Logical inspection, quality inspection method, system, device, terminal and medium for vector data

Abstract

The present application provides a method, system, device, terminal, and medium for logical inspection and quality control of vector data. The method is applied to a vector data update client and includes: extracting the spatial range of the updated vector data in response to input of updated vector data to be detected; sending a benchmark data request to a server based on the spatial range of the updated vector data to obtain benchmark data of the same spatial range; the benchmark data is historical vector tile data; and performing spatial logical analysis based on the updated vector data in combination with the benchmark data to perform a logical inspection on the updated vector data. The vector data update client requests benchmark data from the server to ensure the currency of the benchmark data, thereby effectively improving the quality inspection accuracy of the vector data.

Description

Technical Field

[0001] The present application belongs to the field of database technology and relates to a vector data quality inspection technology, and in particular to a vector data logic inspection, quality inspection method, system, device, terminal and medium. Background Art

[0002] Vector data is a digital representation used to describe the location, shape, and attribute characteristics of geographic spatial features. Due to urban development and changes in the geographic environment, vector data often needs to be updated. To ensure accuracy, updated vector data requires quality control. This involves performing spatial logic analysis on the vector data to be tested in conjunction with existing benchmark data to detect any spatial logical contradictions. This involves checking whether the geographic features in the updated vector data conflict with those in the existing benchmark data. For example, when roads overlap with residential areas, there is a spatial contradiction between the two.

[0003] Currently, vector data logic checking methods typically involve pre-storing existing benchmark data offline on client devices, then performing logic checks on the vector data to be checked based on this benchmark data. However, because benchmark data requires regular updates to maintain its currency, offline storage methods struggle to synchronize data in real time. This results in discrepancies between the benchmark data used for logic checking and the actual benchmark data, leading to inaccurate results in vector data logic checks. Furthermore, due to limitations in the device's storage capacity and computing power, the amount of offline stored benchmark data is limited, making it difficult to support a wide range of vector data quality inspection needs and unable to meet existing vector data quality inspection requirements. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, device, terminal and medium for logical inspection and quality inspection of vector data, which is used to solve the problem in the existing technology that updated vector data is combined with benchmark data stored offline in the client device for logical inspection. Since the offline stored benchmark data is difficult to ensure the timeliness, the quality inspection results may be inaccurate and it is difficult to support the vector data quality inspection needs in a wide range.

[0005] In a first aspect, the present application provides a vector data logic checking method, which is applied to a vector data update client, comprising:

[0006] In response to input of updated vector data to be detected, the spatial range of the updated vector data is extracted; based on the spatial range of the updated vector data, a baseline data request is sent to a server to obtain baseline data of the same spatial range; the baseline data is historical vector tile data; based on the updated vector data, spatial logical analysis is performed in combination with the baseline data to perform a logical check on the updated vector data.

[0007] In one embodiment of the present application, extracting the spatial range of the updated vector data includes: spatially clustering the updated vector data to obtain at least one cluster data group; obtaining the center point coordinates and cluster radius of each cluster data group to determine the spatial range of the updated vector data.

[0008] In one embodiment of the present application, based on the spatial range of the updated vector data, a benchmark data request is sent to the server to obtain the benchmark data, including: obtaining a vector tile level based on a preset quality inspection accuracy; determining a search range based on the spatial range of the updated vector data; based on the search range, calculating, among each tile corresponding to the vector tile level, tiles that intersect with the search range space as tiles to be requested; obtaining the spatial range corresponding to each tile to be requested, and determining whether the spatial range of each tile to be requested intersects with the spatial range corresponding to the cluster data group; if so, using the tile to be requested as the requested tile; and sending a benchmark data request to the server based on the coordinates of each requested tile to obtain the benchmark data.

[0009] In one embodiment of the present application, the spatial logical analysis is performed based on the updated vector data in combination with the benchmark data to perform a logical check on the updated vector data, including: converting both the updated vector data and the benchmark data into a database format; based on the updated vector data and the benchmark data in the database format, extracting the geographic elements of the updated vector data and the benchmark data respectively through a driver corresponding to the database format, and determining whether there is any contradiction between the geographic elements of the two; if not, the logical check of the updated vector data passes.

[0010] In one embodiment of the present application, the spatial clustering of the updated vector data to obtain at least one cluster data group includes: obtaining each feature point of the updated vector data based on the position information of the updated vector data; and dividing the feature points based on the distance between the feature points to obtain at least one cluster data group.

[0011] In the second aspect, the present application provides a vector data quality inspection method, including: performing a data integrity check based on the updated vector data to determine whether the updated vector data meets the integrity requirements; if so, performing a layer information check based on the updated vector data to determine whether the updated vector data meets the spatial coordinate requirements; if so, performing a layer field check based on the updated vector data to determine whether the updated vector data meets the attribute requirements; if so, performing a layer topology check based on the updated vector data to determine whether the updated vector data meets the geometric expression requirements; if so, performing a logic check based on the updated vector data to determine whether the updated vector data conforms to spatial logic; wherein, the implementation method of the logic check on the updated vector data is the vector data logic check method described above.

[0012] In the third aspect, the present application provides a vector data logic checking system, including: an updated vector data client, used to execute the vector data logic checking method as described above in response to the input of updated vector data; a server, which is communicated with the client, used to receive the client's benchmark data request, and generate the corresponding benchmark data based on the benchmark data request, and send it to the client.

[0013] In a fourth aspect, the present application provides a vector data logic checking device, comprising a spatial range acquisition module, a benchmark data request module and a spatial logic analysis module; the spatial range acquisition module is used to extract the spatial range of the updated vector data in response to the input of the updated vector data to be detected; the benchmark data request module is used to send a benchmark data request to the server based on the spatial range of the updated vector data to obtain benchmark data of the same spatial range; the benchmark data is historical vector tile data; the spatial logic analysis module is used to perform spatial logic analysis based on the updated vector data and in combination with the benchmark data to perform a logic check on the updated vector data.

[0014] In a fifth aspect, the present application provides a terminal, comprising: a processor and a memory, wherein the memory is communicatively connected to the processor;

[0015] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the vector data logic inspection method or the vector data quality inspection method as described above.

[0016] In a sixth aspect, the present application provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the vector data logic inspection method or the vector data quality inspection method as described above is implemented.

[0017] As described above, the present application provides a method, system, device, terminal and medium for logical inspection and quality inspection of vector data, which requests benchmark data from the server through the vector data update client, and performs logical inspection on the updated vector data based on the benchmark data to ensure the timeliness of the benchmark data and avoid inaccurate logical inspection results of the updated vector data due to inaccurate benchmark data; at the same time, since the vector data stored by the server is not limited by storage capacity, the logical inspection method provided by the present application can support large-scale vector data quality inspection needs, thereby effectively improving the quality inspection effect of vector data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Shown is a structural schematic diagram of a vector data logic checking system described in an embodiment of the present application.

[0019] Figure 2 Shown is a flow chart of a vector data logic checking method described in an embodiment of the present application.

[0020] Figure 3 Shown is a flow chart of a method for obtaining a spatial range of updated vector data according to an embodiment of the present application.

[0021] Figure 4 Schematic diagram showing each clustered data group after updating vector data space clustering according to an embodiment of the present application

[0022] Figure 5 Shown is a flow chart of a clustering data set acquisition method described in an embodiment of the present application.

[0023] Figure 6 Shown is a flowchart of a method for obtaining benchmark data according to an embodiment of the present application.

[0024] Figure 7 Shown is a flow chart of a logical check process for updating vector data according to an embodiment of the present application.

[0025] Figure 8 Shown is a flow chart of a vector data quality inspection method described in an embodiment of the present application.

[0026] Figure 9 Shown is a structural schematic diagram of a vector data logic checking device described in an embodiment of the present application.

[0027] Figure 10 Shown is a structural schematic diagram of a terminal described in an embodiment of the present application.

[0028] Description of Reference Numerals

[0029] 10: Vector data logic checking system; 11: Vector data update client; 12: Server; 60: Vector data logic checking device; 61: Spatial range acquisition module; 62: Reference data request module; 63: Spatial logic analysis module; 70: Terminal; 71: Processor; 72: Memory; 721: Operating system; 722: Application; 73: User interface; 74: Network interface; 75: Bus system. DETAILED DESCRIPTION

[0030] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0031] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0032] Traditional vector data logic checking methods typically involve pre-storing benchmark data offline on client devices, allowing for logical verification of updated vector data within the client device. Because offline stored benchmark data is difficult to maintain in currency and the client can only store a limited amount of benchmark data, this approach can easily lead to inaccurate quality control results and struggles to support the quality control needs of large-scale vector data.

[0033] The benchmark data is historical vector tile data; the vector tile data refers to the spatial data of each block obtained after dividing the vector data based on the set spatial grid size.

[0034] In response to the technical problems existing in the prior art, the following embodiments of the present application provide a method, system, device, terminal and medium for logical inspection and quality inspection of vector data, which obtains the benchmark data corresponding to the updated vector data by requesting the server to update the benchmark data, and implements logical inspection of the updated vector data in the vector data update client; wherein, the server obtains the latest benchmark data in real time through the network, which can effectively ensure the timeliness of the benchmark data, and thus ensure the accuracy of the logical inspection results of the updated vector data. At the same time, the obtained benchmark data is not limited by the storage capacity of the device, avoiding occupying a large amount of computing resources of the server, thereby realizing efficient and accurate vector data logical inspection, so as to meet the real-time logical inspection needs of large-scale vector data and achieve good vector data quality inspection effects.

[0035] The following embodiments of the present application provide a logical inspection, quality inspection method, system, device, terminal and medium for vector data, including but not limited to vector data quality inspection scenarios applied to geographic information public service platforms such as Tiandi Map, Google Maps, ArcGIS Online, etc. The following will be described using the real-time update quality inspection of vector data as an example.

[0036] like Figure 1 , which is a schematic diagram of an application scenario of the logic checking method provided in this embodiment. Specifically, the logic checking method is applicable to a vector data logic checking system 10 , including a vector data update client 11 and a server 12 that are communicatively connected.

[0037] The vector data update client 11 is used to execute the vector data logic check method on the updated vector data; the vector data update client 11 can be installed on either a mobile terminal or a fixed terminal, and this application does not make any specific limitation here.

[0038] The server 12 is communicatively connected with the vector data update client 11, and is used to receive a request from the vector data update client 11, and generate and send corresponding benchmark data to the vector data update client 11 based on the request, so that the vector data update client 11 receives the benchmark data; the vector data update client 11 uses the benchmark data as a reference benchmark for updating the spatial logic of the vector data, and performs spatial analysis on the benchmark data and the updated vector data to detect whether the spatial logic of the updated vector data is accurate, thereby realizing a logical check of the updated vector data.

[0039] The server 12 can be any one of a cloud server, a server deployed on a physical machine, or an embedded server integrated into a terminal device, and this application does not make any specific limitations here.

[0040] Based on this, the vector data logic check system 10 provided in this embodiment requests the benchmark data from the server 12 through the vector data update client 11, so as to perform a logic check of the updated vector data within the vector data update client 11, thereby ensuring the timeliness of the benchmark data and avoiding resource congestion caused by the logic check performed by the server 12, thereby realizing efficient and accurate vector data logic check.

[0041] In order to solve the technical problems existing in the prior art, this embodiment further provides a vector data logic checking method for implementing a logic check on the updated vector data.

[0042] The following will describe it in detail with reference to the accompanying drawings in the embodiments of the present application.

[0043] like Figure 2 As shown, this embodiment provides a vector data logic checking method, including:

[0044] S100 , in response to input of update vector data to be detected, extracting a spatial range of the update vector data.

[0045] The spatial scope of the updated vector data refers to the geographic area covered by the updated vector data in actual geographic space. Specifically, the geographic area covered by the updated vector data in actual geographic space is obtained as the spatial scope of the updated vector data. This is because logical checks of the updated vector data require obtaining baseline data representing the same geographic area as a reference to facilitate spatial logic analysis of the updated vector data. Based on this, this embodiment obtains the spatial scope of the updated vector data to facilitate subsequent requests for baseline data representing the corresponding spatial scope.

[0046] Furthermore, in order to improve the accuracy of the acquired spatial range and reduce the amount of data calculation, the method of acquiring the spatial range includes: performing spatial analysis on the updated vector data to acquire the spatial range of the updated vector data based on the spatial distribution of each element.

[0047] In some optional embodiments, such as Figure 3 As shown, the method for obtaining the spatial range of the updated vector data includes:

[0048] S110: Perform spatial clustering on the updated vector data to obtain at least one cluster data group.

[0049] Each cluster data group is a set of elements in the updated vector data having similar spatial distribution characteristics. The spatial distribution characteristics are used to characterize the spatial position distribution of each element in the updated vector data in the geographic space.

[0050] like Figure 4 As shown, an exemplary method is given in which a first cluster data group, a second cluster data group, and a third cluster data group are obtained after performing spatial cluster analysis on the updated vector data. The first cluster data group, the second cluster data group, and the third cluster data group are all sets of elements with similar spatial distribution characteristics in the updated vector data, that is, the spatial position distribution of each element in the first cluster data group, the second cluster data group, and the third cluster data group is similar in geographic space.

[0051] S120: Obtain the center point coordinates and cluster radius of each cluster data group to determine the spatial range of the updated vector data.

[0052] Among them, the center point coordinates are the coordinates of the center point of the cluster data group in the actual geographic space. For example, the longitude and latitude of the center point in the actual geographic space are used as the center point coordinates, or the cluster data group is projected to the actual geographic space via the WEB Mercator projection, then the center point coordinates can be represented by the coordinates of the Web Mercator projection coordinate system.

[0053] The cluster radius is the radius of the area covered by the cluster data group in the actual geographic space.

[0054] Based on the center point coordinates and cluster radius of the cluster data group, the subspace range corresponding to the cluster data group is obtained; the subspace ranges corresponding to each cluster data group are integrated, and the integrated total space range is used as the space range of the updated vector data.

[0055] In an optional embodiment, in order to quickly obtain the cluster data group corresponding to the updated vector data, the step S110 is specifically executed as follows: Figure 5 Shown, including:

[0056] S111 : Acquire characteristic points of the updated vector data based on the position information of the updated vector data.

[0057] The characteristic points are used to represent the spatial positions of the characteristic elements constituting the updated vector data.

[0058] Specifically, each element in the update vector data is converted into point data, so that each point data is used as a feature point of the update vector data.

[0059] Exemplarily, the format of the updated vector data is converted into spatialite data, and geometric nodes are obtained by querying SQL (Structured Query Language), so as to extract each geometric node as each feature point.

[0060] S112: Perform division based on the distance between each of the feature points to obtain at least one cluster data group.

[0061] Specifically, based on the spatial position of each feature point, the spatial distance between each feature point is obtained, and each feature point whose spatial distance does not exceed a distance threshold is divided into the same cluster data group. The distance threshold is a preset distance value, illustratively, the distance threshold is 1.5 km.

[0062] Optionally, clustering calculations are performed on each of the feature points using a spatial clustering algorithm to achieve spatial location-based division of each of the feature points, thereby obtaining each of the cluster data groups. Exemplarily, the spatial clustering algorithm is a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, with a clustering distance of 1.5 km to calculate each of the cluster data groups.

[0063] Based on this, each cluster data group is obtained as a set of elements with similar spatial distribution characteristics in the updated vector data.

[0064] The updated vector data is divided into the cluster data groups through spatial cluster analysis, so that the elements with the same spatial distribution are clustered into the same cluster for processing, which is beneficial to improve the accuracy of the acquired spatial range, avoid the redundancy of the acquired benchmark data, thereby reducing the amount of data calculation, improving the quality inspection efficiency, and achieving better quality inspection results.

[0065] S200 : Based on the spatial range of the updated vector data, a reference data request is sent to a server to obtain reference data of the same spatial range.

[0066] The reference data is historical vector tile data; the reference data and each updated vector data are in the same spatial range, that is, the reference data and each updated vector data represent the same geographical area in the actual geographical space.

[0067] It should be noted that the server 12 obtains the latest data in real time via the network to ensure the currency of the benchmark data, thereby ensuring the correctness of the logical check of the updated vector data, thereby improving the accuracy of quality inspection. At the same time, the server 12 generates the corresponding benchmark data based on the request of the vector data update client 11. The server 12 does not need to calculate the corresponding benchmark data based on the updated vector data, thereby reducing the computational workload of the server 12 and avoiding the consumption of server 12 resources, thereby improving quality inspection efficiency.

[0068] In some optional embodiments, such as Figure 6 As shown, the method for obtaining the benchmark data includes:

[0069] S210: Obtain a vector tile level based on a preset quality inspection accuracy.

[0070] The preset quality inspection accuracy is an accuracy value set based on the quality inspection requirements of the updated vector data.

[0071] Since the quality inspection accuracy of vector data is associated with the vector tile level, and the requested benchmark data needs to have the same vector tile level as the updated vector data, in this embodiment, the vector tile level is obtained based on the quality inspection accuracy, so as to facilitate the subsequent request for the benchmark data of the corresponding level from the server 12.

[0072] Specifically, according to the association relationship between the preset vector tile level and the quality inspection accuracy, the vector tile level corresponding to the preset quality inspection accuracy is obtained.

[0073] More specifically, the correlation between the quality inspection accuracy of the vector data and the vector tile level is:

[0074]

[0075] in, Indicates the quality inspection accuracy of vector data, represents the vector tile level, Indicates the grid size of the vector tile, exemplarily, 4096, Represents an equatorial length constant used to characterize the physical size of the equator on the map. For example, for the WEB Mercator projection, the equatorial length constant , that is, the vector data is projected into a square range characterized by the x-axis ranging from -20037508.342789244 to 20037508.342789244 and the y-axis ranging from -20037508.342789244 to 20037508.342789244.

[0076] Based on the association relationship, the vector tile level can be obtained according to the preset quality inspection accuracy.

[0077] Exemplarily, as shown in Table 1, a comparison table of the vector tile level and the quality inspection accuracy obtained based on the above association relationship is displayed. Specifically, based on the quality inspection accuracy, the corresponding vector tile level is obtained from the comparison table.

[0078] Table 1 Comparison table of vector tile levels and quality inspection accuracy.

[0079]

[0080] S220, determining a search range based on the spatial range of the updated vector data; and based on the search range, calculating, from each tile corresponding to the vector tile level, tiles that intersect with the search range as tiles to be requested;

[0081] Wherein, the updated vector data includes at least one of the cluster data groups, and the search range includes each area formed based on the center point and cluster radius of each of the cluster data groups; each of the tiles to be requested is a vector tile contained in the spatial range of the search range at the vector tile level. It should be noted that, in order to ensure the integrity of the obtained benchmark data, in this embodiment, all vector tiles within the spatial range of each cluster data group are used as the tiles to be requested. Specifically, for a single cluster data group, based on the vector tile level, all vector tile coordinates whose distance from the center point of the cluster data group is not greater than the cluster radius are obtained as the coordinates of the corresponding tiles to be requested.

[0082] Exemplarily, the vector tile matrix coordinates corresponding to the center point coordinates of the cluster data group are calculated as the coordinates of the first tile to be requested. The vector tile matrix coordinates are the tile matrix coordinates corresponding to the vector tile in the tile matrix at the vector tile level. The first tile to be requested is used as a reference tile, and the coordinates of each vector tile whose coordinate difference with the first tile to be requested in the horizontal or vertical direction in the tile matrix is ​​1 is calculated as the second tile to be requested; the coordinate difference is changed to 2, and the above steps are repeated to calculate the coordinates of each vector tile whose coordinate difference with the first tile to be requested in the horizontal or vertical direction in the tile matrix is ​​2, and also as the second tile to be requested, and so on, until the calculated distance between the tile to be requested and the center point is greater than the cluster radius, that is, the distance between any tile to be requested and the center point is not greater than the cluster radius; the first tile to be requested and each of the second tiles to be requested are used as the tiles to be requested to obtain the coordinates of each tile to be requested.

[0083] The distance between the tile to be requested and the center point is actually the shortest distance between the spatial range corresponding to the tile to be requested and the center point.

[0084] Based on this, this embodiment actually calculates all vector tiles within the spatial range corresponding to the cluster data group as the tiles to be requested.

[0085] S230, obtaining the spatial range corresponding to each of the tiles to be requested, and determining whether the spatial range of each of the tiles to be requested intersects with the spatial range of the corresponding cluster data group; if so, using the tiles to be requested as the request tiles.

[0086] Specifically, in order to reduce the amount of requested data and improve the generation rate and transmission rate of the benchmark data, in this embodiment, the spatial range of each tile to be requested is compared with the spatial range of the corresponding cluster data group. When the coordinate range of the tile to be requested and the actual geographical area range of the cluster data group are at least partially overlapped, that is, when the tile to be requested intersects with the cluster data group, the tile to be requested is used as the requested tile; otherwise, the tile to be requested is discarded.

[0087] At this time, for a single cluster data group, the spatial range represented by it is the same as the sum of the spatial ranges represented by all the corresponding request tiles.

[0088] S240 : Send a reference data request to a server based on the coordinates of each requested tile to obtain the reference data.

[0089] Specifically, based on the above steps S220-S230, the request tiles corresponding to all the cluster data groups are obtained. At this time, the sum of the spatial ranges represented by all the request tiles is the same as the sum of the spatial ranges represented by all the cluster data groups.

[0090] Based on this, this embodiment generates the benchmark data request in each of the cluster data groups to request the benchmark data from the server 12, thereby ensuring the timeliness of the benchmark data. At the same time, by judging whether the spatial range of each requested tile intersects with the spatial range of the corresponding cluster data group, the non-intersecting tiles to be requested are eliminated, thereby reducing the amount of requested data and improving the generation rate and transmission rate of the benchmark data.

[0091] S300 , performing spatial logic analysis based on the updated vector data in combination with the reference data to perform a logic check on the updated vector data.

[0092] The spatial logic analysis involves extracting geographic features from the updated vector data and the baseline data to determine whether there are any inconsistencies between the geographic features, thereby performing a logical check on the updated vector data. For example, if a geographic feature representing a road passes through a geographic feature representing a green space or residential area, then a conflict exists between the geographic features.

[0093] Specifically, the required geographic features are extracted by writing a driver language or software and checked based on preset spatial logic requirements. For example, the software can be ArcGis or SuperMap, which is installed on a third-party SDK. The driver language is written by writing SQL driver statements to read the data.

[0094] Preferably, this embodiment realizes the extraction and inspection of various geographic elements by writing an SQL driver language. Since the SQL driver language consumes fewer computing resources when running, the extraction and inspection of various geographic elements based on the SQL driver language can reduce the processing time and program complexity of vector data quality inspection, and improve the quality inspection efficiency of the updated vector data.

[0095] It should be noted that, since the corresponding benchmark data is obtained based on the spatial range of the updated vector data in this embodiment, the data volume of the benchmark data is relatively small. Therefore, the extraction and inspection of various geographic elements can be achieved by writing a programming language without the need to use a third-party SDK-based software.

[0096] Specifically, if Figure 7 As shown, the logic checking process of updating vector data includes:

[0097] S310: Convert the updated vector data and the reference data into a database format.

[0098] Specifically, the updated vector data and the reference data are converted into a set of geographical elements stored in a lightweight spatial database, so that subsequent program languages ​​can extract the geographical elements for inspection.

[0099] Exemplarily, both the updated vector data and the reference data are converted into a spatialite database format.

[0100] It should be noted that, since the reference data in this embodiment has a small amount of data, it can be quickly and directly converted into a database format without occupying too many resources, so that the efficiency of the logic checking process is higher.

[0101] S320, based on the updated vector data and the reference data in the database format, extract the geographic elements of the updated vector data and the reference data respectively through the driver corresponding to the database format, and determine whether there is any contradiction between the geographic elements of the two; if not, the updated vector data logic check passes.

[0102] As mentioned above, since the amount of the benchmark data is relatively small, the extraction and inspection of various geographic elements can be achieved through a simple programming language without the need for complex software programs.

[0103] Exemplarily, when both the updated vector data and the reference data are converted into the spatialite database format, the geographic elements of the updated vector data and the reference data are obtained by writing an SQL driver language query, and it is determined whether there are any contradictions between the geographic elements to check whether the updated vector data conforms to spatial logic.

[0104] It should be noted that those skilled in the art can set spatial logic requirements according to actual needs and write corresponding SQL queries to obtain the required geographical features for logical checks. The spatial logic requirements refer to the requirements that the geographical features must meet in order for the updated vector data to pass the logical check.

[0105] In order to facilitate those skilled in the art to understand this solution, the following exemplifies the process of performing a logical check on the updated vector data. For example, if the spatial logic requirement is: roads cannot pass through green areas or residential areas, then the updated vector data and the road lines, green areas and residential areas of the baseline data are obtained respectively to determine whether the road lines of the updated vector data overlap with the green areas or residential areas of the baseline data, and whether the green areas and residential areas of the updated vector data overlap with the road lines of the baseline data. If there is no overlap, the spatial logic requirement is met and the updated vector data logic check passes. The tolerance for determining whether there is an overlap is set to 0.1m, that is, if the overlapping length between the road line and the green area or residential area is greater than 0.1m, then the road line overlaps with the green area or residential area.

[0106] It is worth noting that, during the actual logical check of the updated vector data, multiple spatial logical requirements may be set. When the updated vector data meets all of the spatial logical requirements, the updated vector data logical check passes. For example, as shown in Table 2, for the updated vector data, the spatial logical requirements include: roads crossing green spaces or residential areas, water systems cannot overlap green spaces or residential areas, railway lines cannot cross residential areas, road top lines do not overlap with roads, multiple points cannot overlap, multiple lines cannot overlap, different surfaces cannot overlap, attribute logic of tunnel DISPCLASID and CLASID in roads, attribute logic of primary / secondary roads DISPCLASID, CLASID, and ROUTENUM in roads, attribute logic of bridge / tunnel FORM in roads, attribute logic of road top lines associated with roads, and attribute logic of water systems associated with water system annotation lines.

[0107] Table 2 Quality inspection requirements reference table.

[0108]

[0109] Based on this, this embodiment ensures the currency of the benchmark data by generating a corresponding benchmark data request based on the updated vector data on the vector data update client 11, thereby requesting the benchmark data from the server 12. This effectively improves the accuracy of the logical check results of the updated vector data. At the same time, the vector data update client 11 performs a logical check on the updated vector data based on the received benchmark data. Because the amount of benchmark data is relatively small, this embodiment can implement the logical check process by writing a programming language, without the need for third-party SDK-based software. This effectively reduces the processing time and program complexity of vector data quality inspection, and improves the efficiency of quality inspection of the updated vector data.

[0110] Furthermore, since the logic check process of updating the vector data is executed by the vector data update client 11, there is no need for the server 12 to perform logic check, thereby avoiding the computing resources of the server 12 from being occupied, improving the efficiency of the vector data logic check, and meeting the real-time logic check requirements of multiple users and multiple times of vector data, thereby achieving better vector data quality inspection effects.

[0111] On the other hand, this embodiment also provides a vector data quality inspection method, such as Figure 8 Shown, including:

[0112] S10: Perform a data integrity check based on the updated vector data to determine whether the updated vector data meets integrity requirements.

[0113] The integrity requirement is a preset quality inspection requirement.

[0114] Specifically, the integrity information of the updated vector data is obtained, for example, by writing JavaScript code to obtain the integrity information of the updated vector data, thereby checking the integrity of the updated vector data to determine whether it meets the integrity requirements. It should be noted that those skilled in the art will be aware of the specific method for obtaining the integrity information, and this embodiment will not be explained in detail here.

[0115] Exemplarily, as shown in Table 2, the integrity information includes data organization directory and file naming specifications, data format, file size excess, redundant layers, and layer omissions. Among them, for the data organization directory and file naming, the corresponding directory and file standardization requirements are set based on the preset integrity requirements to determine whether the data organization directory and file naming of the updated vector data comply with the directory and file standardization requirements; for the data format, the corresponding data validity requirements are set based on the preset integrity requirements to determine whether the data format of the updated vector data complies with the data validity requirements; for the file size excess, the corresponding data size requirements are set based on the preset integrity requirements to determine whether the file size excess of the updated vector data complies with the data size requirements; for the redundant layers and the layer omissions, the corresponding layer integrity requirements are set based on the preset integrity requirements to determine whether the redundant layers and the layer omissions of the updated vector data comply with the layer integrity requirements. Specifically, those skilled in the art should be aware of the specific steps for checking the integrity information of the updated vector data, which will not be explained in detail in this embodiment.

[0116] Furthermore, when each piece of integrity information of the updated vector data meets the corresponding integrity requirement, the updated vector data passes the data integrity check.

[0117] S20: If yes, perform layer information check based on the updated vector data to determine whether the updated vector data meets the spatial coordinate requirements.

[0118] Similar to the data integrity check, the spatial coordinate requirement is a preset quality inspection requirement.

[0119] Specifically, layer information of the updated vector data is obtained, for example, by writing GDAL's WASM cross-platform code to obtain the layer information of the updated vector data to determine whether it meets the spatial coordinate requirements. It should be noted that those skilled in the art should be aware of the specific method for obtaining the layer information, and this embodiment will not be specifically explained here.

[0120] Exemplarily, as shown in Table 2, the layer information includes a coordinate system, a geometry type definition, vector data attributes, an attribute code, and an attribute item definition. Specifically, for the coordinate system, a corresponding geodetic datum is set based on the preset spatial coordinate requirement to determine whether the coordinate system of the updated vector data conforms to the geodetic datum; for the geometry type definition, a corresponding geometry type requirement is set based on the preset spatial coordinate requirement to determine whether the geometry type definition of the updated vector data conforms to the geometry type requirement; for the vector data attributes, the attribute code, and the attribute item definition, corresponding attribute requirements are set based on the preset spatial coordinate requirement to determine whether the vector data attributes, attribute code, and attribute item definition of the updated vector data conform to the attribute requirement. Specifically, those skilled in the art should be aware of the specific steps for checking the information of each layer of the updated vector data, which will not be specifically explained in this embodiment.

[0121] Furthermore, when each layer information of the updated vector data meets the corresponding spatial coordinate requirement, the updated vector data passes the layer information check.

[0122] S30: If yes, perform a layer field check based on the updated vector data to determine whether the updated vector data meets the attribute requirements.

[0123] Similar to the data integrity check, the attribute requirement is a preset quality inspection requirement.

[0124] Specifically, field information of the updated vector data is obtained, for example, by writing an SQL query to obtain layer field information of the updated vector data to determine whether it meets the attribute requirements. It should be noted that those skilled in the art should be aware of the specific method for obtaining layer field information, and this embodiment will not be specifically explained here.

[0125] Exemplarily, as shown in Table 2, the field information includes a value range, repeated values, non-empty values, half-width character fields, full-width character fields, format information, image attribute consistency, and sensitive words. Among them, for the value range, repeated values, non-empty values, half-width character fields, full-width character fields, format information, and image attribute consistency, corresponding attribute correctness requirements are set based on the preset attribute requirements to determine whether the value range, repeated values, non-empty values, half-width character fields, full-width character fields, format information, and image attribute consistency of the updated vector data meet the attribute correctness requirements; for the sensitive words, corresponding field compliance requirements are set based on the preset attribute requirements to extract all the text of the updated vector data to determine whether it meets the field compliance requirements. Specifically, those skilled in the art should be aware of the specific steps for checking the field information of the updated vector data, and this embodiment will not be specifically explained here.

[0126] Furthermore, when each field information of the updated vector data meets the corresponding attribute requirements, the updated vector data passes the layer field check.

[0127] S40: If yes, perform a layer topology check based on the updated vector data to determine whether the updated vector data meets the geometric expression requirements.

[0128] Similar to the data integrity check, the attribute requirement is a preset geometric expression requirement.

[0129] Specifically, the layer topology information of the updated vector data is obtained, for example, by writing an SQL query to obtain the layer topology information of the updated vector data to determine whether it meets the geometric expression requirements. It should be noted that those skilled in the art should be aware of the specific method for obtaining the layer topology information, and this embodiment will not be specifically explained here.

[0130] For example, as shown in Table 2, the layer topology information includes geometry type, node count exceeded, small facets, small line fragments, line self-intersections, and surface self-intersections. The geometry type, node count exceeded, small facets, small line fragments, line self-intersections, and surface self-intersections of the updated vector data are obtained to determine whether they meet the geometric expression requirements. Specifically, those skilled in the art will be aware of the specific steps for checking the topology information of each layer of the updated vector data, and this embodiment will not provide a detailed explanation here.

[0131] Furthermore, when each layer topology information of the updated vector data meets the geometric expression requirement, the updated vector data passes the layer topology check.

[0132] S50: If yes, perform a logic check based on the updated vector data to determine whether the updated vector data conforms to spatial logic.

[0133] The method for implementing the logic check of the updated vector data can be found in the above content, which will not be elaborated in this embodiment.

[0134] Based on this, the vector data quality inspection method provided in this embodiment executes each step through the client, thereby avoiding occupying a large amount of computing resources on the server, thereby improving the quality inspection efficiency of the updated vector data and achieving good vector data quality inspection results.

[0135] It should be noted that the above steps of quality inspection of the updated vector data are numbered only to facilitate technical personnel in this field to understand the vector data quality inspection method described in this embodiment, and do not limit the actual execution order of the vector data quality inspection method. That is, in actual applications, the above-mentioned integrity check, layer information check, layer field check, layer topology check and logic check can also be performed on the updated vector data in other orders. This embodiment does not make specific restrictions here.

[0136] like Figure 9 As shown, a vector data logic checking device 60 provided in this embodiment includes a spatial range acquisition module 61 , a reference data request module 62 and a spatial logic analysis module 63 .

[0137] The spatial range acquisition module 61 is configured to perform spatial analysis on the updated vector data in response to input of the updated vector data to be detected, and acquire the spatial range of the updated vector data;

[0138] The benchmark data request module 62 is configured to send a benchmark data request to a server based on the spatial range of the updated vector data to obtain the benchmark data; the benchmark data and the updated vector data represent the same spatial range;

[0139] The spatial logic analysis module 63 is configured to perform spatial logic analysis based on the updated vector data in combination with the reference data, so as to perform a logic check on the updated vector data.

[0140] Based on the same technical concept, the vector data logic inspection method or vector data quality inspection method provided in the embodiment of the present invention can be implemented on the terminal side.

[0141] like Figure 10FIG2 shows an optional hardware structure diagram of a terminal provided in an embodiment of the present invention. Terminal 70 can be a mobile phone, a computer, a tablet device, a personal digital assistant, a factory backend processing device, or the like. Terminal 70 includes at least one processor 71, a memory 72, at least one network interface 74, and a user interface 73. The various components in the device are coupled together via a bus system 75. It will be appreciated that bus system 75 is used to enable communication between these components. In addition to a data bus, bus system 75 also includes a power bus, a control bus, and a status signal bus.

[0142] The user interface 73 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0143] It will be appreciated that the memory 72 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be a read-only memory (ROM) or a programmable read-only memory (PROM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memory featured in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0144] The memory 72 in the embodiment of the present invention is used to store various categories of data to support the operation of the terminal. Examples of these data include: any executable program for operating on the terminal 70, such as an operating system 721 and an application 722; the operating system 721 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 722 can include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., for implementing various application services. The vector data logic inspection method or the vector data quality inspection method provided by the embodiment of the present invention can be included in the application 722.

[0145] The method disclosed in the above embodiment of the present invention can be applied to the processor 71 or implemented by the processor 71. The processor 71 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the hardware integrated logic circuit in the processor 71 or by instructions in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 71 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The processor 71 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory. The processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0146] In an exemplary embodiment, the terminal 70 may be configured to execute the aforementioned method by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs).

[0147] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program is called by a processor, the vector data logic inspection method or the vector data quality inspection method provided by the present invention is implemented.

[0148] Among them, a computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. The computer-readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, and a mechanical encoding device.

[0149] The computer-readable program characterized herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0150] To sum up, the present application calculates each of the requested tiles through the vector data update client 11 to request the benchmark data from the server 12, and after receiving the benchmark data, performs a logical check on the updated vector data to ensure the timeliness of the benchmark data, thereby improving the accuracy of the logical check results of the updated vector data; at the same time, since the logical check process of the updated vector data is executed by the vector data update client 11, the computing resource usage of the server 12 is effectively reduced, thereby avoiding response delays of the server 12, and improving the quality inspection efficiency of the updated vector data, which has high industrial application value.

[0151] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0152] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A vector data logic checking method, applied to a vector data update client, comprising: In response to input of update vector data to be detected, extracting a spatial range of the update vector data; Extracting the spatial range of the updated vector data includes: performing spatial clustering on the updated vector data to obtain at least one cluster data group; obtaining the center point coordinates and cluster radius of each cluster data group to obtain a subspace range corresponding to each cluster data group; and integrating the subspace ranges corresponding to each cluster data group to determine the spatial range of the updated vector data; Based on the spatial range of the updated vector data, a benchmark data request is sent to the server to obtain benchmark data of the same spatial range; the benchmark data is historical vector tile data; based on the spatial range of the updated vector data, a benchmark data request is sent to the server to obtain the benchmark data, including: obtaining a vector tile level based on a preset quality inspection accuracy; determining a search range based on the spatial range of the updated vector data; based on the search range, among each tile corresponding to the vector tile level, calculating a tile that intersects with the search range space as a tile to be requested; obtaining the spatial range corresponding to each tile to be requested, and determining whether the spatial range of each tile to be requested intersects with the spatial range corresponding to the cluster data group; if so, using the tile to be requested as the request tile; based on the coordinates of each request tile, a benchmark data request is sent to the server to obtain the benchmark data; Based on the updated vector data, spatial logic analysis is performed in combination with the reference data to perform a logic check on the updated vector data.

2. The method according to claim 1, characterized in that The performing spatial logic analysis based on the updated vector data and in combination with the reference data to perform logic checking on the updated vector data includes: Converting the update vector data and the reference data into a database format; Based on the updated vector data and the reference data in the database format, the geographic elements of the updated vector data and the reference data are respectively extracted through a driver corresponding to the database format, and it is determined whether there is any contradiction between the geographic elements of the two; if not, the updated vector data logic check passes.

3. The method according to claim 1, characterized in that The performing spatial clustering on the updated vector data to obtain at least one cluster data group includes: Based on the position information of the updated vector data, acquiring each feature point of the updated vector data; The data is divided based on the distance between each of the feature points to obtain at least one cluster data group.

4. A vector data quality inspection method, comprising: Performing a data integrity check based on the updated vector data to determine whether the updated vector data meets integrity requirements; If so, performing a layer information check based on the updated vector data to determine whether the updated vector data meets the spatial coordinate requirements; If yes, perform a layer field check based on the updated vector data to determine whether the updated vector data meets the attribute requirements; If so, performing a layer topology check based on the updated vector data to determine whether the updated vector data meets the geometric expression requirements; If so, performing a logic check based on the updated vector data to determine whether the updated vector data conforms to spatial logic; Wherein, the method for implementing the logic check of the updated vector data is the vector data logic check method according to any one of claims 1-3.

5. A vector data logic checking system, characterized in that: include: An updated vector data client, configured to execute the vector data logic checking method according to any one of claims 1 to 3 in response to input of updated vector data; The server is connected to the client for communication, and is used to receive the benchmark data request from the client, generate the corresponding benchmark data based on the benchmark data request, and send the generated benchmark data to the client.

6. A vector data logic checking device, characterized in that: It includes a spatial range acquisition module, a benchmark data request module, and a spatial logic analysis module; The spatial range acquisition module is configured to extract the spatial range of the updated vector data in response to input of the updated vector data to be detected; the extracting the spatial range of the updated vector data includes: spatially clustering the updated vector data to obtain at least one cluster data group; obtaining the center point coordinates and cluster radius of each cluster data group to obtain a subspace range corresponding to each cluster data group; and integrating the subspace ranges corresponding to each cluster data group to determine the spatial range of the updated vector data; The benchmark data request module is used to send a benchmark data request to the server based on the spatial range of the updated vector data to obtain benchmark data of the same spatial range; the benchmark data is historical vector tile data; the benchmark data request is sent to the server based on the spatial range of the updated vector data to obtain the benchmark data, including: obtaining the vector tile level based on a preset quality inspection accuracy; determining the search range based on the spatial range of the updated vector data; based on the search range, calculating the tiles that intersect with the search range space among the tiles corresponding to the vector tile level as tiles to be requested; obtaining the spatial range corresponding to each tile to be requested, and determining whether the spatial range of each tile to be requested intersects with the spatial range corresponding to the cluster data group; if so, using the tile to be requested as the request tile; sending a benchmark data request to the server based on the coordinates of each request tile to obtain the benchmark data; The spatial logic analysis module is configured to perform spatial logic analysis based on the updated vector data in combination with the reference data, so as to perform a logic check on the updated vector data.

7. A terminal, characterized in that: include: a processor and a memory, wherein the memory is communicatively connected to the processor; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the vector data logic inspection method according to any one of claims 1 to 3 or the vector data quality inspection method according to claim 4.

8. A computer storage medium storing a computer program, wherein: When the computer program is executed by a processor, the vector data logic inspection method according to any one of claims 1 to 3 or the vector data quality inspection method according to claim 4 is implemented.

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