A method, device and equipment for determining a city core area, and a storage medium

Abstract

The present disclosure provides a method and device for determining a city core area, and a storage medium, relates to the field of computer information processing, and particularly to the field of big data. The specific implementation scheme is as follows: acquiring city feature data from at least one data source and position information corresponding to the city feature data; grouping and fusing the city feature data based on grid information of a map and the position information corresponding to the city feature data, to obtain a city feature fusion value corresponding to each grid; determining a candidate grid of the city core area according to the city feature fusion value corresponding to each grid; and determining a boundary of the city core area according to a distribution of the candidate grid. In this way, the city core area can be more accurately determined and automatically generated according to multi-source data from multiple data sources, and is widely applied to application scenarios such as city traffic industry reports, city active population analysis, and city core area initialization.

Description

Technical Field

[0001] This disclosure relates to the field of computer information processing technology, and in particular to the field of big data. Background Technology

[0002] Urban areas, as the basic unit for measuring traffic conditions or other evaluation indicators at the macro level, are used in the generation of authoritative reports on rankings, comparisons, and trend changes in various industries.

[0003] However, the choice of urban area has a significant impact on the final quantitative results. For example, when choosing based on the city's administrative boundaries, the development level and density of different districts (municipal districts and counties) vary greatly, resulting in vastly different traffic conditions. Therefore, it is difficult for any single municipal district or county to truly represent the traffic conditions of the city in question.

[0004] Furthermore, each administrative region is divided into two categories: active areas with a large population and frequent pedestrian activity, and inactive areas with a small population and sparse pedestrian activity. If statistics are compiled based on the entire administrative region, inactive areas would be included, failing to accurately reflect the traffic conditions or regional characteristics of that area.

[0005] Therefore, when measuring a city's traffic conditions or other evaluation indicators, it is usually necessary to identify the city's core area as the primary source and basis for data. The city's core area refers to the most populous and active area within the city where pedestrian traffic is frequent. Summary of the Invention

[0006] This disclosure provides a method, apparatus, device, and storage medium.

[0007] According to one aspect of this disclosure, a method for determining a core urban area is provided, comprising: acquiring urban feature data and corresponding location information from at least one data source; grouping and fusing the urban feature data based on map grid information and the corresponding location information to obtain a fused urban feature value for each grid; determining candidate grids for the core urban area based on the fused urban feature value for each grid; and determining the boundary of the core urban area based on the distribution of the candidate grids.

[0008] According to another aspect of this disclosure, an apparatus for determining a core urban area is provided, comprising: a data acquisition module for acquiring urban feature data and corresponding location information from at least one data source; a fusion value determination module for grouping and fusion processing the urban feature data based on map grid information and the corresponding location information to obtain a fusion value of urban features for each grid; a candidate grid determination module for determining candidate grids for the core urban area based on the fusion value of urban features for each grid; and a boundary determination module for determining the boundary of the core urban area based on the distribution of the candidate grids.

[0009] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method for determining the urban core area.

[0010] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to perform the aforementioned method for determining the core urban area.

[0011] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method for determining the aforementioned urban core area.

[0012] According to another aspect of this disclosure, an electronic map product is provided, including an urban core area determination module for performing a method for determining the urban core area and displaying the boundary of the urban core area in a map displayed by the electronic map product.

[0013] This disclosure provides a method, apparatus, device, and storage medium that determines the urban feature fusion value corresponding to each grid in a map based on multi-source data from multiple data sources. Candidate grids belonging to the urban core area are then determined based on the magnitude of the urban feature fusion value, and the boundary of the urban core area is determined based on the distribution of the candidate grids. In this way, the urban core area can be more accurately determined and automatically generated based on multi-source data from multiple data sources, and it can be widely used in application scenarios such as urban transportation industry reports, urban active population analysis, and urban core area initialization.

[0014] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0015] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0016] Figure 1 This is a flowchart illustrating the method for determining the core urban area according to the first embodiment of this disclosure;

[0017] Figure 2 This is a flowchart illustrating the method for determining the core urban area according to the second embodiment of this disclosure;

[0018] Figure 3 This is a schematic diagram illustrating the determination of the core urban area based on the map grid distribution according to the second embodiment of this disclosure;

[0019] Figure 4 This is a schematic diagram illustrating the smoothing of the boundary of the urban core area according to the second embodiment of this disclosure;

[0020] Figure 5 This is a flowchart illustrating a method for determining the boundary of a core urban area according to a third embodiment of this disclosure;

[0021] Figure 6 This is a schematic diagram illustrating the determination of the boundary points of the urban core area according to the third embodiment of this disclosure;

[0022] Figure 7 This is a schematic diagram of connecting the boundary points of the core urban area to form a boundary according to the third embodiment of this disclosure;

[0023] Figure 8 This is a flowchart illustrating the method for determining the boundary of a core urban area according to the fourth embodiment of this disclosure;

[0024] Figure 9 This is a schematic diagram illustrating the determination of the boundary points of the urban core area according to the fourth embodiment of this disclosure;

[0025] Figure 10 This is a schematic diagram of connecting the boundary points of the core urban area to form a boundary according to the fourth embodiment of this disclosure;

[0026] Figure 11 This is a schematic diagram of the structure of the device for determining the core urban area according to an embodiment of this disclosure;

[0027] Figure 12 This is a block diagram of an electronic device used to implement the method for determining the core urban area according to the embodiments of this disclosure. Detailed Implementation

[0028] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0029] Figure 1 This disclosure illustrates the main flow of a method for determining the core urban area according to an embodiment of the present disclosure, such as... Figure 1 As shown, it includes:

[0030] Operation S110: Obtain urban feature data and corresponding location information from at least one data source;

[0031] Location information refers to information used to describe spatial location. For example, two-dimensional (2D) coordinates or three-dimensional (3D) coordinates.

[0032] Urban characteristic data refers to individual data based on location information. For example, information about an individual's activity location, such as the individual's current location data and Point of Interest (POI) location data.

[0033] Specifically, urban characteristic data includes: 1) static commercial characteristics of the city, such as POIs (points of interest) like shops, supermarkets, shopping centers, and transportation hubs, as well as characteristic data of neighboring areas obtained by radiating and expanding to the surrounding areas based on different types of POIs; 2) dynamic behavioral characteristics of the city, such as data on transportation, residence, work, entertainment, and leisure.

[0034] Urban characteristic data has corresponding location information, such as the location of individual activities. Therefore, based on the location information corresponding to urban characteristic data, calculations and statistics can be performed on urban characteristic data within a certain spatial range.

[0035] Typically, specific types of urban characteristic data often come from specific data sources. For example, individual location information and Points of Interest (POIs) such as shops, shopping malls, and transportation hubs may come from electronic maps; transportation data may come from transportation payment systems; and residential, work, and entertainment data may come from consumer terminal applications, etc.

[0036] The boundaries of the urban core area depend primarily on the selected urban characteristics. Different urban characteristic data will result in different calculated boundaries of the urban core area.

[0037] This disclosure embodiment can acquire urban feature data from multiple data sources and perform fusion processing on urban feature data from multiple data sources. Implementers can select the required urban feature data from multiple data sources according to different scenarios applied in the core urban area.

[0038] Operation S120, based on the grid information of the map and the location information corresponding to the urban feature data, groups and fuses the urban feature data to obtain the urban feature fusion value corresponding to each grid.

[0039] The grid information of the map refers to the division of the map into a grid based on coordinates to obtain a set of sub-regions, where the coordinates of each sub-region are complementary and do not intersect.

[0040] Grouping of urban feature data refers to dividing the urban feature data into various grids on a map based on the location information corresponding to the urban feature data, thus obtaining the urban feature data corresponding to each grid.

[0041] Since the urban feature data in this disclosure can come from multiple data sources, it is necessary to fuse the urban feature data from multiple data sources to obtain a fused urban feature value that can comprehensively reflect the urban features of the grid.

[0042] The fusion processing of urban feature data refers to merging, deduplicating, and normalizing urban feature data from multiple data sources corresponding to each grid to obtain calculable data values. Then, using preset functions or calculation rules, the data values ​​are processed to obtain a fused urban feature value that integrates the characteristics of each urban feature data and can comprehensively reflect the urban features of the grid.

[0043] The urban characteristic fusion value obtained in this way can comprehensively take into account various factors such as population agglomeration, activity behavior, living facilities, and travel behavior. Therefore, the urban core area determined based on the urban characteristic fusion value is more in line with the definition of the urban core area and is more accurate.

[0044] Operation S130 determines candidate grids for the core urban area based on the urban feature fusion value corresponding to each grid.

[0045] After determining the city feature fusion value corresponding to each grid, candidate grids for the city's core area can be determined based on this value.

[0046] Candidate grids for the urban core area refer to grids that meet the characteristics of the urban core area and may belong to the urban core area.

[0047] When determining candidate grids for the core urban area based on the urban feature fusion value corresponding to each grid, the judgment can be made based on the numerical range or numerical characteristics. For example, if the urban feature fusion value is greater than or equal to a certain urban feature fusion value threshold, the grid can be inferred to be a candidate grid; or if the feature fusion value conforms to a certain pattern (for example, it is an even number), the grid can be inferred to be a candidate grid, and so on.

[0048] In this embodiment of the disclosure, the candidate grids for the urban core area are determined based on the urban feature fusion value corresponding to each grid. Grids that do not conform to the characteristics of the urban core area can be filtered out, reducing the amount of subsequent calculations, saving computing resources, and greatly improving the processing speed.

[0049] Operation S140 determines the boundary of the urban core area based on the distribution of candidate grids.

[0050] Since the core urban area mainly refers to the active area with a large population and frequent human activity, it is characterized by the concentration of people in terms of residence, activities, and travel.

[0051] Candidate grids are grids that meet the above characteristics. If such grids are clustered together, then the area containing these candidate grids and where subsequent grids are clustered together is very likely to be the core area of ​​the city.

[0052] Therefore, clustered candidate grids can be identified based on their distribution, such as the number of candidate grids per unit area and the distance between them. Once clustered candidate grids are found, the boundary line encompassing most of these clustered candidate grids can be used to define the boundary of the city's core area.

[0053] In this embodiment, firstly, location information corresponding to urban feature data is acquired through operation 110; then, through operation 120, urban feature data from multiple data sources are fused using map grids as units to obtain a fused urban feature value that comprehensively reflects the urban characteristics of each grid; next, through operation 130, candidate grids that conform to the characteristics of the urban core area are identified based on the fused urban feature value of each grid; then, through operation 140, clustered candidate grids are found based on the candidate grids that conform to the characteristics of the urban core area, and the boundary including the clustered candidate grids is determined as the boundary of the urban core area. In this way, an urban core area with boundaries can be automatically obtained based on urban feature data from multiple data sources without manual intervention, resulting in faster processing speed and greatly simplifying and accelerating the process of determining the urban core area.

[0054] Moreover, as mentioned above, since the embodiments of this disclosure use urban feature data from multiple data sources and determine the urban core area based on the urban feature fusion value obtained by fusing urban feature data from multiple data sources, the urban core area determined in this way is more in line with the definition of urban core area and is more accurate, and can be applied to various application scenarios.

[0055] Figure 2 The main flow of a method for determining the core area of ​​an city, according to another embodiment of this disclosure, is shown.

[0056] in, Figure 2 The embodiment of this disclosure shown is applied to an electronic map product. This electronic map product has a one-click function to display the core urban area. When the user presses the corresponding button, the client of the electronic map product sends a request to the backend server. Upon receiving the request, the backend server calculates the boundary of the core urban area based on urban feature data from multiple data sources obtained from a big data platform, and returns the coordinates of this boundary to the client. Then, the client of the electronic map product displays an electronic map with the boundary of the core urban area marked with a red line. In this way, the core urban area can be quickly and accurately determined and visualized in real time.

[0057] like Figure 2 As shown, the process executed on the server side of this electronic map product to determine the boundaries of the city's core area mainly includes:

[0058] Operation S2010 rasterizes the map based on its spatial location to obtain the map's grid information;

[0059] In this embodiment, map rasterization refers to dividing the map into grids of the same size based on a certain unit length, such as... Figure 3 As shown.

[0060] Grid division breaks down a map into smaller units, allowing calculations to be performed on smaller areas, which greatly reduces the amount of computation and computational complexity.

[0061] In addition, grid division can not only greatly improve the speed of querying and searching, and significantly improve the computing speed and processing power, but also provide data such as spatial adjacency relationships and the distribution around the grid, and perform thermal radiation calculations, providing more decision-making basis for the determination of the core area of ​​the city.

[0062] In the rasterization of the map, the unit size of the grid can be flexibly adjusted according to the amount of computation and processing time, so as to maximize the computational efficiency.

[0063] Operation S2020: Based on the grid information of the map and the location information corresponding to the city feature data, the city feature data is grouped to obtain a subset of city feature data corresponding to each grid.

[0064] Generally speaking, urban characteristic data based on individual activities, accompanying the movement of these individuals, tend to have a certain degree of randomness and discreteness, such as individual travel data and POI data. This randomness and discreteness are not conducive to statistical analysis.

[0065] Therefore, in this embodiment of the disclosure, the urban feature data of these individual activities are mapped onto a grid with fixed coordinates, so that the urban feature data is fixed to a specific location space, and the discrete data form a certain spatial relationship.

[0066] This allows for better statistical analysis and subsequent processing.

[0067] Specifically, in this embodiment, urban feature data from different source data are calculated independently. Individual data within each type of urban feature data are mapped one-to-one to map grids using a hash function based on the X and Y coordinate location information loc(x, y). Then, for each grid, data deduplication is performed to obtain the initial values ​​for each type of urban feature data for each grid.

[0068] In addition, the individual data in each type of urban characteristic data are often collected within a certain period of time. Therefore, it is necessary to unify the time of various types of urban characteristic data and use data from the same period as much as possible.

[0069] In the selection of urban characteristic data, the embodiments of this disclosure are mainly based on big data from the Internet, including four types of data: POI, population, location passenger flow, and travel trajectory, as characteristic data sources for the city, in order to comprehensively consider the distribution of urban facilities, population distribution, living conditions, passenger flow distribution, and traffic conditions.

[0070] Operation S2030 normalizes the urban feature data from different data sources in the urban feature data subset to obtain the normalized value corresponding to each data source.

[0071] Because urban feature data from different data sources have different units, value ranges, data patterns, or distributions, if they are used directly for calculation without any processing, some urban feature data will have a greater influence than others. As a result, the final calculated value will depend more on the numerical characteristics of the different urban feature data from each data source, losing the meaning that the numerical values ​​should represent. This makes it impossible to reflect the characteristics of each urban feature data in a balanced way.

[0072] Therefore, in this embodiment, the urban feature data from different data sources are normalized so that their value ranges all fall within the [0,1] interval. This allows each urban feature data to exert a balanced influence, so that the final calculated urban feature fusion value can evenly reflect the characteristics of each urban feature data.

[0073] In this embodiment of the disclosure, the minimum-maximum (min-max) normalization algorithm is used when normalizing urban feature data from different data sources. Implementers may also choose any other applicable normalization algorithm based on specific implementation needs, conditions, and desired results.

[0074] Furthermore, since normalized data may contain outliers, also known as singular values, and singular values ​​have a significant impact on the final calculation results, the embodiments of this disclosure also include the following processing:

[0075] 1) Based on the distribution of the data, take the value of the higher score range (e.g., 90% to 95%) as the maximum value, rather than taking the value of the highest score (top) as the maximum value;

[0076] 2) By performing peak reduction, values ​​exceeding 1 are set to 1.

[0077] This makes the normalized values ​​more representative.

[0078] Operation S2040: Based on the weight of each data source, perform a weighted summation of the normalized values ​​corresponding to each data source to obtain the city feature fusion value corresponding to each grid.

[0079] In this embodiment of the disclosure, after normalizing the urban feature data from different data sources, the normalized values ​​corresponding to each data source are weighted and summed to compensate for the sparsity of a single data source.

[0080] However, due to the different types of cities (such as different functions, different sizes, etc.) or the different application scenarios in the core urban areas, the types of urban characteristic data that are of interest or value will also be different.

[0081] For example, in first-tier cities, the population in each area is relatively large and the differences are not significant, so the focus may be more on location-based passenger flow and travel patterns; while in second-tier cities, the population distribution is very uneven, but the flow of people is not very frequent, so the focus may be more on population data.

[0082] For example, if the determination of the urban core area is used to reflect population density indicators, then population data is more important; while if the determination of the urban core area is used to reflect traffic status indicators, then data such as passenger flow and travel trajectories are more important.

[0083] Therefore, in this embodiment of the disclosure, a weight coefficient k, k∈[0,1], is set for each type of urban feature data, and the normalized values ​​of each type of urban feature data are weighted and summed according to the weight coefficient.

[0084] Specifically, the city feature fusion value for each grid is calculated using the following formula:

[0085]

[0086] v i k represents the normalized thermal value of the grid corresponding to the i-th type of data source. i This represents the corresponding weighting coefficient.

[0087] In this way, the proportion and influence of various types in the city feature fusion value can be flexibly determined according to the needs of different city types or application scenarios, so that the city feature fusion value is more in line with the characteristics of the city and better meets the needs of the application scenario.

[0088] Operation S2050 is used to normalize the city feature fusion values ​​for each grid.

[0089] Because the weighting coefficients differ across cities, and the data volume and distribution of various types of urban characteristic data may also vary, the resulting urban fusion values ​​obtained after the above fusion process may have significantly different ranges across different cities, making them incomparable. This also renders the standards for determining the core urban areas incomparable.

[0090] Therefore, in this embodiment of the disclosure, the city feature fusion value obtained by weighted summation will be normalized again to make the city feature fusion value comparable among cities and to create conditions and lay the data foundation for providing a unified city feature fusion value threshold; otherwise, it may be necessary to specify a city feature fusion value threshold for each city.

[0091] In this way, a unified threshold for urban feature fusion values ​​can be universally applied to different cities, and the urban feature fusion values ​​can more accurately reflect the degree to which the grid conforms to the characteristics of the core urban area.

[0092] In addition, normalizing the weights can filter out some city feature data with low importance, thereby simplifying subsequent calculations.

[0093] Operation S2060 determines candidate grids for the core urban area based on the urban feature fusion value corresponding to each grid.

[0094] In this embodiment, after obtaining the city feature fusion value corresponding to each grid, the grids are filtered according to a preset city feature fusion value threshold, and grids with city feature fusion values ​​lower than the threshold are removed. Only grids with city feature fusion values ​​higher than or equal to the threshold are retained as candidate grids for subsequent calculations.

[0095] like Figure 3 In the grid shown, the grids filled with diagonal lines are the candidate grids for the urban core area determined based on the urban feature fusion value corresponding to each grid.

[0096] By filtering the grid using a city feature fusion value threshold, grids with low city feature fusion values—those that do not meet the characteristics of the core urban area—can be removed; these are also known as edge grids or long-tailed grids. This not only reduces the amount of data required for subsequent computation and improves processing speed, but also minimizes the impact of invalid data on boundary generation.

[0097] Operation S2070 determines the points representing each candidate grid, obtaining a point set. The points in the point set are then clustered to obtain the target cluster.

[0098] In this embodiment of the disclosure, the center point of each candidate grid is determined as the point representing the corresponding candidate grid. For example... Figure 3 As shown, the solid origin on the diagonally filled grid is the center point of each candidate grid.

[0099] The coordinates of the center point are used as the coordinates of the point representing the candidate grid. In this way, the candidate grid can be transformed into a point in the graph for subsequent graph operations.

[0100] The city feature fusion value (normalized value) of the grid is used as the value of the point. Based on this value, the points representing the grid are clustered according to the spatial clustering algorithm to obtain the target cluster. The grid represented by the point in each target cluster is likely to be the grid in the core area of ​​the city.

[0101] In this embodiment of the disclosure, the DBScan algorithm is used as the spatial clustering algorithm, and its clustering steps include:

[0102] 1) Neighborhood search. Define a spatial distance range Distance. Any adjacent grid within Distance is identified as a neighboring grid.

[0103] 2) Density determination. Calculate the amount of candidate grid data (grid_num) in the neighborhood of the current grid, and the city feature fusion value (grid_value) of each candidate grid. Then, filter the candidate grids using a candidate grid data volume threshold (grid_num_threshold).

[0104] For example, if grid_num >= grid_num_threshold, then the candidate grid is a valid grid, and its neighboring grids are also set as valid grids to be selected; otherwise, it is an invalid grid and will no longer participate in the subsequent grid search.

[0105] 3) Cluster Generation. Based on the results of step 2), valid meshes are added to the cluster set, and the neighboring meshes of valid meshes are used as the search meshes. If the search mesh has been calculated before, it is skipped; otherwise, steps 1)-3) are repeated. Valid meshes are added to the cluster set until the search mesh set is empty, at which point the clusters have been generated.

[0106] 4) Complete Traversal. Following rules 1)-3), traverse all candidate grids for the city, ultimately generating different clusters. Based on statistical values ​​such as the amount of grid data and area contained in different clusters, clusters containing too small an area or too few grids are eliminated to obtain the target clusters.

[0107] If a city obtains multiple target clusters after the above calculations, it means that the city has multiple dispersed urban core areas.

[0108] like Figure 3 As shown in the figure, the points that are relatively concentrated on the left side of the figure form a cluster, which is the target cluster to be determined in this embodiment of the disclosure, while the points distributed in the lower right corner do not belong to this cluster and can be eliminated.

[0109] Operation S2080 determines the boundary of the city's core area based on the points in the target cluster.

[0110] After identifying the points within the target cluster, graph operations can be performed using the coordinates of these points to calculate the boundary line of a polygon that includes all points in each cluster. This boundary line can then serve as the boundary of the core urban area corresponding to that cluster.

[0111] like Figure 3 As shown, the boundary line of the polygon formed by connecting the outermost points and including all points can be used to define the boundary of the urban core area corresponding to this cluster.

[0112] Operation S2090: Smooth the boundary.

[0113] The generated polygonal boundaries may have abnormal protrusions or depressions, so specific algorithms can be used to smooth the boundaries.

[0114] In this embodiment, a third-order Bezier smoothing algorithm is used to smooth the boundary, and the processing effect is as follows: Figure 4 As shown in the figure. The dashed line represents the boundary obtained after processing, which is smoother than the original boundary shown by the solid line.

[0115] Operate S2100 to output the coordinate sequence.

[0116] Once the boundary line is determined, the coordinates of the points on the boundary line can be output as a sequence and sent to the client of the electronic map product.

[0117] Once the client of the electronic map product obtains the coordinate sequence, it can draw and display the boundary of the city's core area on the electronic map based on the coordinates of each point in the coordinate sequence, thus making the city's core area more intuitive.

[0118] exist Figure 2 In the embodiment of this disclosure shown, the boundary of the urban core area is automatically generated by collecting urban feature data in real time. This not only reduces labor costs, but also dynamically updates the boundary of the urban core area in real time based on the updated urban feature data, thus reflecting the development and changes of the city in a timely manner.

[0119] The urban core area identified in this way can also be applied to various scenarios such as urban transportation industry reports, urban active population analysis, and urban core area initialization, providing strong data support for urban development planning and correct decision-making.

[0120] Furthermore, in the numerous embodiments of this disclosure, different implementation methods can be used to determine the boundary of the urban core area based on the candidate grid distribution.

[0121] Figure 5 This disclosure illustrates the main process of determining the boundary of a city core area based on points in a target cluster, according to another embodiment of the present disclosure, including:

[0122] Operation S510: Based on the radiation radius of the center point in the target cluster, perform multiple shrinkage processes on the points in the target cluster to a preset radius threshold, and use the points covered in the shrinkage process as boundary points.

[0123] by Figure 6 Taking the target cluster shown as an example, shrinking the radiation radius from R1 to R2 yields P61, P62, and P63; then shrinking the radiation radius from R2 to R3 yields points P64, P65, and P66.

[0124] Operation S520 sorts and connects the candidate boundary points in a clockwise direction to form an ordered boundary;

[0125] by Figure 6 Taking the target cluster class shown as an example, sorting and connecting them in a clockwise direction yields the ordered boundary {P61, P64, P62, P63, P65, P66}.

[0126] Operate S530 to define the orderly boundary as the boundary of the city's core area.

[0127] This method can more accurately determine the boundaries of the city's core area without covering too many extra areas.

[0128] Figure 8 This disclosure illustrates the main process of determining the boundary of a city core area based on points in a target cluster, according to another embodiment of the present disclosure, including:

[0129] Operation S810 determines the starting boundary vertices from the target cluster points;

[0130] by Figure 9 Taking the target cluster class shown as an example, the point with the highest coordinates, such as point P, can be selected as the starting boundary vertex.

[0131] Operate S820 to calculate the angle between the starting boundary vertex and other points in the target cluster, and determine the point with the largest angle as the next boundary vertex, until the lines connecting all the boundary vertices form an envelope polygon;

[0132] by Figure 9 Taking the target cluster class shown as an example, calculate the angle between the starting boundary vertex P and the neighboring points P1, P2 and P3, and take the point with the largest angle P1 as the next boundary vertex.

[0133] Then repeat the above operation using P1 as the starting boundary vertex until the lines connecting all the boundary vertices form an envelope polygon, such as... Figure 10 As shown.

[0134] Operate S830 to define the envelope polygon as the boundary of the city's core area.

[0135] In this embodiment, the envelope polygon can include all points in the target cluster, covering not only the core urban area but also the radiation area of ​​the core urban area.

[0136] It should be noted that Figure 5 and Figure 8 These are merely illustrative examples of how to determine the boundaries of the urban core area based on the candidate grid distribution, and do not represent a limitation on the embodiments of this disclosure.

[0137] Implementers can flexibly adopt any implementation method according to implementation needs, conditions, and effects to achieve different implementation results.

[0138] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0139] According to embodiments of this disclosure, this disclosure also provides a device for determining a core urban area, such as... Figure 11 As shown, the device 110 includes: a data acquisition module 1101, used to acquire urban feature data and corresponding location information from at least one data source; a fusion value determination module 1102, used to group and fuse the urban feature data based on the grid information of the map and the location information corresponding to the urban feature data, to obtain the urban feature fusion value corresponding to each grid; a candidate grid determination module 1103, used to determine candidate grids for the urban core area based on the urban feature fusion value corresponding to each grid; and a boundary determination module 1104, used to determine the boundary of the urban core area based on the distribution of the candidate grids.

[0140] According to an embodiment of this disclosure, the fusion value determination module 1102 includes: a data grouping submodule, used to group urban feature data based on the grid information of the map and the location information corresponding to the urban feature data, to obtain a subset of urban feature data corresponding to each grid; and a normalization processing submodule, used to normalize the urban feature data from different data sources in the subset of urban feature data, to obtain a normalized value corresponding to each data source.

[0141] The weighted summation submodule is used to perform weighted summation on the normalized values ​​corresponding to each data source according to the weight of each data source, so as to obtain the city feature fusion value corresponding to each grid.

[0142] According to one embodiment of this disclosure, the device 110 further includes: a city feature fusion value normalization processing module, used to normalize the city feature fusion value.

[0143] According to one embodiment of this disclosure, the boundary determination module 1104 includes: a point set acquisition submodule, used to determine the points representing each candidate grid to obtain a point set; a clustering submodule, used to cluster the points in the point set to obtain a cluster class of points; a target cluster class determination submodule, used to determine a target cluster class based on the cluster class of points; and a boundary determination submodule, used to determine the boundary of the urban core area based on the distribution of points in the target cluster class.

[0144] According to one embodiment of this disclosure, the point set acquisition submodule is specifically used to determine the center point of each candidate grid as the point representing the corresponding candidate grid.

[0145] According to an embodiment of this disclosure, the boundary determination submodule includes: a starting boundary vertex determination unit, used to determine the starting boundary vertex from the target cluster points; an angle calculation unit, used to calculate the angle between the starting boundary vertex and other points in the target cluster points, and determine the point with the largest angle as the next boundary vertex, until the line connecting all the boundary vertices forms an envelope polygon; and a boundary determination unit, used to determine the envelope polygon as the boundary of the urban core area.

[0146] According to an embodiment of this disclosure, the boundary determination submodule includes: a boundary point determination unit, used to perform multiple shrinkage processes on the points in the target cluster to a preset radius threshold based on the radiation radius of the center point in the target cluster, and to take the points covered in the shrinkage process as boundary points; a boundary point sorting unit, used to sort and connect the candidate boundary points in a clockwise direction to form an ordered boundary; and a boundary determination unit, used to determine the ordered boundary as the boundary of the urban core area.

[0147] According to one embodiment of this disclosure, the device 110 further includes a boundary smoothing module for smoothing the boundary.

[0148] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0149] Figure 12 A schematic block diagram of an example electronic device 1200 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0150] like Figure 12 As shown, device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in read-only memory (ROM) 1202 or a computer program loaded from storage unit 1208 into random access memory (RAM) 1203. The RAM 1203 may also store various programs and data required for the operation of device 1200. The computing unit 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. Input / output (I / O) interface 1205 is also connected to bus 1204.

[0151] Multiple components in device 1200 are connected to I / O interface 1205, including: input unit 1206, such as keyboard, mouse, etc.; output unit 1207, such as various types of monitors, speakers, etc.; storage unit 1208, such as disk, optical disk, etc.; and communication unit 1209, such as network card, modem, wireless transceiver, etc. Communication unit 1209 allows device 1200 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0152] The computing unit 1201 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1201 performs the various methods and processes described above, such as the method for determining the core urban area of ​​this disclosure. For example, in some embodiments, the method for determining the core urban area of ​​this disclosure can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 1208. In some embodiments, part or all of the computer program can be loaded and / or installed on device 1200 via ROM 1202 and / or communication unit 1209. When the computer program is loaded into RAM 1203 and executed by the computing unit 1201, one or more steps of the method for determining the core urban area of ​​this disclosure described above can be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured by any other suitable means (e.g., by means of firmware) to perform the method for determining the urban core area of ​​this disclosure.

[0153] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0154] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0155] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0156] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0157] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0158] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0159] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0160] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for determining a city's core area, applied in an electronic map product, wherein the electronic map product is equipped with a one-click function to display the city's core area, the method comprising: When a user presses the button corresponding to the function, the client of the electronic map product sends a request to the backend server. After receiving the request, the backend server performs the following operations: Acquire urban feature data from at least one data source and the location information corresponding to the urban feature data; Based on the grid information of the map and the location information corresponding to the city feature data, the city feature data is grouped and fused to obtain the city feature fusion value corresponding to each grid. The grid information of the map includes dividing the map into grids according to coordinates to obtain a group of sub-regions. The coordinates of each sub-region are complementary and do not intersect. Based on the urban feature fusion value corresponding to each grid, candidate grids for the urban core area are determined; The points representing each candidate grid are determined to obtain the point set; Cluster the points in the point set to obtain the point clusters; Determine the target cluster class based on the cluster class of the points; Based on the points in the target cluster, the boundary line of a polygon including each point in each cluster is determined as the boundary of the urban core area; Return the coordinates of the boundary to the client; The client displays an electronic map that marks the boundary of the city's core area based on the coordinates of the boundary.

2. The method according to claim 1, wherein, The map-based grid information and the location information corresponding to the city feature data are used to group and fuse the city feature data to obtain the city feature fusion value corresponding to each grid, including: Based on the grid information of the map and the location information corresponding to the city feature data, the city feature data is grouped to obtain a subset of city feature data corresponding to each grid. The urban feature data from different data sources in the urban feature data subset are normalized to obtain the normalized value corresponding to each data source; Based on the weight of each data source, the normalized values ​​corresponding to each data source are weighted and summed to obtain the city feature fusion value corresponding to each grid.

3. The method according to claim 2, further comprising: The fused values ​​of the city features are normalized.

4. The method according to claim 1, wherein, The process of determining the point representing each candidate grid includes: The center point of each candidate grid is determined as the point representing the corresponding candidate grid.

5. The method according to claim 1, wherein, The step of determining the boundary line of a polygon comprising each point in each cluster as the boundary of the urban core area based on the points in the target cluster includes: Determine the starting boundary vertices from the target cluster points; Calculate the angle between the starting boundary vertex and other points in the target cluster, and determine the point with the largest angle as the next boundary vertex, until the lines connecting all the boundary vertices form an envelope polygon; The envelope polygon is defined as the boundary of the core urban area.

6. The method according to claim 1, wherein, The step of determining the boundary line of a polygon comprising each point in each cluster as the boundary of the urban core area based on the points in the target cluster includes: Based on the radiation radius of the center point in the target cluster, the points in the target cluster are shrunk multiple times to a preset radius threshold, and the points covered in the shrinkage process are taken as boundary points. The boundary points are sorted and connected in a clockwise direction to form an ordered boundary; The ordered boundary is defined as the boundary of the core urban area.

7. The method according to any one of claims 5 or 6, further comprising: The boundary is smoothed.

8. A device for determining a core urban area, applied in an electronic map product, the electronic map product being equipped with a one-click function to display the core urban area, the device comprising a corresponding module for executing the method for determining the core urban area according to claim 1, wherein: The data acquisition module is used to acquire urban feature data from at least one data source and the location information corresponding to the urban feature data; The fusion value determination module is used to group and fuse the urban feature data based on the grid information of the map and the location information corresponding to the urban feature data to obtain the urban feature fusion value corresponding to each grid. The grid information of the map includes a group of sub-regions obtained by dividing the map into grids according to coordinates. The coordinates of each sub-region are complementary and do not intersect. The candidate grid determination module is used to determine the candidate grids for the core urban area based on the urban feature fusion value corresponding to each grid. The boundary determination module includes: a point set acquisition submodule, used to determine the points representing each candidate grid to obtain a point set; a clustering submodule, used to cluster the points in the point set to obtain the point clusters; a target cluster determination submodule, used to determine the target cluster based on the point clusters; and a boundary determination submodule, used to determine the boundary of the urban core area based on the point distribution in the target cluster.

9. The apparatus according to claim 8, wherein, The fusion value determination module includes: The data grouping submodule is used to group the city feature data based on the grid information of the map and the location information corresponding to the city feature data, so as to obtain a subset of city feature data corresponding to each grid. The normalization processing submodule is used to normalize the urban feature data from different data sources in the urban feature data subset to obtain the normalized value corresponding to each data source. The weighted summation submodule is used to perform weighted summation on the normalized values ​​corresponding to each data source according to the weight of each data source, so as to obtain the city feature fusion value corresponding to each grid.

10. The apparatus according to claim 8, further comprising: The city feature fusion value normalization processing module is used to normalize the city feature fusion value.

11. The apparatus according to claim 8, wherein, The point set acquisition submodule is specifically used to determine the center point of each candidate grid as the point representing the corresponding candidate grid.

12. The apparatus according to claim 8, wherein, The boundary determination submodule includes: A starting boundary vertex determination unit is used to determine the starting boundary vertex from the target cluster points; An angle calculation unit is used to calculate the angle between the starting boundary vertex and other points in the target cluster, and to determine the point with the largest angle as the next boundary vertex, until the line connecting all the boundary vertices forms an envelope polygon; A boundary determination unit is used to determine the envelope polygon as the boundary of the urban core area.

13. The apparatus according to claim 8, wherein, The boundary determination submodule includes: The boundary point determination unit is used to perform multiple shrinkage processes on the points in the target cluster class based on the radiation radius of the center point in the target cluster class, and to take the points covered in the shrinkage process as boundary points. A boundary point sorting unit is used to sort and connect the boundary points in a clockwise direction to form an ordered boundary. A boundary determination unit is used to determine the ordered boundary as the boundary of the urban core area.

14. The apparatus of claim 8, further comprising: A boundary smoothing module is used to smooth the boundary.

15. An electronic device comprising: At least one processor; as well as 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 to enable the at least one processor to perform the method for determining the urban core area according to any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to execute the method for determining the urban core area according to any one of claims 1-7.

17. A computer program product comprising a computer program that, when executed by a processor, implements the method for determining a core urban area according to any one of claims 1-7.

18. An electronic map product, comprising an urban core area determination module, for performing the urban core area determination method according to any one of claims 1-7, and displaying the boundary of the urban core area in a map displayed by the electronic map product.

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