Real estate surveying and mapping information intelligent verification method, device, equipment and medium

Abstract

This application relates to a method, apparatus, equipment, and medium for intelligent verification of real estate surveying and mapping information. The method includes: extracting spatial surveying and mapping information and ownership attribute information based on real estate surveying and mapping information; extracting spatial parameters from the spatial surveying and mapping information and constructing a multi-level spatial nesting relationship model through topological analysis; generating spatial consistency verification results based on the model and preset spatial consistency verification rules; generating conflict verification results by combining ownership attribute information and spatial consistency verification results through spatial-ownership conflict verification rules; and integrating spatial surveying and mapping information, ownership attribute information, the multi-level spatial nesting relationship model, spatial consistency verification results, and conflict verification results to generate a structured real estate surveying and mapping verification archive. This method can automatically realize consistency verification and conflict detection of spatial and ownership information.

Description

Technical Field

[0001] This invention belongs to the field of real estate management, and in particular relates to intelligent verification methods, devices, equipment and media for real estate surveying and mapping information. Background Technology

[0002] With the development of technology in the field of real estate management, technologies such as digital surveying and mapping and Geographic Information Systems (GIS) have gradually become widespread. These technologies offer advantages such as efficient data collection and convenient storage, driving the digital transformation of real estate surveying and mapping information management. Currently, the verification of real estate surveying and mapping information mostly employs traditional manual verification combined with simple tools. In traditional techniques, staff need to manually extract spatial geometry and ownership descriptions from surveying data, verify the consistency of spatial data based on experience, manually compare spatial information with ownership attributes for conflicts, and compile the verification results into archives.

[0003] However, this method has obvious drawbacks: manual extraction of multi-source heterogeneous data is inefficient and prone to errors due to human operation; it lacks systematic spatial relationship modeling, making it difficult to accurately verify the consistency of multi-level spatial elements; the verification of the association between spatial and ownership information relies on subjective judgment, and the comprehensiveness and accuracy of conflict detection are insufficient; the integration of verification results lacks a unified standard, the degree of archival structuring is low, which is not conducive to subsequent querying and application. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, equipment, and medium for intelligent verification of real estate surveying and mapping information that can solve the above problems.

[0005] Firstly, this application provides an intelligent verification method for real estate surveying and mapping information, including:

[0006] Based on real estate surveying and mapping information, spatial surveying and mapping information and ownership attribute information are extracted;

[0007] Based on spatial mapping information, spatial parameters are extracted through topological analysis, and a multi-level spatial nesting relationship model is constructed based on the spatial parameters.

[0008] Based on a multi-level spatial nesting relationship model, spatial consistency verification results are generated according to preset spatial consistency verification rules.

[0009] Based on the spatial consistency verification results and ownership attribute information, conflict verification results are generated according to the preset spatial-ownership conflict verification rules.

[0010] Integrate spatial surveying and mapping information, ownership attribute information, multi-level spatial nesting relationship model, spatial consistency verification results, and conflict verification results to generate real estate surveying and mapping verification archives.

[0011] In one embodiment, based on real estate surveying information, spatial surveying information and ownership attribute information are extracted, including:

[0012] Multi-source heterogeneous data extraction is performed on real estate surveying and mapping information to obtain spatial geometric data and descriptive text data;

[0013] According to the preset spatial element classification rules, the spatial geometric data is divided into the corresponding spatial element categories to obtain spatial element data. The spatial element categories include boundary-related elements, parcel outline elements, spatial coordinate elements, geometric morphology elements, and basic topographic elements.

[0014] Based on descriptive text data, a keyword extraction algorithm is used to obtain ownership association data, which includes data on the right holder, data on the core attributes of the right, data on the scope of the right, and data on the restrictions on the right.

[0015] Based on spatial element data, element attribute values ​​are assigned to obtain spatial mapping information;

[0016] Based on ownership association data, ownership attribute information is obtained through ownership hierarchy coding.

[0017] In one embodiment, spatial parameters include spatial topological relationships, spatial element hierarchies, and spatial containment relationships;

[0018] Based on spatial mapping information, spatial parameters are extracted through topological analysis, and a multi-level spatial nesting relationship model is constructed based on these parameters, including:

[0019] Analyze the topological relationships among boundary-related elements, parcel outline elements, and basic topographic elements to obtain spatial topological relationships;

[0020] Based on the spatial topological relationships, the hierarchy among boundary-related elements, parcel outline elements, and basic topographic elements is determined by using a preset hierarchical priority division rule, thus obtaining the spatial element hierarchy.

[0021] Based on boundary-related elements, parcel outline elements, and basic topographic elements, spatial inclusion relationships are obtained by calculating the inclusion degree between the various spatial element levels.

[0022] Using spatial inclusion relationships as the nesting rules and spatial coordinate elements as the location benchmark, the first nested unit is constructed based on boundary-related elements, parcel outline elements, and basic topographic elements.

[0023] Based on the first nested unit, the geometric boundary matching and correction are performed according to the geometric morphology elements to obtain the second nested unit;

[0024] Integrate the second nested unit to form a multi-level spatial nesting relationship model.

[0025] In one embodiment, the spatial consistency verification rules include coordinate accuracy grading standards, topological constraint rules, and spatial parameter threshold ranges; the spatial consistency verification results include coordinate accuracy level, topological conflict detection results, and spatial parameter detection results.

[0026] Based on a multi-level spatial nesting relationship model, spatial consistency verification results are generated according to preset spatial consistency verification rules, including:

[0027] Based on a multi-level spatial nesting relationship model, the coordinate accuracy level is determined by a coordinate accuracy grading standard.

[0028] Based on the coordinate accuracy level, topological constraint rules are used to test the multi-level spatial nesting relationship model, and the topological conflict test results are obtained.

[0029] Based on the coordinate accuracy level, spatial parameters are tested using a spatial parameter threshold range to obtain the spatial parameter test results.

[0030] In one embodiment, the spatial-ownership conflict verification rules include spatial-ownership keyword matching rules, spatial-ownership adaptation calculation rules, conflict level determination rules, and conflict type determination rules.

[0031] Based on the spatial consistency verification results and ownership attribute information, conflict verification results are generated according to preset spatial-ownership conflict verification rules, including:

[0032] Based on the coordinate accuracy level and combined with the data on the delineation of the scope of rights, the spatial ownership verification benchmark is determined;

[0033] Based on the spatial-ownership verification benchmark, spatial-ownership keyword matching rules are used to associate the topological conflict verification results and the core attribute data of rights to obtain topological-ownership association data.

[0034] Based on the coordinate accuracy level, spatial-ownership adaptation calculation rules are adopted to perform adaptation analysis on spatial parameter verification results and rights holder data, and the adaptation analysis results are obtained.

[0035] Based on topology-ownership association data and adaptation analysis results, the conflict level is determined by conflict level determination rules.

[0036] Based on conflict level and rights restriction data, conflict type determination rules are used to identify spatial-ownership conflict types.

[0037] Integrate conflict levels and spatial-ownership conflict types to generate conflict verification results.

[0038] In one embodiment, the calculation formula used for adapting the spatial parameter test results and the rights holder data is as follows:

[0039]

[0040] in, To accommodate the analysis scores, n represents the total number of spatial parameter test results, and i represents the index of the spatial parameter test result. The weighting coefficients for the test results of the i-th spatial parameter are... To adapt weights to spatial parameters, Weights for the complexity of ownership relationships. Let be the standardized deviation value of the test result for the i-th spatial parameter. To calculate the data complexity based on the rights holder's data, The preset precision sensitivity coefficient, The attenuation factor is calculated based on the coordinate accuracy level. The number of entries for which rights are restricted.

[0041] In one embodiment, spatial mapping information, ownership attribute information, multi-level spatial nesting relationship model, spatial consistency verification results, and conflict verification results are integrated to generate a real estate mapping verification file, including:

[0042] Based on a multi-level spatial nesting relationship model, a basic framework for archives is constructed through hierarchical coding;

[0043] Based on the basic archival framework, spatial surveying information and ownership attribute information are linked and mapped to obtain spatial-ownership associated archives;

[0044] Based on the spatial-ownership association archives, the verification results are marked according to the spatial consistency verification results and conflict verification results to obtain the real estate surveying and mapping verification archives.

[0045] Secondly, this application also provides an intelligent verification device for real estate surveying and mapping information, comprising:

[0046] The surveying information extraction module is used to extract spatial surveying information and ownership attribute information based on real estate surveying information;

[0047] The spatial model construction module is used to extract spatial parameters through topological analysis based on spatial mapping information, and to construct a multi-level spatial nesting relationship model based on the spatial parameters.

[0048] The consistency verification module is used to generate spatial consistency verification results based on a multi-level spatial nesting relationship model and according to preset spatial consistency verification rules.

[0049] The space rights conflict verification module is used to generate conflict verification results based on the space consistency verification results and ownership attribute information, according to the preset space-ownership conflict verification rules.

[0050] The archive integration and generation module is used to integrate spatial surveying and mapping information, ownership attribute information, multi-level spatial nesting relationship models, spatial consistency verification results, and conflict verification results to generate real estate surveying and mapping verification archives.

[0051] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described intelligent verification method for real estate surveying information.

[0052] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described intelligent verification method for real estate surveying and mapping information.

[0053] The aforementioned intelligent verification method, device, equipment, and medium for real estate surveying and mapping information extracts spatial surveying and mapping information and ownership attribute information based on real estate surveying and mapping information, avoiding the inefficiency and errors of manually extracting multi-source heterogeneous data; it extracts spatial parameters through topological analysis and constructs a multi-level spatial nesting relationship model to achieve systematic spatial relationship modeling to verify the consistency of multi-level spatial elements; it generates spatial consistency verification results according to preset spatial consistency verification rules, improving the standardization and accuracy of spatial data verification; combining the results with ownership attribute information, it generates conflict verification results through spatial-ownership conflict verification rules, avoiding reliance on subjective judgment and ensuring the comprehensiveness and accuracy of conflict detection; and it integrates various information, models, and verification results to generate real estate surveying and mapping verification archives, achieving standardized integration of verification results, improving the structure of the archives, and facilitating subsequent querying and application. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the intelligent verification method for real estate surveying information of the present invention;

[0056] Figure 2 This is a structural diagram of the intelligent verification device for real estate surveying information according to the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] In one embodiment, such as Figure 1 As shown, an intelligent verification method for real estate surveying information is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this application, the hardware architecture of the implementation environment includes a server with data processing and storage capabilities, and a terminal (such as a surveying instrument, mobile GIS terminal, and PC terminal) for collecting raw real estate surveying data. Application scenarios include: when there are requirements for real estate registration review, ownership change verification, and surveying result acceptance, and when it is necessary to efficiently and accurately complete the consistency verification and conflict detection of spatial surveying information and ownership attribute information, the surveying terminal collects spatial geometric data and ownership description text data and uploads them to the server. The server calls a preset algorithm to extract key information, constructs a multi-level spatial nested relationship model, and executes the verification process. After generating a structured verification file, it feeds it back to the terminal. The terminal supports staff to view the verification result annotations, realizing the standardized and automated verification needs in the field of real estate management.

[0059] In this embodiment, the method includes the following steps:

[0060] S01, Based on real estate surveying and mapping information, extract spatial surveying and mapping information and ownership attribute information.

[0061] Among them, real estate surveying and mapping information refers to multi-source heterogeneous data collected through digital surveying and mapping methods (such as GIS or remote sensing technology), including spatial geometric data (such as point, line, and area coordinates) and descriptive text data (such as ownership registration documents); spatial surveying and mapping information is standardized spatial data obtained by structuring and assigning values ​​to geometric data through spatial element classification rules (such as element categories based on boundaries, land parcel outlines, and topographic foundations), which relies on topological analysis preprocessing to identify geometric shapes and coordinate benchmarks; ownership attribute information is ownership-related data parsed from text data using keyword extraction algorithms (such as those based on natural language processing technology), including hierarchical coded information such as the right holder, core attributes, scope definition, and restrictive clauses. Through the multi-source data extraction interface, data cleaning, element classification, and attribute mapping can be performed to achieve efficient separation and standardized output of spatial information and ownership information, providing structured input for subsequent verification processes.

[0062] S02, based on spatial mapping information, extracts spatial parameters through topological analysis, and constructs a multi-level spatial nesting relationship model based on the spatial parameters.

[0063] Topology analysis employs computational geometry or GIS tools to analyze the topological relationships (such as adjacency, intersection, and containment) between spatial features to extract spatial parameters. These parameters include core attributes such as topological correlation, feature hierarchy priority, and spatial containment. The multi-level spatial nesting relationship model is a hierarchical structure model that organizes spatial features through nesting rules (such as containment relationships). In implementation, feature hierarchy is determined based on topological correlation, nested units are formed through containment calculations, and boundary corrections are performed using geometric morphology data to optimize model consistency. Priority partitioning rules can be used to dynamically allocate hierarchy levels, or iterative integration methods can be used to generate multi-level nested structures, enabling the model to adapt to spatial relationship expressions in different surveying scenarios and providing a standardized spatial benchmark for subsequent verification.

[0064] S03, based on a multi-level spatial nesting relationship model, generates spatial consistency verification results according to preset spatial consistency verification rules.

[0065] The spatial consistency verification rules include various preset verification standards, such as coordinate accuracy grading standards (used to evaluate the accuracy level of spatial data), topological constraint rules (used to verify the consistency of topological relationships between features), and spatial parameter threshold ranges (used to quantify parameter deviations). These rules can be dynamically configured based on domain specifications or algorithms. The spatial consistency verification result is a standardized output generated through rule application, which may include indicators such as coordinate accuracy level, topological conflict verification results, and spatial parameter verification results. In implementation, spatial parameters (such as feature level, containment degree, etc.) can be parsed based on a multi-level model, and a preset rule library can be called to perform item-by-item verification—such as dynamically allocating accuracy levels through accuracy grading standards, using topological constraint rules to identify conflicts such as overlaps and gaps, using parameter threshold ranges to filter abnormal data, and integrating the various sub-results to form the spatial consistency verification result.

[0066] S04. Based on the spatial consistency verification results and ownership attribute information, generate conflict verification results according to the preset spatial-ownership conflict verification rules.

[0067] The spatial-ownership conflict verification rules consist of a set of pre-defined conflict detection logics, including keyword matching, adaptation calculation, and level or type determination. These rules can be dynamically configured to adapt to different verification scenarios, such as semantic association matching using natural language processing technology or calculating spatial-ownership fit using statistical models. The conflict verification result is a standardized output generated through rule application, which may include conflict level and conflict type. In implementation, the verification benchmark can be determined based on spatial consistency verification results (such as coordinate accuracy level) and ownership attribute information (such as rights scope definition data). Keyword matching rules are applied to associate topological conflicts with core ownership attributes. Adaptation calculation rules (such as formulaic analysis based on weighted deviation and complexity factors) are used to evaluate the fit between spatial parameters and ownership subjects. Conflict level and type are dynamically determined through judgment rules, and the conflict verification result is then integrated and generated. This step can be achieved through algorithm iteration (such as introducing machine learning to optimize thresholds) or expanding the rule base (such as integrating international surveying and mapping standards) to ensure the comprehensiveness, adaptability, and repeatability of conflict detection.

[0068] S05 integrates spatial surveying and mapping information, ownership attribute information, multi-level spatial nesting relationship model, spatial consistency verification results, and conflict verification results to generate real estate surveying and mapping verification archives.

[0069] Among them, a basic framework for archives can be constructed based on a multi-level spatial nesting relationship model, through hierarchical coding or similar structured methods (such as tree indexing or graph construction), and spatial surveying information and ownership attribute information can be associated and mapped (such as through key-value pair links) to generate intermediate archives; based on the spatial consistency verification results and conflict verification results, the verification results can be integrated by labeling or embedding (such as adding metadata tags or result fields) to generate a unified real estate surveying verification archive.

[0070] In one embodiment, based on real estate surveying information, spatial surveying information and ownership attribute information are extracted, including:

[0071] S11, extract multi-source heterogeneous data from real estate surveying information to obtain spatial geometric data and descriptive text data;

[0072] S12, according to the preset spatial element classification rules, the spatial geometric data is divided into the corresponding spatial element categories to obtain spatial element data. The spatial element categories include boundary-related elements, parcel outline elements, spatial coordinate elements, geometric morphology elements and basic terrain elements.

[0073] S13. Based on the descriptive text data, a keyword extraction algorithm is used to obtain ownership association data, which includes data on the rights holder, core attributes of the rights, scope of rights definition, and restrictions on rights.

[0074] S14. Based on spatial element data, assign element attribute values ​​to obtain spatial mapping information;

[0075] S15. Based on the ownership association data, ownership attribute information is obtained through ownership hierarchy coding.

[0076] For example, real estate surveying information can be separated into spatial geometric data (such as sets of coordinates of real estate points, lines, and surfaces) and descriptive text data (such as text materials such as ownership registration documents and approval documents). Based on the pre-defined spatial element classification rules of the real estate surveying industry standards, the spatial geometric data is classified into corresponding categories through classification matching. Boundary-related elements correspond to boundary point and boundary line data; land parcel outline elements correspond to land parcel boundary polygon data; spatial coordinate elements correspond to latitude, longitude, and elevation coordinate data; geometric morphology elements correspond to graphic shape and size data; and basic topographic elements correspond to topographic and geomorphological related geometric data, forming spatial element data. For the descriptive text data, the TF-IDF algorithm or a BERT pre-trained model can be used to identify core keywords such as "right holder," "right type," "land boundaries," and "restrictive clauses," and correspondingly extract data on the right holder, core attribute data, scope definition data, and restriction data to form ownership-related data. Based on the preset attribute fields of spatial element categories, such as configuring longitude, latitude, and precision fields for spatial coordinate elements, spatial element data and corresponding attributes are automatically associated and assigned values ​​to generate spatial mapping information; according to the hierarchical structure of "rights subject - core attribute - scope definition - rights restriction", a unique hierarchical code is assigned to the ownership-related data using coding rules to construct ownership attribute information.

[0077] In one embodiment, spatial parameters include spatial topological relationships, spatial element hierarchies, and spatial containment relationships;

[0078] Based on spatial mapping information, spatial parameters are extracted through topological analysis, and a multi-level spatial nesting relationship model is constructed based on these parameters, including:

[0079] S21, Analyze the topological relationships between boundary-related elements, parcel outline elements and basic topographic elements to obtain spatial topological relationships;

[0080] S22. Based on the spatial topological relationship, the hierarchy among boundary-related elements, parcel outline elements and basic topographic elements is determined by using the preset hierarchical priority division rules, thus obtaining the spatial element hierarchy.

[0081] S23. Based on boundary-related elements, parcel outline elements, and basic topographic elements, spatial inclusion relationships are obtained by calculating the inclusion degree between the levels of each spatial element.

[0082] S24, with spatial inclusion relationship as the nesting rule and spatial coordinate elements as the location benchmark, constructs the first nested unit based on boundary-related elements, parcel outline elements and topographic basic elements;

[0083] S25, based on the first nested unit, perform geometric boundary matching correction according to the geometric morphology elements to obtain the second nested unit;

[0084] S26, integrate the second nested unit to form a multi-level spatial nesting relationship model.

[0085] Specifically, GIS topology analysis tools can be used to perform calculations on boundary-related elements, parcel outline elements, and basic terrain elements in spatial mapping information to identify the adjacent, intersecting, and overlapping relationships between elements and extract spatial topological relationships. Based on preset hierarchical priority rules (e.g., boundary-related elements have higher priority than parcel outline elements, and parcel outline elements have higher priority than basic terrain elements), combined with spatial topological relationships, the hierarchical affiliation of the above three types of elements is clarified, resulting in spatial element hierarchies. By calculating the proportion of spatial overlap area between elements corresponding to different spatial element hierarchies, the degree of inclusion between each level is quantified, and spatial inclusion relationships are determined. Using this spatial inclusion relationship as the nesting basis, and using the latitude, longitude, and elevation data of spatial coordinate elements as location benchmarks, boundary-related elements, parcel outline elements, and basic terrain elements are nested hierarchically to construct the first nested unit. The size and shape parameters of geometrical elements can be called and compared with the geometric boundaries of the first nested unit. Boundary deviations are corrected through coordinate fine-tuning, contour fitting, and other methods to obtain the second nested unit. Based on the hierarchical logic of spatial elements, multiple second nested units are linked and integrated to form a multi-level spatial nesting relationship model that can reflect the spatial hierarchy of real estate.

[0086] In one embodiment, the spatial consistency verification rules include coordinate accuracy grading standards, topological constraint rules, and spatial parameter threshold ranges; the spatial consistency verification results include coordinate accuracy level, topological conflict detection results, and spatial parameter detection results.

[0087] Based on a multi-level spatial nesting relationship model, spatial consistency verification results are generated according to preset spatial consistency verification rules, including:

[0088] S31, based on a multi-level spatial nesting relationship model, determines the coordinate accuracy level through a coordinate accuracy grading standard;

[0089] S32, based on coordinate accuracy level, uses topological constraint rules to test the multi-level spatial nesting relationship model and obtain the topological conflict test results;

[0090] S33, based on the coordinate accuracy level, uses the spatial parameter threshold range to verify the spatial parameters and obtain the spatial parameter verification results.

[0091] For example, a preset coordinate accuracy grading standard can be retrieved (e.g., setting three threshold ranges of millimeter, centimeter, and decimeter levels according to real estate surveying industry standards). Actual deviation data of spatial coordinate elements are extracted from a multi-level spatial nesting model. By comparing the deviation values ​​with the grading thresholds one by one, the corresponding coordinate accuracy level is determined (e.g., a deviation ≤ 2 mm is judged as millimeter accuracy). Based on this accuracy level, the corresponding topological constraint rules are matched (higher accuracy levels have stricter constraints; for example, millimeter accuracy requires no overlapping boundary lines and no gaps in the closed land parcel outline, while centimeter accuracy allows for small gaps ≤ 0.5 cm). A GIS topology inspection tool is used to traverse the topological relationships of boundary-related elements, land parcel outline elements, etc., in the model to detect whether there are violations of constraints such as overlap, breakage, or non-closure. The conflict location, type, and severity are recorded to form a topological conflict inspection result. Based on the coordinate accuracy level, the corresponding spatial parameter threshold range can be retrieved (e.g., the spatial coverage threshold is set to 98%-100% for high accuracy level and 95%-100% for low accuracy level). Spatial parameters such as the strength of spatial topological association and spatial coverage in the model can be extracted. The actual values ​​of each parameter are compared with the corresponding threshold range to determine whether they are within a reasonable range. Parameters that exceed the threshold and their deviation magnitude are marked, and spatial parameter inspection results are generated. The topological conflict inspection results and spatial parameter inspection results are integrated to obtain the spatial consistency verification results.

[0092] In one embodiment, the spatial-ownership conflict verification rules include spatial-ownership keyword matching rules, spatial-ownership adaptation calculation rules, conflict level determination rules, and conflict type determination rules.

[0093] Based on the spatial consistency verification results and ownership attribute information, conflict verification results are generated according to preset spatial-ownership conflict verification rules, including:

[0094] S41, Based on the coordinate accuracy level and combined with the rights scope definition data, determine the spatial ownership verification benchmark;

[0095] S42, based on the spatial-ownership verification benchmark, the spatial-ownership keyword matching rule is used to associate the topological conflict verification results and the core attribute data of rights to obtain topological-ownership association data;

[0096] S43, based on coordinate accuracy level, adopts spatial-ownership adaptation calculation rules to perform adaptation analysis on spatial parameter verification results and rights holder data, and obtains adaptation analysis results;

[0097] S44, based on topology-ownership association data and adaptation analysis results, determines the conflict level through conflict level determination rules;

[0098] S45, based on conflict level and rights restriction data, adopts conflict type determination rules to determine the spatial-ownership conflict type;

[0099] S46 integrates conflict levels and spatial-ownership conflict types to generate conflict verification results.

[0100] Specifically, based on the coordinate accuracy level, and combined with information such as land boundary coordinates and ownership boundary descriptions included in the rights scope delineation data, a spatial-ownership verification benchmark is determined through coordinate calibration and scope matching algorithms (e.g., matching the rights scope boundary with millimeter-level coordinates at a high accuracy level to form a precise benchmark line). Based on this benchmark, spatial-ownership keyword matching rules are adopted. Using NLP semantic similarity algorithms, keywords such as "boundary overlap" and "outline break" in the topological conflict verification results are associated with keywords such as "use right type" and "ownership term" in the core rights attribute data, filtering out semantically relevant matches to obtain topological-ownership association data. The parameters of the spatial-ownership adaptation calculation rules are adjusted according to the coordinate accuracy level (e.g., α is set to 0.7 and β to 0.3 at a high accuracy level), substituted into the adaptation analysis calculation formula, and inputting parameters such as the weight coefficients of the spatial parameter verification results, the standardized deviation value, and the complexity of the rights subject data, to calculate the adaptation analysis score and form the adaptation analysis result. Referring to the conflict level determination rules (e.g., AS < 60 points and severe conflicts in related data are Level 1, 60-80 points are Level 2, and ≥ 80 points are Level 3), and combining the conflict degree and fit analysis score of the topology-ownership association data, the conflict level is determined. Based on this conflict level, and combining clauses such as "mortgage" and "seizure" in the rights restriction data, the conflict type determination rules are adopted (e.g., Level 1 conflict + mortgage restriction is determined as "conflict between mortgage ownership and spatial boundary") to determine the spatial-ownership conflict type. The conflict level and conflict type are integrated to generate the conflict verification result.

[0101] In one embodiment, S51, the calculation formula used for adapting the spatial parameter test results and the rights holder data is as follows:

[0102]

[0103] in, To accommodate the analysis scores, n represents the total number of spatial parameter test results, and i represents the index of the spatial parameter test result. The weighting coefficients for the test results of the i-th spatial parameter are... To adapt weights to spatial parameters, Weights for the complexity of ownership relationships. Let be the standardized deviation value of the test result for the i-th spatial parameter. To calculate the data complexity based on the rights holder's data, The preset precision sensitivity coefficient, The attenuation factor is calculated based on the coordinate accuracy level. The number of entries for which rights are restricted.

[0104] For example, the core purpose of this formula is to quantify the degree of fit between the spatial parameter test results and the rights holder data, outputting an objective fit analysis score (AS), providing a quantitative basis for subsequent conflict level determination, and avoiding bias caused by subjective judgment. The formula as a whole is correlated with the variables: the AS score is jointly composed of spatial parameter fit terms (α-weighted part) and ownership relationship fit terms (β-weighted part), where α and β are preset weights and satisfy α+β=1, used to balance the influence weights of the two types of data; in the spatial parameter fit terms, Through weighting coefficients The weighted summation reflects the overall deviation level of the spatial parameter test results, divided by This can offset the interference of the complexity of the rights holder data on the results; that is, the smaller the spatial parameter deviation and the simpler the rights holder data, the higher the score for this item; in the ownership relationship adaptation item, As coordinate accuracy level increases ( (Decrease) shows an increasing trend. The score increases progressively with the number of rights restriction items. The product of the two items directly reflects the impact of the complexity of ownership relationships on adaptability. When the complexity is within a reasonable range, the score of this item more closely reflects the actual adaptability. This formula is closely linked to other technical features: Determined by the coordinate precision level, n, and Derived from the results of spatial parameter verification, Calculated based on rights holder data, The AS score, derived from the rights restriction data, serves as the core output of the adaptation calculation rules in the spatial-ownership conflict verification rules. Together with the topology-ownership association data, it is input into the conflict level determination rules, providing key support for the accurate determination of conflict level and conflict type. It is a quantitative bridge connecting spatial consistency verification results and ownership attribute information.

[0105] In one embodiment, spatial mapping information, ownership attribute information, multi-level spatial nesting relationship model, spatial consistency verification results, and conflict verification results are integrated to generate a real estate mapping verification file, including:

[0106] S61, based on a multi-level spatial nesting relationship model, constructs the basic framework of archives through hierarchical coding;

[0107] S62, based on the basic archival framework, spatial surveying information and ownership attribute information are associated and mapped to obtain spatial-ownership associated archives;

[0108] S63, based on the spatial-ownership association archive, according to the spatial consistency verification results and conflict verification results, the verification results are marked to obtain the real estate surveying and mapping verification archive.

[0109] Specifically, based on the hierarchical structure of a multi-level spatial nesting relationship model, a hierarchical coding rule can be adopted (e.g., boundary-related element codes begin with "1", parcel outline elements begin with "2", and lower-level elements add sub-codes after the upper-level codes) to assign unique codes to each nested unit and spatial element, constructing a tree-structured archival framework and determining the storage path and association logic of data at each level. Based on this framework, a mapping between spatial mapping information and ownership attribute information can be established through code matching. For example, spatial element codes can be key-value bound to corresponding ownership level codes, so that each piece of spatial data (such as parcel outline coordinates) corresponds to the associated rights holder, core rights attributes, and other ownership information, generating a spatial-ownership association archive. In this associated archive, for each set of spatial-ownership association items, a metadata tagging method is used to mark the spatial consistency verification results (such as coordinate accuracy level, topological conflict location and type, spatial parameter deviation range) and conflict verification results (such as conflict level, conflict type) below the associated item. The tagging content includes the result category, specific value and judgment basis, and integrates to form a structured real estate surveying and verification archive with clear hierarchy, close association and visualization of verification results, which is convenient for subsequent quick query and application.

[0110] The aforementioned intelligent verification method for real estate surveying and mapping information automatically extracts spatial surveying and mapping information and ownership attribute information from real estate surveying and mapping information, avoiding the inefficiency and error problems of traditional manual extraction of multi-source heterogeneous data. It utilizes topological analysis to extract spatial parameters and construct a multi-level spatial nesting relationship model, achieving systematic spatial relationship modeling and addressing the pain point of difficulty in verifying the consistency of multi-level spatial elements. Based on preset spatial consistency verification rules, it generates standardized spatial consistency verification results. Combined with spatial-ownership conflict verification rules including keyword matching and adaptation calculations, it correlates these results with ownership attribute information, avoiding the shortcomings of insufficient comprehensiveness and accuracy in conflict detection caused by subjective judgment. By integrating spatial surveying and mapping information, ownership attribute information, models, and various verification results through unified standards, it generates structured real estate surveying and mapping verification archives, solving the problems of lack of unified standards for traditional verification result integration and low degree of archive structure. This achieves automated, standardized, and accurate consistency verification and conflict detection of real estate surveying and mapping information, improving verification efficiency and result reliability, and facilitating subsequent querying and application.

[0111] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0112] Based on the same inventive concept, this application also provides an intelligent verification device for real estate surveying and mapping information for implementing the intelligent verification method for real estate surveying and mapping information described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the intelligent verification device for real estate surveying and mapping information provided below can be found in the limitations of the intelligent verification method for real estate surveying and mapping information described above, and will not be repeated here.

[0113] In one exemplary embodiment, such as Figure 2 As shown, an intelligent verification device for real estate surveying information is provided, comprising:

[0114] The surveying information extraction module 101 is used to extract spatial surveying information and ownership attribute information based on real estate surveying information;

[0115] The spatial model construction module 102 is used to extract spatial parameters through topological analysis based on spatial mapping information, and to construct a multi-level spatial nesting relationship model based on the spatial parameters.

[0116] The consistency verification module 103 is used to generate spatial consistency verification results based on a multi-level spatial nesting relationship model and according to preset spatial consistency verification rules.

[0117] The space rights conflict verification module 104 is used to generate conflict verification results based on the space consistency verification results and ownership attribute information, according to the preset space-ownership conflict verification rules.

[0118] The archive integration and generation module 105 is used to integrate spatial surveying and mapping information, ownership attribute information, multi-level spatial nesting relationship model, spatial consistency verification results and conflict verification results to generate real estate surveying and mapping verification archives.

[0119] In one embodiment, the mapping information extraction module 101 is further configured to:

[0120] Multi-source heterogeneous data extraction is performed on real estate surveying and mapping information to obtain spatial geometric data and descriptive text data;

[0121] According to the preset spatial element classification rules, the spatial geometric data is divided into the corresponding spatial element categories to obtain spatial element data. The spatial element categories include boundary-related elements, parcel outline elements, spatial coordinate elements, geometric morphology elements, and basic topographic elements.

[0122] Based on descriptive text data, a keyword extraction algorithm is used to obtain ownership association data, which includes data on the right holder, data on the core attributes of the right, data on the scope of the right, and data on the restrictions on the right.

[0123] Based on spatial element data, element attribute values ​​are assigned to obtain spatial mapping information;

[0124] Based on ownership association data, ownership attribute information is obtained through ownership hierarchy coding.

[0125] In one embodiment, the spatial parameters in the spatial model construction module 102 include spatial topological relationships, spatial element hierarchies, and spatial inclusion relationships.

[0126] Spatial model building module 102 is also used for:

[0127] Analyze the topological relationships among boundary-related elements, parcel outline elements, and basic topographic elements to obtain spatial topological relationships;

[0128] Based on the spatial topological relationships, the hierarchy among boundary-related elements, parcel outline elements, and basic topographic elements is determined by using a preset hierarchical priority division rule, thus obtaining the spatial element hierarchy.

[0129] Based on boundary-related elements, parcel outline elements, and basic topographic elements, spatial inclusion relationships are obtained by calculating the inclusion degree between the various spatial element levels.

[0130] Using spatial inclusion relationships as the nesting rules and spatial coordinate elements as the location benchmark, the first nested unit is constructed based on boundary-related elements, parcel outline elements, and basic topographic elements.

[0131] Based on the first nested unit, the geometric boundary matching and correction are performed according to the geometric morphology elements to obtain the second nested unit;

[0132] Integrate the second nested unit to form a multi-level spatial nesting relationship model.

[0133] In one embodiment, the consistency verification module 103 includes spatial consistency verification rules such as coordinate accuracy grading standards, topological constraint rules, and spatial parameter threshold ranges; the spatial consistency verification results include coordinate accuracy level, topological conflict detection results, and spatial parameter detection results.

[0134] The consistency verification module 103 is also used for:

[0135] Based on a multi-level spatial nesting relationship model, the coordinate accuracy level is determined by a coordinate accuracy grading standard.

[0136] Based on the coordinate accuracy level, topological constraint rules are used to test the multi-level spatial nesting relationship model, and the topological conflict test results are obtained.

[0137] Based on the coordinate accuracy level, spatial parameters are tested using a spatial parameter threshold range to obtain the spatial parameter test results.

[0138] In one embodiment, the space-ownership conflict verification module 104 includes space-ownership keyword matching rules, space-ownership adaptation calculation rules, conflict level determination rules, and conflict type determination rules in the space-ownership conflict verification module 104.

[0139] The air superiority conflict verification module 104 is also used for:

[0140] Based on the coordinate accuracy level and combined with the data on the delineation of the scope of rights, the spatial ownership verification benchmark is determined;

[0141] Based on the spatial-ownership verification benchmark, spatial-ownership keyword matching rules are used to associate the topological conflict verification results and the core attribute data of rights to obtain topological-ownership association data.

[0142] Based on the coordinate accuracy level, spatial-ownership adaptation calculation rules are adopted to perform adaptation analysis on spatial parameter verification results and rights holder data, and the adaptation analysis results are obtained.

[0143] Based on topology-ownership association data and adaptation analysis results, the conflict level is determined by conflict level determination rules.

[0144] Based on conflict level and rights restriction data, conflict type determination rules are used to identify spatial-ownership conflict types.

[0145] Integrate conflict levels and spatial-ownership conflict types to generate conflict verification results.

[0146] In one embodiment, the airspace rights conflict verification module 104 is further configured to perform an adaptation analysis on the spatial parameter verification results and rights holder data using the following calculation formula:

[0147]

[0148] in, To accommodate the analysis scores, n represents the total number of spatial parameter test results, and i represents the index of the spatial parameter test result. The weighting coefficients for the test results of the i-th spatial parameter are... To adapt weights to spatial parameters, Weights for the complexity of ownership relationships. Let be the standardized deviation value of the test result for the i-th spatial parameter. To calculate the data complexity based on the rights holder's data, The preset precision sensitivity coefficient, The attenuation factor is calculated based on the coordinate accuracy level. The number of entries for which rights are restricted.

[0149] In one embodiment, the file integration and generation module 105 is further configured to:

[0150] Based on a multi-level spatial nesting relationship model, a basic framework for archives is constructed through hierarchical coding;

[0151] Based on the basic archival framework, spatial surveying information and ownership attribute information are linked and mapped to obtain spatial-ownership associated archives;

[0152] Based on the spatial-ownership association archives, the verification results are marked according to the spatial consistency verification results and conflict verification results to obtain the real estate surveying and mapping verification archives.

[0153] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the intelligent verification method for real estate surveying information as described above.

[0154] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0155] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units 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 achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0156] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for intelligent verification of real estate surveying and mapping information, characterized in that, The method includes: Based on real estate surveying and mapping information, spatial surveying and mapping information and ownership attribute information are extracted; Based on the spatial mapping information, spatial parameters are extracted through topological analysis, and a multi-level spatial nesting relationship model is constructed based on the spatial parameters. Based on the multi-level spatial nesting relationship model, spatial consistency verification results are generated according to the preset spatial consistency verification rules. Based on the spatial consistency verification result and the ownership attribute information, a conflict verification result is generated according to the preset spatial-ownership conflict verification rules. By integrating the spatial surveying information, the ownership attribute information, the multi-level spatial nesting relationship model, the spatial consistency verification results, and the conflict verification results, a real estate surveying verification file is generated.

2. The method according to claim 1, characterized in that, The extraction of spatial mapping information and ownership attribute information based on real estate surveying information includes: Multi-source heterogeneous data extraction is performed on the real estate surveying information to obtain spatial geometric data and descriptive text data; According to the preset spatial element classification rules, the spatial geometric data is divided into the corresponding spatial element categories to obtain spatial element data. The spatial element categories include boundary-related elements, parcel outline elements, spatial coordinate elements, geometric morphology elements, and basic terrain elements. Based on the descriptive text data, a keyword extraction algorithm is used to obtain ownership association data, which includes rights subject data, core rights attribute data, rights scope definition data, and rights restriction data. Based on the spatial element data, element attribute values ​​are assigned to obtain the spatial mapping information. Based on the ownership association data, the ownership attribute information is obtained through ownership hierarchy encoding processing.

3. The method according to claim 2, characterized in that, The spatial parameters include spatial topological relationships, spatial element hierarchies, and spatial containment relationships; Based on the spatial mapping information, spatial parameters are extracted through topological analysis, and a multi-level spatial nesting relationship model is constructed based on the spatial parameters, including: The spatial topological relationships are obtained by analyzing the topological relationships among the boundary-related elements, the parcel outline elements, and the basic topographic elements. Based on the spatial topological relationships, a preset hierarchical priority division rule is used to determine the hierarchy among the boundary-related elements, the parcel outline elements, and the basic terrain elements, thereby obtaining the spatial element hierarchy. Based on the boundary-related elements, the parcel outline elements, and the terrain basic elements, the spatial inclusion relationship is obtained by calculating the inclusion degree between the hierarchical levels of each spatial element. Using the spatial inclusion relationship as the nesting rule and the spatial coordinate elements as the location reference, a first nested unit is constructed based on the boundary-related elements, the parcel outline elements, and the terrain basic elements. Based on the first nested unit, a second nested unit is obtained by performing geometric boundary matching correction according to the geometric morphological elements. The second nested unit is integrated to form the multi-level spatial nesting relationship model.

4. The method according to claim 3, characterized in that, The spatial consistency verification rules include coordinate accuracy grading standards, topological constraint rules, and spatial parameter threshold ranges; the spatial consistency verification results include coordinate accuracy level, topological conflict detection results, and spatial parameter detection results. The step of generating spatial consistency verification results based on the multi-level spatial nesting relationship model and according to preset spatial consistency verification rules includes: Based on the aforementioned multi-level spatial nesting relationship model, the coordinate accuracy level is determined using the aforementioned coordinate accuracy grading standard. Based on the coordinate accuracy level, the topological constraint rules are used to test the multi-level spatial nesting relationship model to obtain the topological conflict test results. Based on the coordinate accuracy level, the spatial parameters are tested using the spatial parameter threshold range to obtain the spatial parameter test results.

5. The method according to claim 4, characterized in that, The spatial-ownership conflict verification rules include spatial-ownership keyword matching rules, spatial-ownership adaptation calculation rules, conflict level determination rules, and conflict type determination rules. Based on the spatial consistency verification result and the ownership attribute information, a conflict verification result is generated according to the preset spatial-ownership conflict verification rules, including: Based on the coordinate accuracy level and the rights scope definition data, the spatial ownership verification benchmark is determined. Based on the spatial-ownership verification benchmark, the spatial-ownership keyword matching rule is used to associate the topological conflict verification results and the core attribute data of the rights to obtain topological-ownership association data. Based on the coordinate accuracy level, the spatial-ownership adaptation calculation rules are used to perform adaptation analysis on the spatial parameter verification results and the rights holder data to obtain the adaptation analysis results. Based on the topology-ownership association data and adaptation analysis results, the conflict level is determined by the conflict level determination rules. Based on the conflict level and the right restriction data, the conflict type determination rule is used to determine the spatial-ownership conflict type; The conflict level and the space-ownership conflict type are integrated to generate the conflict verification result.

6. The method according to claim 5, characterized in that, The calculation formula used for the adaptation analysis of the spatial parameter test results and the rights holder data is as follows: in, To accommodate the analysis scores, n represents the total number of spatial parameter test results, and i represents the index of the spatial parameter test result. The weighting coefficients for the test results of the i-th spatial parameter are... To adapt weights to spatial parameters, Weights for the complexity of ownership relationships. Let be the standardized deviation value of the test result for the i-th spatial parameter. To calculate the data complexity based on the rights holder data, The preset precision sensitivity coefficient, The attenuation factor is calculated based on the coordinate accuracy level. The number of entries for which rights are restricted.

7. The method according to claim 1, characterized in that, The process of integrating the spatial mapping information, the ownership attribute information, the multi-level spatial nesting relationship model, the spatial consistency verification results, and the conflict verification results to generate a real estate mapping verification file includes: Based on a multi-level spatial nesting relationship model, a basic framework for archives is constructed through hierarchical coding; Based on the aforementioned basic archival framework, the spatial mapping information and the ownership attribute information are associated and mapped to obtain spatial-ownership associated archives; Based on the spatial-ownership association archive, the verification results are marked according to the spatial consistency verification results and the conflict verification results to obtain the real estate surveying verification archive.

8. An intelligent verification device for real estate surveying information, characterized in that, The device includes: The surveying information extraction module is used to extract spatial surveying information and ownership attribute information based on real estate surveying information; The spatial model construction module is used to extract spatial parameters through topological analysis based on the spatial mapping information, and to construct a multi-level spatial nesting relationship model based on the spatial parameters. The consistency verification module is used to generate spatial consistency verification results based on the multi-level spatial nesting relationship model and according to preset spatial consistency verification rules. The space ownership conflict verification module is used to generate a conflict verification result based on the space consistency verification result and the ownership attribute information, according to the preset space-ownership conflict verification rules. The archive integration and generation module is used to integrate the spatial surveying information, the ownership attribute information, the multi-level spatial nesting relationship model, the spatial consistency verification results, and the conflict verification results to generate real estate surveying verification archives.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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