GIS-based automatic verification methods, systems, and media for urban planning data

Abstract

This invention discloses a GIS-based automatic verification method, system, and medium for urban planning data, relating to the field of data processing technology. The method includes: constructing a current data layer and a planning data layer based on a GIS platform; spatially indexing the current data layer and the planning data layer to obtain an urban planning database; importing a verification base map; performing layer registration by overlaying the urban planning database onto the verification base map through the GIS platform to generate a verification dataset; loading an automatic verification toolset to automatically verify and analyze the verification dataset, generating verification analysis results, which include chart files; storing the chart files sequentially according to a geographic database; and automatically verifying the urban planning database based on the stored information. This invention solves the technical problems of low efficiency and insufficient accuracy of existing manual verification methods, achieving the technical effect of improving verification efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, system, and medium for automatic verification of urban planning data based on GIS. Background Technology

[0002] In urban planning and management, the comparison and verification of planning data with existing data usually relies on manual operations. This requires manually importing base maps, manually overlaying layers, and comparing and analyzing each item one by one, a tedious and time-consuming process. Because the verification process involves the registration and interpretation of multi-source spatial data, it is greatly affected by human factors, making it prone to omissions and misjudgments, and failing to meet the actual needs of rapid and accurate verification of large-scale planning data. Summary of the Invention

[0003] This application provides a GIS-based automatic verification method, system, and medium for urban planning data, which addresses the technical problems of low efficiency and insufficient accuracy of existing manual verification techniques.

[0004] In view of the above problems, this application provides a method, system and medium for automatic verification of urban planning data based on GIS.

[0005] The first aspect of this application provides a method for automatic verification of urban planning data based on GIS, the method comprising:

[0006] Based on a GIS platform, a current status data layer and a planning data layer are constructed. These layers are then spatially indexed to obtain an urban planning database. A verification base map is imported, and the urban planning database is overlaid onto the base map using the GIS platform for layer registration, generating a verification dataset. An automatic verification toolset is loaded to perform automatic verification analysis on the dataset, generating analysis results, which include chart files. These chart files are stored sequentially according to the geographic database, and the urban planning database is automatically verified based on the stored information.

[0007] A second aspect of this application provides a GIS-based automatic verification system for urban planning data, the system comprising:

[0008] The module comprises four layers: a layer construction module, a layer registration module, and a layer storage module. The layer construction module is used to construct a current status data layer and a planning data layer based on a GIS platform. The current status data layer and the planning data layer are spatially indexed to obtain an urban planning database. The layer registration module is used to import a verification base map and, through the GIS platform, overlay the urban planning database onto the verification base map to perform layer registration and generate a verification dataset. The verification analysis module loads an automatic verification toolset to automatically verify and analyze the verification dataset, generating verification analysis results, which include chart files. The sequence storage module stores the chart files sequentially according to the geographic database and performs automated verification of the urban planning database based on the stored information.

[0009] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the GIS-based automatic verification method for urban planning data provided in this application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application constructs a current status data layer and a planning data layer based on a GIS platform, spatially indexes the current status data layer and the planning data layer to obtain an urban planning database; imports a verification base map, and uses the GIS platform to overlay the urban planning database onto the verification base map for layer registration, generating a verification dataset; loads an automatic verification toolset to automatically verify and analyze the verification dataset, generating verification analysis results, which include chart files; stores the chart files sequentially according to the geographic database, and automatically verifies the urban planning database based on the stored information. This invention solves the technical problems of low efficiency and insufficient accuracy of existing manual verification techniques, achieving improved verification efficiency and accuracy through automated layer registration and verification analysis. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the automatic verification method for urban planning data based on GIS provided in the embodiments of this application;

[0014] Figure 2 This is a schematic diagram of the structure of the GIS-based automatic verification system for urban planning data provided in this application embodiment.

[0015] Figure labeling: Layer construction module 11, Layer registration module 12, Verification and analysis module 13, Sequence storage module 14. Detailed Implementation

[0016] This application provides a GIS-based automatic verification method, system, and medium for urban planning data. It addresses the technical problems of low efficiency and insufficient accuracy of existing manual verification techniques by providing an automated layer registration and verification analysis method to improve the efficiency and accuracy of verification.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0019] Example 1, as Figure 1 As shown, this application provides a GIS-based automatic verification method for urban planning data, the method comprising:

[0020] Step S100: Construct a current data layer and a planning data layer based on the GIS platform, and spatially index the current data layer and the planning data layer to obtain an urban planning database.

[0021] In this embodiment, firstly, multi-source spatial data of the planning area is organized and loaded based on a GIS (Geographic Information System) platform. Information such as current land use, building distribution, and infrastructure status is constructed into a current status data layer to reflect the actual use of urban space. Simultaneously, planning outcome documents such as the regulatory detailed planning layer and special planning layers are constructed into a planning data layer to reflect the future spatial layout and management requirements of the city. The regulatory detailed planning layer is used to clarify control elements such as urban land use, development intensity, and road systems, and has high legal binding force. The special planning layer is a technical planning layer prepared for specific areas such as municipal transportation, public facilities, and green space systems. The regulatory detailed planning layer and special planning layers are pre-defined.

[0022] Next, spatial indexing is performed on the existing data layer and the planning data layer. This process begins by implementing hierarchical spatial indexing for both layers, establishing a multi-level index structure. Based on this structure, spatial elements are traversed and analyzed to extract and identify corresponding spatial element parameters. Layer alignment and association are then completed, forming an initial database. Subsequently, conflict detection identifies spatial contradictions between the existing and planning data, and these contradictions are dynamically coordinated and controlled. Ultimately, the initial database is updated, resulting in the urban planning database.

[0023] Furthermore, the method provided in the application embodiment, which constructs a current data layer and a planning data layer based on a GIS platform, spatially indexes the current data layer and the planning data layer to obtain an urban planning database, further includes:

[0024] The existing data layer and the planning data layer are spatially indexed in a hierarchical manner to construct a multi-level index structure. Spatial analysis is performed by traversing the existing data layer and the planning data layer according to the multi-level index structure to determine multiple spatial element parameters. Multiple index items are identified for each of the multiple spatial element parameters, and these index items correspond to the multiple spatial element parameters. Based on these multiple index items, the existing data layer and the planning data layer are aligned and associated to construct an initial database. The initial database is traversed to perform conflict detection on the existing data layer and the planning data layer, identifying spatial conflict and contradiction information. The existing data layer and the planning data layer are dynamically coordinated according to the spatial conflict and contradiction information to generate collaborative control results that update the initial database, thus generating the urban planning database.

[0025] In this embodiment of the application, the existing data layer and the planning data layer are first spatially indexed by the GIS platform. The existing data layers such as the current land use status, the current building distribution status, and the current infrastructure status, as well as the planning data layers such as the control detailed planning layer and the special planning layer, are managed in layers according to spatial hierarchy and attribute characteristics. The hierarchical organization of the layers is realized through the indexing algorithm, and a multi-level index structure is constructed.

[0026] After the multi-level index structure is constructed, the existing data layer and the planning data layer are traversed. Spatial analysis methods are used to extract and analyze the spatial geometry, spatial coordinates, boundary range, feature type and spatial distribution relationship of each layer to determine spatial feature parameters, including land boundary range parameters, building outline geometric parameters and infrastructure network node parameters, as well as land control range parameters of the regulatory detailed plan and facility control line parameters of the special plan.

[0027] After extracting spatial feature parameters, each spatial feature parameter is identified to form an index item. A one-to-one correspondence is established between the index item and the spatial feature parameter through spatial coordinate encoding and attribute field mapping, enabling rapid matching and precise positioning across different data sources. This index item ensures accurate feature correspondence between the current data layer and the planning data layer during subsequent data association processes.

[0028] Next, based on the index items, the current data layer and the planning data layer are aligned and associated. The spatial coordinate system is unified through spatial position offset calculation, rotation angle correction and spatial geometric transformation. The spatial elements are matched based on the element attribute mapping relationship. Finally, the matching results are stored in a unified manner to construct the initial database.

[0029] Subsequently, after the initial database was constructed, the existing data layer and the planning data layer were traversed. Conflict detection was performed through methods such as spatial overlay analysis, topological consistency check and buffer comparison to identify spatial conflict information between the existing data and the planning data, including conflicts such as overlapping land use boundaries, misaligned road control lines and building setbacks.

[0030] Finally, the existing data layer and the planning data layer are dynamically coordinated according to the spatial conflict information. Conflicts are resolved by adjusting spatial elements, correcting boundaries and adjusting control lines. In conjunction with planning control rules, existing elements that do not meet the requirements are marked or adjusted. The adjusted data is then rewritten into the initial database to update and generate the urban planning database.

[0031] Step S200: Import the verification base map, and perform layer registration by overlaying the urban planning database onto the verification base map through the GIS platform to generate the verification dataset.

[0032] In this embodiment, a verification base map is first imported. During this process, a base map metadata archive is constructed by retrieving the basic geographic base map and verifying its coordinates, and then imported into the GIS platform to generate the verification base map. Subsequently, layer feature analysis is performed on the urban planning database and the verification base map to determine multiple registration control points. Dynamic layer registration of the current data layer and the planning data layer is achieved through these multiple registration control points, generating registration results. Then, based on the registration results, spatial correlation analysis is performed to construct a multi-source data spatial correlation array. Finally, the registration results are verified according to the multi-source data spatial correlation array, resulting in a verification dataset.

[0033] Furthermore, in the method provided in the application embodiment, the process of importing a verification base map, overlaying the urban planning database onto the verification base map using a GIS platform for layer registration, and generating a verification dataset further includes:

[0034] The process involves retrieving a base map and verifying its coordinates. Upon successful verification, a base map metadata archive is created. This metadata archive is then imported into a GIS platform to generate a verification base map. Layer feature analysis is performed on the urban planning database and the verification base map to identify multiple registration control points. Dynamic layer registration is then performed by overlaying the current data layer and the planning data layer onto the verification base map according to these control points, generating a registration result. Based on the registration result, spatial correlation analysis is conducted to construct a multi-source data spatial correlation array. Finally, the registration result is verified using the multi-source data spatial correlation array to generate the verification dataset.

[0035] In this embodiment, a pre-prepared base geographic map is first retrieved, and a coordinate system consistency check is performed. The spatial reference, projection parameters, datum ellipsoid, central meridian, and zone number are checked item by item to ensure that the spatial reference of the base geographic map is consistent with the spatial reference of the urban planning database. After the coordinate check passes, a base map metadata file is generated based on the spatial reference parameters, scale, map sheet boundaries, and geographic control information.

[0036] Next, the base map metadata archive is imported into the GIS platform, and a verification base map is generated. The verification base map serves as the reference layer for spatial registration, ensuring that the current data layer and the planning data layer are overlaid under the same spatial reference. Subsequently, layer feature analysis is performed on the urban planning database and the verification base map. By extracting spatial features such as road intersections, control line intersections, and feature boundary nodes, multiple registration control points are selected and determined.

[0037] Then, based on multiple registration control points, the current data layer and the planning data layer are overlaid onto the verification base map for dynamic layer registration. In this process, firstly, registration quality analysis is performed based on multiple registration control points, calculating the spatial accuracy, geometric stability, and positional error of each control point to generate registration quality parameters. Subsequently, the multiple registration control points are classified according to the registration quality parameters to form point categories, and dynamic optimization is performed on this basis, forming optimized registration control points through selection and combination. Next, using the optimized registration control points as references, global registration is first performed to obtain global registration parameters. Then, local registration is performed based on the global registration parameters, correcting local residuals. Through this step, the current data layer and the planning data layer achieve precise spatial alignment relative to the verification base map, ultimately obtaining the registration result.

[0038] Based on the registration results, spatial correlation analysis is performed on the current data layer, the planning data layer, and the verification base map. Geometric relationships such as spatial adjacency, intersection, and inclusion are extracted through topological relationship analysis and attribute mapping to construct a multi-source data spatial correlation array. The multi-source data spatial correlation array is used to describe the spatial and attribute correspondence between different data layers.

[0039] Finally, based on the spatial correlation array of multi-source data, the registration results are checked, and the accuracy of spatial location error, topological consistency and attribute matching is verified to confirm that the registration results meet the verification accuracy requirements. The verified registration results are then output together with the relevant layers to generate a verification dataset.

[0040] Furthermore, in the method provided in the application embodiment, the dynamic layer registration is performed by overlaying the current data layer and the planning data layer onto the verification base map according to the plurality of registration control points to generate a registration result, and further includes:

[0041] Based on the multiple registration control points, registration quality analysis is performed to generate multiple registration quality parameters; based on the multiple registration quality parameters, the multiple registration control points are classified to generate multiple point categories; the multiple registration control points are dynamically optimized according to the multiple point categories, and the optimized points are screened and combined to determine multiple registration optimization control points; according to the multiple registration optimization control points, the current data layer and the planning data layer are superimposed on the verification base map for global registration to generate global registration parameters; according to the global registration parameters, the current data layer and the planning data layer are superimposed on the verification base map for local registration to generate the registration result.

[0042] In this embodiment, during registration quality analysis, based on the corresponding coordinates of multiple registration control points in the verification base map, the current data layer, and the planning data layer, the coordinate differences between the verification base map and the current data layer, and between the verification base map and the planning data layer, are calculated respectively. Specifically, for each registration control point, the difference between the horizontal and vertical coordinates is calculated, and the square root of the sum of the squares of the differences is used to obtain two sets of spatial distance errors, representing the deviation of the control point in the current data layer and the planning data layer, respectively. Each set of spatial distance errors serves as a registration quality parameter to measure the spatial registration accuracy and stability between the corresponding layer and the verification base map. Through this step, multiple registration quality parameters are generated.

[0043] When classifying control points, a grading system is established based on multiple registration quality parameters and the residual range. When the registration residual is less than 0.10 meters, the control point is classified as a high-precision point; when the residual is between 0.10 meters and 0.25 meters, it is classified as a general-precision point; and when the residual is greater than 0.25 meters, it is classified as a low-precision point. This grading applies not only to the current data layer but also to the planning data layer, thus creating two corresponding accuracy evaluations for the same registration control point. For example, if a control point has a residual of 0.08 meters in the current data layer and a residual of 0.20 meters in the planning data layer, it is classified as a high-precision point in the current data layer and a general-precision point in the planning data layer. This step generates multiple point categories.

[0044] During dynamic optimization, all low-precision points are eliminated based on multiple point categories to prevent points with large errors from affecting the overall registration accuracy. Then, among high-precision and general-precision points, selection is performed according to the principle of balanced spatial distribution. For example, the entire registration area is divided into 500m × 500m grid cells, and only one or two control points with the smallest residuals are retained within each grid to ensure uniformity and representativeness of spatial distribution. If a grid contains five control points with residuals of 0.06m, 0.09m, 0.13m, 0.17m, and 0.21m respectively, only the two control points with the smallest residuals are retained. Through this step, multiple registration optimization control points are determined.

[0045] In the global registration stage, based on the registration optimization control points, spatial correspondences are established between the current data layer, the planning data layer, and the verification base map. Through similarity transformation, the translation, rotation angle, and scale factor are calculated to achieve overall spatial alignment between the current data layer and the planning data layer. For example, least squares calculations yield a translation of 0.12 meters, a rotation angle of 0.45°, and a scale factor of 1.0003. This process generates global registration parameters.

[0046] During local registration, spatial deviations in local areas are adjusted based on the residuals calculated using global registration parameters. This is achieved by weighted correction of control point coordinates within a local area, eliminating small-scale offsets. For example, if the residual for a certain area is 0.04 meters, weighted adjustment can reduce it to 0.01 meters. After local adjustments, the current data layer and the planning data layer achieve overall consistency and precise local overlay within the verification base map coordinate system, generating the registration result.

[0047] Step S300: Load the automatic verification toolset to perform automatic verification analysis on the verification dataset and generate verification analysis results, which include chart files.

[0048] In this embodiment, during automated verification analysis, multiple verification rules are first encapsulated and then loaded into the GIS platform. Multiple verification modules within the toolset are then used to perform distributed parallel analysis on the verification dataset to obtain distributed analysis results. Subsequently, multi-level collaborative control of the distributed analysis results generates verification analysis results, which include chart files to visually display spatial and attribute anomaly information during the verification process.

[0049] Furthermore, in the method provided in the application embodiments, loading an automatic verification toolset to automatically verify and analyze the verification dataset and generate verification and analysis results further includes:

[0050] Multiple verification rules are encapsulated and loaded into the automatic verification toolset to the GIS platform. The automatic verification toolset includes multiple verification modules. The multiple verification modules are scheduled to perform distributed parallel analysis processing on the verification dataset to generate distributed analysis results. The distributed analysis results are then subject to multi-level collaborative control to generate the verification analysis results of the multiple verification modules.

[0051] In this embodiment, multiple verification rules are first encapsulated and an automatic verification toolset is loaded into the GIS platform. Verification rules are a set of constraints on verification behavior and judgment criteria, used to determine the consistency and identify anomalies of spatial, topological, and attribute information of data during the execution of the automatic verification toolset. Verification rules include three categories: spatial accuracy threshold rules, topological relationship constraint rules, and attribute field matching rules. Specifically, spatial accuracy threshold rules limit the allowable offset range between spatial elements, such as controlling the error of road red lines, building boundaries, or land use boundaries to within 0.10 meters; topological relationship constraint rules regulate the connection, intersection, and inclusion relationships of geometric elements such as lines and surfaces, such as checking for the existence of hanging lines, repeated line segments, or gaps in planar objects; attribute field matching rules compare the attribute consistency of the same spatial objects in the layer, such as whether information such as plot number, land use nature, and plot ratio corresponds consistently. These verification rules are encapsulated and loaded into the automatic verification toolset.

[0052] Next, multiple verification modules are scheduled to perform distributed parallel analysis on the verification dataset. In this process, the verification dataset is matched by multiple verification modules to formulate a set of verification tasks. Then, spatial region information is introduced, and the verification task set is decomposed according to the spatial region information, forming multiple regional verification sub-tasks. These regional verification sub-tasks are then dynamically allocated in a distributed manner, allowing each verification module to run in parallel within different spatial regions. Real-time tracking is performed based on the task allocation results, resulting in multiple verification monitoring states. Through a parallel data sharing mechanism, these multiple verification monitoring states are integrated to ultimately generate distributed analysis results.

[0053] Next, multi-level collaborative regulation is implemented based on the distributed analysis results. This process begins with heterogeneous analysis of the distributed results, extracting the heterogeneous analysis findings, and unifying the data formats output by different verification modules through data mapping and transformation to obtain standardized analysis results. Subsequently, multi-level conflict detection and identification are performed based on the standardized analysis results to obtain conflict analysis data. Then, a coordinated decision-making process is constructed, performing multiple rounds of collaborative resolution on the conflict analysis data to form a collaborative regulation archive. Next, based on the collaborative regulation archive, multi-dimensional visualization analysis is conducted on the verification analysis results, generating recommended chart parameters and dynamically rendering them to obtain initial chart files. The initial chart files are then evaluated and verified, and the chart content and presentation format are updated and optimized based on the verification score to generate new chart files. Finally, the chart files are added to the verification analysis results, presenting the verification results in a structured, standardized, and visualized manner.

[0054] Furthermore, in the method provided in the application embodiments, scheduling the multiple verification modules to perform distributed parallel analysis processing on the verification dataset and generate distributed analysis results further includes:

[0055] The multiple verification modules are scheduled to match the verification dataset and formulate a verification task set; spatial regional information is introduced, and the verification task set is decomposed according to the spatial regional information to obtain multiple regional verification sub-tasks; the multiple regional verification sub-tasks are distributed and dynamically allocated to the verification dataset, and the multiple verification modules are tracked in real time according to the allocation results to generate multiple verification monitoring statuses; the multiple verification monitoring statuses are shared in parallel to generate the distributed analysis results.

[0056] In this embodiment, a rule-matching method is first used to schedule multiple verification modules to match the verification dataset. The verification rules are mapped one-to-one with layer feature types, attribute fields, and spatial relationships, forming a task list containing module identifiers, layer ranges, feature types, and rule identifiers, thus creating a verification task set. The verification rules consist of spatial precision threshold rules, topological relationship constraint rules, and attribute field matching rules. Spatial precision threshold rules limit the allowable offset range between spatial features, such as controlling the offset of road red lines or land boundaries within 0.10 meters. Topological relationship constraint rules limit the intersection, containment, and connection relationships of geometric features, such as checking for hanging lines, duplicate boundaries, or gaps in polygon features. Attribute field matching rules compare the consistency of attribute information for the same object in different layers, such as whether the plot number, land use nature, and floor area ratio information correspond. This process achieves precise matching between the verification rules and the data structure, ultimately resulting in the verification task set.

[0057] Subsequently, spatial area information is introduced, and a quadtree segmentation method is used to spatially divide the verification scope. Using administrative boundaries or planning zones as the outer bounding box, the spatial area is progressively segmented according to the number of polygon features and area thresholds. When the area of ​​a spatial range exceeds one square kilometer or the number of features exceeds five hundred, it is automatically divided into four equal-area sub-regions, and this recursive segmentation continues until each sub-region meets the set segmentation threshold or reaches the maximum segmentation depth. Task items are assigned to corresponding sub-regions based on the centroid coordinates of the features or the overlap relationship of the minimum bounding rectangle, forming spatial task fragments and obtaining multiple regional verification sub-tasks.

[0058] Next, multiple regional verification subtasks are distributed and dynamically allocated across the verification dataset. A round-robin scheduling method combined with a task queue is used to assign subtasks to each verification module. During this process, subtasks are assigned sequentially according to spatial region, and the task number, spatial region, assignment time, and estimated completion time for each verification module are recorded. When a verification module completes its current subtask, the next regional verification subtask is automatically assigned according to the task queue. When differences in the execution progress of different verification modules are detected, an unexecuted task is reallocated through a rebalancing mechanism to achieve load balancing. After task assignment is completed, a mapping table between tasks and verification modules is generated, yielding the allocation results.

[0059] Next, based on the allocation results, the execution status of each verification module is tracked in real time. Each verification module reports the current subtask number, task progress percentage, analysis time, and anomaly identification information at fixed time intervals. For example, when a module detects a topological anomaly while processing a regional verification subtask, such as overlapping boundaries of adjacent plots, the anomaly is recorded immediately and the verification monitoring information is updated. Through this process, multiple verification monitoring statuses are obtained.

[0060] Finally, multiple verification and monitoring statuses are shared in parallel, employing a publish-subscribe mechanism to achieve real-time information aggregation among multiple modules. Each verification module publishes its interim verification results and metadata to the shared channel, which is subscribed to by the aggregator, which aggregates the analysis results within the same spatial area in 30-second time windows. Duplicate records are deduplicated according to rule identifiers and geometric primary keys, and multiple anomaly types of the same object are integrated. For example, when the same plot of land shows anomalies in both spatial location verification and attribute consistency verification, it is merged into a single multi-anomaly record. Through parallel sharing and data aggregation, structured and standardized analytical information is output, generating distributed analysis results.

[0061] Furthermore, in the method provided in the application embodiments, multi-level collaborative control is implemented on the distributed analysis results to generate the verification analysis results of the multiple verification modules, which further includes:

[0062] The distributed analysis results are subjected to heterogeneous analysis to extract the heterogeneous analysis results. These results are then transformed through data mapping to obtain standardized analysis results. Based on these standardized results, multi-level conflict detection and identification are performed to obtain conflict analysis data. A coordination decision is constructed and executed to collaboratively resolve the conflict analysis data in multiple rounds, resulting in a collaborative control profile. Based on this collaborative control profile, multi-dimensional visualization analysis is performed, generating charts with recommended parameters for dynamic rendering to obtain an initial chart file. The initial chart file is evaluated and verified, and updated and optimized based on the verification score to generate a new chart file. Finally, the chart file is added to the verification analysis results.

[0063] In this embodiment, the distributed analysis results are first subjected to heterogeneous analysis. Results from different verification modules are compared one by one according to data structure characteristics, field attributes, and spatial characteristics. The heterogeneous analysis employs a field standard comparison method, performing format recognition and structure comparison on the vector coordinate data output by the spatial location verification module, the topologically encoded data output by the topological relationship verification module, and the key-value pair data output by the attribute field verification module. By reading each data field individually, the structure of the field name, field type, field length, and geometric type is parsed, marking the fields with differing structures, and extracting the heterogeneous analysis results.

[0064] The heterogeneous analysis results are then transformed through data mapping. This transformation uses a field mapping table to map spatial, topological, and attribute information into spatial identifier fields, topological identifier fields, and attribute identifier fields, respectively. For example, with spatial location data, vector coordinate fields are extracted, and the X and Y coordinate values ​​are mapped to spatial identifier fields; with topological relationship data, topological coding information is mapped to topological identifier fields; and with attribute fields, attributes such as plot number, land use, and floor area ratio are mapped to attribute identifier fields. This process transforms analysis results from different sources into a unified data structure, resulting in standardized analysis results.

[0065] Next, based on the standardized analysis results, multi-level conflict detection and identification are performed. In this process, a multi-dimensional threshold comparison method is used to compare the verification results of the verification module item by item according to three dimensions: spatial location, topological relationship, and attribute consistency. Specifically, in the spatial location dimension, the coordinate values ​​of the same spatial object are calculated for difference; when the deviation exceeds 0.10 meters, it is marked as a spatial conflict. In the topological relationship dimension, the topological codes of adjacent plots are logically compared; when the topological relationship types of the two results are inconsistent, it is marked as a topological conflict. In the attribute consistency dimension, the attribute fields of the same object are compared as strings; when the attribute content is inconsistent, it is marked as an attribute conflict. By identifying conflicts one by one in each dimension, conflict analysis data is obtained.

[0066] Next, a coordinated decision-making process is established to resolve conflicting data through multiple rounds of collaborative analysis. This process employs a conflict-priority rule-based decision-making method, resolving conflict results in multiple rounds based on the spatial accuracy level and data integrity of the conflict source. For example, when the spatial location discrepancy between two verification modules exceeds 0.10 meters, the result from the verification module with higher spatial accuracy is selected as the final result; when attribute conflicts occur, the result with higher field integrity is used as the final result. By processing conflicting objects one by one, updating conflict identifier status, and resolving conflicting data round by round, a collaborative control archive is formed, recording the conflicting object identifier, conflict type, resolution rules, and final judgment result.

[0067] Subsequently, multi-dimensional visualization analysis was conducted based on the collaborative control archives. Through data aggregation and statistical methods, spatial location conflicts, topological relationship conflicts, and attribute consistency conflicts were clustered and statistically analyzed according to their spatial distribution characteristics. Conflict-intensive areas, conflict frequencies, and conflict type proportions were extracted and mapped onto visualization elements. Then, recommended chart parameters were generated, and conflict elements were overlaid with the base map to complete the correspondence between spatial scope and conflict information. Dynamic rendering was then performed to obtain the initial chart file.

[0068] After obtaining the initial chart file, an evaluation and verification process is performed. This evaluation and verification uses a multi-indicator calculation method to calculate the spatial location conflict error rate, topological relationship consistency ratio, and attribute matching rate. For example, the spatial location conflict error rate is calculated as the ratio of the number of conflicting objects to the total number of objects; the topological relationship consistency ratio is calculated as the ratio of the number of consistent objects to the total number of objects; and the attribute matching rate is calculated as the ratio of the number of objects with consistent attributes to the total number of objects. These three indicators are then comprehensively calculated using preset weighting coefficients to obtain a verification score. The initial chart is then updated and optimized based on the verification score. Specifically, when the verification score is lower than a preset standard value, the rendering method and conflict annotation method of the initial chart file are adjusted, such as adjusting the conflict annotation color, modifying the rendering layer transparency, or enhancing debugging information. After adjustment and optimization, the chart file is generated.

[0069] Finally, add the chart files to the verification analysis results.

[0070] Step S400: Store the chart files sequentially according to the geographic database, and automatically verify the urban planning database based on the stored information.

[0071] Furthermore, in the method provided in the application embodiments, storing the map files sequentially according to a geographic database and automatically verifying the urban planning database based on the stored information further includes:

[0072] The process involves traversing the chart files for feature analysis, extracting chart content features for element identification, and determining chart categories. The chart files are then categorized and stored according to these categories, resulting in a multi-level directory structure. A multi-dimensional storage structure is constructed by retrieving the geographic database, and the chart files organized using this multi-level directory structure are stored sequentially to generate storage information. Metadata is intelligently parsed from the chart files to extract spatial attribute data and planning feature data. Based on the spatial attribute data and planning feature data, a planning element feature knowledge base is constructed. Finally, the urban planning database is automatically verified based on the stored information and the planning element feature knowledge base.

[0073] In this embodiment, the map files are stored sequentially according to a geographic database. When automatically verifying the urban planning database based on the stored information, the map files are first traversed for feature analysis to extract content features and identify elements. This process includes identifying spatial elements and planning features by parsing legends, annotation text, and coordinate information. Spatial elements include road boundaries, building outlines, and plot boundaries, while planning features include land use, floor area ratio, and building height limits. By extracting these features, the map category is determined, such as a spatial distribution map, a topological relationship map, or a planning indicator map.

[0074] Next, the chart files are categorized and stored according to their type, forming a multi-level directory structure. In this process, chart files are divided into different first-level directories based on categories such as spatial distribution maps, topological relationship diagrams, and planning indicator maps. Then, they are further categorized into second-level directories based on administrative divisions or planning areas, and finally into third-level directories based on time series or other attributes. This categorized storage method systematically organizes and manages the chart files, ultimately forming a structured multi-level directory structure.

[0075] Subsequently, a multi-dimensional storage structure is constructed by retrieving data from a geodatabase. The chart files are then stored sequentially according to this multi-level directory structure. This process involves building a storage structure based on spatial extent, time dimension, and chart category. Specifically, firstly, the spatial extent of each chart file, such as its bounding rectangle, is indexed and associated with spatial data in the geodatabase to support fast spatial queries. Then, the chart files are indexed by the time dimension to manage charts according to their generation time or update cycle. Finally, charts are categorized by chart category, such as spatial distribution maps, topological relationship maps, and planning indicator maps, ensuring efficient storage by category. These indexes work together to ensure that chart files can be retrieved on demand during storage and to generate corresponding storage information, including path indexes, spatial indexes, timestamps, and category codes.

[0076] Next, the metadata of the multi-level directory structured chart files is intelligently parsed to extract spatial attribute data and planning feature data. During this process, geometric analysis is performed on spatial objects in each chart using coordinate analysis methods to extract spatial attribute data, such as boundary coordinates, geometric type, area, and length. Simultaneously, planning feature data is extracted by parsing attribute fields in the charts, such as land use, plot ratio, building height limit, and road grade. Through the extraction of this data, structured spatial attribute data and planning feature data are obtained.

[0077] Next, a planning element feature knowledge base is constructed by linking and integrating spatial attribute data and planning feature data. This knowledge base is created by linking spatial attribute data and planning feature data from the chart files using unique identifiers. This knowledge base not only records the geometric features of each spatial object but also includes related planning attribute information. Spatial data is optimized and stored using spatial indexes, while planning feature data is standardized. Through this integration, the planning element feature knowledge base provides a comprehensive storage structure for spatial and attribute information, offering strong data support for subsequent verification.

[0078] Finally, the urban planning database is automatically verified based on the stored information and a knowledge base of planning element characteristics. During the verification process, firstly, spatial comparison analysis and buffer zone analysis are used to check whether the spatial elements in the urban planning database are consistent with the data in the chart files, confirming whether the positional deviation meets the accuracy requirements, for example, a deviation of less than 0.10 meters. Secondly, topological relationship verification is used to verify whether the topological relationships between different spatial elements are correct, for example, checking for topological errors such as overlaps, gaps, or hanging lines. Finally, attribute consistency comparison is used to verify whether the attribute data in the urban planning database, such as land use, plot ratio, and building height limits, are consistent with the planning characteristics in the chart files. This process automatically generates verification results, identifies errors or inconsistencies in the planning data, and ensures the accuracy and consistency of the urban planning database.

[0079] In summary, the embodiments of this application have at least the following technical effects:

[0080] This application constructs a current status data layer and a planning data layer based on a GIS platform, spatially indexes the current status data layer and the planning data layer to obtain an urban planning database; imports a verification base map, and uses the GIS platform to overlay the urban planning database onto the verification base map for layer registration, generating a verification dataset; loads an automatic verification toolset to automatically verify and analyze the verification dataset, generating verification analysis results, which include chart files; stores the chart files sequentially according to the geographic database, and automatically verifies the urban planning database based on the stored information. This invention solves the technical problems of low efficiency and insufficient accuracy of existing manual verification techniques, achieving improved verification efficiency and accuracy through automated layer registration and verification analysis.

[0081] Example 2, based on the same inventive concept as the GIS-based automatic verification method for urban planning data in the aforementioned examples, such as... Figure 2As shown, this application provides an automatic verification system for urban planning data based on GIS. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0082] The layer construction module 11 is used to construct a current data layer and a planning data layer based on the GIS platform, and to spatially index the current data layer and the planning data layer to obtain an urban planning database; the layer registration module 12 is used to import the verification base map, and to perform layer registration by overlaying the urban planning database onto the verification base map through the GIS platform to generate a verification dataset; the verification analysis module 13 is used to load an automatic verification toolset to automatically verify and analyze the verification dataset, and to generate verification analysis results, which include chart files; the sequence storage module 14 is used to store the chart files in sequence according to the geographic database, and to automatically verify the urban planning database based on the stored information.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] The existing data layer and the planning data layer are spatially indexed in a hierarchical manner to construct a multi-level index structure. Spatial analysis is performed by traversing the existing data layer and the planning data layer according to the multi-level index structure to determine multiple spatial element parameters. Multiple index items are identified for each of the multiple spatial element parameters, and these index items correspond to the multiple spatial element parameters. Based on these multiple index items, the existing data layer and the planning data layer are aligned and associated to construct an initial database. The initial database is traversed to perform conflict detection on the existing data layer and the planning data layer, identifying spatial conflict and contradiction information. The existing data layer and the planning data layer are dynamically coordinated according to the spatial conflict and contradiction information to generate collaborative control results that update the initial database, thus generating the urban planning database.

[0085] Furthermore, the system is also used to implement the following functions:

[0086] The process involves retrieving a base map and verifying its coordinates. Upon successful verification, a base map metadata archive is created. This metadata archive is then imported into a GIS platform to generate a verification base map. Layer feature analysis is performed on the urban planning database and the verification base map to identify multiple registration control points. Dynamic layer registration is then performed by overlaying the current data layer and the planning data layer onto the verification base map according to these control points, generating a registration result. Based on the registration result, spatial correlation analysis is conducted to construct a multi-source data spatial correlation array. Finally, the registration result is verified using the multi-source data spatial correlation array to generate the verification dataset.

[0087] Furthermore, the system is also used to implement the following functions:

[0088] Based on the multiple registration control points, registration quality analysis is performed to generate multiple registration quality parameters; based on the multiple registration quality parameters, the multiple registration control points are classified to generate multiple point categories; the multiple registration control points are dynamically optimized according to the multiple point categories, and the optimized points are screened and combined to determine multiple registration optimization control points; according to the multiple registration optimization control points, the current data layer and the planning data layer are superimposed on the verification base map for global registration to generate global registration parameters; according to the global registration parameters, the current data layer and the planning data layer are superimposed on the verification base map for local registration to generate the registration result.

[0089] Furthermore, the system is also used to implement the following functions:

[0090] Multiple verification rules are encapsulated and loaded into the automatic verification toolset to the GIS platform. The automatic verification toolset includes multiple verification modules. The multiple verification modules are scheduled to perform distributed parallel analysis processing on the verification dataset to generate distributed analysis results. The distributed analysis results are then subject to multi-level collaborative control to generate the verification analysis results of the multiple verification modules.

[0091] Furthermore, the system is also used to implement the following functions:

[0092] The multiple verification modules are scheduled to match the verification dataset and formulate a verification task set; spatial regional information is introduced, and the verification task set is decomposed according to the spatial regional information to obtain multiple regional verification sub-tasks; the multiple regional verification sub-tasks are distributed and dynamically allocated to the verification dataset, and the multiple verification modules are tracked in real time according to the allocation results to generate multiple verification monitoring statuses; the multiple verification monitoring statuses are shared in parallel to generate the distributed analysis results.

[0093] Furthermore, the system is also used to implement the following functions:

[0094] The distributed analysis results are subjected to heterogeneous analysis to extract the heterogeneous analysis results. These results are then transformed through data mapping to obtain standardized analysis results. Based on these standardized results, multi-level conflict detection and identification are performed to obtain conflict analysis data. A coordination decision is constructed and executed to collaboratively resolve the conflict analysis data in multiple rounds, resulting in a collaborative control profile. Based on this collaborative control profile, multi-dimensional visualization analysis is performed, generating charts with recommended parameters for dynamic rendering to obtain an initial chart file. The initial chart file is evaluated and verified, and updated and optimized based on the verification score to generate a new chart file. Finally, the chart file is added to the verification analysis results.

[0095] Furthermore, the system is also used to implement the following functions:

[0096] The process involves traversing the chart files for feature analysis, extracting chart content features for element identification, and determining chart categories. The chart files are then categorized and stored according to these categories, resulting in a multi-level directory structure. A multi-dimensional storage structure is constructed by retrieving the geographic database, and the chart files organized using this multi-level directory structure are stored sequentially to generate storage information. Metadata is intelligently parsed from the chart files to extract spatial attribute data and planning feature data. Based on the spatial attribute data and planning feature data, a planning element feature knowledge base is constructed. Finally, the urban planning database is automatically verified based on the stored information and the planning element feature knowledge base.

[0097] In Example 3, based on the same inventive concept as the GIS-based automatic verification method for urban planning data in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of any one of the methods described in Example 1 above.

[0098] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0099] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0100] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A GIS-based automatic verification method for urban planning data, characterized in that, The method includes: Based on the GIS platform, a current data layer and a planning data layer are constructed, and the current data layer and the planning data layer are spatially indexed to obtain an urban planning database. Import the verification base map, and perform layer registration by overlaying the urban planning database onto the verification base map through the GIS platform to generate the verification dataset; The automated verification toolset is loaded to perform automated verification analysis on the verification dataset, generating verification analysis results, which include chart files; The chart files are stored sequentially according to the geographic database, and the urban planning database is automatically checked based on the stored information; Import the verification base map, and perform layer registration by overlaying the urban planning database onto the verification base map through a GIS platform to generate a verification dataset. The method includes: Retrieve the base geographic map, verify the coordinates of the base geographic map, and construct the base map metadata file when the verification is successful; Import the base map metadata file into the GIS platform to generate a verification base map; Layer feature analysis was performed on the urban planning database and the verification base map to determine multiple registration control points; Dynamic layer registration is performed by overlaying the current data layer and the planning data layer onto the verification base map according to the multiple registration control points, thereby generating a registration result. Based on the registration results, data spatial correlation analysis is performed to construct a multi-source data spatial correlation array; The registration results are verified based on the multi-source data spatial correlation array to generate the verification dataset; Dynamic layer registration is performed by overlaying the current data layer and the planning data layer onto the verification base map according to the multiple registration control points, generating registration results. The method includes: Based on the multiple registration control points, a registration quality analysis is performed to generate multiple registration quality parameters. Based on the multiple registration quality parameters, the multiple registration control points are classified to generate multiple point categories; The multiple registration control points are dynamically optimized according to the multiple point categories, and the optimized points are screened and combined to determine multiple registration optimization control points. Global registration is performed by overlaying the current data layer and the planning data layer onto the verification base map according to the multiple registration optimization control points, thereby generating global registration parameters. The current status data layer and the planning data layer are overlaid onto the verification base map according to the global registration parameters to perform local registration and generate the registration result.

2. The GIS-based automatic verification method for urban planning data as described in claim 1, characterized in that, Based on a GIS platform, a current data layer and a planning data layer are constructed. Spatial indexing is then performed on the current data layer and the planning data layer to obtain an urban planning database. The method includes: The existing data layer and the planning data layer are spatially indexed in a hierarchical manner to construct a multi-level index structure; Spatial analysis is performed by traversing the current data layer and the planning data layer according to the multi-level index structure to determine multiple spatial element parameters; Multiple index items are identified for the multiple spatial feature parameters, and there is a correspondence between the multiple index items and the multiple spatial feature parameters; Based on the multiple index items, the current data layer and the planning data layer are aligned and associated to construct an initial database; The initial database is traversed to perform conflict detection between the current data layer and the planning data layer, and spatial conflict and contradiction information is identified. Based on the spatial conflict information, the current data layer and the planning data layer are dynamically coordinated to generate collaborative control results, which update the initial database and generate the urban planning database.

3. The automatic verification method for urban planning data based on GIS as described in claim 1, characterized in that, The method includes loading an automated verification toolset to perform automated verification analysis on the verification dataset and generating verification analysis results. Multiple verification rules are encapsulated and loaded into the automatic verification toolset to the GIS platform. The automatic verification toolset includes multiple verification modules. The multiple verification modules are scheduled to perform distributed parallel analysis and processing on the verification dataset to generate distributed analysis results. The distributed analysis results are subjected to multi-level collaborative control to generate the verification analysis results of the multiple verification modules.

4. The GIS-based automatic verification method for urban planning data as described in claim 3, characterized in that, The method for scheduling multiple verification modules to perform distributed parallel analysis on the verification dataset and generate distributed analysis results includes: The multiple verification modules are scheduled to match the verification dataset and formulate a set of verification tasks; By introducing spatial region information, the verification task set is decomposed according to the spatial region information to obtain multiple regional verification sub-tasks; The multiple regional verification subtasks are distributed and dynamically allocated to the verification dataset. Based on the allocation results, the multiple verification modules are tracked in real time, generating multiple verification monitoring statuses. The multiple verification and monitoring statuses are shared in parallel to generate the distributed analysis results.

5. The GIS-based automatic verification method for urban planning data as described in claim 3, characterized in that, The method involves implementing multi-level collaborative control of the distributed analysis results to generate the verification analysis results of the multiple verification modules, including: The distributed analysis results are subjected to heterogeneous analysis, the heterogeneous analysis results are extracted, and the heterogeneous analysis results are subjected to data mapping transformation to obtain standardized analysis results. Based on the standardized analysis results, multi-level conflict detection and identification are performed to obtain conflict analysis data. Construct a coordinated decision-making process, and execute the coordinated decision-making process to perform multiple rounds of collaborative resolution on the analyzed conflict data to obtain a collaborative control archive; Based on the aforementioned collaborative regulation archive, multi-dimensional visualization analysis is performed to generate charts with recommended parameters for dynamic rendering, thereby obtaining an initial chart file. The initial chart file is evaluated and verified, and the initial chart is updated and optimized based on the verification score to generate a new chart file. Add the chart file to the verification analysis results.

6. The GIS-based automatic verification method for urban planning data as described in claim 1, characterized in that, The chart files are stored sequentially according to a geographic database, and the urban planning database is automatically checked based on the stored information. The method includes: The chart files are traversed to perform feature analysis, extract chart content features for element identification, and determine the chart category. The chart files are categorized and stored according to the chart type to obtain a multi-level directory structure for organizing the chart files; The geographic database is retrieved to construct a multi-dimensional storage structure. The chart files are organized according to the multi-level directory structure and stored sequentially according to the multi-dimensional storage structure to generate storage information. The metadata of the chart files organized by the multi-level directory structure is intelligently parsed to extract the spatial attribute data and planning feature data of the chart files. Based on the spatial attribute data and the planning feature data, a planning element feature knowledge base is constructed by linking and integrating them. The urban planning database is automatically verified based on the stored information and the planning element feature knowledge base.

7. A GIS-based automatic verification system for urban planning data, characterized in that: The system is used to execute the GIS-based automatic verification method for urban planning data as described in any one of claims 1-6, and the system includes: The layer construction module is used to construct a current data layer and a planning data layer based on the GIS platform, and to spatially index the current data layer and the planning data layer to obtain an urban planning database. The layer registration module is used to import the verification base map, and perform layer registration by overlaying the urban planning database onto the verification base map through the GIS platform to generate the verification dataset. The verification and analysis module is used to load an automatic verification toolset to automatically verify and analyze the verification dataset and generate verification and analysis results, which include chart files. The sequence storage module is used to store the chart files in sequence according to the geographic database and to automatically check the urban planning database based on the stored information.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the GIS-based automatic verification method for urban planning data as described in any one of claims 1-6.

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