addresses the significant technical limitations of grid algorithms in the application of aggregating points of interest on a map. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the method for aggregating points of interest on a map provided in the aforementioned embodiments, and are not further elaborated here.

[0131] In addition, an embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the map interest point aggregation method as described in the above embodiment.

[0132] The computer program product provided in this application can address the technical limitations of grid algorithms in the application of map point-of-interest aggregation. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as those of the map point-of-interest aggregation method provided in the above embodiments, and will not be elaborated here.

[0133] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for aggregating points of interest on a map, characterized in that: The method comprises: Obtaining map point of interest data, a digital elevation model, and three-dimensional grid data of a target area, wherein the map point of interest data includes two-dimensional coordinates of the map point of interest, and the three-dimensional grid data includes grid data of a three-dimensional grid corresponding to the target area in a Beidou grid code system at a target grid level, and the map point of interest and the three-dimensional grid are in the same coordinate system; Determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model; determining a three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates; Aggregating the map points of interest based on the three-dimensional positional relationship to obtain three-dimensional aggregation point data of the target area at the target grid level, wherein the three-dimensional aggregation point data includes coordinates of the three-dimensional aggregation point and the number of aggregated map points of interest, the three-dimensional aggregation point corresponds to the three-dimensional grid, and the coordinates of the three-dimensional aggregation point are the centroid coordinates of the three-dimensional grid to which the three-dimensional aggregation point belongs, or are the geometric median coordinates of each three-dimensional aggregation point in the three-dimensional grid to which the three-dimensional aggregation point belongs; The step of determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model includes: In a case where the digital elevation model is a grid digital elevation model, determining a preset number of grid cells closest to the map interest point from the grid digital elevation model according to the two-dimensional coordinates; Interpolating the elevation values ​​of a preset number of grid cells closest to the map point of interest to obtain the elevation value of the map point of interest, and combining the two-dimensional coordinates to generate the three-dimensional coordinates of the map point of interest; The grid data includes a Beidou grid code, and the step of determining the three-dimensional positional relationship between the map point of interest and the three-dimensional grid according to the three-dimensional coordinates includes: Decoding the Beidou grid code of each three-dimensional grid to obtain a three-dimensional coordinate range corresponding to each three-dimensional grid; determining the attribution relationship between each map point of interest and each three-dimensional grid based on the three-dimensional coordinates of each map point of interest and the three-dimensional coordinate range corresponding to each three-dimensional grid, and determining the attribution relationship as the three-dimensional position relationship between the map point of interest and the three-dimensional grid; or The three-dimensional coordinates of each map point of interest are encoded according to the target grid level to obtain the Beidou grid code corresponding to each map point of interest at the target grid level; based on the Beidou grid code of each three-dimensional grid and the Beidou grid code corresponding to each map point of interest at the target grid level, the attribution relationship between each map point of interest and each three-dimensional grid is determined respectively, and the attribution relationship is determined as the three-dimensional position relationship between the map point of interest and the three-dimensional grid.

2. The method for aggregating points of interest on a map according to claim 1, wherein: The step of determining the three-dimensional coordinates of the map point of interest using the two-dimensional coordinates and the digital elevation model further includes: In a case where the digital elevation model is a vector digital elevation model, determining a target contour line matching the map point of interest from the vector digital elevation model according to the two-dimensional coordinates; The elevation value of the map interest point is calculated based on the elevation value of the target contour line and the distance between the target contour line and the map interest point, and the three-dimensional coordinates of the map interest point are generated by combining the two-dimensional coordinates.

3. The method for aggregating points of interest on a map according to claim 1 or 2, wherein: The step of obtaining map point of interest data, digital elevation model and three-dimensional grid data of the target area includes: Obtain original map point of interest data, digital elevation model and 3D grid data of the target area; The original map point of interest data is preprocessed to obtain map point of interest data of the target area, wherein the preprocessing includes data cleaning, data standardization and coordinate system conversion, and the coordinate system conversion is used to make the map point of interest and the three-dimensional grid in the same coordinate system.

4. The method for aggregating points of interest on a map according to claim 1 or 2, wherein: The method further comprises: Visualizing the three-dimensional clustering point according to the coordinates of the three-dimensional clustering point and the number of clustered points of interest on the map, wherein the more the number of clustered points of interest on the map at the same grid level is, the larger the visualization icon of the three-dimensional clustering point is when visualizing the three-dimensional clustering point; The three-dimensional grid is visualized according to the coordinates of the three-dimensional aggregation points and the number of map interest points. The more the number of map interest points corresponding to the three-dimensional aggregation points of the three-dimensional grid at the same grid level is, the darker the visualization color of the three-dimensional grid is when the three-dimensional grid is visualized.

5. An electronic device, characterized in that: include: A memory, a processor, and a map interest point aggregation program stored in the memory and executable on the processor, wherein the map interest point aggregation program, when executed by the processor, implements the steps of the map interest point aggregation method according to any one of claims 1 to 4.

6. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the map interest point aggregation method according to any one of claims 1 to 4 are implemented.

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