Event-driven basic geographic information updating method and system

Abstract

The invention relates to a basic geographic information updating method and system based on event driving, and the method comprises the steps: monitoring a planning event in a planning examination and approval system, accessing a corresponding data source to collect data after the planning event is received, carrying out the correlation of the data, extracting pre-updating information, and storing the processed data into a pre-updating information library; monitoring a completion event pushed by the multi-measurement-in-one platform in real time, and triggering a verification event when completion acceptance of a project and multi-measurement-in-one surveying and mapping are completed; obtaining completion data, and extracting geometric and attribute information of an actual construction result; performing comparison verification on actual data in the completion data and corresponding pre-updating information in the pre-updating information base through a three-dimensional comparison algorithm; and identifying the difference area and correcting the difference area to generate final geographic information updating data. Compared with the prior art, the method has the advantages that the pre-updated information is verified and refined, so that the high precision and reliability of the final warehousing data are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of basic geographic information updating, and in particular to a basic geographic information updating package generation method based on planning approval event driving. BACKGROUND

[0002] Traditional basic geographic information updating usually adopts a periodic surveying and mapping or post-update mode. When a city construction project is completed, data is updated in the database through surveying and mapping results such as'multi-measurement integration'. This mode has obvious hysteresis, which leads to a 'time difference' between the information obtained at the application level such as planning decision, city management, and public service and the actual status, affecting the timeliness of the information and the scientificity of the decision. The prior art lacks a closed-loop mechanism of actively triggering updating at the project approval source and verifying and refining using subsequent completion data. SUMMARY

[0003] The purpose of the present application is to solve the problems existing in the current basic geographic information updating scheme, and to provide a basic geographic information updating method and system based on event driving.

[0004] The purpose of the present application can be achieved by the following technical scheme:

[0005] As a first aspect of the present application, a basic geographic information updating method based on event driving is provided, comprising the following steps:

[0006] Listening to planning events in a planning approval system, when a planning event is received, accessing a corresponding data source to collect data, including planning approval drawings, land use approval drawings, red line data, and project attribute information;

[0007] Data correlation and pre-update information extraction are performed on the collected data, and the processed data is stored in a pre-update information database;

[0008] Real-time listening to the completion events pushed by the multi-measurement integration platform, when the project completes the completion acceptance and the multi-measurement integration surveying and mapping, triggering a verification event;

[0009] Obtaining completion data, pre-processing the fused completion data, and extracting geometric and attribute information of the actual construction results;

[0010] Comparing and verifying the actual data in the completion data with the corresponding pre-update information in the pre-update information database through a three-dimensional comparison algorithm; identifying the difference area and correcting the difference area to generate final geographic information updating data.

[0011] As a preferred technical scheme, the method accesses a corresponding data source to collect data, and performs data analysis and format standardization on the collected data, and the specific implementation is as follows:

[0012] For planning approval drawings, call the file download API of the planning approval system, pass in the project's unique code to obtain a list of drawing files; return a list of drawing file URLs, and call the URLs to download the files to the local temporary storage area; for DWG / DXF drawings of planning approval drawings, extract elements including building outlines, roads, and green spaces, and output them as GeoJSON or SHP; for SHP files, use the GDAL / OGR library to read them and convert them to GeoJSON;

[0013] For land use approval maps and red line data, vector data is obtained through SQL and WFS requests; for WFS vector data, GML / GeoJSON is parsed to extract land use red lines and ownership boundaries and output as GeoJSON.

[0014] For project attribute information, it interfaces with the engineering project approval system, calls the project details API of the engineering project approval system and returns JSON format data, parses the JSON to map it to the internal data model, and outputs it to the internal structured object.

[0015] As a preferred technical solution, the data association and pre-update information extraction of the collected data are specifically implemented as follows:

[0016] Data from different data sources are spatially and attribute-associated using a unique project code;

[0017] Spatially overlay the collected planning approval maps, land use approval maps, and red line data:

[0018] The elements in the planning approval map are overlaid with the current basic geographic information database. The elements include the outline of the proposed building, structures, roads and green spaces.

[0019] Identify the spatially changing areas in the planning scheme relative to the existing database, including: new construction areas, demolition areas, reconstruction areas, terrain modification areas, new / reconstructed roads, and new / reconstructed pipeline corridors;

[0020] Based on project attribute information and design drawings, the project attribute information is bound to spatial data to generate pre-updated feature objects.

[0021] As a preferred technical solution, the specific operations for generating pre-updated feature objects include:

[0022] New feature creation: In the pre-updated information database, create the vector geometry and preliminary attributes of features based on the planning approval map;

[0023] Change feature markers: Add status markers to features in the current database that will be demolished or rebuilt, and record their original information as historical archives;

[0024] Predicting Topographic Changes: Based on the vertical design drawings and the existing DEM, calculate and generate the predicted cut and fill areas and the predicted topographic model after completion;

[0025] Initial BIM model generation: Using BIM information from planning approval drawings or based on 2D drawings, a building information model is generated using modeling tools as part of the pre-updated information.

[0026] As a preferred technical solution, the pre-updated information database uses a PostgreSQL database and its extension PostGIS to manage and store pre-updated data; the table structure of the pre-updated information database is divided into a project master table, a pre-updated feature table, and a data traceability table; the pre-updated information database is logically isolated from the main production database, and the data status is set to pending verification, waiting for verification during the completion phase.

[0027] As a preferred technical solution, the extraction of geometric and attribute information of the actual construction results specifically includes:

[0028] Point cloud data: Laser point clouds are obtained from government cloud object storage, and a statistically based radius outlier removal algorithm is used to register the point cloud to the set target coordinate system through an iterative nearest point algorithm;

[0029] BIM data: Download the as-built model from the BIM collaboration platform via API and perform lightweight processing, parse the as-built model file to extract the core building components, and convert it to the set target coordinate system;

[0030] Vector data: Connect to the PostGIS spatial database to extract the as-built mapping vector layer that integrates multiple surveys, perform topology rule checks, eliminate gaps between hanging lines and surfaces, and perform attribute field mapping;

[0031] The extracted actual construction results data are standardized with a unified spatiotemporal reference and converted into a unified internal format.

[0032] As a preferred technical solution, the three-dimensional comparison algorithm is used to compare and verify the actual data in the as-built data with the corresponding pre-updated information in the pre-updated information database as follows:

[0033] Building / Structure Verification: Compare the building outline extracted from the as-built BIM model or point cloud with the pre-built building model in the pre-updated information database, calculate the positional deviation, size deviation and shape difference; determine whether fine-tuning is required based on the set tolerance threshold. If the deviation is within the threshold, it is marked as verified. If the deviation exceeds the limit, a deviation heat map is generated and marked as requiring fine-tuning.

[0034] Topographic change verification: using DEM grid difference analysis method, comparing the digital elevation model generated based on point cloud with the pre-update topographic model, calculating the difference between the pre-judgment and the actual fill and excavation volume, and generating a fill and excavation volume change map; if the slope change is greater than the set slope threshold, it is identified as a topographic mutation area;

[0035] Change element verification, confirming the actual state of the elements to be demolished, and identifying newly added elements that have not been approved, including: demolition element verification and new element verification; the demolition element verification uses image time series analysis method to compare the pixel level of the completed orthographic image with the historical map, to confirm the actual state of the elements to be demolished; if it has been demolished, it is confirmed; if it has not been demolished or the reconstruction is inconsistent, the pre-update information state is updated; the new element verification uses a deep learning driven semantic segmentation model to identify unapproved temporary buildings, checks whether there are actual new elements in the completed data that are not included in the pre-update information library, and records them as new discoveries.

[0036] As a preferred technical solution, the difference area is corrected, and the implementation is as follows:

[0037] Building fine-tuning: when it is detected that the pre-update building model and the completed point cloud have an out-of-limit deviation, a point cloud driven correction algorithm is called to perform fine-tuning operation; taking high-precision completed point cloud as spatial reference, by establishing spatial correspondence between pre-update model vertex and point cloud data, using KD tree to accelerate nearest neighbor search, building contour vertex is dynamically adsorbed to point cloud dense area, realizing coordinate calibration; during the geometric fine-tuning process, the system synchronously retains the semantic identification information of BIM components; after fine-tuning, traverse the building attribute table, update the key fields according to the completed measured data, including: correcting the layer height value according to the elevation profile generated by point cloud, updating the building area based on vector surveying and mapping results, and perfecting the structure attribute by referring to the material parameters in the BIM model, forming the completed building model;

[0038] Topographic element update: generate a digital elevation model through the Poisson surface reconstruction algorithm, which serves as the spatial reference for topographic update; use orthographic images for ground cover classification to distinguish ground types; for the pre-judged fill and excavation area, calculate the elevation difference matrix of the completed DEM and the pre-update DEM through grid algebraic operation, identify the boundary displacement caused by actual construction, and use morphological edge optimization algorithm to regenerate smooth fill and excavation boundary line;

[0039] For changes to elements, classified handling is performed on detected changes outside the plan: For newly added but unapproved elements discovered during the completion phase, an independent spatial entity object is created, a temporary element ID is assigned, and the discovery location, geometric shape, and preliminary classification attributes are recorded, while an anomaly warning is triggered to the monitoring platform; For existing elements that have been confirmed to have been demolished, a logical deletion operation is performed in the main production database, but the historical version snapshot of the element is retained and marked as demolished, its spatial representation is switched to a semi-transparent overlay mode, and the timeliness attribute is updated to the obsolescence date.

[0040] As a preferred technical solution, the method generates a structured update information package after completing the verification and correction operations, and the information package specifically includes:

[0041] Standardized update dataset: Encapsulates 3D spatial entities, including simplified BIM building models calibrated with point clouds, terrain update rasters based on Poisson reconstruction, and change vector layers;

[0042] Metadata: The content includes project information, data source, coordinate system parameters, processing methods, accuracy specifications, and responsible person information; change and update information is in XML format and is published through the service interface;

[0043] Update log: Records the ID, change type, addition, modification, deletion, information before and after the change, update time, and data source of all elements involved in the update; captures the workflow execution process in real time, and records the unique identifier, operation type, addition, modification, deletion, and timestamp of all changed elements;

[0044] Difference Report: Generates documents, comparison bar charts, and heat maps of abnormal areas showing the differences between planned predictions and actual completion.

[0045] As a second aspect of the present invention, an event-driven basic geographic information update system is provided, the system executing the event-driven basic geographic information update method described above, the system comprising:

[0046] At the data source layer, an event listening agent is deployed to listen for and identify planning events in the planning approval system and completion events in the multi-measurement integration system.

[0047] The processing engine layer comprises a pre-update engine and a verification and refining engine; the pre-update engine comprises: a data collection module, which determines data sources to be accessed and collects corresponding data from a planning approval system, a real estate registration system and a land use management system after receiving a planning event; a format standardization module, which unifies different format data collected into a system internal standard format; and a pre-update constructor, which performs data correlation and pre-update information extraction and stores the processed data into a pre-update information database; the verification and refining engine comprises: a vector correction module, which extracts a completed surveying and mapping vector layer in the multi-surveying-in-one system, performs a topological rule check, eliminates hanging lines and surface gaps and performs attribute field mapping; a BIM lightweight module, which performs lightweight processing on a completed model, takes building core components and converts them to a target coordinate system; a point cloud processing module, which registers laser point clouds to a target coordinate system; a three-dimensional comparison module, which performs three-dimensional comparison and verification on completed data and corresponding pre-update information in the pre-update information database, including building / structure verification, topographic change verification and change element verification, and identifies difference areas; and an element refining module, which corrects the difference areas.

[0048] The output layer generates a structured standard update information package through an incremental packager.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] 1) The event-driven basic geographic information update method proposed in the present application verifies and refines pre-update information by using high-precision completed data, thereby ensuring the high precision and reliability of the final database data. The generated standardized and structured reusable change update information package directly serves the geographic information database update, thereby enhancing the data accuracy.

[0051] 2) The present application moves the update trigger point from the project completion stage to the planning approval stage, thereby realizing quasi-real-time or near-real-time updating of geographic information and greatly shortening the information lag period. Through the pre-judgment-verification closed loop and automatic comparison and refining, a large amount of manual comparison and editing work in the traditional post-mapping update is reduced, thereby improving the update efficiency and the degree of automation. DETAILED DESCRIPTION

[0052] Figure 1 A planning approval event-driven basic geographic information update package generation method flow chart of the present application.

[0053] Figure 2 A planning stage pre-update information database construction flow chart of the present application.

[0054] Figure 3 A completed stage automatic verification and refining flow chart of the present application.

[0055] Figure 4 A schematic diagram of a planning approval event-driven basic geographic information update package system structure based on the present application. DETAILED DESCRIPTION

[0056] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solutions of the present application, and detailed implementation methods and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0057] Embodiment 1

[0058] A planning approval event-driven basic geographic information update package generation method. This method significantly moves the key link of basic geographic information update to the planning approval stage, and through the "prediction-verification-update" mechanism, realizes the early perception, pre-update and automatic verification and refinement of geographic information changes, significantly improves the timeliness, accuracy and update efficiency of geographic information. As shown in Figure 1 , the specific steps are as follows:

[0059] Step 1, event listening and triggering: deploy event listening agents in the planning approval system, and automatically identify the event when the urban construction project enters the planning approval process. As shown in Figure 2 , deploy the event listening engine in the planning approval system database log layer to listen to the database changes or business process state updates of the key approval nodes. Capture these state change events through database triggers, message queues or API Webhook methods. Event information includes: project unique code, approval stage, submission time, applicant, project name, etc.

[0060] Step 2, multi-system interface docking and data pulling: after the system receives the event, determine the data sources to be accessed and collect. For planning approval drawings, call the file download API of the planning approval system, input the project unique code, and get the drawing file list. Return the drawing file URL list. The system calls the URL to download the file to the local temporary storage area. For land use approval drawings and red line data (vector data), directly request vector data through SQL and WFS, and spatial data uses WGS84 coordinate system; for project attribute information (structured data), interface with the engineering project approval system, call the project details API of the approval system and return JSON format data.

[0061] Step three, data parsing and format standardization: unify different formats of data into the system internal standard format for subsequent processing. For DWG / DXF drawings, use AutoCAD.NET API or open source libraries (such as netDxf) to parse and extract building contours, roads, green spaces and other features to output as GeoJSON or SHP; for SHP files, use GDAL / OGR library to read and directly convert to GeoJSON; for WFS vector data, parse GML / GeoJSON and extract land use red line and ownership boundaries and output as GeoJSON; for attribute information, directly parse JSON and map to internal data model and output to internal structured objects such as ProjectDTO.

[0062] Step four, data association and pre-update information extraction: associate data from different systems by project unique code for spatial and attribute association. Use GIS spatial analysis engine to perform spatial overlay analysis on collected planning approval drawings, land use approval drawings and red line data: accurately overlay the proposed building contours, structures, roads and green spaces in the planning approval drawings with the current basic geographic information database (such as the latest topographic map, digital elevation model DEM, existing building model). Identify spatial change areas in the planning scheme relative to the current database, including: new construction area, demolition area, reconstruction area, terrain modification area (such as fill and excavation area), new / modified road, new / modified pipeline corridor, etc.

[0063] Based on project attribute information and design drawings, bind project attribute information with spatial data to generate complete "pre-update feature objects", specific operations include:

[0064] New feature creation: in the pre-update information library, create vector geometry (point, line, face) and preliminary attributes (such as "status: proposed", "source: planning approval", "expected completion time") of proposed buildings, roads and green spaces based on planning approval drawings.

[0065] Change feature marking: add "to be demolished / to be reconstructed" status markers to features in the current database that will be demolished or reconstructed, and record their original information as historical archives.

[0066] Topographic change prediction: based on vertical design drawings and current DEM, calculate and generate predicted fill and excavation areas and predicted post-completion topographic model. Preliminary BIM model generation:

[0067] For large or complex projects, preliminary, simplified building information models (LOD 2.0-3.0 level) can be generated using BIM information from planning approval drawings or based on two-dimensional drawings through automated modeling tools as part of the pre-update information.

[0068] Step five, data storage and state management: store the processed data in the pre-update information library, all generated pre-update elements, labels, pre-judgment terrain models, preliminary BIM models and their related metadata are stored in a separate, versioned pre-update information library. The library is logically isolated from the main production database to avoid affecting the accuracy of existing data. PostgreSQL database and its extension PostGIS are used to manage and store pre-update data. The table structure of the database is mainly divided into project main table, pre-update feature table and data source table. Project main table (including project_id, status, created_time, etc.), pre-update feature table (including geometry, attributes, source_system, etc.), data source table (records the source system, collection time, interface call log of each data), sets the data state to "to be verified" and waits for the completion stage verification.

[0069] Step six, completion data receiving and processing: real-time listen to the completion event pushed by the "multi-measurement integration" platform through Kafka message middleware, when the project completes the completion acceptance and completes the "multi-measurement integration" surveying and mapping, trigger the "verification event". As shown in Figure 3 , the completion data is sent to the system through the interface. The fused completion data is preprocessed to extract the geometric and attribute information of the actual construction achievements.

[0070] Point cloud data: Obtain LAZ / LAS format laser point cloud from government cloud object storage, use statistical-based radius outlier removal algorithm, neighborhood radius is set to 0.05m, minimum point number is 16, and point cloud is registered to CGCS2000 through iterative closest point algorithm.

[0071] BIM data: Download IFC format completion model from BIM collaborative platform through REST API, perform lightweight processing, parse IFC file to extract building core components (wall / column / plate / pipeline), and perform geometric simplification to retain 1cm precision features, then convert to CGCS2000 coordinate system.

[0072] Vector data: Directly connect to PostGIS spatial database to extract "multi-measurement integration" completion surveying and mapping vector layer, perform topological rule check, eliminate hanging lines, face gaps, etc., and map attribute fields.

[0073] Unify the space-time reference of these data and convert them to unified internal format GeoJSON, while ensuring that the attribute information between each data source can be correctly matched.

[0074] Step seven, automatic verification: compare the completed data with the corresponding pre-update information in the pre-update information library through three-dimensional comparison algorithm, compare the actual data with the predicted data in the pre-update information library, and identify the difference area. Then automatically correct the difference area, such as adjusting the boundary and updating the attribute, generate the final geographic information update data, and mark it as "verified state".

[0075] 7.1 Building / structure verification

[0076] Three-dimensional geometric comparison: use ICP algorithm or Hausdorff distance calculation to compare the building outline extracted from the completed BIM model or point cloud with the pre-built building model in the pre-update information library. Calculate the position deviation by Procrustes, analyze and calculate the maximum displacement; use Hausdorff distance algorithm to calculate the shape difference, set the threshold value to 0.2m, compare the length, width and height, calculate the size deviation, set the tolerance to ±0.15m, and judge whether it needs to be refined according to the set tolerance threshold. If the deviation is within the threshold, mark it as "verified", if it exceeds the limit, generate a deviation heat map and mark it as "need to be refined".

[0077] 7.2 Topographic change verification:

[0078] Verify the accuracy of the fill and cut area range and topographic form of the pre-judgment. Use DEM grid difference analysis method, generate 1m resolution digital elevation model based on point cloud, compare with pre-update topographic model, calculate fill and cut volume difference, generate fill and cut volume change map, quantify the difference between actual and pre-judgment. If the slope change is >15%, identify it as a topographic mutation area

[0079] 7.3 Change element verification

[0080] Dismantling element verification: use image time series analysis method, compare the completed orthophoto with the historical map at pixel level, confirm the actual state of the element to be demolished. If it has been demolished, it is confirmed; if it has not been demolished or the reconstruction is not consistent, update the pre-update information state.

[0081] New element verification: deep learning driven semantic segmentation model (U-Net architecture) identifies unapproved temporary buildings, checks whether there are actual new elements in the pre-update information library in the completed data, such as unapproved temporary facilities, design changes and updates, and records them as "newly found".

[0082] Step eight, automatic correction, for the "verified" pre-update elements, the system automatically generates formal update instructions, migrates its geometry and attribute information from the pre-update information library to the main production database, and updates the state to "completed". For "need to be refined" elements, call the refinement algorithm:

[0083] 8.1 Building refinement:

[0084] When the system detects an excessive deviation between the pre-updated building model and the as-built point cloud, it automatically invokes the point cloud-driven correction algorithm to perform fine-tuning operations. This algorithm uses the high-precision as-built point cloud as a spatial reference, establishes a spatial correspondence between the vertices of the pre-updated model and the point cloud data, and employs a KD-tree to accelerate nearest neighbor search, dynamically snapping the building outline vertices to dense point cloud areas to achieve millimeter-level coordinate calibration. During geometric fine-tuning, the system simultaneously retains the semantic identification information of BIM components, such as door and window type codes and structural component classifications, ensuring the functional semantic integrity of the model. After fine-tuning, the system automatically traverses the building attribute table, updating key fields based on as-built measured data: correcting floor height values ​​based on the elevation profile generated from the point cloud, updating the building area based on vector mapping results, and refining structural attributes by referring to material parameters in the BIM model, forming an as-built building model with precise matching in both geometric and attribute dimensions.

[0085] 8.2 Terrain Feature Update

[0086] To update terrain features, the system initiates a multi-source data fusion processing flow. First, leveraging the elevation accuracy of laser point clouds, a Poisson surface reconstruction algorithm is used to generate a digital elevation model with centimeter-level precision, serving as the spatial benchmark for terrain updates. Simultaneously, high-resolution orthophotos are introduced for land cover classification, accurately distinguishing between hardened surfaces, green areas, water bodies, and other surface types. For areas with anticipated cut and fill, the system calculates the elevation difference matrix between the as-built DEM and the pre-updated DEM using raster algebra operations. This automatically identifies boundary displacements caused by actual construction, such as over-excavation of slopes and expansion of fill areas, and employs a morphological edge optimization algorithm to regenerate smooth cut and fill boundary lines.

[0087] 8.3 Handling of Changes in Elements

[0088] The system categorizes and handles detected unplanned changes: For newly added but unapproved elements discovered during the completion phase, such as temporary prefabricated houses and unregistered underground pipelines, it automatically creates independent spatial entity objects, assigns them temporary element IDs with the prefix "TMP_", and records their location, geometry, and preliminary classification attributes, while simultaneously triggering an anomaly alert to the monitoring platform; for existing elements confirmed to have been demolished, the system performs a logical deletion operation in the main production database but retains a snapshot of the element's historical version and marks it as "demolished," switches its spatial representation to a semi-transparent overlay mode, and updates its timeliness attribute to the obsolescence date. All change operations are managed through versioning to achieve spatiotemporal retrospection, allowing users to query the evolution of any element's status throughout its entire lifecycle, including the pre-update phase, the final completion phase, and historical versions.

[0089] Step 9: Generate reusable update information packages:

[0090] After the automatic verification and refining operation is completed, a structured update information package conforming to the basic geographic information standard is automatically constructed, and subsequent geographic information database updates are directly called. The data package contains four parts:

[0091] 9.1 Standardized update data set: vector, raster, model data files conforming to national or industry geographic information data standards, which can be directly used for incremental update of the main database. Three-dimensional spatial entities are packaged in GeoPackage format, including simplified BIM building models calibrated by point clouds, terrain update rasters based on Poisson reconstruction, and change vector layers;

[0092] 9.2 Metadata: contains project information, data sources, coordinate system parameters, processing methods, accuracy descriptions, responsible persons, etc. The change update information is described in XML format and can be published through a service interface for reuse by downstream application systems such as planning management, real estate registration, urban management, emergency command, public map services, etc.

[0093] 9.3 Update log: records the IDs of all elements involved in this update, change types, new, modification, deletion, pre-change and post-change information, update time, data sources, etc. By capturing the workflow execution process in real time, all changed elements are recorded in detail, including unique identifier, operation type, new, modification, deletion, and timestamp;

[0094] 9.4 Difference report: summarizes the main differences between the predicted and actual completion, such as average deviation, maximum deviation, and main deviation area, which is used to evaluate the accuracy of the planning prediction and help optimize future prediction models. By using the JasperReport template engine, a PDF document is generated, which contains a building position deviation statistics table, maximum deviation value, average deviation value, fill and dig volume comparison column chart, and abnormal area heat map.

[0095] Embodiment 2

[0096] As another embodiment of the present application, this embodiment provides a planning approval event-driven basic geographic information update package generation system for executing the method as described in Embodiment 1, as shown in Figure 4 The system includes:

[0097] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0098] The preferred embodiments of the present application are described in detail above. It should be understood that those skilled in the art can make many modifications and changes without creative labor based on the concept of the present application. Therefore, any technical solutions obtained by logical analysis, reasoning or limited experiments based on the prior art according to the concept of the present application shall be within the protection scope determined by the claims.

Claims

1. An event-driven method for updating basic geographic information, characterized in that, The steps include: Listen for planning events in the planning approval system. When a planning event is received, access the corresponding data source to collect data, including planning approval map, land use approval map, red line data and project attribute information. The collected data is correlated and pre-update information is extracted. The processed data is then stored in the pre-update information database. Real-time monitoring of completion events pushed by the multi-measurement integration platform; when the project completes final acceptance and multi-measurement integration mapping, a verification event is triggered. Acquire as-built data, preprocess the merged as-built data, and extract the geometric and attribute information of the actual construction results; The actual data in the as-built data is compared and verified with the corresponding pre-updated information in the pre-updated information database using a three-dimensional comparison algorithm. Identify and correct discrepancies in the data to generate the final updated geographic information.

2. The event-driven basic geographic information update method according to claim 1, characterized in that, The method accesses the corresponding data source to collect data, and performs data parsing and format standardization on the collected data. The specific implementation is as follows: For planning approval drawings, call the file download API of the planning approval system, pass in the project's unique code to obtain a list of drawing files; return a list of drawing file URLs, and call the URLs to download the files to the local temporary storage area; for DWG / DXF drawings of planning approval drawings, extract elements including building outlines, roads, and green spaces, and output them as GeoJSON or SHP; for SHP files, use the GDAL / OGR library to read them and convert them to GeoJSON; For land use approval maps and red line data, vector data is obtained through SQL and WFS requests; for WFS vector data, GML / GeoJSON is parsed to extract land use red lines and ownership boundaries and output as GeoJSON. For project attribute information, it interfaces with the engineering project approval system, calls the project details API of the engineering project approval system and returns JSON format data, parses the JSON to map it to the internal data model, and outputs it to the internal structured object.

3. The event-driven basic geographic information update method according to claim 1, characterized in that, The specific implementation of data association and pre-update information extraction of the collected data is as follows: Data from different data sources are spatially and attribute-associated using a unique project code; Spatially overlay the collected planning approval maps, land use approval maps, and red line data: The elements in the planning approval map are overlaid with the current basic geographic information database. The elements include the outline of the proposed building, structures, roads and green spaces. Identify the spatially changing areas in the planning scheme relative to the existing database, including: new construction areas, demolition areas, reconstruction areas, terrain modification areas, new / reconstructed roads, and new / reconstructed pipeline corridors; Based on project attribute information and design drawings, the project attribute information is bound to spatial data to generate pre-updated feature objects.

4. The event-driven basic geographic information update method according to claim 3, characterized in that, The specific operations for generating pre-updated feature objects include: New feature creation: In the pre-updated information database, create the vector geometry and preliminary attributes of features based on the planning approval map; Change feature markers: Add status markers to features in the current database that will be demolished or rebuilt, and record their original information as historical archives; Predicting Topographic Changes: Based on the vertical design drawings and the existing DEM, calculate and generate the predicted cut and fill areas and the predicted topographic model after completion; Initial BIM model generation: Using BIM information from planning approval drawings or based on 2D drawings, a building information model is generated using modeling tools as part of the pre-updated information.

5. The event-driven basic geographic information update method according to claim 1, characterized in that, The pre-updated information database uses a PostgreSQL database and its extension PostGIS to manage and store pre-updated data. The table structure of the pre-updated information database is divided into a project master table, a pre-updated feature table, and a data traceability table. The pre-updated information database is logically isolated from the main production database, and the data status is set to pending verification, waiting for verification during the completion phase.

6. The event-driven basic geographic information update method according to claim 1, characterized in that, The extraction of geometric and attribute information from actual construction results specifically includes: Point cloud data: Laser point clouds are obtained from government cloud object storage, and a statistically based radius outlier removal algorithm is used to register the point cloud to the set target coordinate system through an iterative nearest point algorithm; BIM data: Download the as-built model from the BIM collaboration platform via API and perform lightweight processing, parse the as-built model file to extract the core building components, and convert it to the set target coordinate system; Vector data: Connect to the PostGIS spatial database to extract the as-built mapping vector layer that integrates multiple surveys, perform topology rule checks, eliminate gaps between hanging lines and surfaces, and perform attribute field mapping; The extracted actual construction results data are standardized with a unified spatiotemporal reference and converted into a unified internal format.

7. The event-driven basic geographic information update method according to claim 1, characterized in that, Using the aforementioned three-dimensional comparison algorithm, the actual data in the as-built data is compared and verified with the corresponding pre-updated information in the pre-updated information database as follows: Building / Structure Verification: Compare the building outline extracted from the as-built BIM model or point cloud with the pre-built building model in the pre-updated information database, calculate the positional deviation, size deviation and shape difference; determine whether fine-tuning is required based on the set tolerance threshold. If the deviation is within the threshold, it is marked as verified. If the deviation exceeds the limit, a deviation heat map is generated and marked as requiring fine-tuning. Topographic change verification: Using the DEM raster difference analysis method, the digital elevation model generated based on the point cloud is compared with the pre-updated terrain model to calculate the difference between the predicted and actual cut and fill volumes, and a cut and fill volume change map is generated; if the slope change is greater than the set slope threshold, it is identified as a terrain change area. The verification of change elements confirms the actual status of the elements to be demolished and identifies newly added elements that have not been approved. This includes verification of demolished elements and verification of newly added elements. The verification of demolished elements uses image time-series analysis to compare the as-built orthophoto with historical base maps at the pixel level to confirm the actual status of the elements to be demolished. If the elements have been demolished, the verification is confirmed. If the elements have not been demolished or the reconstruction is inconsistent, the pre-update information status is updated. The verification of newly added elements uses a deep learning-driven semantic segmentation model to identify unapproved temporary buildings and checks whether there are any newly added elements in the as-built data that are not included in the pre-update information database. These are recorded as new discoveries.

8. The event-driven basic geographic information update method according to claim 1, characterized in that, The correction of the difference regions is implemented as follows: Building Refinement: When an excessive deviation is detected between the pre-updated building model and the as-built point cloud, the point cloud-driven correction algorithm is invoked to perform refinement operations; using the high-precision as-built point cloud as the spatial reference, by establishing the spatial correspondence between the vertices of the pre-updated model and the point cloud data, the KD tree is used to accelerate the nearest neighbor search, and the building outline vertices are dynamically snapped to the dense area of ​​the point cloud to achieve coordinate calibration; during the geometric refinement process, the system synchronously retains the semantic identification information of BIM components; After the refinement is completed, the building attribute table is traversed, and key fields are updated based on the as-built measured data, including: correcting the floor height value based on the elevation profile generated by the point cloud, updating the building area based on the vector mapping results, and improving the structural attributes by referring to the material parameters in the BIM model, thus forming the as-built building model. Topographic feature update: A digital elevation model is generated using the Poisson surface reconstruction algorithm as a spatial reference for topographic update; orthophotos are used for surface cover classification to distinguish surface types; for areas with predicted cut and fill, the elevation difference matrix between the as-built DEM and the pre-updated DEM is calculated using raster algebra operations to identify boundary displacements caused by actual construction, and a morphological edge optimization algorithm is used to regenerate smooth cut and fill boundary lines. For changes to elements, classified handling is performed on detected changes outside the plan: For newly added but unapproved elements discovered during the completion phase, an independent spatial entity object is created, a temporary element ID is assigned, and the discovery location, geometric shape, and preliminary classification attributes are recorded, while an anomaly warning is triggered to the monitoring platform; For existing elements that have been confirmed to have been demolished, a logical deletion operation is performed in the main production database, but the historical version snapshot of the element is retained and marked as demolished, its spatial representation is switched to a semi-transparent overlay mode, and the timeliness attribute is updated to the obsolescence date.

9. The event-driven basic geographic information update method according to claim 1, characterized in that, After completing the verification and correction operations, the method generates a structured update information package, which specifically includes: Standardized update dataset: Encapsulates 3D spatial entities, including simplified BIM building models calibrated with point clouds, terrain update rasters based on Poisson reconstruction, and change vector layers; Metadata: The content includes project information, data source, coordinate system parameters, processing methods, accuracy specifications, and responsible person information; change and update information is in XML format and is published through the service interface; Update log: Records the ID, change type, addition, modification, deletion, information before and after the change, update time, and data source of all elements involved in the update; captures the workflow execution process in real time, and records the unique identifier, operation type, addition, modification, deletion, and timestamp of all changed elements; Difference Report: Generates documents, comparison bar charts, and heat maps of abnormal areas showing the differences between planned predictions and actual completion.

10. An event-driven basic geographic information update system, characterized in that, The system executes the event-driven basic geographic information update method as described in any one of claims 1-9, and the system includes: At the data source layer, an event listening agent is deployed to listen for and identify planning events in the planning approval system and completion events in the multi-measurement integration system. The processing engine layer includes a pre-update engine and a verification and refinement engine. The pre-update engine includes: a data acquisition module, which, upon receiving a planning event, determines the data source to be accessed and collects corresponding data from the planning approval system, real estate registration system, and land use management system; a format standardization module, which unifies the collected data of different formats into the system's internal standard format; and a pre-update builder, which performs data association and pre-update information extraction, and stores the processed data in the pre-update information database. The verification and refinement engine includes: a vector correction module, which extracts the as-built mapping vector layer from the multi-measurement integration system, performs topology rule checks, and eliminates hanging lines and surface gaps as well as attribute field mappings; a BIM lightweighting module, which performs lightweighting processing on the as-built model, extracts the core building components, and transforms them to the target coordinate system; and a point cloud processing module, which registers the laser point cloud to the target coordinate system. The 3D comparison module performs 3D comparison and verification between the as-built data and the corresponding pre-updated information in the pre-updated information database, including verification of buildings / structures, verification of terrain changes and verification of changed elements, and identifies areas of difference. The element refinement module corrects discrepancies in certain areas; The output layer generates structured standard update information packets through an incremental packer.

Images