Spatial planning data quality inspection methods, equipment, and media based on embedded databases

Abstract

This invention provides a spatial planning data quality inspection method, device, and medium based on an embedded database. First, quality inspection configuration information is loaded, and the local data packets to be inspected are imported into an embedded database engine that supports spatial expansion, forming a local temporary quality inspection database. Based on the quality inspection configuration information, internal quality checks are performed on the data in the local temporary quality inspection database using spatial SQL functions. Then, an external consistency check is performed by loading a remote database through an external data connection. Finally, the results of the internal quality check and the external consistency check are structured and saved to the result table of the embedded database. This invention directly accesses local data packets and remote databases through the embedded database engine, and uses SQL spatial functions to implement integrated automatic quality inspection by executing quality inspection operators within the database kernel. This reduces the difficulty of developing quality inspection operators and significantly improves the efficiency and standardization of quality inspection of detailed land spatial planning data.

Description

Technical Field

[0001] This invention relates to the field of geographic information systems and land spatial planning data quality management technology, specifically a spatial planning data quality inspection method, equipment, and medium based on an embedded database. Background Technology

[0002] Detailed land use planning is a specific arrangement of the development and protection pattern of national land space, and its data quality directly affects the scientific nature, rationality, and operability of the plan. Accurate and complete detailed land use planning data is the foundation for ensuring the orderly development of national land space, the rational use of resources, ecological environmental protection, and coordinated urban and rural development. However, detailed land use planning data comes from a wide range of sources and is in various formats, covering data from multiple aspects such as geographic information, land use, ecological environment, and infrastructure. These data may be affected by various factors during collection, storage, processing, and transmission, leading to errors, inconsistencies, or incompleteness. Therefore, a comprehensive and accurate quality check of detailed land spatial planning data is crucial.

[0003] Traditional quality inspection methods suffer from significant technical bottlenecks, primarily due to low efficiency in data loading and integration. Limited to a single data environment, these methods cannot directly access local and remote data synchronously. Joint quality inspection requires uploading both remote and local planning data, scattered across various locations, to a centralized environment. This not only incurs high transmission time costs in large-scale data scenarios but also necessitates handling the format, standard, and structural differences of multi-source data, significantly increasing the complexity of quality inspection. Furthermore, traditional quality inspection logic is typically implemented through hard coding or reliance on specific commercial tools, resulting in difficult operator development, poor rule reusability, and difficulty in fully utilizing the spatial indexing acceleration capabilities provided by the underlying database. This further limits the efficiency of quality inspection execution, becoming a key factor restricting the efficiency and quality of land spatial planning data quality inspection. Summary of the Invention

[0004] This invention provides a spatial planning data quality inspection method based on an embedded database. By using spatial SQL functions based on an embedded database engine, quality inspection operators are defined in SQL language and executed natively within the embedded database engine. This fully utilizes the database's underlying native computing and spatial indexing acceleration capabilities, significantly improving the quality inspection efficiency, standardization, and traceability of detailed land spatial planning data, thereby solving the problems in the background technology.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: A spatial planning data quality inspection method based on an embedded database includes the following steps: S1: Load quality inspection configuration information, which includes the standard data structure of the quality inspection layer, the legal enumeration values ​​of the attribute fields, the dictionary mapping relationship, and the definitions of the constraint index layer and the major feature layer; S2: Import or mount the local land and space detailed planning data package to be inspected into the embedded database engine that supports spatial expansion to form a local temporary inspection library; S3: Based on the quality inspection configuration information, perform internal quality checks on the data in the local temporary quality inspection database using spatial SQL functions; S4: Through the external data connection function of the embedded database engine, load the constraint index layer and major feature layer in the remote database, and perform spatial overlay analysis with the patches in the local temporary quality inspection library to perform external consistency checks. S5: The results of internal quality checks and external consistency checks are structured and saved to the result table of the embedded database, and quality inspection reports and quality inspection documents are generated and output based on the result table.

[0006] Preferably, the internal quality checks of S3 include: empty layer check, data geometry type check, multi-part check, patch area check, attribute item check, and patch topology check.

[0007] Preferably, the empty layer check is specifically performed as follows: execute the SQL COUNT(*) statement for each specified quality inspection layer defined in each quality inspection configuration information, count the number of features, and mark it as an empty layer if the number is zero; The data geometry type check specifically involves using the spatial SQL function ST_GeometryType to check whether the geometry type of each feature in the local temporary quality inspection database meets the preset standard. The multi-part check specifically involves using the ST_NumGeometries function to check whether a single feature record contains multiple geometric parts, and identifying features that do not meet the single-part requirements. The patch area check specifically involves: using the spatial function ST_Area to calculate the area of ​​each planar patch, comparing it with the area threshold specified in the standard, and marking areas that are too small or abnormal.

[0008] Preferably, the attribute item check specifically includes: By using SQL JOIN queries and WHERE condition filtering, the attribute table of the data to be inspected is compared with the quality inspection configuration table, including the enumeration value table and the dictionary mapping table, which has been loaded into the embedded database. The inspection includes whether the required fields are empty, whether the field values ​​are within the range of enumeration values, whether the dictionary value mapping between the code and the name is correct, and whether the field value range is reasonable.

[0009] Preferably, the topology check of the patch includes: Gap inspection: By performing the ST_Union merge operation on all adjacent polygons of the same layer in the local temporary quality inspection library to generate continuous polygons, the difference between the merged polygons and the outer polygons is compared by ST_Difference to determine whether gaps exist; Overlap check: The ST_Intersects function is used to determine whether there are intersecting areas between areal features, and the ST_Area function is used to calculate the overlapping area and mark the overlaps that exceed the allowable tolerance.

[0010] Preferably, the external consistency check in S4 includes a constraint indicator check: The binding performance indicators include: Loading constraint indicator layers: Load constraint indicator layer data from a remote database using the external data connection function of the embedded database engine; Overlay analysis of constraint indicators: Spatial overlay analysis is performed on the planning patches in the local temporary quality inspection database and the loaded remote constraint indicator layer, and the ST_Intersection function is used to calculate the intersection area between the two. Constraint indicator difference analysis: The ST_Difference function is used to calculate the portion of local planning patches that exceeds the allowable range of constraint indicators; Statistics on binding indicators: The area of ​​various types of land use is statistically analyzed and compared with the requirements of binding indicators to determine whether the requirements are met.

[0011] Preferably, the external consistency check in S4 includes a major element overlay check: Major element overlay inspections include: Loading critical feature layers: Load critical feature layer data from a remote database using the external data connection function of the embedded database engine; Overlapping area identification: Spatial overlay analysis is performed between the local planning patches and the loaded distant major feature layer, and the overlapping planning patches are identified by the ST_Intersects function; Land cover information extraction: Extract the planned land use type, area, and location information of the land cover patches; Overburden report generation: Based on the extracted overburden information, an overburden analysis report is generated.

[0012] Preferably, the specific steps of S5 include: S51. Result Saving: The results of internal quality checks and external consistency checks are structurally saved to the result table of the embedded database. The results include the element IDs, error types, descriptions, and associated spatial geometric information of elements that pass, fail, or warn. S52. Report Output: Query the result table using SQL, filter and summarize by layer, error type or severity, and generate a structured PDF document containing a quality inspection overview, error details and statistical charts; S53. File Output: Generates standardized GIS format files containing the spatial location and attribute information of erroneous features, facilitating their location and modification in GIS software.

[0013] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0014] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0015] As can be seen from the above technical solution compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves high efficiency, automation, and performance improvement: Based on the DuckDB embedded analysis engine and vectorized execution characteristics, it innovatively utilizes the same SQL engine and spatial functions to seamlessly connect and analyze local data packets and remote databases, directly performing high-speed SQL operations on data in memory. This avoids the frequent IO operations of traditional GIS software and greatly improves the batch quality inspection efficiency of massive spatial data.

[0016] 2. This invention offers flexibility and strong scalability: the entire quality inspection process is driven by configurable metadata and implemented through modular SQL scripts. Adding new inspection rules only requires updating the configuration and writing the corresponding SQL statements, without modifying the core program, making system expansion and maintenance extremely convenient.

[0017] 3. This invention achieves lightweight and low-cost technology: Based on the open-source embedded database DuckDB and its spatial extensions, it eliminates the need to deploy complex and expensive large-scale spatial database systems such as Oracle Spatial or rely on specific commercial GIS software, thus lowering the technical threshold and deployment costs. Process standardization and result traceability: The quality inspection process is solidified into a repeatable sequence of SQL scripts, ensuring standardized and consistent quality inspection procedures. All intermediate and final results are stored in the database, facilitating auditing, traceability, and review. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the method steps in an embodiment of the present invention; Figure 2 This is a schematic diagram of the specific logical flow of the method in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0020] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention, but should not be used to limit the scope of the present invention.

[0021] Example: like Figure 1 As shown in this embodiment, a city is in a critical stage of land and space planning. The city's "control detailed planning" covers an area of ​​approximately 500 square kilometers, involving hundreds of planning plot layers, with a massive amount of data from complex sources. Therefore, the city introduces the method described in this application, employing an SQL quality inspection method based on an embedded database. This method supports simultaneous access to local and remote data without requiring full data migration and centralized integration. Quality inspection operators are defined in SQL language and executed natively within the embedded database engine. This avoids the development barriers associated with traditional hard-coding methods and fully utilizes the database's underlying native computation and spatial indexing acceleration capabilities, effectively overcoming the technical bottlenecks of traditional quality inspection.

[0022] Using a data package of "control and detailed planning" for a certain area as a pilot, automated quality inspection practices were carried out; the specific implementation process is as follows: Figure 2 As shown: Environment preparation and configuration loading; First, the embedded database engine DuckDB, which supports Spatial extensions, was installed on the quality inspection server, and space expansion and PostgreSQL connection plugin loading were completed. Based on national standards and local technical regulations, technicians compiled a quality inspection rule configuration file, which fully defines all metadata required for this quality inspection, including: The standard data structure for each quality inspection layer (e.g., the "Planned Land Parcel" layer must include fields such as land use code and land area, with clearly defined field types and lengths); a list of valid enumerated values ​​for attribute fields (e.g., land use codes must conform to the "Classification of Urban Land Use and Standards for Planning and Construction Land"); a dictionary mapping relationship between land use codes and land use names; geometric and topological rules (e.g., the minimum map area is 50 square meters, and no excessive overlap is allowed between map patches); and the definition of the data source required for external consistency checks, clearly defining "Ecological Protection Red Line" and "Permanent Basic Farmland" as binding indicator layers and "Geological Disaster Prone Zones" as major feature layers in the remote PostGIS database, and configuring the corresponding connection parameters.

[0023] Local data loading and rule initialization; The "Detailed Control Plan" data package (FileGDB format) to be inspected is directly mounted into the temporary mode of the database using DuckDB's data reading function, forming a local temporary quality inspection database. All planning layers are provided as in-memory tables for subsequent analysis. The system then reads the quality inspection rule file configured in step (1) and loads the standard code table, enumeration value list, area threshold, and other rule data into in-memory tables in DuckDB. The entire loading process requires no data import or format conversion, achieving readiness in seconds.

[0024] Perform internal quality checks; The system automatically calls predefined spatial SQL scripts in sequence to perform a comprehensive internal quality check on layers such as "planned plots" in the local temporary quality inspection database, and all inspection results are written to the result table in real time.

[0025] Empty layer check: The system counts the number of features in each specified layer one by one. If a layer has zero features, it is determined to be an empty layer and the error is recorded to avoid invalid analysis in the future.

[0026] Geometry type check: The system traverses each feature and identifies whether its geometry type is consistent with the layer's preset standard. If point or line features appear in a layer that should be a polygon feature, it is marked as a geometry type error.

[0027] Multi-part inspection: The system identifies whether a single feature record contains multiple geometric parts. For layers that require single-part storage, it detects and records such multi-part features.

[0028] Area check of polygons: The system calculates the geometric area of ​​each polygon and compares it with the area threshold specified in the configuration file. Polygons with too small an area and no reasonable comments are marked as having an abnormal area.

[0029] Attribute item check: The system compares the attribute table of the "Planned Land Parcel" layer with the loaded quality inspection configuration table to check whether required fields are empty, whether field values ​​are within the range of enumerated values, whether the dictionary mapping between land use codes and names is correct, and whether the field value range is reasonable. For example, for the "Land Use Code" field, the system performs a left join with its value to the standard code table, filters out records that cannot be matched (i.e., illegal codes), and writes the relevant plot ID and error type "Illegal Attribute" into the results table.

[0030] Topology check of polygons: The system performs spatial relationship analysis on isometric features within the same layer. On one hand, through overlap analysis, it identifies whether there are intersecting areas exceeding the allowable error and calculates the overlapping area; on the other hand, through merging and difference analysis, it identifies whether there are gaps within the layer not covered by polygons. All excessive overlaps and gaps are recorded as topology errors.

[0031] Perform external consistency checks; This step utilizes DuckDB's external data connectivity capabilities to perform collaborative analysis of local planning schemes and remote control data; Constraint indicator check: The system directly reads the "Ecological Protection Red Line" and "Permanent Basic Farmland" layers from the remote PostGIS database via a database connection plugin. Considering that the remote data and local data may use different coordinate systems, the system automatically performs coordinate transformation before spatial analysis.

[0032] Subsequently, the local "Planned Construction Land" map patches are spatially overlaid with the distant "Ecological Protection Red Line" layer to identify intersecting areas. Any construction land map patch intersecting the red line is deemed to violate the binding indicators, and the system writes the relevant map patch ID, intersection area, and error type "overlaying the ecological red line" into the results table. Simultaneously, through difference analysis with the "Permanent Basic Farmland" layer, the area and region of basic farmland occupied by local planning are calculated and compared with the indicator requirements to form an occupation analysis record.

[0033] Major Element Coverage Check: Similarly, the system loads the remote "Geological Hazard Prone Zone" layer and performs spatial overlay analysis with all local planning patches. The system identifies planning patches located within geological hazard prone zones and groups them by risk zone type (e.g., high-risk zone, medium-risk zone) to calculate the number and total area of ​​covered patches within each risk zone type. This detailed coverage data is fully recorded, providing a quantitative basis for subsequent coverage assessment and report generation.

[0034] Result generation and output; After all inspection steps are completed, the DuckDB results table stores a structured record of all errors and warnings from this quality inspection. Each record contains a unique identifier for the erroneous element, its layer, error type, detailed description, and complete spatial geometry information.

[0035] The system then executes a predefined report generation script to perform multi-dimensional statistics and summaries on the results table: counting the number of errors by layer, analyzing the distribution of problems by error type, and generating a quality inspection overview statistics table; for overlay errors, generating a detailed overlay situation analysis table. Based on these statistics, the system automatically generates a structured PDF quality inspection report, which includes a quality inspection overview, error details, statistical charts, and a dedicated chapter on overlay analysis.

[0036] Simultaneously, the system exports the erroneous elements and their attribute information from the results table into a standard GIS FileGDB format file, i.e., a quality inspection result file. This file contains the spatial location and error description of all erroneous polygons, which can be directly opened and viewed in mainstream GIS software, facilitating planning personnel to quickly locate problematic polygons.

[0037] Application of results; After receiving the PDF quality inspection report and GIS quality inspection result file, the planning unit can open the result file in software such as ArcGIS to intuitively see the locations of all marked erroneous patches.

[0038] Based on the error types described in the report and the spatial instructions in the documents, technicians made targeted modifications to the original planning data: correcting illegal attribute codes, adjusting overlapping patches, and avoiding ecological red line areas, etc. After the modifications were completed, the new version of the data package could be resubmitted to the system for review. The system can quickly complete a new round of quality inspection until the data fully meets all quality requirements, forming a complete closed loop from "submission-quality inspection-modification-re-inspection".

[0039] As can be seen from the above examples, the method of the present invention, through an embedded database engine, completes an integrated, automated, and traceable quality inspection process for local data packets and remote databases under a unified SQL engine. This reduces the traditional quality inspection work that takes several days to complete within a few hours, significantly improving the quality inspection efficiency and standardization of detailed land and space planning data, and providing strong technical support for the rapid storage and dynamic maintenance of planning results.

[0040] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0041] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0042] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the spatial planning data quality inspection methods based on embedded databases in the above embodiments.

[0043] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0044] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0045] For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media.

[0046] The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid state disks (SSDs)).

[0047] It should be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0048] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0049] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0050] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A spatial planning data quality inspection method based on an embedded database, characterized in that, Includes the following steps: S1: Load quality inspection configuration information, which includes the standard data structure of the quality inspection layer, the legal enumeration values ​​of the attribute fields, the dictionary mapping relationship, and the definitions of the constraint index layer and the major feature layer; S2: Import or mount the local land and space detailed planning data package to be inspected into the embedded database engine that supports spatial expansion to form a local temporary inspection library; S3: Based on the quality inspection configuration information, perform internal quality checks on the data in the local temporary quality inspection database using spatial SQL functions; S4: Through the external data connection function of the embedded database engine, load the constraint index layer and major feature layer in the remote database, and perform spatial overlay analysis with the patches in the local temporary quality inspection library to perform external consistency checks. S5: The results of internal quality checks and external consistency checks are structured and saved to the result table of the embedded database, and a quality inspection report and quality inspection documents are generated and output based on the result table.

2. The spatial planning data quality inspection method based on an embedded database as described in claim 1, characterized in that: The internal quality checks of S3 include: empty layer check, data geometry type check, multi-component check, patch area check, attribute item check, and patch topology check.

3. The spatial planning data quality inspection method based on an embedded database as described in claim 2, characterized in that: The empty layer check specifically involves executing the SQL COUNT(*) statement on each specified quality inspection layer defined in each quality inspection configuration information, counting the number of elements, and marking it as an empty layer if the number is zero. The data geometry type check specifically involves using the spatial SQL function ST_GeometryType to check whether the geometry type of each feature in the local temporary quality inspection database meets the preset standard. The multi-part check specifically involves using the ST_NumGeometries function to check whether a single feature record contains multiple geometric parts, and identifying features that do not meet the single-part requirements. The patch area check specifically involves: using the spatial function ST_Area to calculate the area of ​​each planar patch, comparing it with the area threshold specified in the standard, and marking areas that are too small or abnormal.

4. The spatial planning data quality inspection method based on an embedded database as described in claim 2, characterized in that: The attribute item check specifically involves: By using SQL JOIN queries and WHERE condition filtering, the attribute table of the data to be inspected is compared with the quality inspection configuration table, including the enumeration value table and the dictionary mapping table, which has been loaded into the embedded database. The inspection includes whether the required fields are empty, whether the field values ​​are within the range of enumeration values, whether the dictionary value mapping between the code and the name is correct, and whether the field value range is reasonable.

5. The spatial planning data quality inspection method based on an embedded database as described in claim 2, characterized in that: The topology check of the patch includes: Gap inspection: By performing the ST_Union merge operation on all adjacent polygons of the same layer in the local temporary quality inspection library to generate continuous polygons, the difference between the merged polygons and the outer polygons is compared by ST_Difference to determine whether gaps exist; Overlap check: The ST_Intersects function is used to determine whether there are intersecting areas between areal features, and the ST_Area function is used to calculate the overlapping area and mark the overlaps that exceed the allowable tolerance.

6. The spatial planning data quality inspection method based on an embedded database as described in claim 1, characterized in that: The external consistency check in S4 includes a constraint indicator check: The binding indicator checks include: Loading constraint indicator layers: Load constraint indicator layer data from a remote database using the external data connection function of the embedded database engine; Overlay analysis of constraint indicators: Spatial overlay analysis is performed on the planning patches in the local temporary quality inspection database and the loaded remote constraint indicator layer, and the ST_Intersection function is used to calculate the intersection area between the two. Constraint indicator difference analysis: The ST_Difference function is used to calculate the portion of local planning patches that exceeds the allowable range of constraint indicators; Statistics on binding indicators: The area of ​​various types of land use is statistically analyzed and compared with the requirements of binding indicators to determine whether the requirements are met.

7. The spatial planning data quality inspection method based on an embedded database as described in claim 1, characterized in that: The external consistency check in S4 includes a major element overlay check: The major element overlay inspection includes: Loading critical feature layers: Load critical feature layer data from a remote database using the external data connection function of the embedded database engine; Overlapping area identification: Spatial overlay analysis is performed between the local planning patches and the loaded distant major feature layer, and the overlapping planning patches are identified by the ST_Intersects function; Land cover information extraction: Extract the planned land use type, area, and location information of the land cover patches; Overburden report generation: Based on the extracted overburden information, an overburden analysis report is generated.

8. The spatial planning data quality inspection method based on an embedded database as described in claim 1, characterized in that: The specific steps of S5 include: S51. Result Saving: The results of internal quality checks and external consistency checks are structurally saved to the result table of the embedded database. The results include the element IDs, error types, descriptions, and associated spatial geometric information of elements that pass, fail, or warn. S52. Report Output: Query the result table using SQL, filter and summarize by layer, error type or severity, and generate a structured PDF document containing a quality inspection overview, error details and statistical charts; S53. File Output: Generates standardized GIS format files containing the spatial location and attribute information of erroneous features, facilitating their location and modification in GIS software.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

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