Vector data logic inspection and quality inspection method, system and device, terminal and medium

Abstract

The invention provides a logic inspection and quality inspection method, system and device of vector data, a terminal and a medium, the logic inspection method of the vector data is applied to a vector data updating client and comprises the following steps: in response to input of to-be-detected updating vector data, extracting a space range of the updating vector data; based on the spatial range of the updated vector data, sending a reference data request to a server to obtain reference data in the same spatial range; the reference data is historical vector tile data; and based on the updated vector data, combining the reference data to carry out spatial logic analysis so as to carry out logic check on the updated vector data. The reference data is requested from the server through the vector data updating client, so that the current situation of the reference data is ensured, and the quality inspection accuracy of the vector data is effectively improved.

Description

Technical Field

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

[0002] Vector data is a digital expression form used to describe the position, shape and attribute characteristics of geographical space elements. Due to urban development and geographical environment changes, vector data often needs to be updated. To ensure correctness, the updated vector data needs to be quality inspected, specifically including performing spatial logical analysis on the vector data to be detected in combination with the existing reference data to detect whether there are spatial logical contradictions, that is, to detect whether there are contradictions between each geographical element in the updated vector data and each geographical element in the existing reference data; for example, when a road overlaps with a residential area, there is a spatial contradiction between the two.

[0003] Currently, the vector data logical inspection method usually pre-stores the existing reference data offline in the client device to perform logical verification on the vector data to be detected based on this reference data. However, since the reference data needs to be updated regularly to maintain its currency, and the offline storage method is difficult to achieve real-time synchronization of data, resulting in differences between the reference data used for logical inspection and the actual reference data, which in turn leads to inaccurate inspection results of vector data logical inspection. At the same time, limited by the storage capacity and computing power of the device, the amount of offline stored reference data is limited, which is difficult to support the quality inspection requirements of vector data in a large range and cannot meet the existing vector data quality inspection requirements. Summary of the Invention

[0004] The purpose of this application is to provide a logical inspection, quality inspection method, system, device, terminal and medium for vector data, which is used to solve the problem in the prior art that when the updated vector data is combined with the reference data stored offline in the client device for logical inspection, due to the difficulty of ensuring the currency of the offline stored reference data, the quality inspection results may be inaccurate and it is difficult to support the quality inspection requirements of vector data in a large range.

[0005] In a first aspect, this application provides a vector data logical inspection method, which is applied to a vector data update client and includes:

[0006] In response to the input of the updated vector data to be detected, extract the spatial range of the updated vector data; based on the spatial range of the updated vector data, send a reference data request to the server to obtain the reference data of the same spatial range; the reference data is historical vector tile data; based on the updated vector data, perform spatial logical analysis in combination with the reference data to perform logical inspection on the updated vector data.

[0007] In an embodiment of the present application, the method for 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 the clustering radius of each cluster data group to determine the spatial range of the updated vector data.

[0008] In an embodiment of the present application, the method for sending a reference data request to a server based on the spatial range of the updated vector data to obtain the reference data includes: obtaining the vector tile level based on a preset quality inspection accuracy; determining a search range based on the spatial range of the updated vector data; calculating, in each tile corresponding to the vector tile level based on the search range, tiles that spatially intersect with the search range 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 of the corresponding cluster data group; if so, using the tile to be requested as the requested tile; and sending a reference data request to the server based on the coordinates of each requested tile to obtain the reference data.

[0009] In an embodiment of the present application, the method for performing spatial logic analysis based on the updated vector data in combination with the reference data to perform logic check on the updated vector data includes: converting both the updated vector data and the reference data into a database format; based on the updated vector data and the reference data in the database format, respectively extracting the geographic features of the updated vector data and the reference data through a driver corresponding to the database format, and determining whether there are contradictions between the geographic features of the two; if not, the logic check of the updated vector data passes.

[0010] In an embodiment of the present application, the method for performing spatial clustering on 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 based on the distances between the feature points to obtain at least one cluster data group.

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

[0012] Third aspect, the present application provides a vector data logic check system, including: an updated vector data client, configured to execute the vector data logic check method as described above in response to the input of updated vector data; a server, communicatively connected to the client, configured to receive the benchmark data request from the client and generate the corresponding benchmark data based on the benchmark data request and send it to the client.

[0013] Fourth aspect, the present application provides a vector data logic check device, including 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 the input of the updated vector data to be detected; the benchmark data request module is configured to send a benchmark data request to the server based on the spatial range of the updated vector data to obtain the benchmark data of the same spatial range; the benchmark data is historical vector tile data; the spatial logic analysis module is configured to perform spatial logic analysis based on the updated vector data in combination with the benchmark data to perform logic check on the updated vector data.

[0014] Fifth aspect, the present application provides a terminal, including: a processor and a memory, communicatively connected between the memory and the processor;

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

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

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

[0018] Figure 1 It shows a schematic structural diagram of a vector data logical checking system according to an embodiment of the present application.

[0019] Figure 2 It shows a schematic flow diagram of a vector data logical checking method according to an embodiment of the present application.

[0020] Figure 3 It shows a schematic flow diagram of a method for obtaining the spatial range of updated vector data according to an embodiment of the present application.

[0021] Figure 4 It shows a schematic diagram of each cluster data group after spatial clustering of updated vector data according to an embodiment of the present application

[0022] Figure 5 It shows a schematic flow diagram of a method for obtaining a cluster data group according to an embodiment of the present application.

[0023] Figure 6 It shows a schematic flow diagram of a method for obtaining reference data according to an embodiment of the present application.

[0024] Figure 7 It shows a schematic flow diagram of a logical checking process of updated vector data according to an embodiment of the present application.

[0025] Figure 8 It shows a schematic flow diagram of a vector data quality inspection method according to an embodiment of the present application.

[0026] Figure 9 It shows a schematic structural diagram of a vector data logical checking device according to an embodiment of the present application.

[0027] Figure 10 It shows a schematic structural diagram of a terminal according to an embodiment of the present application.

[0028] DESCRIPTION OF THE 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 program; 73: User interface; 74: Network interface; 75: Bus system. Detailed implementation manners

[0030] The following uses specific specific examples to illustrate the implementation manners of the present application. 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 implementation manners. Various 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, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0031] It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner. Therefore, only the components related to the present application are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, quantities, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0032] In traditional vector data logic checking methods, reference data is usually pre-stored offline in a client device to perform logical verification on updated vector data within the client device. Since it is difficult to ensure the currency of the offline-stored reference data and the amount of reference data that can be saved by the client is limited, the quality inspection results of this vector data logic checking method are prone to inaccuracy and it is difficult to support the quality inspection requirements of large-scale vector data.

[0033] Among them, the reference data is historical vector tile data; vector tile data refers to the respective divided spatial data obtained after dividing vector data based on the set spatial grid size.

[0034] In view of 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. By requesting the server to update the reference data corresponding to the vector data, the reference data is obtained, and the logical inspection of the updated vector data is implemented in the vector data update client. Among them, the server obtains the latest reference data in real time through the network, which can effectively ensure the currency of the reference data, and then ensure the accuracy of the logical inspection result of the updated vector data. At the same time, the obtained reference data is not limited by the storage capacity of the device, avoiding occupying a large amount of computing resources of the server, and then realizing efficient and accurate logical inspection of vector data to meet the real-time logical inspection requirements of large-scale vector data, achieving a good quality inspection effect of vector data.

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

[0036] As Figure 1 shown, it is a schematic diagram of the application scenario of the logical inspection method provided by this embodiment. Specifically, the logical inspection method is applicable to a vector data logical inspection system 10, including a vector data update client 11 and a server 12 that are communicatively connected.

[0037] Among them, the vector data update client 11 is used to execute the vector data logical inspection 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 the present application does not make specific limitations here.

[0038] The server 12 is communicatively connected to the vector data update client 11, and is used to receive the request of the vector data update client 11, generate and send the corresponding reference data to the vector data update client 11 based on this request, so that the vector data update client 11 can receive the reference data. The vector data update client 11 uses the reference data as a reference benchmark for the spatial logic of the updated vector data, and through joint spatial analysis of the reference data and the updated vector data, detects whether the spatial logic of the updated vector data is accurate, so as to realize the logical inspection 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 in a terminal device, and the present application does not make specific limitations here.

[0040] Based on this, the vector data logic check system 10 provided in this embodiment requests the reference data from the server 12 through the vector data update client 11 to perform the logic check of the updated vector data inside the vector data update client 11, so as to ensure the currency of the reference data and avoid resource congestion caused by the server 12 performing the logic check, thereby achieving efficient and accurate vector data logic check.

[0041] To solve the technical problems existing in the prior art, this embodiment also provides a vector data logic check method for implementing the logic check of the updated vector data.

[0042] Next, it will be described in detail in conjunction with the accompanying drawings in the embodiments of the present application.

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

[0044] S100, in response to the input of the updated vector data to be detected, extract the spatial range of the updated vector data.

[0045] The spatial range of the updated vector data refers to the geographical area covered by the updated vector data in the actual geographical space. That is, obtain the geographical area covered by the updated vector data in the actual geographical space as the spatial range of the updated vector data. This is because for the logic check of the updated vector data, it is necessary to obtain the reference data representing the same geographical area as the reference benchmark in order to analyze the spatial logic of the updated vector data. Based on this, this embodiment obtains the spatial range of the updated vector data to facilitate subsequent requests for the reference data representing the corresponding spatial range.

[0046] Further, in order to improve the accuracy of the obtained spatial range and reduce the data calculation amount, the obtaining method of the spatial range includes: performing spatial analysis on the updated vector data to obtain the spatial range of the updated vector data based on the spatial distribution of each element.

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

[0048] S110, perform spatial clustering on the updated vector data to obtain at least one clustering data group.

[0049] Among them, each clustering data group is a set of elements with similar spatial distribution characteristics in the updated vector data. The spatial distribution characteristics are used to characterize the spatial position distribution of each element in the updated vector data in the geographical space.

[0050] As Figure 4 shown, an example is given of obtaining a first cluster data group, a second cluster data group, and a third cluster data group after updating vector data for spatial clustering analysis. Among them, 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 positions of the elements within the first cluster data group, the second cluster data group, and the third cluster data group are similar in the geographical space.

[0051] S120. Obtain the center point coordinates and cluster radii of each of the cluster data groups 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 geographical space. Exemplarily, the longitude and latitude of the center point in the actual geographical space are used as the center point coordinates, or if the cluster data group is projected onto the actual geographical space through the WEB Mercator projection method, 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 geographical space.

[0054] Based on the center point coordinates and cluster radii of the cluster data group, obtain the subspace range corresponding to the cluster data group; synthesize the subspace ranges corresponding to each cluster data group, and use the synthesized total spatial range as the spatial range of the updated vector data.

[0055] In an optional embodiment, to quickly obtain the cluster data group corresponding to the updated vector data, when specifically executing step S110, as Figure 5 shown, it includes:

[0056] S111. Based on the position information of the updated vector data, obtain each feature point of the updated vector data.

[0057] Among them, each of the feature points is used to represent the spatial position of each feature element that makes up the updated vector data.

[0058] Specifically, convert each element in the updated vector data into point data, and use each of the point data as each feature point of the updated vector data.

[0059] Exemplarily, convert the format of the updated vector data into spatialite data, and obtain geometric nodes through SQL (Structured Query Language) query to extract each of the geometric nodes as each of the feature points.

[0060] S112, divide based on the distances of each of the feature points to obtain at least one of the clustering data groups.

[0061] Specifically, based on the spatial positions of each of the feature points, obtain the spatial distances between each of the feature points, and divide the feature points with spatial distances not exceeding the distance threshold into the same clustering data group. Wherein, the distance threshold is a preset distance value. Exemplarily, the distance threshold is 1.5 km.

[0062] Optionally, perform clustering calculation on each of the feature points through a spatial clustering algorithm to achieve the division of each of the feature points based on spatial position, so as to obtain each of the clustering data groups. Exemplarily, the spatial clustering algorithm is the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise), and the clustering distance is 1.5 km to calculate each of the clustering data groups.

[0063] Based on this, obtain each of the clustering data groups as each set of elements with similar spatial distribution characteristics in the updated vector data.

[0064] Dividing the updated vector data into each of the clustering data groups through spatial clustering analysis to cluster elements with the same spatial distribution into the same cluster for processing is beneficial to improving the accuracy of the obtained spatial range, avoiding redundancy of the obtained reference data, thereby reducing the data calculation amount, improving the quality inspection efficiency, and thus achieving a better quality inspection effect.

[0065] S200, based on the spatial range of the updated vector data, send a reference data request to the server to obtain reference data for the same spatial range.

[0066] Wherein, the reference data is historical vector tile data; the reference data and each of the updated vector data are in the same spatial range, that is, the reference data and each of the 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 through the network to ensure the currency of the reference data, and further ensure the correctness of the logical check of the updated vector data, so as to improve the accuracy of quality inspection. At the same time, the server 12 generates corresponding reference data based on the request of the client 11 for vector data update, without the need for the server 12 to calculate the corresponding reference data based on the updated vector data, thereby reducing the calculation amount of the server 12, avoiding occupying and consuming the resources of the server 12, and further improving the quality inspection efficiency.

[0068] In some alternative embodiments, such as Figure 6 shown, the method for obtaining the reference data includes:

[0069] S210, obtaining the vector tile level based on a preset quality inspection accuracy.

[0070] Wherein, 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 the vector data is associated with the vector tile level, and the requested reference data needs to have the same vector tile level as the updated vector data, therefore, in this embodiment, the vector tile level is obtained based on the quality inspection accuracy, so as to facilitate subsequent requests to the server 12 for the reference data of the corresponding level.

[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 association relationship between the quality inspection accuracy of the vector data and the vector tile level is:

[0074]

[0075] Wherein, represents the quality inspection accuracy of the vector data, represents the vector tile level, represents the grid size of the vector tile, exemplarily, it is 4096, represents an equatorial length constant, which is used to characterize the physical size of the equator on the map. Exemplarily, for the WEB Mercator projection method, the equatorial length constant , that is, the vector data is projected onto a square range where the x-axis ranges from -20037508.342789244 to 20037508.342789244, and the y-axis ranges from -20037508.342789244 to 20037508.342789244.

[0076] Based on this association relationship, the vector tile level can be obtained from 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 shown. Specifically, based on the quality inspection accuracy, the corresponding vector tile level is obtained from this comparison table.

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

[0079]

[0080] S220. Based on the spatial range of the updated vector data, determine the search range; based on this search range, among the tiles corresponding to the vector tile level, calculate the tiles that spatially intersect with the search range as the tiles to be requested.

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

[0082] Exemplarily, calculate the vector tile matrix coordinates corresponding to the center point coordinates of the clustering data group as the coordinates of the first tile to be requested. Among them, the vector tile matrix coordinates are the tile matrix coordinates corresponding to this vector tile within the tile matrix at the vector tile level. Use the first tile to be requested as the reference tile, calculate the coordinates of each vector tile whose coordinate difference from the first tile to be requested in the horizontal or vertical direction in the tile matrix is 1 as the second tile to be requested; change the coordinate difference to 2, repeat the above steps, calculate the coordinates of each vector tile whose coordinate difference from the first tile to be requested in the horizontal or vertical direction in the tile matrix is 2, also as the second tile to be requested, and so on, until the distance between the calculated tile to be requested and the center point is greater than the clustering radius, that is, the distance between any tile to be requested and the center point is not greater than the clustering radius; use the first tile to be requested and each of the second tiles to be requested as the tiles to be requested to obtain the coordinates of each tile to be requested.

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

[0084] Based on this, in this embodiment, all the vector tiles within the spatial range corresponding to the clustering data group are actually calculated as each of the to-be-requested tiles.

[0085] S230. Obtain the spatial range corresponding to each of the to-be-requested tiles, and determine whether the spatial range of each of the to-be-requested tiles intersects with the spatial range of the corresponding clustering data group; if so, use the to-be-requested tile as the requested tile.

[0086] Specifically, in order to reduce the amount of data requested and improve the generation rate and transmission rate of the reference data, in this embodiment, based on the spatial range of each of the to-be-requested tiles, it is compared with the spatial range of the corresponding clustering data group. When there is at least partial overlap between the coordinate range of the to-be-requested tile and the actual geographical area range of the clustering data group, that is, when the to-be-requested tile intersects with the clustering data group, the to-be-requested tile is used as the requested tile; otherwise, the to-be-requested tile is excluded.

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

[0088] S240. Based on the coordinates of each of the requested tiles, send a reference data request to the server to obtain the reference data.

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

[0090] Based on this, in this embodiment, by generating the reference data request in each of the clustering data groups to request the reference data from the server 12, the currency of the reference data is ensured. At the same time, by determining whether the spatial range of each of the to-be-requested tiles intersects with the spatial range of the corresponding clustering data group, each of the non-intersecting to-be-requested tiles is excluded, thereby reducing the amount of data requested and improving the generation rate and transmission rate of the reference data.

[0091] S300. Based on the updated vector data, perform spatial logic analysis in combination with the reference data to perform logical checks on the updated vector data.

[0092] Among them, the spatial logic analysis refers to extracting each geographical feature in the updated vector data and the reference data to determine whether there are contradictions between the geographical features, so as to realize the logical check of the updated vector data. Exemplarily, if the geographical feature element representing a road passes through the geographical feature element representing a green space or a residential area, there is a contradiction between these geographical feature elements.

[0093] Specifically, the required geographical features are extracted by writing a driver program language or software and checked based on preset spatial logic requirements. Exemplarily, the software can be software such as ArcGis or SuperMap carried by a third-party SDK; the driver program language is written as an SQL driver program statement to read the data.

[0094] Preferably, in this embodiment, the SQL driver program language is written to extract and check each geographical feature. Since the SQL driver program language consumes less computing resources, extracting and checking each geographical feature based on the SQL driver program language can reduce the processing time and program complexity of the vector data quality inspection, and improve the quality inspection efficiency of the updated vector data.

[0095] It should be noted that since the corresponding reference data is obtained based on the spatial range of the updated vector data in this embodiment, and the data volume of the reference data is small, the extraction and inspection of each geographical feature can be realized by writing a program language, without using software carried by a third-party SDK.

[0096] Specifically, as Figure 7 shown, the logical check process of the updated vector data includes:

[0097] S310, converting both the updated vector data and the reference data into a database format.

[0098] Specifically, both the updated vector data and the reference data are converted into a set of geographical features stored in a lightweight spatial database, so as to facilitate the subsequent program language to extract each geographical feature for inspection.

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

[0100] It should be noted that since the data volume of the reference data in this embodiment is small, it can be quickly and directly converted into a database form without occupying too many resources, so that the efficiency of the logical check process is relatively high.

[0101] S320. Based on the updated vector data and the reference data in the database format, the geographical features of the updated vector data and the reference data are respectively extracted through the driver corresponding to the database format, and it is determined whether there are contradictions in the geographical features of the two; if not, the logical check of the updated vector data passes.

[0102] As mentioned above, since the data volume of the reference data is small, the extraction and inspection of each geographical feature can be realized through simple programming languages, without the need to be realized through complex software programs.

[0103] Exemplarily, when both the updated vector data and the reference data are converted into the spatialite database format, the geographical features of the updated vector data and the reference data are obtained by querying through writing SQL driver language, and it is determined whether there are contradictions in the quality inspection of each geographical feature to check whether the updated vector data conforms to the spatial logic.

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

[0105] To facilitate the understanding of this solution by those skilled in the art, the process of logically checking the updated vector data is exemplarily described below. For example, the spatial logic requirement is that roads cannot cross green spaces or residential areas. Then, the road lines, green areas, and residential areas of the updated vector data and the reference data are respectively obtained, and it is determined whether there is an overlap between the road lines of the updated vector data and the green areas or residential areas of the reference data, and whether there is an overlap between the green areas and residential areas of the updated vector data and the road lines of the reference data. If there is no overlap in both cases, it conforms to the spatial logic requirement, and the logical check of the updated vector data passes. Among them, 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, there is an overlap between the road line and the green area or residential area.

[0106] It should be noted 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 the spatial logical requirements, the logical check of the updated vector data passes. Exemplarily, as shown in Table 2, for the updated vector data, the spatial logical requirements include: roads crossing green spaces or residential areas, water system surfaces not covering green spaces or residential areas, railway lines not crossing residential areas, the top-level lines of roads not coinciding with the roads, no overlap between multiple points, no overlap between multiple lines, no overlap between different surfaces, the attribute logic of tunnel DISPCLASID and CLASID in roads, the attribute logic of main / secondary roads DISPCLASID, CLASID, and ROUTENUM in roads, the attribute logic of bridges / tunnels FORM in roads, the associated attribute logic between the top-level lines of roads and the roads, and the associated attribute logic between water systems and water system annotation lines.

[0107] Table 2 Reference table for quality inspection requirements.

[0108]

[0109] Based on this, in this embodiment, the vector data update client 11 generates a corresponding reference data request based on the updated vector data to request the reference data from the server 12, thereby ensuring the currency of the reference data and effectively improving the accuracy of the logical check result 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 reference data. Since the data volume of the reference data is small, the logical check process can be implemented by writing a programming language without using a third-party SDK to load software, effectively reducing the processing time and program complexity of vector data quality inspection and improving the quality inspection efficiency of the updated vector data.

[0110] Furthermore, since the logical check process of the updated vector data is executed by the vector data update client 11 without the need for the server 12 to perform a logical check, the computing resources of the server 12 are avoided from being occupied, the efficiency of vector data logical check is improved, the real-time logical check requirements of vector data for multiple users and multiple times are met, and a better vector data quality inspection effect is achieved.

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

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

[0113] Among them, the integrity requirement is a preset quality inspection requirement.

[0114] Specifically, obtain the integrity information of the update vector data. For example, write JavaScript code to obtain the integrity information of the update vector data, so as to check the integrity of the update vector data and determine whether it meets the integrity requirements. It should be noted that those skilled in the art should know the specific method for obtaining the integrity information, and this embodiment does not elaborate on it here.

[0115] Exemplarily, as shown in Table 2, the integrity information includes the data organization directory and file naming specification, data format, file size exceeding, redundant layers, and missing layers. Among them, for the data organization directory and file naming, set the corresponding directory and file standardization requirements based on the preset integrity requirements to determine whether the data organization directory and file naming of the update vector data meet the directory and file standardization requirements; for the data format, set the corresponding data validity requirements based on the preset integrity requirements to determine whether the data format of the update vector data meets the data validity requirements; for the file size exceeding, set the corresponding data size requirements based on the preset integrity requirements to determine whether the file size exceeding of the update vector data meets the data size requirements; for the redundant layers and missing layers, set the corresponding layer integrity requirements based on the preset integrity requirements to determine whether the redundant layers and missing layers of the update vector data meet the layer integrity requirements. Specifically, those skilled in the art should know the specific steps for checking the integrity information of the update vector data, and this embodiment does not elaborate on it here.

[0116] Further, when all the integrity information of the update vector data meets the corresponding integrity requirements, the update vector data passes the data integrity check.

[0117] S20, if so, perform a layer information check based on the update vector data to determine whether the update vector data meets the spatial coordinate requirements.

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

[0119] Specifically, obtain the layer information of the update vector data. For example, write the wasm cross-platform code of GDAL to obtain the layer information of the update vector data to determine whether it meets the spatial coordinate requirements. It should be noted that those skilled in the art should know the specific method for obtaining the layer information, and this embodiment does not elaborate on it here.

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

[0121] Further, when each layer information of the updated vector data conforms to the corresponding spatial coordinate requirements, the updated vector data passes the layer information check.

[0122] S30, if so, 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 requirements are a preset quality inspection requirement.

[0124] Specifically, obtain the field information of the updated vector data. For example, obtain the layer field information of the updated vector data by writing an SQL query 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 the layer field information, and this embodiment does not make a specific explanation here.

[0125] Exemplarily, as shown in Table 2, the field information includes value range, duplicate value, non-null value, half-width character field, full-width character field, format information, graphic attribute consistency, and sensitive words. Among them, for the value range, the duplicate value, the non-null value, the half-width character field, the full-width character field, the format information, and the graphic attribute consistency, corresponding attribute correctness requirements are set based on the preset attribute requirements, and it is determined whether the value range, duplicate value, non-null value, half-width character field, full-width character field, format information, and graphic 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, and all the text of the updated vector data is extracted to determine whether it meets the field compliance requirements. Specifically, those skilled in the art should know the specific steps for checking each field information of the updated vector data, and this embodiment does not make specific explanations here.

[0126] Further, 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 so, 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, obtain the layer topology information of the updated vector data. For example, obtain the layer topology information of the updated vector data by writing an SQL query to determine whether it meets the geometric expression requirements. It should be noted that those skilled in the art should know the specific method for obtaining the layer topology information, and this embodiment does not make specific explanations here.

[0130] Exemplarily, as shown in Table 2, the layer topology information includes geometric type, excessive node quantity, tiny surface, tiny line, line self-intersection, and surface self-intersection. Obtain the geometric type, excessive node quantity, tiny surface, tiny line, line self-intersection, and surface self-intersection of the updated vector data to determine whether it meets the geometric expression requirements. Specifically, those skilled in the art should know the specific steps for checking each layer topology information of the updated vector data, and this embodiment does not make specific explanations here.

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

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

[0133] For the implementation method of performing the logical check on the updated vector data, please refer to the foregoing content, and this embodiment will not elaborate here.

[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 side, and then improving the quality inspection efficiency of the updated vector data, achieving a good vector data quality inspection effect.

[0135] It should be noted that the above numbers are assigned to each step of performing quality inspection on the updated vector data only for the convenience of those skilled in the art 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 integrity check, layer information check, layer field check, layer topology check, and logical check can also be performed on the updated vector data in other orders, and this embodiment does not make specific restrictions here.

[0136] As Figure 9 shown, a vector data logical check 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] Among them, the spatial range acquisition module 61 is used to respond to the input of the updated vector data to be detected, perform spatial analysis on the updated vector data, and obtain the spatial range of the updated vector data;

[0138] The reference data request module 62 is used to send a reference data request to the server based on the spatial range of the updated vector data to obtain the reference data; the reference data represents the same spatial range as the updated vector data;

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

[0140] Based on the same inventive concept, the vector data logical check method or vector data quality inspection method provided in the embodiments of the present invention can be implemented on the terminal side.

[0141] As Figure 10As shown, it is a schematic diagram of an optional hardware structure of a terminal provided by an embodiment of the present invention. The terminal 70 may be a mobile phone, a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc. The terminal 70 includes: at least one processor 71, a memory 72, at least one network interface 74, and a user interface 73. Each component in the device is coupled together through a bus system 75. It can be understood that the bus system 75 is used to implement the connection and communication between these components. In addition to the data bus, the bus system 75 also includes a power bus, a control bus, and a status signal bus.

[0142] Among them, the user interface 73 may include a display, a keyboard, a mouse, a trackball, a click gun, a button, a touchpad, or a touch screen, etc.

[0143] It can be understood that the memory 72 may be a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM, Read Only Memory), a programmable read-only memory (PROM, Programmable Read-Only Memory), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM, Static Random Access Memory), synchronous static random access memory (SSRAM, Synchronous Static Random Access Memory). The memory characterized by the embodiments of the present invention is intended to include but not limited to these and any other suitable categories of memory.

[0144] The memory 72 in the embodiments 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 program 722; the operating system 721 contains 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 program 722 may include various application programs, such as a media player (Media Player), a browser (Browser), etc., for implementing various application services. Implementing the vector data logic check method or the vector data quality inspection method provided by the embodiments of the present invention may be included in the application program 722.

[0145] The method disclosed in the embodiments of the present invention above 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. In the implementation process, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor 71 or instructions in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), 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 embodiments of the present invention. The processor 71 may be a microprocessor or any conventional processor, etc. Combining the steps of the accessory optimization method provided by the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module may be located in the storage medium, and this storage medium is located in the memory. The processor reads the information in the memory and combines its hardware to complete the steps of the foregoing method.

[0146] In an exemplary embodiment, the terminal 70 may be one or more application-specific integrated circuits (ASICs, Application Specific Integrated Circuit), DSPs, programmable logic devices (PLDs, Programmable Logic Device), complex programmable logic devices (CPLDs, Complex Programmable Logic Device) for executing the foregoing method.

[0147] The embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the program is called by a processor, it implements the vector data logic check method or the vector data quality inspection method provided by the present invention.

[0148] Among them, the computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. The computer-readable storage medium may 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 of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), static random access memories (SRAM), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, mechanical encoding devices.

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

[0150] In summary, in this application, the vector data update client 11 calculates each of the requested tiles to request the reference data from the server 12, and after receiving the reference data, performs a logical check on the updated vector data, thereby ensuring the currency of the reference data and further improving the accuracy of the logical check result 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 occupancy of the server 12 is effectively reduced, thereby avoiding the response delay 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 respective drawings each have their own focuses. For parts not detailed in a certain process or structure, reference can be made to the relevant descriptions of other processes or structures.

[0152] The above embodiments merely illustrate the principles and effects of this application and are not intended to limit this application. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or changes completed by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in this application should still be covered by the claims of this application.

Claims

1. A vector data logical check method, applied to a vector data update client, includes: In response to the input of updated vector data to be detected, extracting the spatial range of the updated vector data; Based on the spatial range of the updated vector data, sending a reference data request to the server to obtain reference data within the same spatial range; The reference data is historical vector tile data; Based on the updated vector data, performing spatial logical analysis in combination with the reference data to perform logical check on the updated vector data.

2. The method according to claim 1, characterized in that, The extracting the spatial range of the updated vector data includes: Performing spatial clustering on the updated vector data to obtain at least one clustering data group; Obtaining the center point coordinates and clustering radius of each clustering data group to determine the spatial range of the updated vector data.

3. The method according to claim 1, wherein The based on the spatial range of the updated vector data, sending a reference data request to the server to obtain the reference data includes: Based on a preset quality inspection accuracy, obtaining the vector tile level; Based on the spatial range of the updated vector data, determining a search range; based on this search range, calculating, among the tiles corresponding to the vector tile level, the tiles that spatially intersect with the search range as the 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 of the corresponding clustering data group; if so, taking the tile to be requested as the requested tile; Based on the coordinates of each requested tile, sending a reference data request to the server to obtain the reference data.

4. The method according to claim 1, wherein The based on the updated vector data, performing spatial logical analysis in combination with the reference data to perform logical check on the updated vector data includes: Converting both the updated vector data and the reference data into a database format; Based on the updated vector data and the reference data in database format, respectively extracting the geographic features of the updated vector data and the reference data through a driver corresponding to the database format, and determining whether there are contradictions between the geographic features of the two; if not, the logical check of the updated vector data passes.

5. The method according to claim 2, characterized in that, The performing spatial clustering on the updated vector data to obtain at least one clustering data group includes: Based on the location information of the updated vector data, obtaining each feature point of the updated vector data; Based on the distance between each feature point, performing division to obtain at least one clustering data group.

6. A vector data quality inspection method, includes: Performing data integrity check based on updated vector data to determine whether the updated vector data meets the integrity requirements; If so, performing layer information check based on the updated vector data to determine whether the updated vector data meets the spatial coordinate requirements; If so, performing layer field check based on the updated vector data to determine whether the updated vector data meets the attribute requirements; If so, performing layer topology check based on the updated vector data to determine whether the updated vector data meets the geometric expression requirements; If so, perform a logical check based on the updated vector data to determine whether the updated vector data conforms to the spatial logic; Among them, the implementation method of performing the logical check on the updated vector data is the vector data logical check method described in any one of claims 1-5.

7. A vector data logical check system, characterized in that It includes: An updated vector data client, configured to execute the vector data logical check method described in any one of claims 1-5 in response to the input of the updated vector data; A server, communicatively connected to the client, configured to receive the reference data request from the client, generate the corresponding reference data based on the reference data request, and send it to the client.

8. A vector data logic check device, characterized in that It includes a spatial range acquisition module, a reference 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 the input of the updated vector data to be detected; The reference data request module is configured to send a reference data request to the server based on the spatial range of the updated vector data to obtain the reference data of the same spatial range; The reference data is historical vector tile data; The spatial logic analysis module is configured to perform a spatial logic analysis based on the updated vector data in combination with the reference data to perform a logical check on the updated vector data.

9. A terminal, characterized in that, It includes: A processor and a memory, communicatively connected between the memory and the processor; The memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory so that the terminal executes the vector data logical check method described in any one of claims 1-5 or the vector data quality inspection method described in claim 6.

10. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the vector data logical check method described in any one of claims 1-5 or the vector data quality inspection method described in claim 6.

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