Multi-level geographic entity data consistency determination method and device, equipment and storage medium

Abstract

The invention provides a multi-level geographic entity data consistency determination method and device, equipment and a storage medium, and relates to the technical field of surveying and mapping geographic information. The method comprises the steps of performing fusion processing on geographic entity data of different spatial scale hierarchies to form a unified database; based on geometric, semantic and attribute features, a differentiation matching model is adopted to carry out automatic identity determination on point-shaped, linear and planar entities of different hierarchies, and comprehensive judgment is carried out by utilizing multiple geometric quantities such as distance, buffer area overlapping proportion, area overlapping degree, curvature, trend and the like; and performing unified coding and association relationship construction on the entities according to an affirmation result. According to the method, the problems that the same entity in the multi-level geographic entity data is inconsistent in affirmation and non-uniform in coding are solved, automatic and high-efficiency consistency affirmation and integrated management are realized, and the data processing accuracy and efficiency are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of surveying and mapping geographic information technology, and in particular to a method, apparatus, equipment and storage medium for confirming the consistency of multi-level geographic entity data. Background Technology

[0002] Geographic entity data is an important data outcome in the new basic surveying and mapping system. It has the characteristics of being entity-based, semantic, and structured, breaking the constraints of traditional basic surveying and mapping data outcomes such as points, lines, areas, and scales, and laying the data foundation for the combined development of surveying and mapping geographic information data and artificial intelligence technology.

[0003] Based on the region and category of geographic entities, geographic entity data is divided into city-level geographic entities and topographic-level geographic entities. Topographic-level geographic entity data production, aggregation, and database construction are carried out at the national and provincial levels, while city-level geographic entity data production and database construction are carried out at the prefecture-level and county-level units. Because geographic entity data is mostly converted from existing basic surveying and mapping data, there are various problems such as inconsistent data standards and differences in the representation of the same object at different scales. Furthermore, according to the geographic entity spatial identity coding rules, it must include three parts: a unique identifier field, a standard field, and an extended field. The standard field needs to be encoded according to the geometric position and shape of the entity object, based on the BeiDou grid location code grid coding rules (GB / T). (39409-2020) When calculating entity location codes, during the production and database construction phase of city and county-level geographic entities, it is impossible to calculate and assign geographic entity location codes from a provincial or higher-level perspective. Considering the above factors, this results in several core differences between provincial and city / county-level geographic entity data, including scale, graphic representation, and cross-regional hierarchical differences. These differences directly affect the identification of the same entity or parent-child entities. Specific differences are as follows: scale differences lead to inconsistent geometric precision for the same entity; graphic representation differences lead to inconsistent presentation of the same entity; cross-regional hierarchical differences, with some provincial entities spanning multiple city / county administrative regions, causing ambiguity in parent-child hierarchical relationships.

[0004] Current technical approaches and methods lack the technical means to verify the consistency of geographic entity data at the provincial, municipal, and county levels. Verification and identification must be done manually, one entity at a time. For example, a road geographic entity might be represented by multiple centerline graphics at the provincial level, while at the municipal and county levels it might be represented by a centerline plus an outer contour surface. Furthermore, the operational basis for geographic entities differs across levels. The provincial level uses remote sensing imagery to collect centerlines, while municipal and county levels use high-resolution UAV imagery or mesh 3D models to collect contour surfaces and centerlines, resulting in significant differences in the geometric shape and location of geographic entities. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and storage medium for identifying consistency of multi-level geographic entity data. It addresses the core differences in geographic entity data formed at different levels (such as provincial, municipal, and county-level units) due to variations in scale, graphic representation, and cross-regional levels. When summarizing and building a database of geographic entity data for the entire province, it solves the problem of identifying and recognizing the same geographic entity expressed at different levels in the real world.

[0006] Firstly, this application provides a method for verifying the consistency of multi-level geographic entity data, including:

[0007] The source geographic entity data at different spatial scales are converted in format, hierarchically identified and merged to form a unified multi-level geographic entity data.

[0008] Based on the multi-level geographic entity data, entities at different levels are automatically identified and recognized according to their geometric, semantic, and attribute features to determine whether they are the same entity or entities with hierarchical relationships, and the recognition result is obtained.

[0009] Based on the identification results, multi-level entities identified as the same entity are coded uniformly, and coded associations are established for entities identified as having hierarchical relationships, resulting in unified coding results;

[0010] Based on the unified coding results, an association table is established between entities at different spatial scale levels to realize the structured association of multi-level geographic entity data; wherein, the association table includes at least the lower-level entity code, the higher-level entity code, and the entity relationship type field, and the association relationship between entities is stored through code matching.

[0011] In one possible design, source geographic entity data at different spatial scales are format-converted, hierarchically labeled, and merged to form a unified, multi-level geographic entity data set, including:

[0012] Based on entity data structure templates and relation mapping files, geographic entity data from multiple sources is converted into a unified database file;

[0013] Add identifiers to the converted data layers to distinguish the sources of different spatial scale levels;

[0014] Data engineering at multiple spatial scales is merged into a single database file, including graph data, entity data, semantic relationship tables, and entity-graph element association tables.

[0015] In one possible design, when automatically identifying and recognizing entities at different levels, the identified entity categories include at least one of rivers, lakes, islands, reservoirs, railways, subways, and highways; the recognition scenarios include entities with different geometric precisions, entities with different graphic representations of the same entity, and entities with hierarchical relationships across administrative regions.

[0016] In one possible design, based on the multi-level geographic entity data, entities at different levels are automatically identified and defined according to their geometric, semantic, and attribute characteristics to determine whether they are the same entity or entities with hierarchical relationships, thus obtaining the definition result, including:

[0017] For point-like entities, identification is based on the entity name and the distance between points, including calculating the distance d between two points:

[0018]

[0019] in, and These are the coordinates of a point-like entity selected from the entities at the first spatial scale level. and The coordinates of another point entity selected from the entities at the second spatial scale level;

[0020] If the distance is less than or equal to the preset threshold, and the entity type and name are the same, then they are considered to be the same entity.

[0021] For linear entities, identification is based on entity type, name, line direction, and buffer overlap ratio. This includes creating a buffer for the linear entity, calculating the overlap area between the buffer of the input entity data and the buffer of the reference entity data, and calculating the ratio of the overlap area to the total area of ​​the input entity buffer as a first ratio and the ratio to the total area of ​​the reference entity buffer as a second ratio. The proportional similarity is determined based on the first ratio and the second ratio.

[0022] If the proportional similarity is greater than or equal to the set threshold, and the entity type and name are the same, then they are initially identified as the same entity.

[0023] If the overlap is greater than or equal to the set threshold, and the entity type and name are the same, they are initially identified as the same entity;

[0024] For planar entities, identification is based on entity type, name, the distribution range of faces, and the proportion of overlapping area. This includes calculating the overlapping area S_overlap between input face A1 and reference face A2, and calculating the bidirectional overlap ratio as the similarity s.

[0025]

[0026] Where Area(A1) represents the total area of ​​input surface A1, and Area(A2) represents the total area of ​​reference surface A2;

[0027] If the similarity s is greater than or equal to the set threshold, and the entity type and name are the same, then they are initially identified as the same entity.

[0028] In one possible design, for linear entities, when the proportional similarity is less than a set threshold, the curvature of the linear entity is further calculated for auxiliary judgment. The formula for calculating the curvature k of the arc segment is:

[0029]

[0030]

[0031] Where L is the arc length, S is the chord length, and h is the arch height; the overall curvature is the weighted average of the curvatures of each arc segment, with the weight being the proportion of the arc length.

[0032] And / or, calculate the azimuth using the coordinates of the first and last points of the line segment. To determine the direction:

[0033]

[0034] in, and Let these be the coordinates of the first point of the line segment. and The coordinates of the end point of the line segment;

[0035] If the difference in azimuth angle between two linear entities is not greater than a preset angle threshold, the proportional similarity is increased.

[0036] In one possible design, for planar entities, after initially identifying them as the same entity, further determination is made by using the distance between the center points of the planar entities. Let the planar entity consist of n vertex coordinates (x1, y1), (x2, y2), ..., (x...). n ,y n Composed of ) and center point ( , The formula for calculating ) is:

[0037]

[0038]

[0039] Let the center point of the input surface be ( , The center point of the reference plane is ( , Then, the formula for calculating the distance between the center points is:

[0040]

[0041] If the center point distance Within the set threshold, it serves as an auxiliary condition for identifying the same entity.

[0042] In one possible design, based on the identification result, multi-level entities identified as the same entity are encoded uniformly, and coded associations are established for entities identified as having hierarchical relationships, resulting in a unified coding result, including:

[0043] If entities at different spatial scales are identified as the same entity but have different codes, the code of the higher-level entity shall prevail, and the lower-level entity shall be coded, assigned a value, and marked with a duplicate code.

[0044] If entities at different spatial scales are identified as having a hierarchical relationship, a new field is added to the lower-level entity to store the code of the higher-level entity it is associated with, so as to establish a coded relationship.

[0045] Entities with duplicate codes that cannot be identified as the same entity or have a hierarchical relationship are re-encoded and marked with a duplicate code identifier.

[0046] Secondly, this application provides a multi-level geographic entity data consistency verification device, the device comprising:

[0047] The data fusion module is configured to convert the format, identify and merge the source geographic entity data at different spatial scales, forming a unified multi-level geographic entity data.

[0048] The identity determination module is configured to automatically identify and determine entities at different levels based on the multi-level geographic entity data, according to the geometric features, semantic features, and attribute features of the entities, to determine whether they are the same entity or entities with hierarchical relationships, and to obtain the determination result.

[0049] The unified coding module is configured to perform unified coding on multi-level entities identified as the same entity based on the identification result, and to establish coding association relationships for entities identified as having hierarchical relationships, thereby obtaining unified coding results;

[0050] The entity association module is configured to establish an association table between entities at different spatial scale levels based on the unified coding result, thereby realizing the structured association of multi-level geographic entity data. The association table includes at least lower-level entity codes, higher-level entity codes, and entity relationship type fields, and the association relationship between entities is stored through code matching.

[0051] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the multi-level geographic entity data consistency determination method as described in the first aspect and various possible designs of the first aspect.

[0052] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the multi-level geographic entity data consistency determination method described in the first aspect and various possible designs of the first aspect.

[0053] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the multi-level geographic entity data consistency determination method described in the first aspect and various possible designs of the first aspect.

[0054] The method, apparatus, equipment, and storage medium for confirming the consistency of multi-level geographic entity data provided in this application have at least the following beneficial effects:

[0055] This application, by combining entity consistency verification tools, can quickly achieve the identity verification of geographic entities at the provincial, municipal, and county levels. It accurately and effectively organizes and integrates real-world entities in digital form, solving problems such as inconsistent entity identification and spatial identity coding of the same geographic entity across multiple data levels. It achieves the identity and uniqueness of identity codes for the same geographic entity across different database levels, realizing automated consistency verification of geographic entities. Data experiments show that using this technical solution for consistency verification of multi-level geographic entities achieves an accuracy rate of over 90%. Using provincial, municipal, and county-level geographic entity data in Lianyungang, approximately 3 million element objects and 2.1 million entity objects were processed for consistency verification in about 2 hours. Compared to manual processing, this technical solution significantly improves the efficiency of identifying the same geographic entity at different levels. Attached Figure Description

[0056] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0057] Figure 1 Example diagrams illustrating the differences in graphic representation and geometric precision between provincial and municipal levels provided in this application embodiment;

[0058] Figure 2Example diagram of cross-regional hierarchical differences provided in embodiments of this application;

[0059] Figure 3 A technical roadmap for a multi-level geographic entity data consistency assessment method provided in this application embodiment;

[0060] Figure 4 A flowchart illustrating a method for determining the consistency of multi-level geographic entity data provided in this application embodiment;

[0061] Figure 5 A flowchart of multi-source data fusion provided in the embodiments of this application;

[0062] Figure 6 Example diagram of the relationship mapping file provided in the embodiments of this application;

[0063] Figure 7 Example diagrams of the same lake entity with different geometric precisions provided in the embodiments of this application;

[0064] Figure 8 Example diagram of the entity representation of "Changjiang Road" at the Taicang city level provided in the embodiments of this application;

[0065] Figure 9 An example diagram illustrating the parent-child relationship between the various city and county-level segmented entities and the Jiangsu section of the Yangtze River, provided for embodiments of this application;

[0066] Figure 10 A flowchart for determining entity identity provided in the embodiments of this application;

[0067] Figure 11 A flowchart illustrating the point entity identification technology provided in this application embodiment;

[0068] Figure 12 A flowchart illustrating the technical process for determining the identity of linear entities provided in this application embodiment;

[0069] Figure 13 A flowchart illustrating the technical process for determining the identity of planar entities provided in this application embodiment;

[0070] Figure 14 A structural diagram of the multi-level geographic entity data consistency verification device provided in the embodiments of this application.

[0071] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0072] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0073] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0074] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0075] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0076] Provincial and municipal / county-level geographic entity data exhibit core differences in the representation of the same geographic entity at different levels, such as scale, graphic representation, and cross-regional hierarchical differences. These differences directly affect the identification of the same entity or parent-child entities. Figure 1 and Figure 2 As shown, the specific differences are as follows: differences in scale lead to inconsistent geometric precision for the same entity; differences in graphic representation result in inconsistent presentation of the same entity; and differences in cross-regional hierarchy, with some provincial entities spanning multiple city and county administrative regions, causing ambiguity in parent-child hierarchical relationships. When geographic entity data is aggregated and databased at the city or provincial level, the same geographic entity object can only be manually verified one by one through interactive methods. To address the above problems, this application provides a method for confirming the consistency of multi-level geographic entity data. It can automatically identify the same entity in different levels of data by using computer algorithms based on the geometric location, orientation, and attribute information of provincial and city / county-level geographic entities, ultimately achieving the consistency confirmation of the same entity in different levels of data.

[0077] like Figure 3The diagram shows the technical roadmap for this multi-level geographic entity data consistency identification method. Its overall technical approach centers on multi-source data fusion → identity identification → unified coding → entity association. Through standardization processing and hierarchical association, it achieves the integration and unification of provincial and municipal / county-level geographic entities. In the resource acquisition stage, multi-source geographic entity data is first collected, specifically covering three categories: municipal-level geographic entities (corresponding to urban development boundaries), county-level geographic entities (corresponding to built-up areas), and provincial-level geographic entities (corresponding to the entire province). This provides a basic data source covering different administrative levels and spatial ranges for subsequent processing. In the multi-level geographic entity data fusion stage, the collected multi-source data is used as input. Data format standardization, data structure unification, and layer suffix identification are performed sequentially. By adapting data formats, aligning data structures, and adding unique layer suffixes, geographic entity data from different levels and sources are integrated into standardized fused data. In the multi-level geographic entity association and identification stage, based on the fused standardized data, the process proceeds sequentially: identity verification (clarifying the correspondence between geographic entities at different levels), unified entity coding (standardizing and associating entity codes at each level), and entity association construction (establishing associations between provincial and municipal / county-level entities), completing the integrated association processing of provincial and municipal / county-level geographic entities. In the multi-level data entry stage, after the aforementioned multi-source data fusion, identity verification, unified coding, and entity association processes, the integrated and unified provincial and municipal / county-level geographic entity data is incorporated into the multi-level data entry stage, completing centralized data storage and management, and providing unified geographic entity data support for subsequent applications.

[0078] like Figure 4 As shown, this multi-level geographic entity data consistency identification method can be implemented through the following steps S10-S40.

[0079] S10: Convert the format, identify and merge the source geographic entity data at different spatial scales to form a unified multi-level geographic entity data.

[0080] It should be noted that the source geographic entity data at different spatial scale levels can be provincial and municipal / county-level geographic entity data, but this is only an example. It can also be any two or more geographic entity data at different scales, such as national, provincial, municipal, district / county, and township levels. This application does not limit the specific division method of different spatial scale levels, and includes any entity data level with different geographic coverage or level of detail formed for the purpose of data production, management, or application.

[0081] In some embodiments, such as Figure 5 As shown, step S10 can be implemented through the following steps S101-S103.

[0082] S101: Based on entity data structure templates and relation mapping files, convert geographic entity data from multiple sources into a unified database file.

[0083] The purpose of step S101 is to achieve data format conversion. Specifically, this embodiment processes and merges geographic entity data from multiple sources, including provinces, cities, and counties. Using a geographic entity conversion tool and based on an entity data structure template, it exchanges data using relational mapping files according to the entity spatial identity code, primitive code, entity classification code, and primitive classification code in the data. It parses multi-level geographic entity data from different sources and converts the data into an SQLite database file for storage. The relational mapping file is as follows: Figure 6 As shown.

[0084] S102: Add identifiers to the converted data layers to distinguish the sources of different spatial scale levels.

[0085] The purpose of step S102 is to achieve data layer identification. Specifically, using a data mapping tool, the correspondence between data elements and entities from different sources is created. The data structure of database files from different sources is reorganized, and suffixes such as "_province", "_city", and "_county" are added to distinguish the data from different levels of sources, forming multiple levels of data projects stored separately at the provincial, municipal, and county levels. An example of the converted layer names is shown in Table 1.

[0086] Table 1. Layer Names After Conversion

[0087]

[0088] In this embodiment, the layer code DL_A represents the area road geographic entity data layer, DL_L represents the line road geographic entity data layer, and STJBB_DL represents the road entity basic attribute information data layer.

[0089] S103: Merge data engineering at multiple spatial scale levels into a single database file. The merged content includes map data, entity data, semantic relationship tables, and entity-map element association tables.

[0090] The purpose of step S103 is to achieve multi-level data merging. Specifically, this embodiment uses an entity project merging tool to load multi-level data from the same region and merge entity data from provincial, municipal, and county-level multi-level data projects, combining multiple levels of data into a single database file. The merged content includes graphic elements, entities, semantic relationships, entity-graphic element association tables, etc. Please refer to Table 2 for the corresponding table structures before and after multi-level geographic entity data merging.

[0091] Table 2. Table structure before and after fusion of multi-level geographic entity data

[0092]

[0093] S20: Based on multi-level geographic entity data, entities at different levels are automatically identified and recognized according to their geometric, semantic, and attribute characteristics to determine whether they are the same entity or entities with hierarchical relationships, and the recognition result is obtained.

[0094] In some embodiments, when automatically identifying and recognizing entities at different levels, the identified entity categories include at least one of rivers, lakes, islands, reservoirs, railways, subways, and highways; the recognition scenarios include entities with different geometric precisions, entities with different graphic representations, and entities with hierarchical relationships across administrative regions.

[0095] Specifically, for the identification of entity categories, as shown in Table 3, based on the characteristics of geographic entity data at different levels and the needs of actual applications, and combined with the geometric shape and attributes of geographic entity data, identity is determined according to the geographic entity data category, which mainly includes rivers, lakes, islands, reservoirs, railways, subways, highways, etc.

[0096] Table 3. Categories of Entities for Identity Verification

[0097]

[0098] The situations for determining entity identity are mainly divided into three types: the same entity with different geometric precision, the same entity with different graphic representations, and parent-child entities across administrative regions.

[0099] 1) Different geometric precisions for the same entity

[0100] Because the topographic map scales used in the conversion of provincial and municipal / county-level geographic entity data are different, the geometric positional accuracy of provincial entity graphics is lower, while that of municipal / county-level entity graphics is higher. Therefore, it is necessary to determine whether the two are the same entity through identity judgment.

[0101] For example, the "Xinghai Lake" entity produced at the provincial and Lianyungang city levels differs in geometric positional precision. The Lianyungang city-level entity of "Xinghai Lake" is more refined in its graphic representation compared to the provincial-level entity. Therefore, an identity verification process is needed to determine that they are "the same lake entity with different geometric precisions." Figure 7 As shown.

[0102] 2) Different graphical representations of the same entity

[0103] Because the granularity of geographic entity data differs between provincial and municipal / county levels, provincial entities only represent line primitives for some entities, while municipal / county entities add polygon primitives. Therefore, identity verification is required to confirm that the two are the same entity.

[0104] For example, the "Changjiang Road" entities produced at the provincial and Taicang city levels, such as... Figure 8 As shown, the Taicang municipal-level "Changjiang Road" entity includes both the road centerline (line element) and the road surface (surface element), while the provincial-level "Changjiang Road" entity only includes the road centerline (line element). Therefore, it is necessary to determine whether the two are "different graphic representations of the same road entity" through identity verification.

[0105] 3) Parent-child entities spanning administrative regions

[0106] Since the scope of provincial entities spans multiple city and county administrative regions, and the scope of city and county-level entities is divided according to administrative boundaries, it is necessary to clarify the parent-child entity hierarchy between the two through identification.

[0107] For example, the provincial-level entity "Jiangsu Section of the Yangtze River" is divided into city and county-level entities based on administrative regions, such as "Nanjing Section of the Yangtze River," "Wuxi Section of the Yangtze River," and "Changzhou Section of the Yangtze River." It is necessary to determine the parent-child relationship between each city / county-level segment entity and the "Jiangsu Section of the Yangtze River," such as... Figure 9 As shown.

[0108] Step S20 is used to determine the identity of entities. Based on the different characteristics of point, line, and area entities, and comprehensively considering features such as the entity's location, geometry, semantics, and attribute information, a differentiated matching model is formulated to determine the identity of provincial and municipal / county-level geographic entities. The identity determination of point entities focuses on the entity name and location; the identity determination of line entities focuses on the entity type, entity name, and line direction; and the identity determination of area entities focuses on the entity type, entity name, and the distribution range of the area.

[0109] In some embodiments, such as Figure 10 The diagram shows the entity identity identification process. This process takes provincial and municipal basic geographic entities obtained during the resource collection phase as input. The specific steps are as follows: First, multi-dimensional information of the entity to be matched is extracted from these two types of basic geographic entities, covering spatial location, geometric features, semantic features, and attribute information. Then, the corresponding matching models are called to perform multi-dimensional matching operations, including spatial location matching models, geometric feature matching models, and semantic and attribute matching models, to comprehensively match the extracted information. After the matching is completed, the process enters the determination phase of whether the information matches: If the determination result is that they are the same entity, the unified entity coding process of the multi-level geographic entity association and identification phase is executed to uniformly encode the entity; if the determination result is that they are different entities, the current identity identification process ends.

[0110] In practice, step S20 can be implemented based on the entity identity verification tool, which specifically includes the following steps S201-S203.

[0111] S201: For point-like entities, identification is based on the entity name and the distance between the points.

[0112] like Figure 11 As shown, for point entities, with provincial and city-level basic geographic entities (points) as input objects, the model principle is as follows: by calculating the distance between two points and comparing it with a preset threshold, it determines whether the two point entities match. The application scenario is suitable for matching point geographic entities, such as point-based water conservancy entities and place name entities. The matching characteristic is that the matching accuracy can be controlled by adjusting the threshold; the smaller the threshold, the higher the matching accuracy. Based on the above model, distance judgment is performed on the input provincial and city-level point-based basic geographic entities: if the distance between two points is ≤ the preset threshold, then the two entities are marked as matched; if the distance between two points is > the preset threshold, then they are directly determined to be different entities.

[0113] In practical implementation, for point-like entities such as islands, the identification system is used to automatically identify multi-level data from different sources. The identification steps are as follows:

[0114] S2011: Using the entity data layer in the input entity data, filter the data categories that need to be identified by conditions, and select the entity objects that meet the conditions.

[0115] S2012: Select entity objects in the reference entity layer that meet the filtering criteria, and calculate the distance between the two points.

[0116]

[0117] in, and These are the coordinates of a point-like entity selected from the entities at the first spatial scale level. and The coordinates of another point-like entity selected from the entities at the second spatial scale level are compared with a preset threshold to determine whether they are the same entity.

[0118] S2013: Filter multi-level entities with overlapping point-like entity buffers. If the city / county level entity and the provincial level entity have the same name and the same entity type, the software will initially determine that the city / county level entity and the provincial level entity are the same entity, and then manually verify it.

[0119] S2014: If a city / county level entity and a provincial level entity are of the same type and the distance between the entities is less than or equal to the threshold, but their names are different, then it shall be manually determined whether they are the same entity, and semantic consistency processing shall be performed on the identified entities.

[0120] S202: For linear entities, identification is based on entity type, name, line direction, and buffer overlap ratio.

[0121] like Figure 12 As shown, for linear entities, provincial-level and city-level basic geographic entities (lines) are used as input objects. Matching is performed based on a deviation threshold model. The specific logic is as follows: The model principle is to create a buffer for linear entities, calculate the overlap area ratio, and use this to determine whether the entities match. Its application scenario is suitable for matching linear geographic entities such as roads and water systems. The matching feature is that it can handle the curvature and extension of line elements well. The matching process is as follows: First, the overlap area is calculated for the input provincial and city-level linear basic geographic entities, and then a threshold judgment is performed: If the overlap area ratio is ≥ the threshold, the two linear entities are directly marked for matching; if the overlap area ratio is < the threshold, the geometric feature check is entered to judge the similarity of their curvature / direction, and attribute assistance is combined. Finally, based on these results, they are either judged as the same entity or as different entities.

[0122] In practical implementation, for linear geographic entities such as roads and waterways, the identification system creates buffer zones for these entities, calculates the percentage of overlapping areas, and determines whether they belong to the same entity based on the similarity of their areas. The system automatically identifies multi-level data from different sources, and the identification steps are as follows:

[0123] S2021: Using the entity data layer in the input entity data and the entity data layer in the reference entity data, filter the data categories that need to be identified by conditions.

[0124] S2022: Based on the set buffer distance, using the linear entity as the central axis, expand outwards to both sides to form a strip-shaped polygonal region. Generate a buffer zone for linear objects from the objects that meet the conditions in step S2021.

[0125] S2023: Calculate the overlap area Soverlap of the input entity data and reference entity data buffers, and calculate the overlap degree between the input entity and the reference entity, including a first ratio and a second ratio, where the first ratio a1 = Soverlap / S1; the second ratio a2 = Soverlap / S2, and S1 and S2 are the total area of ​​the input entity buffer and the total area of ​​the reference entity buffer, respectively. Based on the overlap degree of the graphics, i.e., the first ratio and the second ratio, calculate the proportional similarity and determine whether the proportional similarity is greater than or equal to a set threshold. The calculation method of proportional similarity includes, but is not limited to, calculating the absolute difference between the two ratios.

[0126] S2024: Compare the attributes of the objects that meet the threshold setting conditions in step S2022. If the city / county level entity and the provincial level entity have the same entity type and the same name, the software will initially determine that the city / county level entity and the provincial level entity are the same entity, and then manually verify it.

[0127] S2025: If a city / county level entity and a provincial level entity have the same entity type and a graphic similarity greater than or equal to the threshold, but different names, then it shall be manually determined whether they are the same entity, and semantic consistency processing shall be performed on the identified entities.

[0128] S2026: If city / county level entities and provincial level entities have the same entity type and their graphic similarity is less than the threshold, further use software to calculate geometric features such as curvature or direction, supplemented by attribute comprehensive judgment to determine whether they are the same entity, and perform semantic consistency processing on the identified entities.

[0129] Curvature calculation: For each arc segment, let the arc length L and the chord length S. Then the curvature of the arc segment The overall curvature is the weighted average of each arc segment (the weight is the proportion of the arc length).

[0130] Direction calculation: Calculate the orientation using the coordinates of the first and last points of the line segment.

[0131]

[0132] in, and Let these be the coordinates of the first point of the line segment. and The coordinates of the end point of the line segment;

[0133] If the azimuth difference is ≤15°, increase the similarity.

[0134] S203: For planar entities, identification is based on entity type, name, the distribution range of the faces, and the proportion of overlapping areas.

[0135] like Figure 13 As shown, for isal entities, provincial and city-level basic geographic entities (areas) are used as input objects. Matching is performed based on a deviation threshold model. The model principle is to create a buffer for isal entities, calculate the overlap area ratio, and use this to determine whether the entities match. The application scenario is suitable for matching linear geographic entities such as roads and water systems. The matching feature is that it can handle the curvature and extension of line features well. The specific matching process is as follows: First, the overlap area of ​​the input provincial and city-level isal basic geographic entities is calculated, and then a threshold judgment is performed: if the overlap area ratio is greater than or equal to the threshold, the two isal entities are directly marked for matching; if the overlap area ratio is less than the threshold, the geometric feature check is performed to judge the similarity of their curvature / direction, and attributes are used to assist in the judgment. Finally, based on these results, they are either judged as the same entity or as different entities.

[0136] In practical implementation, for areal entities such as lakes, the identification system calculates the percentage of overlapping area and determines whether they belong to the same entity based on the area overlap ratio. The system automatically identifies multi-level data from different sources, and the identification steps are as follows:

[0137] S2031: Using the entity data layer in the input entity data and the entity data layer in the reference entity data, filter the data categories that need to be identified by conditions.

[0138] S2032: Use software to calculate the ratio of overlapping area of ​​surface features to total area, determine whether the graphic similarity threshold is met by the area similarity ratio, and calculate the distance between the center points of the surfaces.

[0139] S2033: Obtain the total area Area(A1) and Area(A2) of the input plane (A1) and the reference plane (A2), calculate the overlap area S_overlap, and calculate the similarity based on the bidirectional overlap ratio.

[0140] In this embodiment, input surface A1 is a planar geographic entity at a lower spatial scale level (such as city or county level) to be matched, and reference surface A2 is a planar geographic entity at a higher spatial scale level (such as province level) to be matched as a benchmark. The definitions of the above two surfaces are custom matching object settings for multi-level geographic entity cross-scale matching scenarios in this application, used to clearly distinguish the hierarchical attributes of the entity to be matched and the benchmark entity.

[0141] Calculation of the center point coordinates of a surface: Suppose a surface entity consists of n vertices with coordinates (x1, y1), (x2, y2), ..., (x...). n ,y n Composed of ) and center point ( , The calculation formula is:

[0142] ;

[0143] .

[0144] Center point distance calculation: Let the center point of the input surface be ( , The center point of the reference plane is ( , Then the distance from the center point .

[0145] S2034: Based on the processing results of step S2032, if the city / county level entity and the provincial level entity have the same entity type and the same name, and the distance between the center points of the surfaces is within the threshold, and the graphic similarity of the surfaces is greater than or equal to the threshold (the “threshold” can be adjusted according to the identification results), the software will initially determine that the city / county level entity and the provincial level entity are the same entity, and then conduct manual verification.

[0146] If a city / county-level entity and a provincial-level entity have the same entity type and a graphic similarity greater than or equal to the threshold, but different names, then it will be manually determined whether they are the same entity, and semantic consistency processing will be performed on the identified entities.

[0147] In this embodiment, the similarity calculation is bidirectional similarity, that is, the ratio of the processed entity to the reference entity and the ratio of the reference entity to the processed entity are both satisfied, and the similarity condition is met.

[0148] The similarity calculation methods for different primitives are shown in Table 4.

[0149] Table 4 Similarity Calculation Method

[0150]

[0151] Based on the matching models of different types of entities, the identity of municipal and county-level geographical entities and provincial-level geographical entities is identified. The municipal and county-level geographical entities are marked with the identifiers "identified as the same entity" or "identified as parent and child entities", and the information of the identified municipal and county-level entities and provincial-level entities is recorded.

[0152] S30: Based on the identification results, the multi-level entities identified as the same entity are coded uniformly, and the entities identified as having hierarchical relationships are coded with a relationship, resulting in a unified coding result.

[0153] In this embodiment, in step S30, based on the identity determination result, data that are determined to be the same entity at the city / county level and the provincial level are uniformly encoded, and data that are determined to be parent-child entities at the city / county level and the provincial level are associated and recorded to obtain the geographical entity coding association results of the province, city, and county.

[0154] To achieve one code per geographic entity, it is necessary to carry out unified coding of entities across levels. In some embodiments, the specific process is as follows: steps S301-S303, identifying duplicate codes between provincial and municipal / county level entities through geographic entity code matching; uniformly processing duplicate codes belonging to the same entity to ensure that the codes of the same entity are consistent and that the codes of parent and child entities are related; and eliminating conflicts by recoding municipal / county level entities that cannot be identified as the same entity but have duplicate codes.

[0155] S301: Uniform coding for the same entity across different levels.

[0156] Referring to the multi-level entity information at the provincial, municipal, and county levels in the identity determination records, objects marked as "identified as the same entity" or "identified as parent-child entities" in the municipal and county-level entities are coded according to the following rules.

[0157] (1) The same entity has been identified and uniformly coded to avoid "different codes for the same item".

[0158] If the code of a city or county-level entity is the same as that of the corresponding provincial entity, the code is retained directly; if the codes are different, the provincial entity code shall prevail, the provincial "entity spatial identity code" shall be assigned to the same-name field of the city or county-level entity, and a "duplicate code identifier" field shall be added and filled in "already duplicated".

[0159] For example, if there is an entity called "Qinshan Island" at both the provincial and municipal / county levels, and the "entity spatial identity codes" of the two are different, the provincial code will be assigned to the municipal / county level "Qinshan Island" to achieve code unification.

[0160] (2) The parent and child entities have been identified as having added fields, and the parent and child codes are associated.

[0161] Create a new "Parent Entity Spatial Identity Code" field in the city and county level entities, and fill in the "Entity Spatial Identity Code" of the corresponding provincial entity into this field to establish a parent-child relationship.

[0162] For example, the provincial-level river "Jiangsu Section of the Yangtze River" corresponds to the city / county-level rivers "Nanjing Section of the Yangtze River," "Wuxi Section of the Yangtze River," and "Changzhou Section of the Yangtze River." A new "Parent Entity Spatial Identity Code" field is created within the city / county-level entities, and the code for the provincial-level "Jiangsu Section of the Yangtze River" is entered to clarify the parent-child relationship.

[0163] S302: Deduplication of identical codes for different entities across different levels.

[0164] By matching geographic entity codes, duplicate codes between provincial and municipal / county-level entities are identified. For municipal / county-level entities that cannot be identified as the same entity but have duplicate codes, they are recoded to eliminate coding conflicts, ensure "one item, one code", and a "duplicate code identifier" field is added, filled with "recoded".

[0165] S303: Unified coding feedback at the city and county levels.

[0166] The city and county-level entity data with the "re-coded" mark added to the unified entity coding are sorted and summarized by region, forming a re-coded entity list and fed back to each city and county to update the coding of city and county-level geographic entity data, so as to ensure the consistency between city and county-level geographic entity coding and provincial entity coding.

[0167] S40: Based on the unified coding results, establish a relationship table between entities at different spatial scale levels to realize the structured association of multi-level geographic entity data; wherein, the relationship table includes at least the lower-level entity code, the higher-level entity code, and the entity relationship type field, and realizes the storage of the relationship between entities through code matching.

[0168] Entity association construction is divided into two scenarios: "same entity association" and "parent-child entity association". The specific rules and implementation methods are as follows.

[0169] (1) Association rules

[0170] 1) Association of the same entity

[0171] For the same entity in provincial and municipal / county-level geographic entities, unified coding has been completed, and association can be achieved directly through entity coding.

[0172] 2) Parent-child entity relationship

[0173] Parent-child relationships between provincial and municipal / county-level geographic entities are established based on the condition that "the 'parent entity spatial identity code' of the municipal / county-level entity is the same as the 'entity spatial identity code' of the provincial entity".

[0174] (2) Entity Relationship Table Design

[0175] An entity relationship table named "GXBZC" (meaning "composition relationship table") is established to record the association relationships between provincial and municipal / county level entities. The table structure design is shown in Table 5.

[0176] Table 5 Table Structure

[0177]

[0178] This application also provides a multi-level geographic entity data consistency verification device for implementing the methods described in any of the above embodiments, such as... Figure 14 As shown, the multi-level geographic entity data consistency verification device includes:

[0179] The data fusion module 1401 is configured to perform format conversion, hierarchical identification and merging of source geographic entity data at different spatial scale levels to form multi-level geographic entity data for unified storage.

[0180] The identity determination module 1402 is configured to automatically identify and determine entities at different levels based on the multi-level geographic entity data, according to the geometric features, semantic features and attribute features of the entities, to determine whether they are the same entity or entities with hierarchical relationships, and to obtain the determination result.

[0181] The unified coding module 1403 is configured to perform unified coding on multi-level entities identified as the same entity based on the identification result, and to establish coding association relationship for entities identified as having hierarchical relationship, thereby obtaining a unified coding result;

[0182] The entity association module 1404 is configured to establish an association table between entities at different spatial scale levels based on the unified coding result, thereby realizing the structured association of multi-level geographic entity data. The association table includes at least a lower-level entity code, a higher-level entity code, and an entity relationship type field, and the association relationship between entities is stored through code matching.

[0183] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0184] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0185] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0186] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0187] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the multi-level geographic entity data consistency determination method described in the above embodiments.

[0188] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the multi-level geographic entity data consistency identification method in the above embodiments.

[0189] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0190] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0191] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0192] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0193] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0194] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0195] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0196] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0197] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0198] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for determining the consistency of multi-level geographic entity data, characterized in that, The method includes: The source geographic entity data at different spatial scales are converted in format, hierarchically identified and merged to form a unified multi-level geographic entity data. Based on the multi-level geographic entity data, entities at different levels are automatically identified and recognized according to their geometric, semantic, and attribute features to determine whether they are the same entity or entities with hierarchical relationships, and the recognition result is obtained. Based on the identification results, multi-level entities identified as the same entity are coded uniformly, and coded associations are established for entities identified as having hierarchical relationships, resulting in unified coding results; Based on the unified coding results, an association table is established between entities at different spatial scale levels to realize the structured association of multi-level geographic entity data; wherein, the association table includes at least the lower-level entity code, the higher-level entity code, and the entity relationship type field, and the association relationship between entities is stored through code matching.

2. The method for determining the consistency of multi-level geographic entity data according to claim 1, characterized in that, Geographic entity data from different spatial scales is format-converted, hierarchically labeled, and merged to form a unified, multi-level geographic entity data system, including: Based on entity data structure templates and relation mapping files, geographic entity data from multiple sources is converted into a unified database file; Add identifiers to the converted data layers to distinguish the sources of different spatial scale levels; Data engineering at multiple spatial scales is merged into a single database file, including graph data, entity data, semantic relationship tables, and entity-graph element association tables.

3. The method for determining the consistency of multi-level geographic entity data according to claim 1, characterized in that, When automatically identifying and recognizing entities at different levels, the entity categories to be recognized include at least one of rivers, lakes, islands, reservoirs, railways, subways, and highways; the recognition scenarios include entities with different geometric precisions, entities with different graphic representations of the same entity, and entities with hierarchical relationships across administrative regions.

4. The method for determining the consistency of multi-level geographic entity data according to claim 1, characterized in that, Based on the aforementioned multi-level geographic entity data, entities at different levels are automatically identified and recognized according to their geometric, semantic, and attribute characteristics. This determines whether they are the same entity or entities with hierarchical relationships, yielding the recognition results, including: For point-like entities, identification is based on the entity name and the distance between points, including calculating the distance d between two points: ； in, and These are the coordinates of a point-like entity selected from the entities at the first spatial scale level. and The coordinates of another point entity selected from the entities at the second spatial scale level; If the distance is less than or equal to the preset threshold, and the entity type and name are the same, then they are considered to be the same entity. For linear entities, identification is based on entity type, name, line direction, and buffer overlap ratio. This includes creating a buffer for the linear entity, calculating the overlap area between the buffer of the input entity data and the buffer of the reference entity data, and calculating the ratio of the overlap area to the total area of ​​the input entity buffer as a first ratio and the ratio to the total area of ​​the reference entity buffer as a second ratio. The proportional similarity is determined based on the first ratio and the second ratio. If the proportional similarity is greater than or equal to the set threshold, and the entity type and name are the same, then they are initially identified as the same entity. If the overlap is greater than or equal to the set threshold, and the entity type and name are the same, they are initially identified as the same entity; For planar entities, identification is based on entity type, name, the distribution range of faces, and the proportion of overlapping area. This includes calculating the overlapping area S_overlap between input face A1 and reference face A2, and calculating the bidirectional overlap ratio as the similarity s. ； Where Area(A1) represents the total area of ​​input surface A1, and Area(A2) represents the total area of ​​reference surface A2; If the similarity s is greater than or equal to the set threshold, and the entity type and name are the same, then they are initially identified as the same entity.

5. The method for determining the consistency of multi-level geographic entity data according to claim 4, characterized in that, For linear entities, when the proportional similarity is less than a set threshold, the curvature of the linear entity is further calculated for auxiliary judgment. The formula for calculating the curvature k of the arc segment is: ； ； Where L is the arc length, S is the chord length, and h is the arch height; the overall curvature is the weighted average of the curvatures of each arc segment, with the weight being the proportion of the arc length. And / or, calculate the azimuth using the coordinates of the first and last points of the line segment. To determine the direction: ； in, and Let these be the coordinates of the first point of the line segment. and The coordinates of the end point of the line segment; If the difference in azimuth angle between two linear entities is not greater than a preset angle threshold, the proportional similarity is increased.

6. The method for determining the consistency of multi-level geographic entity data according to claim 4, characterized in that, For planar entities, after initially identifying them as the same entity, further auxiliary judgment is made based on the distance between the center points of the planar entities. Let the planar entity consist of n vertex coordinates (x1, y1), (x2, y2), ..., (x...). n ,y n Composed of ) and center point ( , The formula for calculating ) is: ； ； Let the center point of the input surface be ( , The center point of the reference plane is ( , Then, the formula for calculating the distance between the center points is: ； If the center point distance Within the set threshold, it serves as an auxiliary condition for identifying the same entity.

7. The method for determining the consistency of multi-level geographic entity data according to claim 1, characterized in that, Based on the identification results, multi-level entities identified as the same entity are coded uniformly, and coded associations are established for entities identified as having hierarchical relationships, resulting in unified coding results, including: If entities at different spatial scales are identified as the same entity but have different codes, the code of the higher-level entity shall prevail, and the lower-level entity shall be coded, assigned a value, and marked with a duplicate code. If entities at different spatial scales are identified as having a hierarchical relationship, a new field is added to the lower-level entity to store the code of the higher-level entity it is associated with, so as to establish a coded relationship. Entities with duplicate codes that cannot be identified as the same entity or have a hierarchical relationship are re-encoded and marked with a duplicate code identifier.

8. A multi-level geographic entity data consistency verification device, characterized in that, The device includes: The data fusion module is configured to convert the format, identify and merge the source geographic entity data at different spatial scales, forming a unified multi-level geographic entity data. The identity determination module is configured to automatically identify and determine entities at different levels based on the multi-level geographic entity data, according to the geometric features, semantic features, and attribute features of the entities, to determine whether they are the same entity or entities with hierarchical relationships, and to obtain the determination result. The unified coding module is configured to perform unified coding on multi-level entities identified as the same entity based on the identification result, and to establish coding association relationships for entities identified as having hierarchical relationships, thereby obtaining unified coding results; The entity association module is configured to establish an association table between entities at different spatial scale levels based on the unified coding result, thereby realizing the structured association of multi-level geographic entity data. The association table includes at least lower-level entity codes, higher-level entity codes, and entity relationship type fields, and the association relationship between entities is stored through code matching.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the multi-level geographic entity data consistency identification method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the multi-level geographic entity data consistency determination method as described in any one of claims 1-7.

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