A dynamic cooperative processing method of a natural resource right registration data management and updating system

Abstract

This invention discloses a dynamic collaborative processing method for a natural resource ownership registration data management and updating system, relating to the field of natural resource ownership registration technology. The method includes the following steps: identifying conflicts in heterogeneous data and constructing a standardized basic data resource pool; performing multi-scale spatial alignment and map boundary optimization on the data; automatically generating natural resource registration units with unique codes; constructing a multi-source information association network; outputting review opinions and driving iterative correction of survey results; encrypting and storing the announcement text and registration unit maps; and differentially capturing and tracing historical versions of changes in the register, triggering automatic updates of associated information. This invention solves the problems of multi-source data conflicts, complex pre-planning of registration units, delayed information association, and uncoordinated register updates in natural resource ownership registration by constructing a full-process dynamic collaborative processing mechanism, thereby improving the intelligence level and data management efficiency of ownership registration.

Description

Technical Field

[0001] This invention relates to the field of natural resource ownership registration technology, and in particular to a dynamic collaborative processing method for a natural resource ownership registration data management and updating system. Background Technology

[0002] Natural resource registration refers to the act of recording the ownership, natural condition, and public control status of various natural resources within a natural ecological space in the natural resource register. Natural resources encompass types such as watercourses, forests, grasslands, wetlands, wastelands, and mineral resources with proven reserves. They are an important foundation for ecological civilization construction. The purpose of carrying out natural resource registration is to clarify the ownership subjects of natural resources, clearly define the property rights relationships of various natural resources within the national land space, and provide a legal basis for unified management of natural resources, ecological protection and restoration, paid use of assets, and supervision and assessment.

[0003] Currently, in the process of managing and updating data for natural resource ownership registration, the data from various departments is scattered and heterogeneous, with inconsistent format standards. Conflicts between multiple data sources rely on manual identification and processing, resulting in low data fusion efficiency and poor accuracy. The pre-planning of registration units involves multiple types of spatial areas such as nature reserves, watercourses, and land use patches, which overlap and intersect. Traditional methods struggle to optimize topological constraints and automatically resolve spatial conflicts, leading to highly subjective and easily overlooked unit divisions. Furthermore, ownership information, public control information, and related information such as real estate rights, mining rights, and water abstraction permits are fragmented and scattered across different business systems, resulting in delayed information updates and a lack of dynamic synchronization mechanisms, making it impossible to guarantee data consistency.

[0004] Therefore, a dynamic collaborative processing method for the natural resource ownership registration data management and updating system is proposed to solve the above problems. Summary of the Invention

[0005] The main objective of this invention is to provide a dynamic collaborative processing method for a natural resource ownership registration data management and updating system, in order to solve the problems mentioned in the background above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: a dynamic collaborative processing method for a natural resource ownership registration data management and updating system, the method comprising the following steps: S1. By using a multi-source data fusion engine, spatial topology rules and attribute logic rules are employed to identify conflicts in heterogeneous data and construct a standardized basic data resource pool. S2. A spatiotemporal data fusion algorithm is used to perform multi-scale spatial alignment and patch boundary optimization of the survey base map data, orthophoto data and field supplementary survey data to generate a standardized cadastral survey base map data layer. S3. Using the dynamic pre-planning model of registration units, based on the boundary lines of nature reserves, water flow management boundary lines, and land use patches, natural resource registration units with unique codes are automatically generated through topological constraint optimization and spatial conflict resolution. S4. By using a correlation mapping mechanism, the ownership information, public control information and real estate rights information, mining rights information and water abstraction permit information in the cadastral survey results are dynamically bound and synchronized in real time to construct a multi-source information correlation network. S5. Perform logical comparison and integrity verification on the final cadastral survey form, result verification form, and registration unit map, output review opinions, and drive iterative correction of survey results; S6. Digital watermarking and electronic signature technologies are used to encrypt and preserve the announcement text and the attached diagrams of the registration unit, so as to realize the online submission, automatic verification and feedback of the processing results of objection information. S7. Implement the dynamic update strategy for the register to capture changes in the register in a differentiated manner and trace historical versions, and trigger the automatic update of related information to form a dynamic collaborative closed loop for data management and updates.

[0007] Preferably, the construction of the standardized basic data resource pool in S1 includes the following steps: S11. Based on data lineage analysis technology, extract metadata features from the collected results of the Third National Land Survey, special surveys, and real estate registration data, and establish mapping relationships between multi-source data. S12. Using spatial topology rules and attribute logic rules, perform integrity verification and conflict identification on data items with the same name provided by different departments, and generate a data quality report. S13. By using a fusion rule base based on conflict resolution, the identified conflict data is automatically arbitrated and marked for processing, thus constructing a standardized basic data resource pool.

[0008] Preferably, the generation of the standardized cadastral survey base map data layer in S2 includes the following steps: S21. Feature point-based image registration algorithm performs geometric fine correction and spatial alignment on the latest orthophoto map and digital line map data to eliminate coordinate system errors; S22. Through multi-scale segmentation and merging techniques, the administrative division elements, cadastral elements and field supplementary survey data in the survey base map are optimized for map boundary and attribute inheritance. S23. Using spatial overlay analysis and edge matching techniques, the fused data is subjected to boundary regularization and consistency checks to generate a standardized cadastral survey base map data layer.

[0009] Preferably, the automatic generation of a natural resource registration unit with a unique code in step S3 includes the following steps: S31. Construct a pre-planning logical model for registration units based on a graph database, using the boundaries of nature reserves, water management boundaries, and land use patches as nodes, and storing spatial inclusion, intersection, and adjacency relationships as edges; S32. Using a topological constraint-based optimization algorithm, prioritizing and dividing overlapping and intersecting spatial ranges according to the principles of prioritizing nature reserves and water flow, spatial conflicts are eliminated. S33. A spatial clustering and automatic coding generation module is used to dynamically cluster and normalize the boundaries of the processed closed spatial range, and automatically generate a unique 15-digit registration unit code according to the coding rules.

[0010] Preferably, the step S31, which involves constructing a pre-planning logical model for registration units based on a graph database, includes the following steps: S311. Take the approval boundary line of nature reserves, the water flow management boundary line and the latest land survey land category map as spatial element nodes, and extract the unique identifier and geometric boundary information of each element respectively; S312. Establish spatial inclusion relationship edges, spatial intersection relationship edges, and spatial adjacency relationship edges, connect nodes with topological associations, and label the relationship type and overlapping area attributes. S313. The indexing mechanism of the graph database is used to store nodes and edges in a hierarchical manner, supporting multi-dimensional queries and retrievals based on spatial range, feature type and topological relationship.

[0011] Preferably, the construction of the multi-source information association network in step S4 includes the following steps: S41. A message queue-based association mapping mechanism is adopted to dynamically bind the real estate unit number, exploration permit number, and water extraction permit number in the cadastral survey results to the registration unit as unique identifiers. S42. By using graph database-based association network construction technology, ownership information, public regulation information, real estate rights information, mining rights information, and water abstraction permit information are organized into a multimodal association network, and support fast retrieval by node and relationship; S43. Implement a real-time synchronization strategy based on message queues. When any related information changes in the source system, the related information in the register is triggered to be updated by comparing the status with the message push.

[0012] Preferably, implementing the message queue-based real-time synchronization strategy in step S43 includes the following steps: S431. Deploy message queue middleware between the register management system and each source system to establish a unified channel for subscribing to and publishing change events; S432. When the associated information in the mining rights management system, water abstraction permit management system and real estate registration system changes, the source system will encapsulate the changed data and operation type into a change message and push it to the message queue. S433. The register management system obtains change messages in real time through message listeners and triggers automatic updates and status synchronization of related information fields in the register based on message content and preset mapping rules.

[0013] Preferably, the step S5, which outputs review comments and drives iterative revision of the investigation results, includes the following steps: S51. Construct a knowledge base of key review points, and transform the requirements in the technical specifications regarding boundary line connection, ownership boundary closure, and complete review signatures and seals into executable logical rules; S52. The rule engine performs logical comparison and integrity verification on the final cadastral survey form, result verification form and registration unit map, and automatically detects unclosed boundary lines, unmarked ownership dispute areas and missing or abnormal related information. S53. Utilize machine learning anomaly detection models to learn from historically approved cases, conduct risk level assessments of current investigation results, generate supplementary review opinions, and feed these opinions back to the investigation process for data correction.

[0014] Preferably, the online submission, automatic verification, and processing result feedback of objection information in step S6 includes the following steps: S61. Adopt digital watermark-based announcement document protection technology to embed invisible watermarks when generating announcement text and registration unit attached diagrams; S62. Through an electronic signature-based evidence storage platform, the publication of announcements, the submission of objection materials, and the notification of objection processing results are stored with hash values ​​and timestamps. S63. Implement a collaborative workflow for handling objections. After the objecting party submits objection materials through the online platform, the system automatically associates them with the registration unit and notifies the reviewers. After verification, the system automatically updates the processing status and generates an objection handling notification.

[0015] Preferably, the cave update of the associated information in S7 includes the following steps: S71. Employs incremental backup-based register update technology to capture the differences in each registration and change operation of the register, recording only the data differences before and after the change, and generating a version snapshot; S72. Through a version control-based traceability mechanism, a unique version number is generated for each update operation, and the operator, operation time, and operation type are recorded, supporting the backtracking of any historical version along the timeline; S73. Implement a linked update trigger. When the core data of the register changes, the associated natural resource cadastral map and registration unit map will be automatically updated synchronously, and the metadata will be updated.

[0016] The present invention has the following beneficial effects: 1. In this invention, spatial topology rules and attribute logic rules are used to identify conflicts in multi-source heterogeneous data. A spatiotemporal data fusion algorithm is used to perform multi-scale spatial alignment and patch boundary optimization of survey base map data, orthophoto data, and field supplementary survey data. This enables automatic integrity verification and geometric position correction of heterogeneous data scattered across multiple departments such as natural resources, water conservancy, forestry and grassland, and ecological environment. This solves the problems of low data fusion efficiency, inconsistent coordinate systems, and difficulty in discovering boundary contradictions under traditional manual processing methods. It improves the quality and efficiency of basic data integration and provides a standardized data foundation for subsequent registration unit pre-planning and cadastral surveys.

[0017] 2. In this invention, a dynamic pre-planning model for registration units based on a graph database is used. The boundaries of nature reserves, water management boundaries, and land parcels are stored as nodes, and spatial topological relationships are stored as edges. Registration units with unique codes are automatically generated through topological constraint optimization and spatial conflict resolution. A message queue-based association mapping mechanism is used to dynamically bind and synchronize ownership information, public control information, real estate rights information, mining rights information, and water abstraction permit information in real time. This solves the problems of strong subjectivity in registration unit pre-planning, difficulty in handling overlapping and cross-cutting information, and data inconsistency caused by fragmented and scattered related information and delayed updates. It realizes intelligent delineation of registration units and collaborative linkage of multi-source information.

[0018] 3. In this invention, a rule engine and machine learning anomaly detection model are used to logically compare and verify the integrity of the final cadastral survey form, result verification form, and registration unit map through the review announcement and dynamic update terminal. Anomalies such as unclosed boundary lines and unmarked ownership dispute areas are automatically detected, and review opinions are output to drive data correction. Digital watermarking and electronic signature technology are used to encrypt and preserve the announcement text and attached maps to realize online verification of objections. The dynamic update strategy of the register based on incremental backup and version control is executed to trigger the synchronous update of related maps. This solves the problems of low efficiency of traditional review relying on manual item-by-item comparison, opaque announcement objection handling process and difficulty in evidence preservation, inability to automatically synchronize maps after register update and inability to trace historical versions. It improves the level of intelligent review, ensures the credibility of the announcement process, and forms a dynamic collaborative closed loop of full-process data management and update. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to the present invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] Example 1, please refer to Figure 1 A dynamic collaborative processing method for a natural resource ownership registration data management and updating system, comprising the following steps: Data fusion processing: S1. By using a multi-source data fusion engine, spatial topology rules and attribute logic rules are employed to identify conflicts in heterogeneous data and construct a standardized basic data resource pool. S2. A spatiotemporal data fusion algorithm is used to perform multi-scale spatial alignment and patch boundary optimization of the survey base map data, orthophoto data and field supplementary survey data to generate a standardized cadastral survey base map data layer. Registration Unit and Related Network Construction: S3. Using the dynamic pre-planning model of registration units, based on the boundary lines of nature reserves, water flow management boundary lines, and land use patches, natural resource registration units with unique codes are automatically generated through topological constraint optimization and spatial conflict resolution. S4. By using a correlation mapping mechanism, the ownership information, public control information and real estate rights information, mining rights information and water abstraction permit information in the cadastral survey results are dynamically bound and synchronized in real time to construct a multi-source information correlation network. Review announcements and updates: S5. Perform logical comparison and integrity verification on the final cadastral survey form, result verification form, and registration unit map, output review opinions, and drive iterative correction of survey results; S6. Digital watermarking and electronic signature technologies are used to encrypt and preserve the announcement text and the attached diagrams of the registration unit, so as to realize the online submission, automatic verification and feedback of the processing results of objection information. S7. Implement the dynamic update strategy for the register to capture changes in the register in a differentiated manner and trace historical versions, and trigger the automatic update of related information to form a dynamic collaborative closed loop for data management and updates.

[0022] Building a standardized basic data resource pool in S1 includes the following steps: S11. Based on data lineage analysis technology, extract metadata features from the collected results of the Third National Land Survey, special surveys, and real estate registration data, and establish mapping relationships between multi-source data. Specifically, in implementation: The results of the Third National Land Survey, as well as special surveys on water resources, forests, grasslands, and wetlands, and real estate registration data, were uniformly imported into the data fusion engine. Data lineage analysis technology was then used to extract metadata features from each data file, including: data source department identifiers. Data production timestamp Data version number Coordinate system type Data format type and the geometric boundaries of the data coverage area A multi-source data mapping table is established based on the extracted metadata features, using the data item name (Name) as the key to record the attribute differences of the data item in different source departments, including: field name, alias, and mapping. Numerical unit conversion factor and classification code reference table .

[0023] S12. Using spatial topology rules and attribute logic rules, perform integrity checks and conflict identification on data items with the same name provided by different departments, and generate a data quality report. In practice: For data items within the same administrative region (such as river surface boundaries, forest land parcel boundaries, and collective land ownership boundaries), firstly, all data are uniformly converted to the CGCS2000 coordinate system (using the Bursa seven-parameter model, with a conversion residual error not exceeding 0.1 meters), and then the following verification is performed: Geometric integrity check: Check whether planar features form closed loops and do not self-intersect; for linear features, if they should be closed (such as lake shorelines), determine whether the distance between the first and last points meets the following requirements: ; in , These represent the coordinates of the first and last points of the line element, and the closed tolerance. Determined based on the data scale: 0.5 meters for a scale of 1:10000, and 0.1 meters for a scale of 1:2000; Spatial conflict detection: For features with the same name at the same geographical location, calculate the percentage of overlapping area. ; in Let be the area of ​​the intersection of elements A and B. For the area of ​​the union, when It was determined to be a geometric conflict at that time; Attribute validation: Check whether required fields are empty, whether enumerated values ​​are within the preset range, and whether numerical values ​​exceed the reasonable range; for attribute values ​​of different departments in the same position, if the relative deviation exceeds 5% (or the absolute deviation exceeds the standard allowable value), it is judged as an attribute conflict; All conflict records are compiled to generate a data quality report, which includes the coordinates of the conflict location, the departments involved, the conflict type, a detailed description, and suggested handling methods.

[0024] S13. By using a fusion rule base based on conflict resolution, the identified conflict data is automatically arbitrated and marked for processing, thus constructing a standardized basic data resource pool. Specifically, in implementation: Each conflict record in the data quality report is input into the conflict resolution rule engine. The rule base is constructed based on the technical specifications for natural resource ownership registration and includes: Priority arbitration rules: In case of geometric conflicts, the final boundary shall be determined in the priority order of the approved scope line of nature reserve > water flow management scope line > land survey land category map line; for overlapping areas, the clipping method shall be adopted: retain the complete geometry of high priority elements, cut off the parts of low priority elements that overlap with high priority elements, and perform a 0.5-meter buffer smoothing process on the clipped boundary; Authoritative data source determination rules: In case of attribute conflict, if the field exists in the results of the Third National Land Survey, the results of the Third National Land Survey shall prevail; if it does not exist, the results of the corresponding special survey shall prevail; if it still does not exist, the results of the latest annual change survey shall prevail. When multiple data sources have different times of availability, the data updated at the production time shall be given priority. Automatic arbitration and manual intervention: For conflicts that can be automatically arbitrated, the engine directly performs the fusion and records the operation log; for complex conflicts that cannot be automatically arbitrated (such as data from more than three departments contradicting each other, or the disputed area exceeding 10% of the total area of ​​the registered units), the engine marks the conflict as pending, generates processing suggestions, and pushes them to the manual review interface. All data items that have been arbitrated or manually verified are uniformly converted to the CGCS2000 coordinate system (Bursa seven parameters, residual ≤0.1m) and converted to a unified data format (such as GeoPackage or FileGeodatabase), ultimately forming a standardized basic data resource pool with consistent structure, unified coordinates, and complete attributes.

[0025] The process of generating a standardized cadastral survey base map data layer in S2 includes the following steps: S21. Feature-point-based image registration algorithm: Performs geometrical fine correction and spatial alignment between the latest orthophoto image and digital line drawing data to eliminate coordinate system errors. Specific implementation details: Acquire the latest high-resolution orthophoto image and the corresponding standard-scale digital line map of the region. Use the SIFT algorithm to extract common feature points from the two images, calculate a 128-dimensional orientation descriptor for each feature point, and match the feature point set of the orthophoto image with the feature point set of the digital line map based on the Euclidean distance between the descriptors. ; in The i-th dimension feature descriptors in the orthophoto and digital line drawing maps are used respectively. Reliable matching point pairs are selected using the nearest neighbor and second nearest neighbor distance ratio test (threshold 0.8), and false matches are eliminated using the RANSAC algorithm (inlier distance threshold 3 pixels, iterations 1000). For flat terrain areas, an affine transformation model is used. ; in Original coordinates For the corrected coordinates, For rotation and scaling parameters, , The translation parameters are used; for areas with large terrain undulations or obvious image distortion, a quadratic polynomial transformation is adopted, and the transformation parameters are solved by the least squares method. The orthophoto map is transformed pixel by pixel to the coordinate space of the digital line map, and geometric correction and spatial alignment are completed.

[0026] S22. Through multi-scale segmentation and merging techniques, the administrative division elements, cadastral elements, and supplementary field survey data in the base map are optimized for map boundary and attribute inheritance. Specifically, the implementation involves: Based on the corrected data, administrative division boundaries, land parcel boundaries, and boundary points of map patches obtained from supplementary field surveys were extracted. A fractal network evolution method was used for multi-scale segmentation: three scales were set: 5m, 10m, and 20m, with a shape weight of 0.5 and a compactness weight of 0.5. After segmentation, adjacent small map patches were merged based on spectral distance. ; in , For each of the two patches A and B in the first... The average pixel value of the bands (m bands in total), the merging threshold is taken as 15% of the image's grayscale dynamic range, that is... After the merger is completed, the supplementary field survey data (land category name, ownership nature, measurement time, etc.) will be inherited to the optimized map features through spatial overlay analysis: point data collected in the field will be assigned values ​​according to whether the point is in the area, and area data will be assigned values ​​according to the principle of area dominance. S23. Using spatial overlay analysis and edge matching techniques, the fused data is subjected to boundary normalization and consistency checks to generate a standardized cadastral survey base map data layer. In specific implementation: The optimized map data is spatially overlaid with administrative division boundaries and cadastral survey boundaries. The maximum deviation distance is calculated for the common boundaries of adjacent map features. ; in , These are the coordinates of two points on the boundary of a map patch. If the deviation exceeds the allowable threshold (0.5 times the ground resolution corresponding to the data scale, e.g., 0.5 meters for 1:10000 data), then a distance-inverse weighted average is used to smooth the boundary coordinates. ; in , The distance from the boundary point to the centroid of the two patches is used. For patches whose boundaries are inconsistent with the administrative division boundary, they are normalized according to the administrative division boundary. Finally, a topological consistency check is performed (no gaps, no overlaps, no hanging lines, and all patches are located within the administrative area). The attribute fields are unified into the format of patch identifier code, land category code, area, ownership unit, etc., to generate a standardized cadastral survey base map data layer.

[0027] Example 2: The automatic generation of natural resource registration units with unique codes in S3 includes the following steps: S31. Construct a pre-planning logical model for registration units based on a graph database, using the boundaries of nature reserves, water flow management boundaries, and land use patches as nodes, and storing spatial inclusion, intersection, and adjacency relationships as edges. In specific implementation: S311. Using the approval boundary line of nature reserves, the water flow management boundary line, and the latest land classification map patches from the national land survey as spatial element nodes, extract the unique identifier and geometric boundary information of each element. In specific implementation: Three types of spatial elements are read from the standardized basic data resource pool: the approved scope line of nature reserves, the water flow management scope line delineated by the water conservancy department, and the latest land type map patches from the national land survey. For each element, a unique identifier and geometric boundary are extracted and used as nodes in the graph database. Each node records: node type (Type), unique identifier (ID), geometric boundary (Geometry), and original attribute set (Attributes).

[0028] S312. Establish spatial inclusion, spatial intersection, and spatial adjacency edges, connect nodes with topological relationships, and label the relationship type and overlapping region attributes. In specific implementation: Traverse all node pairs, determine the spatial topological relationships in the following order, and establish the corresponding edges: Inclusion relation: If all vertices of node A are inside the geometric boundary of node B, and at least one vertex of B is outside A, then A is considered to be contained in B, and a directed edge is established. (Direction B→A), and record the depth (nesting level); Intersection relationship (prerequisite: containment relationship not satisfied): Calculate the intersection area: ; in Let be the region of intersection of two geometric boundaries, if Then an undirected edge is established. and mark the overlapping area. and the geometric boundaries of overlapping regions; Adjacency relationship (prerequisite: neither intersecting nor containing): Calculate the minimum Euclidean distance between two geometric boundaries: ; in , Let A and B be the sets of boundary points, respectively. And less than the adjacent threshold (Take a data scale that is twice the ground resolution, for example, 2 meters for 1:10000 data), then establish undirected edges. And record the lengths of adjacent boundaries: ; That is, the distance on the boundary is less than The total length of continuous line segments, all nodes and edges are stored in the graph database.

[0029] S313. Employ a graph database indexing mechanism to store nodes and edges hierarchically, supporting multi-dimensional queries and retrieval based on spatial extent, feature type, and topological relationships. In specific implementation: Nodes are stored hierarchically by type (Protect layer, Water layer, Parcel layer), with an R-tree spatial index built for each layer for fast spatial range queries. Edges are hierarchically stored by relation type ( , , It also creates an adjacency table index for each node (recording all edges connected to it and neighboring nodes), supporting three query methods: by spatial range (rectangle / polygon), by feature type, and by topological relationship (enter node ID and relationship type to return all associated nodes).

[0030] S32. Using a topology-constrained optimization algorithm, prioritizing nature reserves and water flow, overlapping and intersecting spatial areas are prioritized and their boundaries are cut to eliminate spatial conflicts. Specifically, in implementation: Read all intersecting node pairs from the graph database and assign priorities according to the technical specifications for natural resource ownership registration: the approval boundary line of nature reserves has the highest priority (P=1), followed by the water flow management boundary line (P=2), and the land survey land category patches have the lowest priority (P=3); for each intersecting node pair (A, B), compare the priorities and cut the boundaries of nodes with lower priorities (larger P values). ; in The geometric boundary of the high-priority node. The original geometric boundary of the low-priority nodes. The resulting geometric boundary (which may contain multiple separate sub-polygons) is defined as follows: After cutting, low-priority nodes are split into several child nodes. Each child node inherits all attributes of the original node and is marked with the ID of the node that generated the cutting and the node from which it originated. If the area of ​​a child node is less than the minimum area threshold (100 square meters), the child node is merged into the adjacent high-priority node. The graph database is updated by deleting the original intersecting edges, inserting new child nodes, and reconstructing the containing and adjacent edges according to the new spatial relationships. Ultimately, this ensures that the geometric boundaries of all nodes only have containing or adjacent relationships, without any overlap or intersection.

[0031] S33. A spatial clustering and automatic code generation module is used to dynamically cluster and normalize the boundaries of the processed closed spatial range, and automatically generate a unique 15-digit registration unit code according to the coding rules. In specific implementation: Read all geometrically closed boundaries and areas greater than or equal to the area threshold of the smallest registered unit from the graph database. For nodes covering 1000 square meters, a density-based spatial clustering algorithm (DBSCAN) is used for clustering: the neighborhood radius Eps is twice the average distance between adjacent nodes, and the minimum number of points MinPts is 1 (i.e., connectivity is defined as long as there is an adjacent relationship); for each cluster, the union of the geometric boundaries of all nodes is calculated, and then the outer boundary of the union (non-convex hull, to preserve the true concave boundary) is extracted. The Douglas-Puk algorithm (threshold 0.5 meters) is used to simplify the boundary and remove small jagged edges; adjacent straight line segments are merged; and orthogonal adjustments are made to corners close to 90° (corrected to right angles when the angle deviation is less than 5°). Generate a 15-digit registration unit code: The first 6 digits: the county-level administrative division code where the registration unit is located, obtained through spatial overlay analysis; 7th position: Initial registration agency level code. If the registration unit spans two or more county-level administrative regions, take 3 (provincial level); if it does not span counties and the area is greater than 1,000 hectares, take 2 (municipal level); otherwise, take 4 (county level); national level (1) is manually designated; The 8th and 9th digits are the natural resource registration unit type code, which is mapped to a two-digit code based on the main natural resource types within the unit (nature protected areas, watercourses, forests, wetlands, etc.) (refer to Appendix C, Table C.3 of the Guide). The last 6 digits: the registration unit sequence number, which increments from 000001 under the same natural resource feature code; The generated 15-bit code is associated with and stored in relation to the geometric boundaries and attributes of the registration unit, thus completing the automatic generation of the natural resource registration unit.

[0032] Constructing a multi-source information association network in S4 includes the following steps: S41. A message queue-based association mapping mechanism is adopted, using the real estate unit number, exploration permit number, and water extraction permit number in the cadastral survey results as unique identifiers, and dynamically binding them with the registration unit. In specific implementation: Extract real estate unit numbers from cadastral survey results Exploration License Number Water withdrawal permit number Extract the registration unit code from the registration unit data. Create a bound topic in the message queue. Create a mapping table Mapping rules: The real estate unit number is bound one-to-one with the registration unit where it is located; The exploration license number is linked to the registration unit covering the mining right area; The water intake permit number is linked to the registration unit where the water intake is located; Binding request message format: ; in For source identifier, For identifier types (real estate, exploration, water intake). To register unit codes, To bind the time, the registry management system persists the mapping relationship after consuming messages.

[0033] S42. Through graph database-based relational network construction technology, ownership information, public regulation information, real estate rights information, mining rights information, and water abstraction permit information are organized into a multimodal relational network, supporting fast retrieval by node and relationship. In specific implementation: Ownership information, public regulation information, real estate rights information, mining rights information, and water abstraction permit information are used as nodes in the graph database: Ownership information nodes: Ownership subject, representative and agent exercising rights, and exercise method; Publicly regulated areas: ecological protection red lines, land use controls, and special protection regulations; Real estate rights details: Real estate unit number, type of right, right holder, registration date, registration authority; Mining rights details: license number, mining rights holder, mining area, and validity period; Water abstraction permit details: permit number, water rights holder, water abstraction volume, and validity period; Establish related edges: Ownership node → Registration unit: Ownership relationship; Public control node → Registration unit: Constraint relationship; Real estate rights node → Registration unit: Overlapping relationship (indicate the type of rights); Mining rights node → Registration unit: Rights and interests; Water intake permit node → Registration unit: Water intake association; Edges with the same right holder are established between nodes with the same right holder. The system uses labels and relationship indexes from a graph database to support fast retrieval by node ID, relationship type, and attribute value.

[0034] S43. Implement a real-time synchronization strategy based on message queues. When any related information changes in the source system, the related information in the register is updated by pushing messages and comparing the status. Specifically, the following measures are taken: S431. Deploy message queue middleware between the register management system and each source system to establish a unified channel for subscribing to and publishing change events. Specific implementation details include: Deploy a message queue (RabbitMQ / Kafka) on the data center server, enable publish-subscribe mode, and create a consumer group (Consumer_Group_registry) for the registry management system. Create topics for the three source systems: Topic_mining (mining rights management system), Topic_water (water extraction permit management system), and Topic_realestate (real estate registration system). Configure message persistence to ensure no message loss in case of failure. The registry system subscribes to the above three topics through a long connection via the client SDK. Each source system obtains the corresponding topic's publish permission and sets the message acknowledgment mode to manual acknowledgment (Ack_mode=manual) to ensure that messages are removed after successful processing.

[0035] S432. When the associated information in the mining rights management system, water abstraction permit management system, and real estate registration system changes, the source system encapsulates the changed data and operation type into a change message and pushes it to the message queue. Specifically, in implementation: Deploy change monitoring plugins in each source system to capture insertion, update, and deletion operations of related information. Extract the license number (or property unit number) L, operation type Op (create / update / delete), complete data after change D, and timestamp T, encapsulate them into a JSON format message, call the message queue API, publish the message to the corresponding topic, and set the message key to L to ensure sequential consumption of messages with the same identifier.

[0036] S433. The register management system obtains change messages in real time through message listeners and triggers automatic updates and status synchronization of related information fields in the register based on the message content and preset mapping rules. In specific implementation: The registration management system runs a persistent background service containing a message listener that continuously polls and subscribes to topics. Upon receiving a message, it parses it, extracting S, L, Op, D, and T. Based on S and L, it queries the mapping table Map constructed in step S41 to obtain the registration unit code. If no match is found, the message will be stored in the unmatched log and skipped (retry once per hour, and an alarm will be triggered if it fails for 24 consecutive hours). If the query is successful, then execute according to Op: create: Inserts a new record into the register association information table and establishes an association edge with the register unit; update: Retrieves the last update time of this field in the register. ,like If so, update to D (optimistic locking); otherwise, discard. delete: Marks associated information as invalid, but retains historical records; After a successful update, a manual acknowledgment (ACK) is sent to the message queue, and a synchronization log is recorded (source system, change identifier, registration unit code, operation type, time, and status). If the process fails (e.g., database error), no ACK is sent, and the message queue automatically retryes (up to 3 times, with exponential backoff). If it still fails, it is transferred to the dead letter queue and an alarm is triggered.

[0037] Example 3: Outputting review comments and driving iterative revision of investigation results in S5 includes the following steps: S51. Construct a knowledge base of key review points, transforming the requirements in the technical specifications regarding boundary line connection, ownership boundary closure, and complete review signatures and seals into executable logical rules. In specific implementation: Extract three types of executable logic rules from the technical specifications: Boundary line connection rules: The boundary lines of adjacent registered units (or parcels) should be joined end to end. The distance between the endpoints of adjacent boundary lines should be calculated as follows: ; in This is the end point of the first boundary line. This marks the starting point of the second boundary line. If (Take 0.1 meters) and it is determined to be a non-connection; Property Boundary Closure Rule: The boundary line of each property parcel should form a closed loop. Calculate the distance between the first and last points: ; like (Take 0.1 meters), and it is determined to be non-closed; Complete signature and seal rules: The fields of reviewer's signature, unit seal, and review date in the cadastral survey form and results verification form must not be empty. Check whether the specified fields are NULL or empty strings. If any one is missing, it is judged as incomplete. The above rules are encoded as condition-conclusion pairs and stored in the knowledge base.

[0038] S52. The rule engine performs logical comparison and integrity checks on the final cadastral survey form, result verification form, and registration unit map, automatically detecting issues such as unclosed boundary lines, unmarked ownership dispute areas, and missing or abnormal related information. Specifically, in implementation: The vector and attribute data of cadastral survey forms, results verification forms, and registration unit maps are read from the survey results database and loaded into the working memory of the rule engine. The engine then matches these data against the rules in the knowledge base. Traverse all adjacent boundary pairs and calculate Record the locations where there is no connection and the parcel number; Traverse each land parcel and calculate Records of unclosed land parcels; Check areas with ownership disputes: If there are disputed areas but the names of the disputing parties, area, and reason are not marked, it is judged as an abnormality of not being marked; Check related information: Compare the real estate unit number, exploration permit number, and water extraction permit number in the cadastral survey form with the related information on the registration unit map. If any omissions are found, record the missing related information. Generate a list of exceptions, including the exception type, location, data identifier, and the rule violated.

[0039] S53. Utilize machine learning anomaly detection models to learn from historically approved cases, assess the risk level of current investigation results, generate supplementary review opinions, and feed these opinions back to the investigation stage for data correction. In specific implementation: Collect historical approved case data, extract feature vectors (number of boundary points, length of ownership boundary line, number of related information items, completeness rate of signatures and seals, etc.) and review result labels (approved / rejected) for each case, and train an anomaly detection model using the isolated forest algorithm: ; in This is the feature vector of the current survey results. The path length of the sample on the isolated tree. This represents the average path length across multiple trees (default 100 trees). For a given number of samples The average path length normalization factor (calculated using the formula is) is given by the time period. , (for harmonic numbers) The closer it is to 1, the more abnormal it is; Input the feature vector of the current survey results into the model to obtain the anomaly score s, and set the risk threshold: , ,like Determined to be high-risk (it is recommended to focus on verifying boundary lines and areas of ownership dispute); if The risk level is assessed as medium (note the importance of ensuring the completeness of related information); if The risk level is determined to be low (approval is recommended); supplementary review comments are generated and pushed to the investigator's interface along with the anomaly list. The investigator corrects the data based on the feedback, and then re-enters the review process until all anomalies are eliminated.

[0040] The online submission, automatic verification, and processing result feedback of objection information in S6 includes the following steps: S61. Employ digital watermarking-based announcement document protection technology, embedding an invisible watermark when generating the announcement text and registration unit attachments. Specifically: A digital watermarking component is integrated into the announcement generation module. For the announcement text, it is first rendered as an image, divided into 8×8 pixel blocks, and a Discrete Cosine Transform (DCT) is performed on each block. The mid-frequency coefficient position set P is selected, and the watermark information (registration unit code) is then added. Announcement Number The generation time (T) is encoded into a bit sequence. ,according to ; Modify the intermediate frequency coefficient ,in (Watermark intensity coefficient), then inversely transform to obtain the watermarked image. For the attached diagram (vector image) of the registration unit, multiply the coordinates of the inflection point by 1000 and round down (to retain millimeter precision). Embed the watermark bit in the least significant bit of the integer value, and then divide by 1000 to restore the coordinates. The embedded announcement file can be used for subsequent copyright tracing and integrity verification.

[0041] S62. Through an electronic signature-based evidence storage platform, the publication of announcements, the submission of objection materials, and the notification of objection processing results are stored using hash values ​​and time stamps. Specifically, in implementation: An electronic signature and evidence storage platform is built, providing hash calculation and timestamp services. When an announcement is published, the announcement file is read. And attached diagram Calculate the concatenated SHA-256 hash value: ; This indicates binary data concatenation. Including the release time Announcement Number When submitted to the platform, the platform calls the National Time Service Center to generate a trusted timestamp, signs it with the platform's private key, and stores it as evidence. When the objecting party submits objection materials, the hash value is also calculated. And solidify and preserve the evidence; after the objection handling result notification is generated, calculate... The evidence is permanently stored, and all evidence records are stored in an immutable database to support subsequent auditing.

[0042] S63. Implement a collaborative workflow for handling objections. After the objecting party submits objection materials through the online platform, the system automatically associates the registration unit and notifies the reviewer. After verification, the system automatically updates the processing status and generates an objection handling notification. Specifically, the implementation is as follows: The online announcement platform provides an entry point for submitting objections. Objectors can enter their objections and upload supporting documentation. The system automatically retrieves the registration unit code for the current announcement page. Generate objection records and based on Query the list of relevant reviewers, send a pending notification via internal messaging service, and have the reviewers log in to verify the objection materials: If the objection is not valid: the system will directly generate an objection handling notice, stating that the submitted evidence is invalid and the objection is not valid. The notice will be sent to the objecting party after being affixed with an electronic signature, and the objection status will be updated to "rejected". If the objection is valid: the system will automatically trigger a reinvestigation process, update the registration unit status to "objection in progress", and assign the original investigation agency to re-verify. After the reinvestigation is completed, a notification will be generated based on the new results (the content of which is whether the reinvestigation results are consistent with or inconsistent with the announcement, and whether subsequent legal processing will be carried out), and the notification will be sent to the objecting party and stored as evidence. All operations throughout the entire process (submission, acceptance, verification, reinvestigation, and notification generation) are automatically recorded with timestamps and operator logs, enabling full-process traceability and auditability of objection handling.

[0043] The cave update of associated information in S7 includes the following steps: S71. Employ incremental backup-based register update technology to capture the differences in each registration and change operation in the register, recording only the data differences before and after the change, and generating a version snapshot. In specific implementation: Configure an update interception module in the register management system to capture all insert, update, and delete operations. Extract a snapshot of the data before the operation. and post-operation data snapshot By comparing differences at the field level, a difference set is generated: ; in For field names, , These are the field values ​​before and after the operation, only... Operation metadata (operation type, time, operator) is stored as an incremental backup record; at the same time, a complete snapshot is generated for each registration unit after each registration operation (a snapshot is also triggered if the cumulative number of difference records exceeds 100 or the number of difference fields exceeds 20). The snapshot is stored in compressed form and a CRC32 checksum is calculated. Both incremental records and snapshots are stored in an independent version storage area.

[0044] S72. Through a version control-based traceability mechanism, a unique version number is generated for each update operation, and the operator, operation time, and operation type are recorded. It supports traversing any historical version along the timeline. In specific implementation: Assign a unique version number to each update operation: ; in The Unix timestamp (in seconds) of when the operation occurred. This indicates a left shift of 16 bits. The version record is the sequence number of operations within the same second (0-65535). The version record includes: version number, operator, operation time, operation type, and difference set. Register the unit code, write it to the version log table, and create an index by the registered unit; during backtracking, find the most recent complete snapshot before the target time T. Then apply in forward order from chronological order. All incremental differences between T and T (Total k items), Reconstruct the target state: ; in This indicates that the update is performed field by field based on the difference set.

[0045] S73. Implement a linked update trigger. When the core data of the register changes, automatically trigger the synchronous update of the associated natural resource cadastral map and registration unit map, and update the metadata. Specifically, the implementation should include: Create database triggers on the core data tables of the register (main table, ownership table, natural status table, and related information table). When the data changes, the triggers capture the register unit code. And change type, send the synchronization task message to the synchronization topic in the message queue. The message body is The cadastral map service module and the registration unit map service module subscribe to this topic and, upon receiving the message, according to: The system reads the latest ownership boundaries, resource type boundaries, and other data, calls the GIS rendering engine to regenerate vector graphics, replaces the old version, and records the correspondence between the map version number and the register version number. At the same time, it updates the metadata table (map update time, version, storage path). If map generation fails, the message queue automatically retryes 3 times (with exponential backoff at intervals). If it still fails, it is transferred to the dead letter queue and an alarm is triggered. The entire linkage ensures that the register data and map data are eventually consistent.

[0046] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as defined by the appended claims and their equivalents.

Claims

1. A dynamic collaborative processing method for a natural resource ownership registration data management and updating system, characterized in that, The method includes the following steps: S1. By using a multi-source data fusion engine, spatial topology rules and attribute logic rules are employed to identify conflicts in heterogeneous data and construct a standardized basic data resource pool. S2. A spatiotemporal data fusion algorithm is used to perform multi-scale spatial alignment and patch boundary optimization of the survey base map data, orthophoto data and field supplementary survey data to generate a standardized cadastral survey base map data layer. S3. Using the dynamic pre-planning model of registration units, based on the boundary lines of nature reserves, water flow management boundary lines, and land use patches, natural resource registration units with unique codes are automatically generated through topological constraint optimization and spatial conflict resolution. S4. By using a correlation mapping mechanism, the ownership information, public control information and real estate rights information, mining rights information and water abstraction permit information in the cadastral survey results are dynamically bound and synchronized in real time to construct a multi-source information correlation network. S5. Perform logical comparison and integrity verification on the final cadastral survey form, result verification form, and registration unit map, output review opinions, and drive iterative correction of survey results; S6. Digital watermarking and electronic signature technologies are used to encrypt and preserve the announcement text and the attached diagrams of the registration unit, so as to realize the online submission, automatic verification and feedback of the processing results of objection information. S7. Implement the dynamic update strategy for the register to capture changes in the register in a differentiated manner and trace historical versions, and trigger the automatic update of related information to form a dynamic collaborative closed loop for data management and updates.

2. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The construction of the standardized basic data resource pool in S1 includes the following steps: S11. Based on data lineage analysis technology, extract metadata features from the collected results of the Third National Land Survey, special surveys, and real estate registration data, and establish mapping relationships between multi-source data. S12. Using spatial topology rules and attribute logic rules, perform integrity verification and conflict identification on data items with the same name provided by different departments, and generate a data quality report. S13. By using a fusion rule base based on conflict resolution, the identified conflict data is automatically arbitrated and marked for processing, thus constructing a standardized basic data resource pool.

3. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The generation of the standardized cadastral survey base map data layer in S2 includes the following steps: S21. Feature point-based image registration algorithm performs geometric fine correction and spatial alignment on the latest orthophoto map and digital line map data to eliminate coordinate system errors; S22. Through multi-scale segmentation and merging techniques, the administrative division elements, cadastral elements and field supplementary survey data in the survey base map are optimized for map boundary and attribute inheritance. S23. Using spatial overlay analysis and edge matching techniques, the fused data is subjected to boundary regularization and consistency checks to generate a standardized cadastral survey base map data layer.

4. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The automatic generation of natural resource registration units with unique codes in S3 includes the following steps: S31. Construct a pre-planning logical model for registration units based on a graph database, using the boundaries of nature reserves, water management boundaries, and land use patches as nodes, and storing spatial inclusion, intersection, and adjacency relationships as edges; S32. Using a topological constraint-based optimization algorithm, prioritizing and dividing overlapping and intersecting spatial ranges according to the principles of prioritizing nature reserves and water flow, spatial conflicts are eliminated. S33. A spatial clustering and automatic coding generation module is used to dynamically cluster and normalize the boundaries of the processed closed spatial range, and automatically generate a unique 15-digit registration unit code according to the coding rules.

5. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 4, characterized in that, The step S31, which involves constructing a pre-planning logical model for registration units based on a graph database, includes the following steps: S311. Take the approval boundary line of nature reserves, the water flow management boundary line and the latest land survey land category map as spatial element nodes, and extract the unique identifier and geometric boundary information of each element respectively; S312. Establish spatial inclusion relationship edges, spatial intersection relationship edges, and spatial adjacency relationship edges, connect nodes with topological associations, and label the relationship type and overlapping area attributes. S313. The indexing mechanism of the graph database is used to store nodes and edges in a hierarchical manner, supporting multi-dimensional queries and retrievals based on spatial range, feature type and topological relationship.

6. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The construction of the multi-source information association network in S4 includes the following steps: S41. A message queue-based association mapping mechanism is adopted to dynamically bind the real estate unit number, exploration permit number, and water extraction permit number in the cadastral survey results to the registration unit as unique identifiers. S42. By using graph database-based association network construction technology, ownership information, public regulation information, real estate rights information, mining rights information, and water abstraction permit information are organized into a multimodal association network, and support fast retrieval by node and relationship; S43. Implement a real-time synchronization strategy based on message queues. When any related information changes in the source system, the related information in the register is triggered to be updated by comparing the status with the message push.

7. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 6, characterized in that, The implementation of the message queue-based real-time synchronization strategy in S43 includes the following steps: S431. Deploy message queue middleware between the register management system and each source system to establish a unified channel for subscribing to and publishing change events; S432. When the associated information in the mining rights management system, water abstraction permit management system and real estate registration system changes, the source system will encapsulate the changed data and operation type into a change message and push it to the message queue. S433. The register management system obtains change messages in real time through message listeners and triggers automatic updates and status synchronization of related information fields in the register based on message content and preset mapping rules.

8. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The steps involved in outputting review comments and driving iterative revisions of investigation results in S5 are as follows: S51. Construct a knowledge base of key review points, and transform the requirements in the technical specifications regarding boundary line connection, ownership boundary closure, and complete review signatures and seals into executable logical rules; S52. The rule engine performs logical comparison and integrity verification on the final cadastral survey form, result verification form and registration unit map, and automatically detects unclosed boundary lines, unmarked ownership dispute areas and missing or abnormal related information. S53. Utilize machine learning anomaly detection models to learn from historically approved cases, conduct risk level assessments of current investigation results, generate supplementary review opinions, and feed these opinions back to the investigation process for data correction.

9. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The online submission, automatic verification, and processing result feedback of objection information in S6 includes the following steps: S61. Adopt digital watermark-based announcement document protection technology to embed invisible watermarks when generating announcement text and registration unit attached diagrams; S62. Through an electronic signature-based evidence storage platform, the publication of announcements, the submission of objection materials, and the notification of objection processing results are stored with hash values ​​and timestamps. S63. Implement a collaborative workflow for handling objections. After the objecting party submits objection materials through the online platform, the system automatically associates them with the registration unit and notifies the reviewers. After verification, the system automatically updates the processing status and generates an objection handling notification.

10. The dynamic collaborative processing method for a natural resource ownership registration data management and updating system according to claim 1, characterized in that, The cave update of the associated information in S7 includes the following steps: S71. Employs incremental backup-based register update technology to capture the differences in each registration and change operation of the register, recording only the data differences before and after the change, and generating a version snapshot; S72. Through a version control-based traceability mechanism, a unique version number is generated for each update operation, and the operator, operation time, and operation type are recorded, supporting the backtracking of any historical version along the timeline; S73. Implement a linked update trigger. When the core data of the register changes, the associated natural resource cadastral map and registration unit map will be automatically updated synchronously, and the metadata will be updated.

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