Territorial space planning spatial data checking method

Abstract

This application discloses a method for inspecting spatial data of territorial spatial planning, belonging to the field of territorial spatial planning information management technology. It includes: preprocessing territorial spatial planning data to obtain standardized spatial data; determining the upper bound parameter of overlay error and generating dynamic tolerance parameters based on the scale or resolution information of the standardized spatial data; organizing the standardized spatial data into blocks to obtain a spatial index structure; performing spatial conflict detection based on the standardized spatial data and spatial index structure, and filtering false conflict results according to the dynamic tolerance parameters to obtain conflict detection results; performing rule matching and reasoning on the conflict detection results based on a pre-constructed policy rule knowledge graph to obtain rule matching and reasoning results; and structurally expressing the rule matching and reasoning results, generating result versions and establishing version associations.

Description

Technical Field

[0001] This application relates to the field of land and space planning information management technology, and more specifically, to methods for inspecting land and space planning spatial data. Background Technology

[0002] With the continuous improvement of my country's territorial spatial planning system, the quantity and types of territorial spatial planning data are showing a rapid growth trend, including planned land use data, ecological red line data, land use status data, and basic geographic information data. These spatial data play a crucial role in planning, management, and approval processes, providing fundamental support for scientific decision-making. However, due to factors such as diverse data sources, inconsistent surveying and mapping standards, and untimely updates to historical data, territorial spatial planning data exhibit significant differences and potential conflicts in terms of projected coordinates, spatial accuracy, and topological relationships.

[0003] Existing spatial data inspection methods largely rely on manual review or simple overlay analysis, resulting in inefficiency, high false positive rates, and difficulty in adapting to the processing needs of large-scale, multi-source spatial data. While some automated inspection methods can identify overlaps or topological anomalies between spatial data to a certain extent, they lack comprehensive consideration of differences in data accuracy, scale adaptation, and policy constraints, making it difficult to effectively distinguish between genuine and pseudo-conflicts. Furthermore, the lack of systematic version management and change tracking mechanisms makes the tracing and updating of historical planning data extremely difficult.

[0004] Furthermore, the policies and rules involved in territorial spatial planning are complex and diverse, and there are overlaps and conflicts between the constraints of different planning objectives and management levels. Traditional inspection methods cannot achieve intelligent reasoning and judgment based on rule knowledge, nor can they systematically classify and label the types, severity and difficulty of rectification of conflicts, resulting in inaccurate quality control of planning data and increased risks in data use.

[0005] In summary, how to construct an efficient, accurate, and traceable spatial data inspection method for multi-source, multi-scale, and multi-format land spatial planning data, while taking into account accuracy control, conflict identification, rule constraints, and version management, has become an urgent technical problem to be solved. Summary of the Invention

[0006] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide a method for checking spatial data of land and space planning, which includes the following steps.

[0007] The spatial data of the national land spatial planning is preprocessed to obtain standardized spatial data.

[0008] Based on the scale or resolution information of standardized spatial data, the upper bound parameter of the superposition error is determined and the dynamic tolerance parameter is generated.

[0009] The standardized spatial data is divided into blocks to obtain a spatial index structure.

[0010] Spatial conflict detection is performed based on standardized spatial data and spatial index structure, and false conflict results are filtered out according to dynamic tolerance parameters to obtain the conflict detection results.

[0011] Based on a pre-built policy rule knowledge graph, rule matching and reasoning are performed on the conflict detection results to obtain the rule matching and reasoning results.

[0012] The rule matching reasoning results are expressed in a structured manner, resulting in version versions and establishing version association relationships.

[0013] Determine if there are any non-compliant items in the result version.

[0014] If it does not exist, a change tracking record will be generated based on the resulting version.

[0015] If they exist, the non-compliant items are categorized and marked, and a detailed description of the corresponding non-compliant items is output.

[0016] In some embodiments, the method for acquiring standardized spatial data is as follows.

[0017] The projection parameter information of the spatial data of the national land spatial planning is analyzed to obtain the initial coordinate system identification result.

[0018] The initial coordinate system identification results are comprehensively evaluated to determine the target coordinate system type and its confidence level, and the coordinate system identification results are obtained based on the confidence level.

[0019] Based on the coordinate system identification results, the corresponding coordinate transformation model is selected, and coordinate system transformation processing is performed on the spatial data of the land and space planning.

[0020] Perform transformation quality control processing on the coordinate system transformation process, including boundary verification, transformation residual calculation, abnormal feature marking and alarm, and generate transformation log.

[0021] Perform topology consistency repair on the spatial data after coordinate transformation to obtain the topology repair result.

[0022] Based on the coordinate system identification results, transformation logs, and topology repair results, standardized spatial data is generated.

[0023] In some embodiments, the method for determining the upper bound parameter of the superposition error and generating the dynamic tolerance parameter is as follows.

[0024] Based on the scale or spatial resolution information of standardized spatial data, the data type is determined and the corresponding basic parameters are extracted.

[0025] A baseline tolerance function model is constructed based on the aforementioned basic parameters. When the spatial data is vector data, a mapping relationship between the scale and the baseline tolerance is established based on the scale denominator. When the spatial data is raster data, a mapping relationship between the resolution and the baseline tolerance is established based on the spatial resolution.

[0026] By introducing data acquisition method factors and time factors, the baseline tolerance function model is parameterized and modified to obtain the dynamic tolerance model and the dynamic tolerance corresponding to each spatial data layer.

[0027] When multiple spatial data sources exist, the dynamic tolerance model is weighted and fused based on the area ratio of each spatial data layer to obtain the superposition error upper bound model, and a unified superposition error upper bound parameter is output.

[0028] In some embodiments, determining the upper bound of the superposition error further includes an adaptive correction step based on measured control points, specifically including the following steps.

[0029] Within the standardized spatial data coverage area, control points with known precise coordinates are deployed at a preset density, so that the control points cover different terrain areas and multi-source data boundary areas.

[0030] Obtain the corresponding position of each control point in the standardized spatial data, match it with its known precise coordinates, and calculate the position residual.

[0031] The statistical error index is calculated based on the location residual. The statistical error index includes the root mean square error and the quantile error corresponding to the preset confidence level.

[0032] The quantile error is compared with the theoretical tolerance to determine the deviation state of the upper bound of the superposition error.

[0033] The upper bound parameter of the superposition error is adaptively adjusted according to the deviation state to obtain the corrected upper bound parameter of the superposition error.

[0034] When the adjustment range of the superposition error upper bound parameter exceeds the preset range threshold, a data quality warning message is output.

[0035] The dynamic tolerance parameter is updated based on the corrected upper bound parameter of the superposition error, and a correction record containing control point distribution and error statistics is generated.

[0036] In some embodiments, the method for establishing the spatial index structure is as follows.

[0037] Based on the number of elements corresponding to each spatial data layer, an R-tree index for each spatial data layer is constructed, and the index node capacity parameter is adaptively determined according to the number of elements to obtain the first-layer spatial index structure.

[0038] A grid index is constructed within the spatial data coverage area, and the grid scale is adaptively determined based on the data density to ensure that the number of features within the grid cell meets the preset constraints, thus obtaining the second-layer spatial index structure.

[0039] The first-level spatial index structure is associated and integrated with the second-level spatial index structure to obtain a two-level spatial index structure.

[0040] In some embodiments, the method for obtaining conflict detection results is as follows.

[0041] Obtain the topological and geometric relationships between elements in the spatial data layer.

[0042] Based on the topological and geometric relationships, multiple types of spatial conflict detection are performed in a preset order, including self-overlay detection within the same spatial data layer, pairwise overlay detection between different spatial data layers, containment relationship detection, connectivity and integrity detection, and boundary consistency detection, to obtain the initial conflict result.

[0043] Based on dynamic tolerance parameters, the initial conflict results are filtered by area threshold to remove conflict areas whose area does not exceed the tolerance threshold, thus obtaining the first filtering result.

[0044] Boundary collinearity analysis is performed on the first filtering result to filter out narrow pseudo-collisions caused by boundary overlap or digitization errors, resulting in a second filtering result.

[0045] Based on historical result version data, spatial matching and state determination are performed on the second filtering result. Conflicts that meet preset conditions are marked as inheritance conflicts, and the inheritance conflicts are filtered to obtain the third filtering result.

[0046] The third filtering results are summarized to obtain conflict detection results, and corresponding pseudo-conflict filtering statistics are generated.

[0047] In some embodiments, the method for constructing a policy rule knowledge graph is as follows.

[0048] The policy and rule texts are structured and parsed to extract rule clause information, element information, and conflict type information, and to identify rule entities, element type entities, and conflict type entities.

[0049] Based on the rule entities, element type entities, and conflict type entities, an entity layer of the knowledge graph is constructed, and attribute information is configured for the rule entities. The attribute information includes at least the rule source, rule type, rule logic, and scope of application.

[0050] Based on the entity layer and combined with the attribute information of the rule entities, semantic relationship edges between entities are established. The semantic relationship edges include at least the applicability relationship between the rule entity and the feature type entity, the triggering relationship between the rule entity and the conflict type entity, and the constraint relationship between the rule entities, thus obtaining the rule semantic association structure.

[0051] Based on the semantic association structure of rules, a validity period range is set for each rule entity, and a substitution relationship is established between the old and new rule entities when the rule entity is updated.

[0052] Differentiated rules are introduced and priority relationships are set to form a collaborative constraint relationship between rules at different levels, resulting in a policy rule knowledge graph.

[0053] The policy and rule knowledge graph is modeled and stored in an ontology format, and query and rule maintenance interfaces are provided to support the addition, modification and deactivation of rules.

[0054] In some embodiments, the method for performing rule matching and reasoning on the conflict detection results is as follows.

[0055] Features are extracted from each conflict record in the conflict detection results to construct the corresponding conflict feature vector.

[0056] Based on the conflict feature vector, rule matching is performed in a pre-constructed policy rule knowledge graph, and a candidate rule set is obtained by filtering according to preset triggering conditions.

[0057] The candidate rule set is prioritized and sorted to obtain an ordered rule set, wherein the priority is determined based on the rule type, rule level and implementation time.

[0058] Based on an ordered set of rules, rule reasoning is performed to determine the target rule corresponding to each conflict record.

[0059] During the rule reasoning process, when there are multiple rule constraints, conflict resolution is performed on the rules in the candidate rule set; when conflict resolution cannot be completed, rule conflict identification information is generated.

[0060] Record the reasoning path information during the rule matching and reasoning process, and output the rule matching and reasoning results.

[0061] In some embodiments, the method for establishing version association is as follows.

[0062] Construct a directed acyclic graph data structure for version evolution relationships.

[0063] Generate a unique version identifier for the current rule matching inference result and obtain the corresponding parent version identifier.

[0064] Based on the unique version identifier and the parent version identifier, an association relationship between the current version and the parent version is established in the directed acyclic graph data structure.

[0065] In some embodiments, the method for classifying and labeling non-compliant items is as follows.

[0066] Construct a classification and labeling system, which includes at least a severity dimension, a conflict type dimension, and a rectification difficulty dimension.

[0067] Based on the aforementioned classification and labeling system, the non-compliant items are graded and determined according to the severity dimension to obtain the corresponding severity label.

[0068] Based on the conflict detection results corresponding to the non-compliant items, the non-compliant items are classified and labeled in the conflict type dimension to obtain the corresponding conflict type labels.

[0069] Based on the attribute and association information of the non-compliant items, the non-compliant items are evaluated in terms of rectification difficulty, and the corresponding rectification difficulty label is obtained.

[0070] The severity marker, conflict type marker, and rectification difficulty marker are combined and encoded to obtain the classification marker result.

[0071] Compared with existing technologies, this application has the following advantages: through a multi-layer spatial index structure, high-precision conflict detection, pseudo-conflict filtering, rule matching reasoning and version management, it can achieve automatic identification, classification and labeling of spatial data conflicts and non-compliant items, as well as change tracking. Attached Figure Description

[0072] Figure 1 This is a flowchart illustrating the spatial data inspection method for land and space planning disclosed in an embodiment of this application. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0074] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0076] like Figure 1 As shown, the method for checking spatial data in land and space planning includes the following steps.

[0077] The spatial data of the national land spatial planning is preprocessed to obtain standardized spatial data.

[0078] Based on the scale or resolution information of standardized spatial data, the upper bound parameter of the superposition error is determined and the dynamic tolerance parameter is generated.

[0079] The standardized spatial data is divided into blocks to obtain a spatial index structure.

[0080] Spatial conflict detection is performed based on standardized spatial data and spatial index structure, and false conflict results are filtered out according to dynamic tolerance parameters to obtain the conflict detection results.

[0081] Based on a pre-built policy rule knowledge graph, rule matching and reasoning are performed on the conflict detection results to obtain the rule matching and reasoning results.

[0082] The rule matching reasoning results are expressed in a structured manner, resulting in version versions and establishing version association relationships.

[0083] Determine if there are any non-compliant items in the result version.

[0084] If it does not exist, a change tracking record will be generated based on the resulting version.

[0085] If they exist, the non-compliant items are categorized and marked, and a detailed description of the corresponding non-compliant items is output.

[0086] The spatial data inspection method for territorial spatial planning described in this application ensures spatial consistency and topological integrity of data from different sources and scales under a unified coordinate system by performing standardized processing and coordinate transformation on multi-source spatial data. It improves the accuracy of identifying conflicts arising from the overlay of multi-source data through dynamic tolerance modeling based on scale and resolution and adaptive correction of the upper bound parameter of overlay error. Furthermore, it accelerates spatial conflict detection using a two-layer spatial index structure and combines it with a policy rule knowledge graph for intelligent rule matching and reasoning, generating structured results and categorized non-compliant items. This significantly enhances the automation, accuracy, and traceability of territorial spatial planning data inspection, meeting the needs of high-precision, systematic planning data management.

[0087] In some embodiments, the method for acquiring standardized spatial data is as follows.

[0088] The projection parameter information of the spatial data of the national land spatial planning is analyzed to obtain the initial coordinate system identification result.

[0089] The initial coordinate system identification results are comprehensively evaluated to determine the target coordinate system type and its confidence level, and the coordinate system identification results are obtained based on the confidence level.

[0090] Based on the coordinate system identification results, the corresponding coordinate transformation model is selected, and coordinate system transformation processing is performed on the spatial data of the national land spatial planning.

[0091] Perform transformation quality control processing on the coordinate system transformation process, including boundary verification, transformation residual calculation, abnormal feature marking and alarm, and generate transformation log.

[0092] Perform topology consistency repair on the spatial data after coordinate transformation to obtain the topology repair result.

[0093] Based on the coordinate system identification results, transformation logs, and topology repair results, standardized spatial data is generated.

[0094] The method for obtaining standardized spatial data described in this application ensures the accuracy of coordinate identification for multi-source data by analyzing the projection parameters of the spatial data and comprehensively determining the types and confidence levels of the initial and target coordinate systems. It achieves reliable transformation of spatial data under a unified coordinate system by selecting the corresponding coordinate transformation model and performing transformation quality control, including boundary verification, residual calculation, and anomaly feature marking and logging. Based on this, standardized spatial data is generated by combining topology consistency repair processing, thereby significantly improving the spatial consistency, topological integrity, and usability of subsequent analysis of territorial spatial planning data, meeting the needs of high-precision planning management.

[0095] In some embodiments, the method for determining the upper bound parameter of the superposition error and generating the dynamic tolerance parameter is as follows.

[0096] Based on the scale or spatial resolution information of standardized spatial data, the data type is determined and the corresponding basic parameters are extracted.

[0097] A baseline tolerance function model is constructed based on the aforementioned basic parameters. When the spatial data is vector data, a mapping relationship between the scale and the baseline tolerance is established based on the scale denominator. When the spatial data is raster data, a mapping relationship between the resolution and the baseline tolerance is established based on the spatial resolution.

[0098] By introducing data acquisition method factors and time factors, the baseline tolerance function model is parameterized and modified to obtain the dynamic tolerance model and the dynamic tolerance corresponding to each spatial data layer.

[0099] When multiple spatial data sources exist, the dynamic tolerance model is weighted and fused based on the area ratio of each spatial data layer to obtain the superposition error upper bound model, and a unified superposition error upper bound parameter is output.

[0100] The method described in this application for determining the upper bound parameter of the overlay error and generating dynamic tolerance parameters constructs a benchmark tolerance function model based on the scale or resolution information of standardized spatial data to ensure the tolerance accuracy and spatial consistency of vector and raster data. By introducing data acquisition method factors and time factors to parameterize and correct the benchmark model, the method achieves self-adaptation of dynamic tolerance for each spatial data layer. On this basis, the method performs weighted fusion of multi-source spatial data to generate the upper bound parameter of the overlay error, thereby significantly improving the accuracy of multi-source spatial data overlay conflict identification and the reliability of planning data management.

[0101] In some embodiments, determining the upper bound of the superposition error further includes an adaptive correction step based on measured control points, specifically including the following steps.

[0102] Within the standardized spatial data coverage area, control points with known precise coordinates are deployed at a preset density, so that the control points cover different terrain areas and multi-source data boundary areas.

[0103] Obtain the corresponding position of each control point in the standardized spatial data, match it with its known precise coordinates, and calculate the position residual.

[0104] The statistical error index is calculated based on the location residual. The statistical error index includes the root mean square error and the quantile error corresponding to the preset confidence level.

[0105] The quantile error is compared with the theoretical tolerance to determine the deviation state of the upper bound of the superposition error.

[0106] The upper bound parameter of the superposition error is adaptively adjusted according to the deviation state to obtain the corrected upper bound parameter of the superposition error.

[0107] When the adjustment range of the superposition error upper bound parameter exceeds the preset range threshold, a data quality warning message is output.

[0108] The dynamic tolerance parameter is updated based on the corrected upper bound parameter of the superposition error, and a correction record containing control point distribution and error statistics is generated.

[0109] The adaptive correction steps based on measured control points described in this application assess the deviation status of the upper bound parameter of the superposition error by deploying control points with known precise coordinates in the standardized spatial data coverage area and calculating their positional residuals and statistical error indices. Through adaptive adjustment based on the deviation status and dynamic tolerance parameter updates, combined with over-threshold early warning and correction record generation, real-time correction of the superposition accuracy of multi-source spatial data is achieved. On this basis, the accuracy of spatial conflict detection, the controllability of data quality, and the reliability of land and space planning data management are significantly improved, meeting the needs of high-precision planning and analysis.

[0110] In some embodiments, the method for establishing the spatial index structure is as follows.

[0111] Based on the number of elements corresponding to each spatial data layer, an R-tree index for each spatial data layer is constructed, and the index node capacity parameter is adaptively determined according to the number of elements to obtain the first-layer spatial index structure.

[0112] A grid index is constructed within the spatial data coverage area, and the grid scale is adaptively determined based on the data density to ensure that the number of features within the grid cell meets the preset constraints, thus obtaining the second-layer spatial index structure.

[0113] The first-level spatial index structure is associated and integrated with the second-level spatial index structure to obtain a two-level spatial index structure.

[0114] The method for establishing a spatial index structure described in this application achieves a first-layer spatial index structure by constructing an R-tree index based on the number of elements in each spatial data layer and adaptively determining the node capacity; a second-layer spatial index structure is achieved by constructing an adaptive grid index within the data coverage area; and the two-layer index structures are then linked and integrated to form a two-layer spatial index structure, thereby significantly improving the access efficiency of multi-source spatial data, the speed of spatial conflict retrieval, and the overall performance of planning data management.

[0115] In some embodiments, the method for obtaining conflict detection results is as follows.

[0116] Obtain the topological and geometric relationships between elements in the spatial data layer.

[0117] Based on the topological and geometric relationships, multiple types of spatial conflict detection are performed in a preset order, including self-overlay detection within the same spatial data layer, pairwise overlay detection between different spatial data layers, containment relationship detection, connectivity and integrity detection, and boundary consistency detection, to obtain the initial conflict result.

[0118] Based on dynamic tolerance parameters, the initial conflict results are filtered by area threshold to remove conflict areas whose area does not exceed the tolerance threshold, thus obtaining the first filtering result.

[0119] Boundary collinearity analysis is performed on the first filtering result to filter out narrow pseudo-collisions caused by boundary overlap or digitization errors, resulting in a second filtering result.

[0120] Based on historical result version data, spatial matching and state determination are performed on the second filtering result. Conflicts that meet preset conditions are marked as inheritance conflicts, and the inheritance conflicts are filtered to obtain the third filtering result.

[0121] The third filtering results are summarized to obtain conflict detection results, and corresponding pseudo-conflict filtering statistics are generated.

[0122] The method for obtaining the aforementioned conflict detection results in this application acquires the topological and geometric relationships of elements in the spatial data layer and performs multi-type spatial conflict detection in a preset order to obtain initial conflict results. It then introduces dynamic tolerance parameters for area threshold filtering and combines boundary collinearity analysis to eliminate false conflicts, thus obtaining accurate conflict results. Based on this, it performs spatial matching and inherited conflict filtering based on historical result versions and summarizes and processes the data to generate the final conflict detection results and false conflict statistics, thereby significantly improving the accuracy and processing efficiency of spatial data conflict identification and meeting the needs of high-precision land spatial planning inspection.

[0123] In some embodiments, the method for constructing a policy rule knowledge graph is as follows.

[0124] The policy and rule texts are structured and parsed to extract rule clause information, element information, and conflict type information, and to identify rule entities, element type entities, and conflict type entities.

[0125] Based on the rule entities, element type entities, and conflict type entities, an entity layer of the knowledge graph is constructed, and attribute information is configured for the rule entities. The attribute information includes at least the rule source, rule type, rule logic, and scope of application.

[0126] Based on the entity layer and combined with the attribute information of the rule entities, semantic relationship edges between entities are established. The semantic relationship edges include at least the applicability relationship between the rule entity and the feature type entity, the triggering relationship between the rule entity and the conflict type entity, and the constraint relationship between the rule entities, thus obtaining the rule semantic association structure.

[0127] Based on the semantic association structure of rules, a validity period range is set for each rule entity, and a substitution relationship is established between the old and new rule entities when the rule entity is updated.

[0128] Differentiated rules are introduced and priority relationships are set to form a collaborative constraint relationship between rules at different levels, resulting in a policy rule knowledge graph.

[0129] The policy and rule knowledge graph is modeled and stored in an ontology format, and query and rule maintenance interfaces are provided to support the addition, modification and deactivation of rules.

[0130] The method for constructing the policy rule knowledge graph described in this application involves structured parsing of policy rule texts to extract rule clauses, elements, and conflict type information, thereby establishing rule entities, element type entities, and conflict type entities. Based on these entities, an entity layer of the knowledge graph is constructed, and attribute information such as rule source, rule type, rule logic, and scope of application is configured. Semantic relationship edges are established between entities to form a semantic association structure encompassing rule application, triggering, and constraints. Combined with validity periods, rule substitution relationships, and differentiated priority settings, collaborative constraints between rules at different levels are achieved. Furthermore, the knowledge graph is ontologically modeled, stored, and provided with query and maintenance interfaces, significantly improving the application efficiency and maintainability of policy rules in spatial data inspection, and meeting the needs of high-precision territorial spatial planning compliance analysis.

[0131] In some embodiments, the method for performing rule matching and reasoning on the conflict detection results is as follows.

[0132] Features are extracted from each conflict record in the conflict detection results to construct the corresponding conflict feature vector.

[0133] Based on the conflict feature vector, rule matching is performed in a pre-constructed policy rule knowledge graph, and a candidate rule set is obtained by filtering according to preset triggering conditions.

[0134] The candidate rule set is prioritized and sorted to obtain an ordered rule set, wherein the priority is determined based on the rule type, rule level and implementation time.

[0135] Based on an ordered set of rules, rule reasoning is performed to determine the target rule corresponding to each conflict record.

[0136] During the rule reasoning process, when there are multiple rule constraints, conflict resolution is performed on the rules in the candidate rule set; when conflict resolution cannot be completed, rule conflict identification information is generated.

[0137] Record the reasoning path information during the rule matching and reasoning process, and output the rule matching and reasoning results.

[0138] The method described in this application for rule matching and reasoning of conflict detection results involves extracting features from each conflict record in the conflict detection results to construct a corresponding conflict feature vector; based on the feature vector, rule matching is performed in a pre-constructed policy rule knowledge graph to filter and obtain a candidate rule set; the candidate rule set is prioritized and sorted to form an ordered rule set, and rule reasoning is performed in combination with rule type, level, and implementation time to determine the target rule corresponding to each conflict record; during the rule reasoning process, conflict resolution is performed on multiple rule constraints, and a rule conflict identifier is generated for cases that cannot be resolved; the complete reasoning path is recorded and the rule matching reasoning result is output, thereby realizing automated compliance judgment and intelligent analysis of spatial conflicts, significantly improving the accuracy and efficiency of land and space planning data inspection.

[0139] In some embodiments, the method for establishing version association is as follows.

[0140] Construct a directed acyclic graph data structure for version evolution relationships.

[0141] Generate a unique version identifier for the current rule matching inference result and obtain the corresponding parent version identifier.

[0142] Based on the unique version identifier and the parent version identifier, an association relationship between the current version and the parent version is established in the directed acyclic graph data structure.

[0143] The method for establishing version associations described in this application constructs a directed acyclic graph data structure of version evolution relationships; generates a unique version identifier for the current rule matching inference result and obtains the corresponding parent version identifier; based on the unique version identifier and the parent version identifier, establishes an association between the current version and the parent version in the directed acyclic graph, thereby realizing versioned management and historical tracking of the results of land and space planning inspections, significantly improving the traceability of data changes and management efficiency.

[0144] In some embodiments, the method for classifying and labeling non-compliant items is as follows.

[0145] Construct a classification and labeling system, which includes at least a severity dimension, a conflict type dimension, and a rectification difficulty dimension.

[0146] Based on the aforementioned classification and labeling system, the non-compliant items are graded and determined according to the severity dimension to obtain the corresponding severity label.

[0147] Based on the conflict detection results corresponding to the non-compliant items, the non-compliant items are classified and labeled in the conflict type dimension to obtain the corresponding conflict type labels.

[0148] Based on the attribute and association information of the non-compliant items, the non-compliant items are evaluated in terms of rectification difficulty, and the corresponding rectification difficulty label is obtained.

[0149] The severity marker, conflict type marker, and rectification difficulty marker are combined and encoded to obtain the classification marker result.

[0150] The method described in this application for classifying and marking non-compliant items involves constructing a classification and marking system that includes dimensions of severity, conflict type, and rectification difficulty. Based on the severity dimension, non-compliant items are graded to obtain severity markings. Based on conflict detection results, non-compliant items are classified and labeled according to conflict type to obtain conflict type markings. Based on the attributes and related information of non-compliant items, the rectification difficulty is assessed to obtain rectification difficulty markings. These markings are then combined and coded to form the final classification and marking results. This achieves multi-dimensional hierarchical management and refined evaluation of non-compliant items, improving the accuracy and operability of planning data management.

[0151] To verify the effectiveness of the spatial data inspection method for land and space planning disclosed in this application in practical applications, the following uses a provincial urban and rural planning dataset as an example.

[0152] This urban and rural planning dataset encompasses multiple spatial data layers, including current land use data, planned land use data, ecological red line data, and basic geographic information data, covering a total area of ​​approximately 12,000 square kilometers. The scale ranges from 1:5000 to 1:50000, and the data sources include the Municipal Surveying and Mapping Institute, district and county planning departments, and historical planning results. The data update period spans from 2018 to 2024. Due to the dispersed data sources and different collection methods, issues such as inconsistent coordinate systems, missing topological relationships, overlapping boundary lines, and differences in scale among multiple data sources exist. Traditional manual review and single overlay analysis methods are insufficient to comprehensively identify potential conflicts and non-compliance items. Therefore, based on the technical solution of this application, a land spatial planning intelligent inspection platform was constructed. This platform performs systematic inspections, rule-based reasoning, and version management on the dataset, enabling automatic identification, classification, labeling, and change tracking of spatial data conflicts and non-compliance items.

[0153] First, the standardization module performs coordinate transformation, projection standardization, and topology repair on each data layer to generate standardized spatial data. During the coordinate transformation process, the system parses the initial coordinate system parameters of each data layer, selects the target coordinate system type through comprehensive judgment, and chooses the corresponding transformation model for coordinate unification based on the coordinate system identification confidence level. Simultaneously, the system performs boundary verification, transformation residual calculation, and anomaly feature marking, generates a transformation log, and combines the topology repair results to generate the final standardized spatial data, ensuring that data from different sources and scales maintain spatial consistency and topological integrity under a unified coordinate system.

[0154] Subsequently, based on the scale and spatial resolution information of each data layer, a dynamic tolerance model is constructed and the upper bound parameter of the superposition error is calculated. During this process, data acquisition method factors and time factors are introduced to correct the baseline tolerance function, while area-weighted fusion of multi-source data is performed. When control points are set within the coverage area of ​​standardized spatial data, statistical error indices are calculated using the residuals of the control point locations to adaptively correct the upper bound parameter of the superposition error, ensuring that the dynamic tolerance parameter accurately adapts to the characteristics of each data layer.

[0155] During the spatial index construction phase, a two-layer spatial index structure is constructed based on the number and spatial distribution of features in each data layer. This structure includes a first-layer R-tree index and a second-layer grid index. The node capacity and grid scale are adaptively determined to ensure the efficiency and coverage integrity of subsequent spatial conflict detection.

[0156] The spatial conflict detection module performs multi-type conflict identification based on standardized spatial data and spatial indexes, including self-overlay detection within the same data layer, pairwise overlay detection between different data layers, containment relationship detection, connectivity and integrity detection, and boundary consistency detection. Preliminary conflict results are filtered for pseudo-conflicts with areas below a threshold using dynamic tolerance, and further refined through boundary collinearity analysis to eliminate elongated pseudo-conflicts. Simultaneously, historical version data is used to determine the inheritance of conflict states, resulting in the final conflict detection result.

[0157] Conflict detection results are input into a pre-constructed policy rule knowledge graph for rule matching and inference. A feature vector is constructed for each conflict record. Candidate rules are selected from the knowledge graph and prioritized according to rule type, level, and implementation time, generating an ordered rule set. Under multiple rule constraints, conflict resolution is performed, and the inference path is recorded. This process yields the rule matching and inference results, clarifying the target rule corresponding to each conflict record and its applicability.

[0158] In the results management phase, the rule-matching reasoning results are presented in a structured manner, generating result versions and establishing a directed acyclic graph of version evolution relationships to achieve correlation and traceability between versions. Subsequently, it is determined whether any non-compliant items exist in the result versions: if not, change tracking records are generated to support subsequent data updates and historical comparisons; if they exist, non-compliant items are labeled in multiple dimensions based on a classification and labeling system, including severity, conflict type, and rectification difficulty, and a detailed description is output to provide actionable rectification references for the planning and management department.

[0159] Taking this city dataset as an example, a total of 27 conflicts between planned land use and ecological red line boundaries were identified, of which 5 were high-risk and required priority rectification; 10 conflicts were pseudo-conflicts due to overlapping boundaries, which were successfully removed through dynamic tolerance filtering, reducing the false judgment rate by 37%. The consistency rate between rule matching inference results and manual review results reached 92%, and the conflict classification labels were clear and accurate. Version management and change tracking mechanisms ensured that the results of each inspection were traceable and comparable, providing a reliable basis for planning revision, policy implementation, and data updates.

[0160] By applying this method, urban planning management departments can identify potential conflicts in real time, scientifically arrange data corrections, and assess the impact of non-compliance on the overall planning implementation. For example, a high-risk conflict involves approximately 12 hectares of planned land; if not rectified promptly, it will affect the protection of surrounding ecological red lines and the layout of public facilities, with significant potential socio-economic impacts. Based on rule-based reasoning and version management, targeted rectification suggestions and priority processing sequences are provided to ensure the scientific and compliant implementation of the plan.

[0161] In summary, the technical solution presented in this application achieves automated inspection and management of spatial data for territorial spatial planning through standardized data processing, dynamic tolerance modeling, multi-layer spatial index construction, and intelligent reasoning based on policy rule knowledge graphs. Compared with existing technologies, this method demonstrates significant advantages in conflict identification accuracy, pseudo-conflict removal efficiency, rule determination capability, and version traceability. Practical application verification shows that this technical solution achieves significant technical and economic benefits in improving spatial data quality, reducing manual review costs, optimizing planning decisions, and supporting scientific management.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for checking spatial data in land and space planning, characterized in that, Includes the following steps: Preprocessing spatial data for land spatial planning yields standardized spatial data; Based on the scale or resolution information of standardized spatial data, determine the upper bound parameter of the superposition error and generate dynamic tolerance parameters. The standardized spatial data is divided into blocks to obtain a spatial index structure; Spatial conflict detection is performed based on standardized spatial data and spatial index structure, and false conflict results are filtered out according to dynamic tolerance parameters to obtain the conflict detection results. Based on a pre-built policy rule knowledge graph, rule matching and reasoning are performed on the conflict detection results to obtain the rule matching and reasoning results. The rule matching reasoning results are expressed in a structured manner, resulting in version versions and establishing version association relationships; Determine if there are any non-compliant items in the result version: If it does not exist, a change tracking record will be generated based on the resulting version; If they exist, the non-compliant items are categorized and marked, and a detailed description of the corresponding non-compliant items is output.

2. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for obtaining standardized spatial data is as follows: The projection parameter information of the spatial data of the land spatial planning is analyzed to obtain the initial coordinate system identification result; The initial coordinate system identification results are comprehensively evaluated to determine the target coordinate system type and its confidence level, and the coordinate system identification result is obtained based on the confidence level. Based on the coordinate system identification results, the corresponding coordinate transformation model is selected, and coordinate system one transformation processing is performed on the spatial data of the land spatial planning. Perform transformation quality control processing on the coordinate system transformation process, including boundary verification, transformation residual calculation, and abnormal feature marking and alarm, and generate transformation log; Perform topology consistency repair on the spatial data after coordinate transformation to obtain the topology repair result; Based on the coordinate system identification results, transformation logs, and topology repair results, standardized spatial data is generated.

3. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for determining the upper bound parameter of the superposition error and generating the dynamic tolerance parameter is as follows: Based on the scale or spatial resolution information of standardized spatial data, determine the data type and extract the corresponding basic parameters; A baseline tolerance function model is constructed based on the aforementioned basic parameters. When the spatial data is vector data, a mapping relationship between the scale and the baseline tolerance is established based on the scale denominator. When the spatial data is raster data, a mapping relationship between the resolution and the baseline tolerance is established based on the spatial resolution. By introducing data acquisition method factors and time factors, the baseline tolerance function model is parameterized and modified to obtain the dynamic tolerance model and the dynamic tolerance corresponding to each spatial data layer. When multiple spatial data sources exist, the dynamic tolerance model is weighted and fused based on the area ratio of each spatial data layer to obtain the superposition error upper bound model, and a unified superposition error upper bound parameter is output.

4. The method for checking spatial data of land and space planning according to claim 3, characterized in that, The determination of the upper bound of the superposition error also includes an adaptive correction step based on measured control points, specifically including the following steps: Within the standardized spatial data coverage area, control points with known precise coordinates are deployed at a preset density to cover different terrain areas and multi-source data boundary areas. Obtain the corresponding position of each control point in the standardized spatial data, match it with its known precise coordinates, and calculate the position residual; Statistical error indices are calculated based on location residuals, including root mean square error and quantile error corresponding to a preset confidence level. The quantile error is compared with the theoretical tolerance to determine the deviation state of the upper bound of the superposition error; Based on the deviation state, the upper bound parameter of the superposition error is adaptively adjusted to obtain the corrected upper bound parameter of the superposition error. When the adjustment range of the superposition error upper bound parameter exceeds the preset range threshold, a data quality warning message is output. The dynamic tolerance parameter is updated based on the corrected upper bound parameter of the superposition error, and a correction record containing control point distribution and error statistics is generated.

5. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for establishing the spatial index structure is as follows: Based on the number of elements corresponding to each spatial data layer, an R-tree index for each spatial data layer is constructed, and the index node capacity parameter is adaptively determined according to the number of elements to obtain the first-layer spatial index structure. A grid index is constructed within the spatial data coverage area, and the grid scale is adaptively determined based on the data density to ensure that the number of features within the grid cell meets the preset constraints, thus obtaining the second-layer spatial index structure. The first-level spatial index structure is associated and integrated with the second-level spatial index structure to obtain a two-level spatial index structure.

6. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for obtaining collision detection results is as follows: To obtain the topological and geometric relationships between elements in the spatial data layer; Based on the topological and geometric relationships, multiple types of spatial conflict detection are performed in a preset order, including self-overlap detection within the same spatial data layer, pairwise overlap detection between different spatial data layers, containment relationship detection, connectivity and integrity detection, and boundary consistency detection, to obtain the initial conflict result. Based on dynamic tolerance parameters, the initial conflict results are filtered by area threshold to remove conflict areas whose area does not exceed the tolerance threshold, thus obtaining the first filtering result. Boundary collinearity analysis is performed on the first filtering result to filter out narrow pseudo-collisions caused by boundary overlap or digitization errors, resulting in a second filtering result. Based on historical result version data, spatial matching and state determination are performed on the second filtering result. Conflicts that meet preset conditions are marked as inherited conflicts, and the inherited conflicts are filtered to obtain the third filtering result. The third filtering results are summarized to obtain conflict detection results, and corresponding pseudo-conflict filtering statistics are generated.

7. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for constructing a policy and rule knowledge graph is as follows: The policy and rule texts are structured and parsed to extract rule clause information, element information, and conflict type information, and to identify rule entities, element type entities, and conflict type entities. Based on the rule entities, element type entities, and conflict type entities, an entity layer of the knowledge graph is constructed, and attribute information is configured for the rule entities. The attribute information includes at least the rule source, rule type, rule logic, and scope of application. Based on the entity layer and combined with the attribute information of the rule entities, semantic relationship edges between entities are established. The semantic relationship edges include at least the applicability relationship between the rule entity and the feature type entity, the triggering relationship between the rule entity and the conflict type entity, and the constraint relationship between the rule entities, thus obtaining the rule semantic association structure. Based on the semantic association structure of rules, a validity period range is set for each rule entity, and a substitution relationship between the old and new rule entities is established when the rule entity is updated; Differentiated rules are introduced and priority relationships are set to form a collaborative constraint relationship between rules at different levels, resulting in a policy rule knowledge graph; The policy and rule knowledge graph is modeled and stored in an ontology format, and query and rule maintenance interfaces are provided to support the addition, modification and deactivation of rules.

8. The method for checking spatial data of land and space planning according to claim 7, characterized in that, The method for rule matching and inference of conflict detection results is as follows: Feature extraction is performed on each conflict record in the conflict detection results to construct the corresponding conflict feature vector; Based on the conflict feature vector, rule matching is performed in a pre-constructed policy rule knowledge graph, and a candidate rule set is obtained by filtering according to preset triggering conditions. The candidate rule set is prioritized and sorted to obtain an ordered rule set, wherein the priority is determined based on the rule type, rule level and implementation time; Based on an ordered set of rules, rule reasoning is performed to determine the target rule corresponding to each conflict record; During the rule reasoning process, when multiple rule constraints exist, conflict resolution is performed on the rules in the candidate rule set; when conflict resolution cannot be completed, rule conflict identification information is generated. Record the reasoning path information during the rule matching and reasoning process, and output the rule matching and reasoning results.

9. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for establishing version association is as follows: Construct a directed acyclic graph data structure to establish version evolution relationships; Generate a unique version identifier for the current rule matching inference result and obtain the corresponding parent version identifier; Based on the unique version identifier and the parent version identifier, an association relationship between the current version and the parent version is established in the directed acyclic graph data structure.

10. The method for checking spatial data of land and space planning according to claim 1, characterized in that, The method for classifying and marking non-compliant items is as follows: Construct a classification and labeling system, which includes at least a severity dimension, a conflict type dimension, and a rectification difficulty dimension; Based on the aforementioned classification and labeling system, the non-compliant items are graded and determined according to the severity dimension to obtain the corresponding severity labels; Based on the conflict detection results corresponding to the non-compliant items, the non-compliant items are classified and labeled in the conflict type dimension to obtain the corresponding conflict type labels. Based on the attribute and association information of the non-compliant items, the non-compliant items are evaluated in terms of rectification difficulty, and the corresponding rectification difficulty label is obtained. The severity marker, conflict type marker, and rectification difficulty marker are combined and encoded to obtain the classification marker result.

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