Natural resource right confirmation registration information processing method, system and equipment

Abstract

The invention provides a natural resource right confirmation registration information processing method, system and device, and relates to the technical field of geographic information processing, and the method comprises the steps: obtaining composite natural resource multi-source heterogeneous data across provincial administrative regions; performing coordinate reference unification and attribute structure standardization processing on the multi-source heterogeneous data to obtain a standardized data set with a unified space-time reference; recognizing and extracting a natural resource ownership space conflict region based on a standardized data set with a unified space-time reference; and according to the space conflict region, determining a topological intersection node of a trans-provincial enclave administrative jurisdiction and a natural resource type boundary, a dispute overlapping region mass center of a latest demarcation result of a historical forest authority record boundary and an ecological protection red line, and a feature region center of which a resource type is substantially changed before and after implementation of an ecological restoration project. According to the method, the boundary of the conflict area can be accurately defined, and the judgment efficiency and objectivity are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic information processing, in particular to a natural resource right registration information processing method, system and device. BACKGROUND

[0002] Natural resource right registration is a basic work for clarifying resource ownership and standardizing management. Especially for complex natural resources across provincial administrative regions, the processing accuracy directly affects the coordination of resource utilization and ecological protection because it involves multi-department supervision, multi-type superposition and multi-period ownership records.

[0003] Although the existing technology has formed processes such as data collection, preprocessing, conflict identification and manual arbitration, it still has the following technical defects: lack of unified spatial reference and key spatial control points to construct a precise boundary optimization and intelligent arbitration mechanism for spatial topology analysis unit. For example, the multi-source heterogeneous characteristics of cross-provincial data are obvious, different provincial department data uses different coordinate reference systems, and there is no unified standard for attribute structure, making it difficult to form a unified spatial reference framework. In the ownership conflict processing, the existing technology relies on single-dimensional comparison or manual judgment, and does not establish a spatial reference control system covering cross-provincial enclaves, historical disputes and type changes after ecological restoration, which cannot provide a stable reference for boundary definition.

[0004] Due to the lack of a unified spatial reference framework based on key spatial control points, the conflict area boundary is not optimized, often appearing fuzzy and low precision, resulting in a lack of objective spatial basis for ownership arbitration. At the same time, arbitration relies mainly on manual analysis, and an intelligent model integrating multi-source evidence chain and priority rules has not been constructed, which is low in efficiency and prone to result in difficulty in cross-provincial mutual recognition due to human differences. These problems restrict the quality and efficiency of cross-provincial complex natural resource right registration. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a natural resource right registration information processing method, system and device, which realizes the precise definition of the conflict area boundary, improves the arbitration efficiency and objectivity, and guarantees the cross-provincial mutual recognition consistency.

[0006] To solve the above technical problems, the technical solution of the present application is as follows: In a first aspect, a natural resource right registration information processing method is provided, which comprises: acquiring multi-source heterogeneous data of complex natural resources across provincial administrative regions; performing coordinate reference unification and attribute structure standardization processing on the multi-source heterogeneous data to obtain a standardized data set with unified space-time reference; based on the standardized data set with unified space-time reference, identifying and extracting natural resource ownership spatial conflict areas; According to the spatial conflict area, determine the topological intersection node of the provincial administrative jurisdiction boundary crossing the territory, the disputed overlapping area centroid of the historical forest right certificate recorded boundary and the latest red line of ecological protection, and the characteristic area center of the resource type which has substantial changes before and after the implementation of ecological restoration project; Based on the three spatial reference control points of the topological intersection node, the disputed overlapping area centroid and the characteristic area center, a spatial topology analysis unit is constructed, the spatial topology analysis unit is divided into several circular arc sub-regions, and an adjustment coefficient is obtained by curve fitting calculation and adjustment of each circular arc sub-region; Based on the adjustment coefficient, a boundary precision correction parameter is generated, and the boundary geometric characteristics of the spatial conflict area are optimized through the boundary precision correction parameter to obtain an optimized conflict area; By constructing an intelligent conflict resolution model, the optimized conflict area is automatically ruled to obtain a ruling result; According to the ruling result, the right registration information of the composite natural resource unit is generated; and a dynamic monitoring and right registration information linkage updating mechanism of natural resources is established.

[0007] Further, the multi-source heterogeneous data of the composite natural resources across the provincial administrative region is obtained, including: Collecting paper ownership archives provided by cross-provincial natural resource management departments; Obtaining land space planning spatial vector data sets of different coordinate reference systems; Collecting multi-temporal satellite remote sensing images and their interpretation results; Receiving dynamic monitoring data sets of the ecological restoration project implementation process; Integrating special investigation data of forestry, water conservancy, ecological environment and other departments; Establishing a full-factor data set covering paper ownership archives, land space planning spatial vector data sets, multi-temporal satellite remote sensing images and their interpretation results, dynamic monitoring data sets and special investigation data, i.e. multi-source heterogeneous data of composite natural resources.

[0008] Further, the multi-source heterogeneous data is processed in coordinate reference unification and attribute structure standardization to obtain a standardized data set with unified space-time reference, including: Carrying out spatial reference normalization processing on the land space planning spatial vector data set to convert all coordinate systems to the national 2000 geodetic coordinate system to obtain the normalized spatial vector data set; Digitally reconstructing the paper ownership archives to generate structured ownership vector data with spatial coordinate information; Establishing a unified natural resource classification system and attribute field standard to standardize the reconstruction of multi-source attribute data to form reconstructed multi-source attribute data; spatial-temporal reference alignment processing is performed on the multi-temporal satellite remote sensing images and the dynamic monitoring data set to eliminate the temporal differences, so as to obtain the satellite remote sensing images and the dynamic monitoring data set after the spatial-temporal reference alignment; The spatial vector data set after the normalization processing, the structured ownership vector data with the spatial coordinate information, the reconstructed multi-source attribute data, the satellite remote sensing images and the dynamic monitoring data set after the spatial-temporal reference alignment are integrated to construct a standardized data set with a unified spatial-temporal reference.

[0009] Further, based on the standardized data set with the unified spatial-temporal reference, a natural resource ownership spatial conflict region is identified and extracted, including: spatial overlay analysis is performed on the ownership boundary and the ecological protection red line in the standardized data set to identify the conflict region of the ownership and the control requirement; based on the ownership vector data and the remote sensing image features in the standardized data set, a cross-provincial enclave use right and ownership separation region is identified and extracted; by comparing the historical ownership data with the current situation investigation data, a disputed region of the inserted mountain boundary is identified and located; by combining the multi-temporal resource type data, a resource type identification ambiguous region of the wetland and the forest land is identified; all the identified ownership and control requirement conflict regions, the cross-provincial enclave use right and ownership separation regions, the disputed region of the inserted mountain boundary, and the resource type identification ambiguous region of the wetland and the forest land are integrated to construct a complete natural resource ownership spatial conflict region.

[0010] Further, according to the spatial conflict region, a topological intersection node of the cross-provincial enclave administrative jurisdiction boundary and the natural resource type boundary, a disputed overlapping region centroid of the historical forest ownership record boundary and the latest ecological protection red line demarcation result, and a characteristic region center of a region where the resource type has a substantial change before and after the ecological restoration project implementation are determined, including: spatial topological relationship analysis is performed on the spatial conflict region to extract the intersection point of the cross-provincial enclave administrative jurisdiction boundary and the natural resource type boundary as the topological intersection node; based on the historical forest ownership record boundary and the latest ecological protection red line demarcation result, spatial overlay analysis is performed to calculate the geometric centroid of the disputed overlapping region; the geometric centroid of the disputed overlapping region is taken as the disputed overlapping region centroid; by comparing the resource type data before and after the ecological restoration project implementation, a region where the resource type has a substantial change is identified to calculate the geometric center of the region where the substantial change occurs, and the geometric center is taken as the characteristic region center.

[0011] Further, based on the three spatial reference control points of the topological intersection node, the centroid of the disputed overlapping area, and the characteristic area center, a spatial topology analysis unit is constructed, the spatial topology analysis unit is divided into a plurality of arc sub-regions, an adjustment coefficient is obtained by curve fitting calculation adjustment for each arc sub-region; a boundary accuracy correction parameter is generated based on the adjustment coefficient, the boundary geometric features of the spatial conflict area are optimized through the boundary accuracy correction parameter, and an optimized conflict area is obtained, including: Taking the topological intersection node, the centroid of the disputed overlapping area, and the characteristic area center as key control points, a spatial topology analysis unit surrounding the spatial conflict area is constructed; The boundary profile of the spatial topology analysis unit is discretized to divide it into continuous arc sub-regions; For each arc sub-region, a polygon simplification method is used to extract boundary feature points, and key geometric feature vertices are identified and retained; Based on the key geometric feature vertices, a smooth curve is reconstructed for each arc sub-region by curve fitting, and a fitted boundary curve is generated; The geometric deviation value between the fitted boundary curve and the original boundary is calculated, and the boundary adjustment coefficient of each arc sub-region is determined based on the deviation value; The boundary adjustment coefficients of all arc sub-regions are integrated to form a boundary accuracy correction parameter set for overall boundary optimization; Through the boundary accuracy correction parameter set, the original boundary geometric features of the spatial conflict area are modified and smoothed segment by segment to form an optimized conflict area with precise boundary definition.

[0012] Further, by constructing an ownership conflict intelligent resolution model, the optimized conflict area is automatically ruled to obtain a ruling result, including: An ownership conflict intelligent resolution model is constructed, which integrates an ownership priority rule library and a multi-source evidence chain weight distribution mechanism; The optimized conflict area with precise boundary definition is input into the ownership conflict intelligent resolution model as a benchmark spatial framework for automatic ruling; In the benchmark spatial framework, according to the ownership priority rule library, the historical ownership archives and current control requirements in the optimized conflict area are analyzed in terms of space-time features to form an ownership data set with space-time feature labels; Based on the ownership data set with space-time feature labels, through the multi-source evidence chain weight distribution mechanism, the boundary accuracy correction parameter set and the geometric feature analysis result of the optimized conflict area are fused to form a multi-source evidence fusion data set; Based on the multi-source evidence fusion data set, through adaptive arbitration operation of the ownership conflict intelligent resolution model, a preliminary ruling result of the cross-provincial ownership dispute area is obtained; The preliminary decision result is cross-province mutual recognition consistency verification, and the final right decision result is formed.

[0013] Further, the right registration information of the composite natural resource unit is generated according to the decision result; a dynamic monitoring and right registration information linkage update mechanism of natural resources is established, including: Based on the final right decision result, the ownership boundary and resource type attribute of the composite natural resource unit are determined within the range of the optimized conflict area and the accurate boundary definition; According to the ownership boundary and resource type attribute, the ecological protection responsibility subject and use control requirement of the corresponding natural resource unit are determined; Based on the information of the ownership boundary, the resource type attribute and the responsibility subject, the right registration information of the composite natural resource unit is generated in accordance with the cross-province linkage right requirements; A two-way mutual recognition mechanism of flying-in registration and flying-out confirmation is established to complete the cross-province collaborative verification and unified filing of the right registration information, and form a benchmark registration information database; Based on the benchmark registration information database, a natural resource dynamic monitoring system is constructed by combining multi-temporal satellite remote sensing images and ecological restoration dynamic monitoring data, and a resource state change detection and early warning mechanism is established; Through the resource state change detection and early warning mechanism, the dynamic change characteristics of natural resources are captured in real time, and the right registration information linkage update process is triggered to realize the continuous optimization and maintenance of the benchmark registration information database.

[0014] Secondly, a natural resource right registration information processing system, comprising: An acquisition module is configured to acquire multi-source heterogeneous data of composite natural resources across provincial administrative regions; A processing module is configured to perform coordinate benchmark unification and attribute structure standardization processing on the multi-source heterogeneous data to obtain a standardized data set with unified space-time benchmark; An identification module is configured to identify and extract a natural resource ownership spatial conflict area based on the standardized data set with unified space-time benchmark; A determination module is configured to determine, according to the spatial conflict area, a topological intersection node of the cross-province enclave administrative jurisdiction boundary and the natural resource type boundary, a disputed overlapping area center of the latest ecological protection red line demarcation results and the historical forest right certificate recorded boundary, and a characteristic area center where the resource type has substantially changed before and after the implementation of the ecological restoration project; An optimization module is configured to construct a spatial topology analysis unit based on three spatial reference control points, i.e., a topology intersection node, a disputed overlapping area centroid and a feature area center, divide the spatial topology analysis unit into a plurality of arc sub-areas, calculate and adjust each arc sub-area to obtain an adjustment coefficient, generate a boundary precision correction parameter based on the adjustment coefficient, optimize the boundary geometric features of the spatial conflict area based on the boundary precision correction parameter, and obtain an optimized conflict area. A ruling module is configured to automatically rule on the optimized conflict area by constructing an ownership conflict intelligent resolution model to obtain a ruling result. An establishment module is configured to generate right registration information of the composite natural resource unit according to the ruling result, and establish a dynamic monitoring and right registration information linkage update mechanism.

[0015] In a third aspect, a natural resource right registration information processing device includes: One or more processors; A storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0016] In a fourth aspect, a computer readable storage medium stores a program, when the program is executed by a processor, the method is implemented.

[0017] The above-mentioned scheme of the present application at least has the following beneficial effects: Because the coordinate reference is unified and the attribute structure is standardized by processing the multi-source heterogeneous data of the cross-provincial composite natural resource, the spatial topology analysis unit is constructed based on three spatial reference control points, i.e., a topology intersection node, a disputed overlapping area centroid and a feature area center, the conflict area boundary is optimized by curve fitting, the ownership conflict intelligent resolution model is constructed by integrating the ownership priority rule library and the multi-source evidence chain weight distribution mechanism, and the dynamic monitoring and right registration information linkage update mechanism of the natural resource is established, the technical problems in the prior art, i.e., lack of unified spatial reference for multi-source heterogeneous data, low fuzzy precision of conflict area boundary, low efficiency and difficulty in cross-provincial mutual recognition of artificial ownership ruling, are effectively overcome, the conflict area boundary is accurately defined, the efficiency and objectivity of the ownership ruling are improved, the cross-provincial mutual recognition consistency of the right registration result is ensured, the dynamic optimization and continuous maintenance of the right registration information are realized, and reliable support is provided for the standardized management and ecological protection of the cross-provincial composite natural resource. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 is a flowchart of a natural resource right registration information processing method provided by an embodiment of the present application.

[0019] Figure 2 is a natural resource right registration information processing system schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0021] As Figure 1 shown, an embodiment of the present application proposes a natural resource right registration information processing method, the method comprising the following steps: Step 1, acquiring multi-source heterogeneous data of composite natural resources across provincial administrative regions; Step 2, performing coordinate reference unification and attribute structure standardization processing on the multi-source heterogeneous data, obtaining a standardized data set with unified space-time reference; Step 3, based on the standardized data set with unified space-time reference, identifying and extracting natural resource right conflict regions; Step 4, according to the spatial conflict regions, determining the topological intersection nodes of cross-provincial enclave administrative jurisdiction boundaries and natural resource type boundaries, the disputed overlapping region centroids of the latest ecological protection red line demarcation results and historical forest right certificate recorded boundaries, and the feature region centers of the regions where the resource types have substantially changed before and after the implementation of ecological restoration projects; Step 5, based on the three spatial reference control points of topological intersection nodes, disputed overlapping region centroids and feature region centers, constructing a spatial topology analysis unit, dividing the spatial topology analysis unit into a plurality of circular arc sub-regions, calculating and adjusting the curve fitting of each circular arc sub-region to obtain an adjustment coefficient; generating a boundary precision correction parameter based on the adjustment coefficient, optimizing the boundary geometric features of the spatial conflict regions through the boundary precision correction parameter, and obtaining the optimized conflict regions; Step 6, constructing a right conflict intelligent resolution model to automatically adjudicate the optimized conflict regions to obtain the adjudication results; Step 7, generating right registration information of composite natural resource units according to the adjudication results; establishing a natural resource dynamic monitoring and right registration information linkage update mechanism.

[0022] In the embodiments of the present application, through systematic process design, the whole-chain optimization of cross-provincial composite natural resource right registration is realized. At the data level, through coordinate reference unification and attribute structure standardization processing, the barriers of multi-source heterogeneous data are broken down; the conflict identification link accurately locates the conflict areas of ownership and control requirements, cross-provincial enclave separation, boundary disputes and other types of spatial conflicts, ensuring that no problems are missed; by determining three types of key control points, i.e., topological intersection nodes, disputed overlapping area centroids and feature area centers, stable spatial reference support is provided for boundary optimization, combined with circular arc sub-region division and curve fitting adjustment, the geometric precision and standardization of the conflict area boundary are effectively improved, effectively solving the pain points of boundary ambiguity and insufficient precision; the ownership conflict intelligent resolution model realizes automatic adjudication of disputes, reduces the subjectivity and inefficiency problems caused by manual intervention, and ensures the objectivity and fairness of the adjudication results; the finally generated right registration information meets the cross-provincial linkage requirements, and the dynamic monitoring and linkage update mechanism can respond to resource state changes in real time, ensuring the timeliness and accuracy of the registration information, and comprehensively improving the quality, efficiency and collaborative management level of cross-provincial composite natural resource right registration.

[0023] In a preferred embodiment of the present application, step 1 can include: Step 1.1, collecting paper ownership archive data provided by cross-provincial natural resource management departments, specifically including: interfacing with various provincial natural resource management departments related to cross-provincial administrative regions, collecting paper ownership archive data related to composite natural resources through on-site visits, receiving, etc. ; these data include historical forest right certificates, land contract management right certificates, ownership boundary agreements, resource right history approval documents, etc. During the collection process, the integrity and effectiveness of the archives are checked, and key information such as the formation time, ownership subject, and recorded boundary range of each archive is registered in detail to ensure that important historical ownership basis is not missed.

[0024] Step 1.2, obtaining land space planning spatial vector data sets of different coordinate reference systems, specifically including: establishing data sharing channels with provincial land space planning departments, and obtaining land space planning spatial vector data sets prepared by them; these data sets cover core spatial data such as ecological protection red line, permanent basic farmland, urban development boundary, and natural resource utilization partition. Due to the differences in coordinate reference systems used by different provincial departments, the coordinate system type corresponding to each data set needs to be recorded when obtaining, including local independent coordinate system, Xi'an 80 coordinate system, Beijing 54 coordinate system, etc. At the same time, it is confirmed whether the vector format of the data conforms to the general standard of the geographic information industry, to prepare for coordinate reference unification processing.

[0025] Step 1.3, collect multi-temporal satellite remote sensing images and their interpretation results, including: from authoritative satellite remote sensing data service platforms, professional remote sensing data providers, etc. Collect multi-temporal satellite remote sensing images and their interpretation results covering the target cross-provincial area; image selection needs to cover key natural resource change nodes, including pre-implementation, implementation, and post-implementation of ecological restoration projects, as well as same-period images of different years, to ensure that the spatial resolution and spectral resolution requirements are met; interpretation results focus on collecting resource type distribution vector maps, land feature change detection results, vegetation coverage analysis data, etc., while recording key metadata information such as image shooting time, sensor type, and image resolution.

[0026] Step 1.4, receive dynamic monitoring data sets of the implementation process of ecological restoration projects, including: establishing data transmission mechanisms with responsible departments, construction units and professional monitoring agencies responsible for cross-provincial ecological restoration project implementation, and receiving dynamic monitoring data sets in real time or periodically during the implementation process; the data set includes boundary monitoring coordinate data of the implementation range, real-time records of resource type changes during construction, spatial location data corresponding to project progress, soil quality, vegetation restoration, and other restoration effect monitoring index data, with data time span covering from the basic monitoring data of the project approval stage to the long-term tracking monitoring data after the project is completed, ensuring complete reflection of the impact of ecological restoration on natural resources.

[0027] Step 1.5, integrate special investigation materials of forestry, water conservancy, and ecological environment departments, including: coordinating provincial forestry, water conservancy, ecological environment, and natural resource departments, systematically collecting special investigation materials of cross-provincial complex natural resources; forestry departments focus on collecting forest resource survey data, ancient tree protection archives, and forest type division materials; water conservancy departments focus on collecting water area shoreline survey data, water resource distribution materials, and watershed division files; ecological environment departments focus on collecting ecological function zoning materials, environmental quality monitoring data, and pollution emission control related materials; after collecting, format the materials and extract key information, ensuring that each material contains spatial location, resource attribute, and regulatory requirements.

[0028] Step 1.6, a full-factor data set covering paper property archives, land space planning spatial vector data sets, multi-temporal satellite remote sensing images and their interpretation results, dynamic monitoring data sets, and thematic survey data, i.e. composite natural resource multi-source heterogeneous data, specifically including: comprehensively classifying and sorting the paper property archives collected in the previous steps, the land space planning spatial vector data sets obtained, the multi-temporal satellite remote sensing images and their interpretation results collected, the ecological restoration dynamic monitoring data sets received, and the multi-department thematic survey data integrated; dividing into five categories according to data types, namely property archives, spatial vectors, remote sensing images, dynamic monitoring, and thematic surveys, establishing a unified data index system, and clearly defining the spatial, temporal, and attribute relationships between the various data; through data format conversion, key information completion, and other processing, a full-factor data set covering cross-provincial composite natural resource property, space, type, dynamic change, and regulatory requirements, i.e. composite natural resource multi-source heterogeneous data, is finally formed.

[0029] In the embodiments of the present application, by comprehensively collecting paper property archives of cross-provincial natural resource management departments, land space planning spatial vector data sets of different coordinate reference systems, multi-temporal satellite remote sensing images and their interpretation results, ecological restoration engineering dynamic monitoring data sets, and multi-department thematic survey data of forestry, water conservancy, and ecological environment, and integrating them into a full-factor composite natural resource multi-source heterogeneous data set, the cross-provincial composite natural resource related data is covered and systematically collected, which not only guarantees the diversity and integrity of the data source, but also avoids the limitations of single data dimension, provides comprehensive and solid data support for coordinate reference unification, attribute structure standardization processing, and accurate identification of property space conflict areas, effectively reduces the subsequent processing deviation caused by data missing or one-sidedness, and helps to ensure data quality from the source for cross-provincial composite natural resource right registration work.

[0030] In a preferred embodiment of the present application, the above step 2 can include: Step 2.1. Normalization processing of spatial reference is performed on the spatial vector data set of territorial space planning to convert all coordinate systems to the national 2000 geodetic coordinate system, to obtain a normalized spatial vector data set, which specifically includes: first, checking the collected spatial vector data set of territorial space planning by piece, by reading the data meta file and attribute description document, to clarify the original coordinate reference system type, projection method and coordinate unit of each data set; applying for official published coordinate conversion parameters from the provincial surveying and mapping geographic information department in charge, and additionally collecting the joint survey results of the local independent coordinate system and the national 2000 geodetic coordinate system; using seven-parameter conversion method for Xi'an 80 coordinate system and Beijing 54 coordinate system data, and using self-defined parameter conversion method for local independent coordinate system data combined with joint survey results, to convert all data sets to national 2000 geodetic coordinate system one by one; at least 3 national A-level or B-level GPS control points distributed uniformly and covering the target area are selected for accuracy check during conversion to ensure that the horizontal position error of the converted data set is not more than ±0.5 meters; comprehensive topological check is performed on the converted data set, focusing on repairing topological errors such as polygon overlap, boundary crack and node non-coincidence, to ensure correct data topological relationship through automatic repair combined with manual adjustment, to finally form a normalized spatial vector data set.

[0031] Step 2.2. Paper ownership archive data is digitized and reconstructed to generate structured ownership vector data with spatial coordinate information, which specifically includes: first, high-definition scanning of the collected paper ownership archive data to generate lossless digital image files; then extracting the text information in the archives, including ownership subject, boundary four-to-description, approval time and other key contents; combining the normalized spatial vector data set, based on the boundary description recorded in the archives, boundary vectorization is drawn on the digital image; by comparing the known coordinates of landmarks, roads, rivers and other geographic features, accurate national 2000 geodetic coordinate system coordinates are assigned to the vectorized boundary; attribute association is performed on the generated ownership vector data, the extracted text information is entered into the attribute field, and the consistency of coordinate accuracy and attribute information is repeatedly checked, to finally form structured ownership vector data with spatial coordinate information.

[0032] Step 2.3. Establish a unified natural resource classification system and attribute field standard, normalize and reconstruct multi-source attribute data, and form reconstructed multi-source attribute data, which specifically includes: referring to relevant national natural resource classification standards, combining the management needs of cross-provincial complex natural resources, establishing a unified natural resource classification system covering multiple types such as forest land, wetland, cultivated land, and water area; clarifying the division basis, coding rules, and naming specifications of each type of resource to ensure that the classification standard is uniformly applicable within the cross-provincial region; at the same time, a unified attribute field standard is developed to clearly define the name, data type, field length, and filling specifications of property class, spatial class, management class, and dynamic class attribute fields; the collected multi-source attribute data is checked one by one, the attribute field name is modified, the data format is adjusted, and the necessary attribute information that is missing is supplemented according to the unified standard; for attribute data that is repeated or conflicting between different departments, the original materials are verified and corrected to finally form the reconstructed multi-source attribute data.

[0033] Step 2.4. Align the spatio-temporal reference of multi-temporal satellite remote sensing images and dynamic monitoring data sets to eliminate temporal differences and obtain spatio-temporally aligned satellite remote sensing images and dynamic monitoring data sets, which specifically includes: first, determine a unified time reference, divide the time nodes with the Gregorian calendar year and quarter as the standard, and correspond the multi-temporal satellite remote sensing images and dynamic monitoring data of different periods to the corresponding time nodes; for satellite remote sensing images, use the latest high-resolution image as the reference, and use image registration technology to align the images of other periods to eliminate spatial shifts caused by satellite shooting angles and terrain undulations; for dynamic monitoring data sets, associate the spatio-temporal reference of the monitoring data with the aligned remote sensing images according to the collection time and spatial position of the monitoring data; interpolate the missing values during the data collection interval, and use smoothing processing to eliminate abnormal fluctuation data; perform consistency verification on the processed images and data sets to ensure that the data of the same spatial position at different periods can be accurately corresponded, and finally obtain the spatio-temporally aligned satellite remote sensing images and dynamic monitoring data sets.

[0034] Step 2.5, integrate the normalized spatial vector data set, structured ownership vector data with spatial coordinate information, reconstructed multi-source attribute data, satellite remote sensing images after spatio-temporal reference alignment and dynamic monitoring data set, construct a standardized data set with unified spatio-temporal reference, specifically including: building a unified data integration platform, importing the normalized spatial vector data set, structured ownership vector data with spatial coordinate information, reconstructed multi-source attribute data, satellite remote sensing images after spatio-temporal reference alignment and dynamic monitoring data set into the platform according to data types; taking geographic location coding as a spatial key field, establishing a one-to-one correspondence between spatial vector data and attribute data; taking data collection date as a time key field, realizing spatio-temporal accurate matching of remote sensing images and dynamic monitoring data; formulating clear data priority rules, measured data is better than remote sensing interpretation data, latest monitoring data is better than historical data, official approval data is better than survey and statistical data, strictly following the priority rules to deal with data conflicts, format incompatibility and other problems in the integration process; conducting comprehensive quality inspection on the integrated data set, requiring data completeness, no missing in core fields, data coverage rate reaching 100%, coordinate consistency, all data strictly following national 2000 geodetic coordinate system, no coordinate offset, attribute specification, field filling conforming to standards, no format error, spatio-temporal correlation, spatial data and time data of the same element matching correctly; adopting a combination of manual sampling inspection and automatic detection, sampling inspection of each type of data at a proportion of 30%, marking the detected problem data, feeding back to the data provider for verification and correction, then reimporting and integrating; after multiple rounds of verification and confirmation that the data quality fully meets the requirements of the right registration work, finally constructing a standardized data set with unified spatio-temporal reference.

[0035] In the embodiment of the present application, by implementing spatial reference normalization processing on the land space planning spatial vector data set, the data of various coordinate reference systems is uniformly converted to the national 2000 geodetic coordinate system, solving the problem of non-uniform spatial reference of cross-provincial data and laying a unified spatial foundation for data integration. The paper-based ownership archive data is digitized and reconstructed and given accurate spatial coordinate information, so that the historical ownership data is converted into structured vector data that can be directly used for spatial analysis, realizing the association and matching with other spatial data. A unified natural resource classification system and attribute field standard are established, and the multi-source attribute data is standardized and reconstructed, eliminating the inconsistency of different department data attribute definitions and the inconsistency of formats, ensuring the comparability and integrability of various data at the attribute level. The multi-temporal satellite remote sensing image and dynamic monitoring data set are processed by time and space reference alignment, accurately eliminating the temporal difference and spatial offset, and ensuring the effective comparative analysis of dynamic change data under the same time and space framework. Finally, all the standardized processed data sets are integrated to form a standardized data set with unified time and space reference, effectively overcoming the difficulty of multi-source heterogeneous data integration in the prior art, and providing comprehensive, consistent and reliable data support for accurate identification of natural resource ownership space conflict areas, boundary optimization and intelligent decision-making.

[0036] In a preferred embodiment of the present application, step 3 can include: Step 3.1, spatial overlay analysis is performed on the ownership boundary and ecological protection red line in the standardized data set to identify the conflict area between ownership and control requirements, specifically including: loading the ownership boundary vector data and ecological protection red line vector data in the standardized data set in the unified data integration platform, ensuring that both types of data are based on the national 2000 geodetic coordinate system; using spatial overlay analysis function, selecting intersection analysis mode to operate the two types of data, focusing on checking whether the natural resource range corresponding to the ownership boundary exceeds the ecological protection red line delineation area, or whether the ownership type within the ecological protection red line range is inconsistent with the control requirements; attribute association is performed on the intersection area generated after overlay, and key information such as ownership subject, resource type, control level, etc. of the area is extracted; according to the control rules of the ecological protection red line, it is judged whether there is a conflict situation such as use restriction or prohibition of development in the area, and finally the spatial range meeting the conflict judgment condition is marked to form a data set of conflict area between ownership and control requirements.

[0037] Step 3.2, based on the ownership vector data in the standardized data set and the remote sensing image features, to identify and extract the cross-provincial enclave use right and ownership separation area, specifically including: extracting structured ownership vector data and high-resolution remote sensing images aligned with spatial and temporal reference from the standardized data set; first, filter the attribute fields of the ownership vector data to distinguish the registration information of the ownership subject and the use right subject; focus on investigating records where the ownership subject belongs to a different provincial administrative region than the use right subject, and preliminarily lock the potential cross-provincial enclave range; spatially overlay these potential ranges with remote sensing images to identify whether the spatial form of the area is a plot isolated from the ownership subject administrative region; verify the independence of the plot in combination with surrounding geographical elements, and exclude areas adjacent or connected to the ownership subject administrative region; accurately outline the boundaries of the confirmed isolated plot, extract its complete spatial range, and form a cross-provincial enclave use right and ownership separation area data set.

[0038] Step 3.3, identify and locate the disputed area of the inserted mountain by comparing historical ownership data with current survey data, specifically including: retrieving historical ownership data and current survey data from the standardized data set, historical ownership data including digitized reconstructed structured ownership vector data and original paper archive textual description information, and current survey data including the latest remote sensing interpretation results and field survey data; spatially overlay historical ownership boundary vector data and current ownership boundary vector data, and use difference analysis mode to identify areas where the boundaries do not coincide; check the historical ownership records and current survey results of the difference areas one by one, focusing on key information such as boundary four-to-description and adjacent ownership subject confirmation; combined with the geographical division characteristics of cross-provincial administrative regions, determine whether the difference area belongs to the inserted mountain category; through supplementary materials such as ownership dispute mediation records and cross-provincial coordination files, verify whether the area has a boundary dispute, and finally determine the precise range of the disputed area of the inserted mountain and form a data set.

[0039] Step 3.4, by combining multi-temporal resource type data, to identify the wetland and forest resource type identification ambiguous area, specifically comprising: extracting the multi-temporal satellite remote sensing image interpretation results and dynamic monitoring data set from the standardized data set, and screening out the resource type data of different time periods covering the same spatial range; focusing on comparing the resource type classification results of adjacent time nodes, and investigating the spatial area alternately determined as wetland or forest; combining the spectral feature analysis of remote sensing image, observing whether the key indicators such as vegetation coverage type and water content of the area are between the typical characteristics of wetland and forest; extracting the vegetation coverage and soil moisture in the dynamic monitoring data, and referring to the division standard of wetland and forest in the natural resource classification system; marking the area whose indicators are in the critical interval and the type determination is inconsistent, supplementing the auxiliary information such as topography, hydrology of the area, and finally forming the wetland and forest resource type identification ambiguous area data set.

[0040] Step 3.5, integrating all the identified conflict areas of ownership and management requirements, cross-provincial enclave ownership separation areas, disputed areas of inserted mountain boundaries, and wetland and forest resource type identification ambiguous areas, to construct a complete natural resource ownership spatial conflict area, specifically comprising: importing the four types of conflict area data sets identified into a unified data integration platform, ensuring that the spatial reference and data format of all data sets remain consistent; batch integrating the four types of areas, automatically processing the overlapping parts between areas, and retaining the complete spatial range; adding a conflict type attribute field to each spatial unit after integration, and clearly marking which one of the ownership and management requirement conflict, cross-provincial enclave ownership separation, inserted mountain boundary dispute or resource type identification ambiguity; establishing a conflict area index system to associate the original data source, key attribute information and determination basis of each area; conducting a comprehensive spatial check on the integrated data set to ensure that there is no area missing and no boundary overlap error, and finally constructing a complete natural resource ownership spatial conflict area data set covering all conflict types.

[0041] In the embodiment of the present application, by spatial overlay analysis of the ownership boundary and ecological protection red line in the standardized data set, the ownership and management requirement conflict area is effectively identified; based on the ownership vector data and remote sensing image features, the cross-provincial enclave use right and ownership separation area is effectively extracted; by comparing the historical ownership data and the present situation investigation data, the inserted mountain boundary dispute area is clearly located; combined with multi-temporal resource type data, the wetland and forest resource type identification ambiguous area is clearly identified, and finally all the identified conflict areas are integrated to construct a complete natural resource ownership spatial conflict area. The problem of one-sided and insufficient precision in the prior art is solved, and the wide coverage and precise positioning of various types of ownership conflict areas of cross-provincial composite natural resources are realized, which provides clear and comprehensive target objects for spatial reference control point layout, boundary optimization and intelligent resolution of ownership conflicts.

[0042] In a preferred embodiment of the application, step 4 above can include: Step 4.1, spatial topological relationship analysis is performed on the spatial conflict area to extract the intersection points of the cross-provincial enclave administrative jurisdiction boundary and the natural resource type boundary as topological intersection nodes, specifically including: loading the cross-provincial enclave use right and ownership separation area vector data, provincial administrative jurisdiction boundary vector data and natural resource type boundary vector data in the standardized data set, all data strictly follow the national 2000 geodetic coordinate system, ensuring that the spatial reference is completely consistent; after data loading, pre-processing is performed first, and topological errors such as boundary cracks and node misalignment are repaired to ensure the integrity and standardization of each type of boundary data; after the precise spatial range of the cross-provincial enclave is determined, the boundaries of various natural resource types such as forest land, wetland, farmland and water area within the corresponding area are extracted according to the natural resource classification system, and complete data of the provincial administrative jurisdiction boundary within the range is also extracted; spatial topological relationship analysis is performed on the administrative jurisdiction boundary and the natural resource type boundary using the plane scanning method, and by traversing the boundary coordinates point by point, the points where the two types of boundaries intersect directly in space are screened out; a distance threshold of 0.3 meters is set to exclude pseudo-intersection points, and adjacent points with a distance less than the threshold are determined as false intersections caused by data errors, and only the real boundary intersection points are retained; for the screened effective intersection points, the coordinate information to the sixth decimal place is recorded according to the national geodetic measurement accuracy requirements, and at the same time, the attribute data such as the name of the provincial administrative region, the administrative jurisdiction boundary code, the natural resource type and the boundary code is associated, and finally a complete and accurate topological intersection node set is formed.

[0043] Step 4.2, based on the historical forest ownership certificate recorded boundary and the latest ecological protection red line demarcation results, the geometric center of the disputed overlapping area is calculated by spatial overlay analysis; the geometric center of the disputed overlapping area is taken as the center of the disputed overlapping area, which specifically includes: loading the standardized data set of the historical forest ownership certificate recorded boundary vector data, the latest ecological protection red line demarcation results vector data, and the attribute complete digital reconstruction of the conflict area data set identified in the previous stage; taking the spatial range of the ownership and control requirement conflict area as the screening condition, the intersection operation of spatial overlay is performed on the historical forest ownership certificate recorded boundary and the latest ecological protection red line demarcation results, and the overlapping spatial range of the two boundaries in the conflict area is preliminarily locked; for the preliminarily locked overlapping area, the corresponding historical forest ownership certificate paper file digital image is retrieved one by one, the consistency of the text description of the boundary four sides, the attached figure annotation and the vector data in the file is checked, and the latest ecological protection red line demarcation technical specification, approval file is consulted to confirm the legality and accuracy of the demarcation basis; for the overlapping range which is checked without error, the historical forest ownership certificate recorded boundary and the latest ecological protection red line demarcation results are clearly defined as the disputed overlapping area; the geometric center of the area is calculated by using the polygon arithmetic average method, the coordinates of all vertexes of the vector boundary of the disputed overlapping area are extracted, and then the arithmetic average operation is performed on the x coordinates and y coordinates of all vertexes respectively to obtain the accurate coordinate value of the center of mass; the coordinate decimal point after six digits is retained according to the requirement, and the corresponding historical forest ownership certificate number, ownership subject, ecological protection red line control level, demarcation batch and other key attribute information are associated, and finally the center of the disputed overlapping area is determined.

[0044] Step 4.3, by comparing the resource type data before and after the implementation of the ecological restoration project, identifying the areas where the resource type has undergone substantial changes, calculating the geometric center of the areas where substantial changes have occurred, and taking the geometric center as the feature area center, specifically comprising: loading the multi-temporal resource type vector data before and after the implementation of the ecological restoration project, the ecological restoration dynamic monitoring data set, and the natural resource ownership spatial conflict area data in the standardized data set after the spatio-temporal reference alignment, ensuring that the spatio-temporal reference of all data is completely matched; according to the unified natural resource classification system, the determination standard of resource type substantial change is clear, that is, the resource type changes from the first class to the cross-class, or the second class changes fundamentally and is accompanied by the change of core ecological characteristics; comparing the resource type data before and after the implementation, combining the vegetation coverage, soil moisture, hydrological conditions and other indicators in the dynamic monitoring data, screening out areas that meet the determination standard, such as the conversion of forest land to wetland and the conversion of farmland to ecological protection land; superimposing these preliminary screened areas with the natural resource ownership spatial conflict area to lock the target spatial range that belongs to the conflict area and has undergone substantial changes in resource type; extracting the ecological restoration project establishment file, construction record, completion report and other information in the target range, combining the seasonal vegetation coverage change and soil quality test results in the dynamic monitoring data to verify that the resource type change is a substantial change directly caused by the ecological restoration project, and exclude changes caused by natural evolution or other human activities; the geometric center calculation method is used to determine the feature area center, the abnormal values in the boundary coordinates of the target area are removed, and then the x and y coordinates of the remaining vertices are averaged to obtain the center point coordinates of the area; record the center point coordinates and keep the last six digits, and associate the corresponding ecological restoration project name, implementation start and end time, resource type code and name before and after the change, core monitoring index change data and other information, and finally determine the feature area center.

[0045] In the embodiments of the present application, by extracting the topological intersection nodes of the cross-provincial enclave administrative jurisdiction boundary and the natural resource type boundary, calculating the mass center of the controversial overlapping area of the historical forest right certificate recorded boundary and the latest ecological protection red line demarcation results, and determining the feature area center of the area where the resource type has undergone substantial changes before and after the implementation of the ecological restoration project, a spatial reference control point system covering key scenarios such as cross-provincial enclave ownership separation, historical ownership dispute, and ecological restoration type change is constructed. These spatial reference control points provide accurate and stable spatial reference for the construction of spatial topology analysis units, effectively making up for the defect that the existing boundary optimization lacks unified spatial reference support, ensuring that the subsequent conflict area boundary optimization has clear and reliable spatial basis, and at the same time ensuring the consistency and logical coherence of the core terms in the technical process, laying a solid foundation for further improving the accuracy of cross-provincial complex natural resource right registration.

[0046] In a preferred embodiment of the present application, step 5 above can include: Step 5.1, taking the topological intersection node, the center of mass of the disputed overlapping area and the characteristic area center as the key control point, constructing a spatial topology analysis unit surrounding the spatial conflict area, specifically including: loading the three types of key spatial benchmark control point data of topological intersection nodes, the center of mass of the disputed overlapping area and the characteristic area center, and synchronously loading the complete natural resource ownership spatial conflict area vector data; all data strictly follow the national 2000 geodetic coordinate system, first de-duplicate the control point data, eliminate redundant points with coordinates repeated or errors exceeding 0.1 meters, and ensure the uniqueness and accuracy of the control points; taking the de-duplicated three types of control points as the core input, using the Graham scan method to calculate the convex hull algorithm, sorting the control point polar angle and gradually constructing the convex polygon to form the initial convex hull framework that can completely surround the entire spatial conflict area; in combination with the actual geometric form of the spatial conflict area, locally adjusting the initial convex hull framework, supplementing the boundary inflection point as an auxiliary control point for the concave parts of the conflict area to avoid excessive expansion or omission of the edge conflict area of the framework; connecting the core control points and auxiliary control points in clockwise order to form a polygon structure that is closed, without self-intersection and boundary cracks, which is the spatial topology analysis unit specially surrounding the spatial conflict area; finally, verify the boundary continuity of the analysis unit through the topology checking tool to ensure that it meets the basic requirements of discretization processing.

[0047] Step 5.2, discretizing the boundary profile of the spatial topology analysis unit to divide it into continuous circular arc sub-regions, specifically including: extracting the complete boundary profile data of the spatial topology analysis unit, clearly defining the national 2000 geodetic coordinate system coordinate information of the boundary starting point, ending point and all vertices, and calculating the total boundary length and complexity; dividing the segmentation granularity according to the total boundary length, using a 5-degree central angle threshold when the boundary length exceeds 5 kilometers, a 10-degree threshold when the length is between 2 and 5 kilometers, and a 15-degree threshold when the length is less than 2 kilometers, to ensure that the division of the circular arc sub-region is both fine and efficient; starting from the boundary starting point, taking the line connecting the starting point and the geometric center of the analysis unit as the reference, and cutting the boundary profile in segments according to the set central angle threshold, recording the starting point, ending point coordinates and corresponding central position of each circular arc sub-region after each cutting; in the segmentation process, real-time check whether the end point coordinates of adjacent sub-regions are completely coincident, and if there is a deviation, adjust the cutting parameters in time to ensure that each circular arc sub-region is continuously connected in space without overlap or gap; continuously number all the segmented circular arc sub-regions in the order of cutting, and establish the association index of the number, original boundary paragraph and corresponding control point.

[0048] Step 5.3, for each circular arc sub-region, the boundary feature point extraction is performed by using the polygon simplification method, the key geometric feature vertices are identified and retained, specifically including: for each numbered circular arc sub-region, the Douglas-Peucker polygon simplification algorithm is used to perform boundary feature point extraction, and the algorithm parameters are strictly matched with the accuracy requirements of the cross-provincial natural resource right registration; according to the relevant national technical specifications, the distance tolerance threshold is set to 0.2 meters, which can both eliminate redundant points and completely retain the boundary core features; traverse the boundary coordinate points of each circular arc sub-region, starting from the starting point, calculate the connecting line between the current point and the next candidate point, and then solve the perpendicular distance of the remaining points on the original boundary to the connecting line; mark the points with a perpendicular distance greater than 0.2 meters as key geometric feature vertices, and record their coordinate information in detail; points with a perpendicular distance less than 0.2 meters are determined as unnecessary redundant points and are directly eliminated; pay special attention to the inflection points, turning points and curvature mutation points of the boundary during the extraction process, which are the core elements reflecting the geometric form of the circular arc sub-region; arrange the key geometric feature vertices extracted for each sub-region in a clockwise order to form an ordered feature point sequence, and calculate the similarity of the polyline formed by the feature point sequence and the original boundary, to ensure that the similarity is not less than 95%, and to ensure that the feature point sequence can accurately restore the boundary form of the sub-region.

[0049] Step 5.4, based on the key geometric feature vertices, the curve fitting method is used to reconstruct the smooth curve of each circular arc sub-region, and the fitting boundary curve is generated, which specifically includes: based on the ordered key geometric feature vertex sequence of each circular arc sub-region, first, the feature points are checked twice to confirm that the coordinate order of all vertices is consistent with the direction of the original boundary, and there is no reversal or misplacement, ensuring the accuracy of the basis data for curve reconstruction; according to the arrangement order of the feature points, taking the adjacent two key feature vertices as the basis segment, the curve is gradually constructed, and the curve passes through each vertex in turn, while referring to the spatial position relationship of the adjacent feature points before and after, adjusting the bending trend of the current segment curve, ensuring that the adjacent two curves transition naturally at the connection point, without breakpoints, abrupt turns or angle mutations; set the curve smoothness constraint condition, combined with the natural form characteristics of natural resource boundaries such as forest land, wetland and cultivated land, limit the minimum curvature radius of the curve to be not less than 5 meters, avoid the appearance of sharp corners or excessive distortion, and make the curve form fit the actual distribution law of the natural resource boundary; the reconstructed curve is segmented according to the feature points, and each segment is checked, by comparing the bending degree of adjacent paragraphs and observing whether the overall trend of the curve is coherent, to determine whether the curvature change of each curve is uniform, if a certain segment of the curve appears local excessive bending, bending direction mutation or hard connection with adjacent paragraphs, it is judged as abnormal form; for paragraphs with abnormal form, first analyze the abnormal reason, if it is caused by uneven weight distribution of feature points, give higher weight to key feature points such as inflection points and turning points to enhance their influence on the curve form; if it is caused by insufficient number of feature points, supplement 1 to 2 auxiliary feature points at the curve mutation or hard connection position to refine the bending transition of the curve; after supplementing or adjusting, the curve is reconstructed again, and then the form of the paragraph is rechecked, and repeated iteration adjustment is carried out until the bending degree of all segmented curves is uniform and consistent, and the overall curve is highly consistent with the boundary trend of the original circular arc sub-region, which not only retains the core features of the original boundary, but also has a smooth and natural form, finally generating a fitting boundary curve that can accurately reflect the boundary features of the sub-region.

[0050] Step 5.5, calculate the geometric deviation value between the fitted boundary curve and the original boundary, determine the boundary adjustment coefficient of each circular arc sub-region based on the deviation value, specifically including: obtaining the original boundary coordinate data of each circular arc sub-region and the coordinate data of the fitted boundary curve, synchronously sampling the two types of data at a uniform interval of 0.1 meters to ensure that the spatial positions of the sampling points are one-to-one corresponding; using the Euclidean distance calculation method, point-by-point solving the straight line distance between the sampling points on the fitted boundary curve and the corresponding sampling points on the original boundary, which is the geometric deviation value of a single point; statistically analyzing the geometric deviation values of all sampling points in each circular arc sub-region, calculating the average, maximum and standard deviation of the deviation to comprehensively grasp the deviation degree of the fitted curve and the original boundary of the sub-region; establishing a corresponding relationship between the deviation value and the boundary adjustment coefficient, the adjustment coefficient is 1.0 when the average deviation is ≤0.1 meters, 1.1 when the average deviation is between 0.1 and 0.2 meters, and 1.2 when the average deviation is >0.2 meters, the adjustment coefficient value range is strictly controlled between 0.8 and 1.2, which ensures the correction effect and avoids excessive adjustment leading to boundary distortion; binding and storing the deviation statistical results of each sub-region with the corresponding boundary adjustment coefficient to form the boundary correction basis data exclusive to the sub-region.

[0051] Step 5.6, integrate the boundary adjustment coefficients of all circular arc sub-regions to form a boundary precision correction parameter set for overall boundary optimization, specifically including: collecting the boundary adjustment coefficients and associated deviation statistical data of all circular arc sub-regions, systematically organizing and sorting them in order of sub-region number; supplementing complete associated attribute information for each boundary adjustment coefficient, including the corresponding circular arc sub-region number, start and end point coordinates, feature point sequence identifier, average deviation, maximum deviation, etc. key content, to ensure that the coefficient and the sub-region are accurately matched without confusion; establish a unified format standard for the boundary precision correction parameter set, clearly define the meaning, data type and format requirements of each field, where the coordinate data retains six digits after the decimal point, the deviation data retains three digits after the decimal point, and the adjustment coefficient retains two digits after the decimal point; perform consistency check on the organized adjustment coefficients, check whether there are abnormal values outside the value range of 0.8 to 1.2, and whether the associated attributes are complete and the logic is consistent; if abnormal values are found, trace back to the deviation calculation link to verify the original data and calculation process, and re-integrate after correction; if there are missing attributes or logical conflicts, supplement the relevant information and then integrate; integrate all adjustment coefficients and associated attribute information that pass the verification in a unified format to form a boundary precision correction parameter set that is structurally standardized, data complete, and directly callable.

[0052] Step 5.7, the original boundary geometry of the spatial conflict area is corrected and smoothed by the boundary precision correction parameter set to form an optimized conflict area with accurate boundary definition, specifically including: loading the original boundary data of the spatial conflict area and the boundary precision correction parameter set, establishing a one-to-one association between them through the arc sub-region number, and ensuring that the correction parameter corresponds to the target boundary paragraph accurately; starting from the first arc sub-region, calling the boundary adjustment coefficient corresponding to the region, and adjusting each sampling point on the original boundary point by point using the coordinate correction formula, the adjustment logic is to add (fitting coordinates - original coordinates) multiplied by the adjustment coefficient to the original coordinates, so that the corrected boundary point approaches the fitting boundary curve and maintains a reasonable amplitude; after completing the point-by-point correction of a single arc sub-region, the modified boundary of the region is locally smoothed using a 3-point moving average window to eliminate minor fluctuations and make the boundary transition more natural; all arc sub-regions are processed in order, and the boundary connection of adjacent sub-regions is checked in real time during processing. If there is a connection deviation, the boundary end point coordinates are fine-tuned to ensure the continuity of the overall boundary; after all sub-regions are processed, the overall boundary is topologically checked to repair possible node misalignment, boundary cracks and other problems; finally, the boundary precision is verified by the random sampling method, 20 points are randomly selected on the boundary of the optimized conflict area, the average deviation from the fitting boundary curve is calculated to ensure that the planar position mean error is not more than ±0.3 meters, and finally the optimized conflict area with accurate boundary definition is formed.

[0053] In the embodiments of the present application, by taking the topological intersection node, the disputed overlapping area centroid and the feature area center as the key support, a complete conflict area boundary precision optimization scheme is formed by systematically carrying out spatial topology analysis unit construction, boundary discretization division, key feature point extraction, smooth curve reconstruction, geometric deviation calculation, correction parameter integration and boundary section optimization. This process first focuses on the features of different boundary paragraphs through regional processing, then retains the core geometric information of the boundary by feature point extraction, combines the smooth curve reconstruction to make the boundary form conform to the natural distribution law of natural resources, and finally solves the problems of fuzzy boundary, insufficient precision and irregular form of the conflict area boundary in the prior art through deviation calculation and targeted correction. The finally generated optimized conflict area has accurate boundary definition, which not only retains the core features of the original boundary, but also improves the geometric precision and standardization of the boundary, providing a precise and stable spatial framework for intelligent resolution of ownership conflicts, and further improving the accuracy and reliability of the cross-provincial composite natural resource right registration results.

[0054] In a preferred embodiment of the present application, step 6 described above can include: Step 6.1, build an intellectual resolution model for ownership conflicts, which integrates an ownership priority rule base and a multi-source evidence chain weight distribution mechanism, specifically including: taking the State Natural Resource Right Registration Regulations, the Interim Regulations on Real Estate Registration and other laws and regulations as the core basis, combining with the cross-provincial regional coordination management method, building a three-layer architecture of the intellectual resolution model for ownership conflicts, including data input layer, feature processing layer and arbitration output layer; the ownership priority rule base adopts hierarchical design principle, and is divided into three levels of rules according to legal validity, data reliability and time validity. The legal level rule clearly states that laws are higher than administrative regulations, administrative regulations are higher than departmental rules, and departmental rules are higher than local normative documents. The ownership type rule defines that ownership is higher than usufruct, and usufruct is higher than security right. The legal registration ownership is higher than the fact of occupying ownership, and the time sequence rule determines that the effective ownership is higher than the expired ownership, and the latest registration ownership is higher than the historical unchanging ownership. The multi-source evidence chain weight distribution mechanism is based on the principle of analytic hierarchy process, and experts in the fields of ownership management, surveying and mapping engineering and policy regulations are invited to form an evaluation group. According to the data source reliability, the data precision, the data timeliness, and the three dimensions of the measured data, the official archives, the remote sensing interpretation data and the investigation records, the data precision of centimeter, decimeter and meter, and the data timeliness of three years, three to ten years and more than ten years, the fixed coefficients of various types of evidence are determined through weight calculation. The measured data is 0.4, the official archives is 0.3, the remote sensing interpretation data is 0.15, the investigation record is 0.1, and the policy document is 0.05. At the same time, a dynamic adjustment interface is reserved, and the weight can be flexibly corrected according to the conflict type.

[0055] Step 6.2, input the optimized conflict area with accurate boundary definition into the intellectual resolution model for ownership conflicts as the benchmark spatial framework for automatic arbitration, specifically including: loading the complete data of the generated optimized conflict area with accurate boundary definition, including boundary coordinate information, conflict type attribute, associated topological intersection node and other spatial benchmark control point data; according to the format requirements of the data input layer of the intellectual resolution model for ownership conflicts, the optimized conflict area data is format-converted, the boundary coordinates are arranged according to the national 2000 geodetic coordinate system standard, and the conflict type attribute is uniformly labeled according to the preset coding specification; the converted data set is subjected to integrity check to confirm that the boundary coordinates have no missing, the conflict type labeling has no omission, and the associated control point data has no error; the optimized conflict area data passing the check is input into the intellectual resolution model for ownership conflicts, the model automatically reads the spatial range information in the data, and builds a closed benchmark spatial framework based on the range; a spatial index system is established in the framework, and is indexed according to the geographical location and range size of the conflict area, so as to facilitate the subsequent time and space feature analysis and arbitration operation, and ensure the efficient progress of automatic arbitration process.

[0056] Step 6.3, in the reference spatial framework, according to the ownership priority rule base, the historical ownership archives and the current management requirements in the optimization conflict area are analyzed in time and space characteristics, and the ownership data set with time and space characteristic markers is formed, which specifically includes: in the reference spatial framework, according to the hierarchical rules of the ownership priority rule base, the historical ownership archives and the current management requirements in the optimization conflict area are analyzed in time and space characteristics; the time characteristics of the historical ownership archives are extracted by using the time sequence analysis principle, the effective date, the expiration date, the number of ownership changes, the time node and the reason of each change, the spatial characteristics are extracted by using the spatial superposition analysis principle, the ownership boundary coordinates, the range area, the spatial relationship of adjacent ownerships, the overlap or adjacency, the resource type and distribution corresponding to the ownership; for the current management requirements, the policy adaptability analysis principle is used to analyze the management level of the ecological protection red line, the first level and the second level, the use control type, the prohibition of development, the restriction of development, the permission of development, the development restriction conditions, the building density, the volume rate, the vegetation protection requirements and other management characteristics, the implementation starting time, the applicable spatial range and whether it is a cross-provincial unified implementation policy of the management requirements are clarified; the analyzed time, space and management characteristics are marked to the corresponding ownership data entries one by one according to the structure of ownership ID, characteristic type, characteristic value and confidence, the confidence is determined according to the data source reliability, the official archives are marked 0.9, the measured data are marked 0.95, and the remote sensing data are marked 0.8, and finally the ownership data set with complete time and space characteristic markers is formed.

[0057] Step 6.4, based on the ownership data set with spatio-temporal feature marking, through the multi-source evidence chain weight distribution mechanism, the boundary accuracy correction parameter set and the geometric feature analysis result of the optimized conflict area are fused to form a multi-source evidence fusion data set, specifically including: loading the ownership data set with spatio-temporal feature marking, boundary accuracy correction parameter set and geometric feature data of the optimized conflict area, all data are associated through the unique identification ID of the conflict area; first, the ownership data set and the boundary accuracy correction parameter set are matched and fused, according to the conflict area number and the circular arc sub-area number, the corresponding boundary adjustment coefficient and deviation statistical data are associated to each ownership data entry, and the boundary accuracy feature information of the ownership data is supplemented; then extract the geometric feature data of the optimized conflict area, including boundary length, range area, boundary curvature change, spatial relationship with adjacent conflict areas, etc., after quantizing these geometric features according to the unified standard, associate them to the corresponding ownership data; start the multi-source evidence chain weight distribution mechanism, according to the source type of the ownership data, such as measured data, official archives, remote sensing interpretation data, etc., give the corresponding fixed weight coefficient; combined with the spatio-temporal features of the ownership data, such as data timeliness, spatial correlation, fine-tune the fixed weight coefficient, such as the weight of the measured data in the past three years can be increased by 0.05, and the weight of the policy documents uniformly implemented across the province can be increased by 0.03; integrate the fused ownership data, boundary accuracy correction parameters, geometric feature data and adjusted weight coefficients according to the unified structure, each data entry contains ownership basic information, spatio-temporal feature marking, boundary accuracy parameter, geometric feature quantization value, evidence weight and other core contents, finally form a multi-source evidence fusion data set with complete structure, close association and can be directly used for arbitration operation.

[0058] Step 6.5. Based on the multi-source evidence fusion dataset, the preliminary ruling result of the cross-provincial right-of-use dispute area is obtained through the adaptive arbitration operation of the right-of-use conflict intelligent resolution model. Specifically, the multi-source evidence fusion dataset is input into the right-of-use conflict intelligent resolution model, and the model realizes automatic matching of rules and data based on the production rule reasoning principle. By performing conflict identification calculation to traverse the dataset, conflict points are extracted, including right-of-use subject disputes, boundary range disputes, control requirement application disputes, and resource type identification disputes. In combination with the weight proportion of multi-source evidence, the conflict points are prioritized according to the conflict influence range, involving ecological protection red lines, major projects, and public interests, and the conflict severity, including complete conflict, partial conflict, and slight conflict. The conflict involving the ecological protection red line has the highest priority and is processed first. For each conflict point, the right-of-use priority rule library is called to match the corresponding ruling rules. For example, the right-of-use subject dispute is matched with the right-of-use type and time sequence rules. Through fuzzy reasoning operation, the sufficiency of the evidence of the conflict parties is determined. The evidence with a weight proportion of more than 0.6 is considered as core evidence, the evidence with a weight proportion of 0.4 to 0.6 is considered as auxiliary evidence, and the evidence with a weight proportion of less than 0.4 is considered as invalid evidence. The right-of-use ownership is ruled based on the core evidence and auxiliary evidence, the precise boundary coordinates are determined, the control requirement execution standard is determined, and the evidence source and rule basis corresponding to each ruling result are recorded. Finally, the cross-provincial right-of-use dispute area preliminary ruling result containing complete ruling information is output.

[0059] Step 6.6. The preliminary ruling result is subjected to cross-provincial mutual recognition consistency verification to form the final right-of-use ruling result. Specifically, a cross-provincial right-of-use dispute ruling verification group is established, and the preliminary ruling result is sent to the provincial natural resource management departments involved in the conflict. The verification content includes whether the ruling result conforms to the relevant right-of-use management regulations of the two provinces, whether the boundary coordinates are consistent with the division of the administrative regions of the two provinces, whether the control requirements are consistent with the ecological protection and resource utilization policies of the two places, and whether the ruling basis has sufficient legal and data support. The verification opinions of the two provinces are collected, and the parts with objections, such as boundary connection deviation and policy application doubts, are rechecked in combination with the multi-source evidence fusion dataset. According to the verification results, the inconsistent items in the preliminary ruling are corrected to ensure that the results meet the management requirements and mutual recognition standards of the two provinces, and finally the final right-of-use ruling result that is recognized by both parties, has sufficient basis, and can be implemented is formed.

[0060] Specifically, the construction and training process of the right-of-use conflict intelligent resolution model is as follows: To meet the needs of cross-provincial composite natural resource ownership conflict resolution, a rule-driven and evidence-fusion dual-core model architecture is constructed. First, a three-layer functional architecture is designed. The data input layer is responsible for receiving standardized ownership data, including historical ownership archives, current control requirements, spatial reference control network data, and triangular subdivision grid analysis results. It is converted into a structured data format in the national 2000 geodetic coordinate system, ensuring coordinate precision to centimeter level and attribute field completeness. The feature processing layer analyzes the time features, such as ownership validity, expiration time, and policy implementation nodes, spatial features, such as ownership boundary coordinates and resource type distribution, and attribute features, such as ownership subject qualifications and control levels, and marks the feature confidence, with official archives at 0.9, measured data at 0.95, and remote sensing data at 0.8. The arbitration output layer integrates the ownership priority rule reasoning module and the multi-source evidence fusion module. The former is responsible for matching the arbitration rules, and the latter is responsible for weighted integration of evidence, and finally outputs the arbitration results.

[0061] When constructing the ownership priority rule library, the Natural Resources Ownership Registration Regulations, the Interim Regulations on Real Estate Registration, and other laws and regulations are used as the basis, and a three-level rule system is established according to legal level, ownership type, and time sequence. The legal level rule clearly defines the priority of effectiveness, with laws above administrative regulations, administrative regulations above departmental rules, and departmental rules above local normative documents. The ownership type rule defines the priority of rights, with ownership above usufructuary rights, and usufructuary rights above security rights. The time sequence rule determines the priority of time effect, with valid ownership above expired ownership, and the latest registered ownership above historical unaltered ownership. The rule library uses an extensible structure to support the addition of cross-provincial coordination policy clauses, such as cross-provincial enclave management methods.

[0062] Based on the analytic hierarchy process, experts in ownership management, surveying and mapping engineering, and policy and regulation fields are invited to form an evaluation group to weight the evidence from three dimensions: data reliability, precision, and timeliness. In the data reliability dimension, measured data such as field boundary survey has a weight of 0.4, official archives such as forest right certificates have a weight of 0.3, remote sensing interpretation data have a weight of 0.15, survey records have a weight of 0.1, and policy documents have a weight of 0.05. In the precision dimension, centimeter-level data have a weight of 0.6, decimeter-level data have a weight of 0.3, and meter-level data have a weight of 0.1. In the timeliness dimension, data within the last three years have a weight of 0.7, data between three and ten years have a weight of 0.2, and data over ten years have a weight of 0.1. The evidence comprehensive weight is calculated by weighted summation, and a dynamic adjustment interface is reserved. For special conflict types such as cross-provincial enclaves and inserted mountains, the weight coefficients can be manually corrected, such as increasing the weight of administrative jurisdiction boundary evidence in the enclave case.

[0063] The specific training process of the intellectual property conflict intelligent resolution model is as follows: collect cross-provincial natural resource ownership dispute cases in the past 20 years, covering cross-provincial enclaves, inserted mountain boundary disputes, wetland and forest type conflicts, etc. Each case contains original ownership data, historical archives, control files, spatial data, boundary coordinates, remote sensing images, processing records, mediation opinions, and final ruling results. A total of 1000 valid cases are selected as the training set, and 300 cases are selected as the validation set. Preprocess the data, unify the coordinates to the national 2000 geodetic coordinate system, and complete the missing ownership features, such as the effective time not recorded, according to the conflict type, such as ownership subject dispute and boundary range dispute, and label the case label.

[0064] Input the case data in the training set into the model and call the ownership priority rule library for rule matching test. For each case, the model automatically identifies the core conflict point, such as the conflict between ownership and use right, matches the corresponding hierarchical rule, such as ownership priority, and outputs the simulated ruling result. Compare the simulated result with the actual ruling result, calculate the rule matching accuracy, and if the accuracy of a certain type of conflict, such as ownership dispute within the ecological protection red line, is less than 80%, backtrack the rule library, supplement the corresponding special provisions, such as use right within the ecological protection red line subject to control requirements, and repeat the training until the rule matching accuracy of all conflict types reaches more than 90%.

[0065] Based on the model trained through rule adaptation, introduce a multi-source evidence chain weight distribution mechanism to fuse the evidence of the cases in the training set. The model calculates the comprehensive weight of each type of evidence in each case, and according to the weight proportion, the core evidence is greater than 0.6 and the auxiliary evidence is 0.4 to 0.6. The reasoning ruling result is compared with the deviation from the actual result. If the deviation exceeds 10%, such as misjudgment caused by excessive reliance on remote sensing data, adjust the weight coefficient of the corresponding evidence, such as reducing the weight of low-precision remote sensing data, and optimize the fusion algorithm to enhance the relevance of core evidence and rules, such as when the ownership evidence weight is greater than 0.7, the ownership priority rule is triggered directly. Through 50 rounds of iteration, the ruling accuracy after evidence fusion is improved to more than 95%.

[0066] Test the generalization ability of the model with the validation set, focusing on complex scenarios such as cross-provincial enclaves use right and ownership separation, and inserted mountain boundary disputes. For misjudgment cases in the validation set, such as boundary coordinate matching deviation, analyze the reasons and supplement the boundary precision correction parameters, such as the boundary geometric deviation value of the circular arc sub-region, as evidence input, and optimize the sensitivity of the model to spatial features. Finally, through the validation set test, ensure that the ruling accuracy of the model in various cross-provincial conflict scenarios is stable at more than 92%, meeting the actual application requirements.

[0067] In the embodiment of the present application, by constructing an ownership conflict intelligent resolution model integrating ownership priority rule library and multi-source evidence chain weight distribution mechanism, inputting the optimized conflict area with accurate boundary as the reference framework, analyzing the ownership space-time characteristics, fusing multi-source evidence, and obtaining the final decision result through adaptive arbitration operation and cross-provincial mutual recognition verification. This process replaces the traditional manual decision, greatly improves the efficiency of ownership dispute processing, multi-source evidence fusion makes the decision result more objective and accurate, cross-provincial mutual recognition verification ensures the consistency of the result in the cross-provincial area, effectively solves the problems of low decision efficiency and difficult mutual recognition of the result in the prior art, and provides reliable ownership decision support for cross-provincial complex natural resource right registration.

[0068] In a preferred embodiment of the present application, step 7 can include: Step 7.1, based on the final ownership decision result, determining the ownership boundary and resource type attribute of the complex natural resource unit within the accurate boundary definition range of the optimized conflict area, specifically including: taking the final ownership decision result as the core basis, focusing on the accurate boundary range of the optimized conflict area; extracting key information such as ownership subject and boundary coordinate string in the decision result, calibrating the boundary point position according to the national 2000 large coordinate system; combining the multi-temporal resource type data in the standardized data set, determining the specific type of each natural resource unit according to the natural resource classification standard; ensuring that the ownership boundary and resource type attribute correspond one by one, the boundary of each unit is closed and has no overlap, and finally determining the accurate ownership boundary and detailed resource type attribute of the complex natural resource unit.

[0069] Step 7.2, according to the ownership boundary and resource type attribute, determining the ecological protection responsibility subject and use control requirement of the corresponding natural resource unit, specifically including: according to the determined ownership boundary, determining the ownership attribution subject of each natural resource unit; according to the administrative jurisdiction range corresponding to the ownership subject, dividing the ecological protection responsibility subject, the provincial ownership corresponding to the provincial natural resource management department, and the local ownership corresponding to the local management department; combining the resource type attribute and the existing control policy, matching the corresponding use control requirement, clearly defining the vegetation protection, felling restriction and other requirements of forest land, and the prohibition of development, ecological restoration and other standards of wetland; associating the responsibility subject and the control requirement to the corresponding natural resource unit to form a clear responsibility list and control details.

[0070] Step 7.3, based on the information of the ownership boundary, resource type attribute and responsible subject, generate the composite natural resource unit right registration information that meets the cross-provincial linkage right requirements, including: integrating the accurate coordinates of the ownership boundary, the detailed classification of the resource type, and the specific information of the responsible subject; standardizing the information format according to the data standards of cross-provincial linkage right, unifying the coordinate precision, attribute coding and other requirements; the registration information contains the core content of ownership subject name, unified social credit code, ownership boundary coordinates, resource type and code, ecological protection responsibility subject, use control requirements, decision basis number, etc.; add cross-provincial cooperation identifier to mark the involved provincial administrative regions to ensure that the information can be recognized and called by the relevant departments of the two provinces, and generate the right registration information that meets the cross-provincial linkage right requirements.

[0071] Step 7.4, establish a two-way mutual recognition mechanism of fly-in and fly-out registration and confirmation to complete the cross-provincial cooperative verification and unified filing of right registration information, and form a benchmark registration information library, including: based on the principle of distributed data synchronization, establish a two-way information interaction channel between fly-in and fly-out, the channel uses encryption transmission protocol to ensure data security, and set a time limit rule that feedback within 24 hours after information push; after completing the local right registration information input, the fly-in place arranges the information according to the national right registration data specification, including the core verification items of ownership boundary coordinate precision, resource type code consistency, and responsibility subject qualification, and pushes it to the provincial right management platform of the fly-out place through the channel; the fly-out place platform verifies item by item according to the same data specification, the boundary connection error should be less than 0.2 meters, the resource type code should meet the national unified classification standard, and the responsible subject should have corresponding jurisdiction, and the confirmation receipt with electronic seal is generated after the verification is passed; if there is objection, the fly-out place needs to mark the specific problem items and basis, and return to the fly-in place for modification, and after modification, re-push for verification until both parties confirm consistency; the confirmed information is synchronized to the national natural resource right registration platform through the cross-provincial interface, the platform indexes according to the provincial partition and resource type, sets hierarchical access rights, completes unified filing, and finally forms a benchmark registration information library with complete data, unified format and real-time searchable.

[0072] Step 7.5, based on the benchmark registration information base, by combining multi-temporal satellite remote sensing image and ecological restoration dynamic monitoring data, to build a natural resource dynamic monitoring system, to establish a resource state change detection and early warning mechanism, including: according to the principle of multi-source data fusion, taking the benchmark registration information base as the spatial benchmark, accessing the monthly updated 1 meter resolution satellite remote sensing image, the image needs to be processed by radiation correction and geometric registration to ensure that the error with the benchmark library coordinate system is less than 0.5 meters; integrate ecological restoration dynamic monitoring data, including hourly vegetation coverage and soil moisture data collected by ground monitoring stations, and quarterly ecological index evaluation reports, match different sources and different frequencies of data to the same time dimension and spatial unit through time and space alignment calculation; build a natural resource dynamic monitoring system, which includes a data collection layer responsible for summarizing remote sensing and ground data, a preprocessing layer to eliminate data noise and bias, an analysis layer to identify resource changes through pixel-level comparison, and an early warning layer to trigger alerts according to preset thresholds; reference the ecological protection red line management regulations and resource type ecological sensitivity value, set the change threshold, trigger general warning when the forest vegetation coverage decreases by more than 15% per month, the wetland water area decreases by more than 10% per month, and the ownership boundary moves more than 1 meter per year, and trigger emergency warning when the change amplitude exceeds 30%, forming a resource state change detection and early warning mechanism.

[0073] Step 7.6, through the resource state change detection and early warning mechanism, real-time capture natural resource dynamic change characteristics, trigger the right registration information linkage update process, to realize the continuous optimization and maintenance of the benchmark registration information base, including: based on the closed-loop control principle, the resource state change detection and early warning mechanism runs continuously, compares the latest remote sensing image with the historical image of the benchmark registration information base through difference analysis method, verifies the change authenticity combined with ground monitoring data, real-time capture natural resource type conversion such as forest land to farmland, boundary offset such as ownership line movement caused by geological disasters, ecological state deterioration such as wetland drying, etc. Dynamic characteristics; when changes that meet the early warning threshold are detected and confirmed by continuous 3 times monitoring that are not temporary fluctuations, the right registration information linkage update process is automatically triggered, a verification notice containing the change area coordinates, feature description, and supporting data is sent to the corresponding ecological protection responsibility subject, requiring 7 working days to complete the field survey and feedback the results; after the responsibility subject verifies, if the change is legal and compliant such as approved development and utilization, submit the change application and supporting materials; if it is illegal change such as illegal encroachment, it is simultaneously processed to the law enforcement department, and after rectification, the change is applied again; after the audit is passed, the ownership boundary, resource type attribute and other contents in the benchmark registration information base are automatically updated, and the data is pushed to the registration system of the flying-in and flying-out places synchronously, to ensure the consistency of the data, and record the update track for tracing, to realize the continuous optimization and maintenance of the benchmark registration information base.

[0074] In the embodiment of the present application, the ownership boundary and resource attribute of the composite natural resource unit are determined based on the final ownership adjudication result, the ecological protection responsibility subject and the use control requirement are clarified, the right registration information meeting the cross-provincial linkage requirement is generated, the cross-provincial collaborative verification and archiving are completed through the bidirectional mutual recognition mechanism, the monitoring system and the early warning mechanism are constructed in combination with the dynamic monitoring data, and finally the linkage update and continuous optimization of the registration information are realized. This process accurately defines the ownership and attribute of the cross-provincial natural resources, clarifies the management responsibility, guarantees the cross-provincial mutual recognition and unification of the right information, and realizes the real-time update of the information through dynamic monitoring, effectively solving the problems of inaccurate cross-provincial right registration, difficulty in collaboration and information lag in the prior art, and providing precise, unified and dynamic registration support for the long-term management of the cross-provincial composite natural resources.

[0075] As shown in Figure 2 the embodiment of the present application also provides a natural resource right registration information processing system, comprising: an acquisition module configured to acquire composite natural resource multi-source heterogeneous data of a cross-provincial administrative region; a processing module configured to perform coordinate reference unification and attribute structure standardization processing on the multi-source heterogeneous data to obtain a standardized data set with unified space-time reference; an identification module configured to identify and extract a natural resource ownership spatial conflict area based on the standardized data set with unified space-time reference; a determination module configured to determine, according to the spatial conflict area, a topological intersection node of the cross-provincial enclave administrative jurisdiction boundary and the natural resource type boundary, a disputed overlapping area centroid of the latest ecological protection red line demarcation result and the historical forest right certificate recorded boundary, and a characteristic area center of a resource type that has undergone substantial changes before and after the implementation of an ecological restoration project; an optimization module configured to construct a spatial topology analysis unit based on the three spatial reference control points of the topological intersection node, the disputed overlapping area centroid and the characteristic area center, divide the spatial topology analysis unit into a plurality of circular arc sub-areas, calculate and adjust a curve fitting for each circular arc sub-area to obtain an adjustment coefficient, generate a boundary precision correction parameter based on the adjustment coefficient, and optimize the boundary geometric features of the spatial conflict area through the boundary precision correction parameter to obtain an optimized conflict area; an adjudication module configured to automatically adjudicate the optimized conflict area through the construction of an ownership conflict intelligent resolution model to obtain an adjudication result; an establishment module configured to generate right registration information of a composite natural resource unit according to the adjudication result, and establish a dynamic monitoring and right registration information linkage update mechanism.

[0076] The above is the preferred embodiment of the present application, it should be noted that for those skilled in the art, without departing from the principles described in the present application, can also be made several improvements and refinements, these improvements and refinements should also be considered the scope of protection of the present application.

Claims

1. A method for processing information on natural resource ownership registration, characterized in that, The method includes: Acquire complex, multi-source, heterogeneous natural resource data spanning provincial administrative regions; The multi-source heterogeneous data is subjected to coordinate benchmark unification and attribute structure standardization processing to obtain a standardized data set with a unified spatiotemporal benchmark; Based on a standardized dataset with a unified spatiotemporal benchmark, we identify and extract areas of spatial conflict over natural resource ownership. Based on the aforementioned spatial conflict areas, determine the topological intersection nodes of the administrative jurisdiction boundary of the cross-provincial enclave and the boundary of natural resource types, the centroid of the disputed overlapping area between the historical forest tenure certificate record boundary and the latest demarcation results of the ecological protection red line, and the characteristic area center where resource types have undergone substantial changes before and after the implementation of ecological restoration projects. A spatial topology analysis unit is constructed based on three spatial reference control points: the topological intersection node, the centroid of the disputed overlapping area, and the center of the feature area. The spatial topology analysis unit is divided into several circular arc sub-regions. An adjustment coefficient is obtained by curve fitting calculation and adjustment for each circular arc sub-region. Boundary accuracy correction parameters are generated based on the adjustment coefficient. The boundary geometric features of the spatial conflict area are optimized by the boundary accuracy correction parameters to obtain the optimized conflict area. By constructing an intelligent model for resolving ownership conflicts, the optimized conflict areas are automatically adjudicated to obtain the adjudication results; Based on the ruling, information on the confirmation and registration of rights for complex natural resource units will be generated; a mechanism for linking and updating natural resource dynamic monitoring and confirmation and registration information will be established.

2. The method for processing natural resource ownership registration information according to claim 1, characterized in that, Based on a standardized dataset with a unified spatiotemporal benchmark, areas of spatial conflict over natural resource ownership are identified and extracted, including: Spatial overlay analysis of ownership boundaries and ecological protection red lines in standardized datasets is conducted to identify areas where ownership and control requirements conflict. Based on ownership vector data and remote sensing image features in a standardized dataset, we can identify and extract areas where the right to use and the ownership of cross-provincial enclaves are separated. By comparing historical ownership data with current survey data in a standardized dataset, the disputed area of ​​Chahua Mountain boundary can be identified and located. By combining multi-temporal resource type data from a standardized dataset, ambiguous areas can be identified in relation to wetland and forest resource types. By integrating all identified areas with conflicting ownership and control requirements, areas where cross-provincial enclaves have separate use rights and ownership, areas with disputed boundaries of interspersed mountains, and areas with ambiguous identification of wetland and forest land resource types, a complete spatial conflict area for natural resource ownership is constructed.

3. The method for processing natural resource ownership registration information according to claim 2, characterized in that, Based on the aforementioned spatial conflict areas, determine the topological intersection nodes of the administrative jurisdiction boundaries of cross-provincial enclaves and the boundaries of natural resource types, the centroid of the disputed overlapping areas of the historical forest tenure certificate records and the latest demarcation results of the ecological protection red line, and the characteristic area centers where resource types have undergone substantial changes before and after the implementation of ecological restoration projects, including: Spatial topological relationship analysis is conducted on spatial conflict areas to extract the intersection points of cross-provincial enclave administrative jurisdiction boundaries and natural resource type boundaries as topological intersection nodes; Based on the latest demarcation results of the historical forest tenure certificate records and the ecological protection red line, spatial overlay analysis is conducted to calculate the geometric centroid of the disputed overlapping area; the geometric centroid of the disputed overlapping area is used as the centroid of the disputed overlapping area. By comparing resource type data before and after the implementation of ecological restoration projects, areas where resource types have undergone substantial changes are identified. The geometric center of the areas where substantial changes have occurred is calculated, and the geometric center is used as the center of the feature region.

4. The method for processing natural resource ownership registration information according to claim 3, characterized in that, A spatial topological analysis unit is constructed based on three spatial reference control points: the topological intersection node, the centroid of the disputed overlapping area, and the center of the characteristic area. The spatial topological analysis unit is divided into several circular arc sub-regions. Curve fitting calculation and adjustment are performed on each circular arc sub-region to obtain an adjustment coefficient. Boundary accuracy correction parameters are generated based on adjustment coefficients. These parameters are then used to optimize the boundary geometric features of the spatial conflict region, resulting in the optimized conflict region, which includes: Using topological intersection nodes, centroids of disputed overlapping regions, and centers of characteristic regions as key control points, a spatial topological analysis unit is constructed to enclose the spatial conflict region. The boundary contours of the spatial topology analysis unit are discretized to divide them into continuous circular arc sub-regions; For each arc sub-region, a polygon simplification method is used to extract boundary feature points, identify and retain key geometric feature vertices; Based on key geometric feature vertices, a curve fitting method is used to reconstruct a smooth curve for each circular arc sub-region, generating a fitted boundary curve. Calculate the geometric deviation between the fitted boundary curve and the original boundary, and determine the boundary adjustment coefficient for each circular arc sub-region based on the deviation value; Integrate the boundary adjustment coefficients of all circular arc sub-regions to form a set of boundary accuracy correction parameters for overall boundary optimization; By using a boundary precision correction parameter set, the original boundary geometric features of the spatial conflict region are corrected and smoothed piece by piece to form an optimized conflict region with precise boundary definition.

5. The method for processing natural resource ownership registration information according to claim 4, characterized in that, By constructing an intelligent conflict resolution model, the optimized conflict areas are automatically adjudicated to obtain the adjudication results, including: A smart model for resolving ownership conflicts is constructed, which integrates an ownership priority rule base and a multi-source evidence chain weight allocation mechanism. An optimized conflict region with precise boundary definitions is input into the ownership conflict intelligent resolution model as a benchmark spatial framework for automatic adjudication. Within the baseline spatial framework, the historical ownership archives and current control requirements within the optimization conflict area are analyzed for spatiotemporal characteristics based on the ownership priority rule base, forming an ownership dataset with spatiotemporal feature labels. Based on the ownership dataset with spatiotemporal feature labels, a multi-source evidence fusion dataset is formed by integrating the boundary precision correction parameter set and the geometric feature analysis results of the optimized conflict region through a multi-source evidence chain weight allocation mechanism. Based on a multi-source evidence fusion dataset, preliminary arbitration results for cross-provincial ownership dispute areas are obtained through adaptive arbitration calculations of an intelligent ownership conflict resolution model. The preliminary ruling results are verified for cross-provincial consistency to form the final ownership ruling result.

6. The method for processing natural resource ownership registration information according to claim 5, characterized in that, Based on the ruling, information on the confirmation and registration of ownership of complex natural resource units will be generated; Establish a mechanism for linking dynamic monitoring of natural resources with information on confirmation and registration of property rights, including: Based on the final ownership ruling, within the scope of the precise boundary definition of the optimized conflict area, the ownership boundaries and resource type attributes of the composite natural resource unit are determined. Based on the ownership boundaries and resource type attributes, determine the ecological protection responsibility entities and land use control requirements for the corresponding natural resource units; Based on information on ownership boundaries, resource type attributes, and responsible entities, composite natural resource unit ownership registration information that meets the requirements of cross-provincial joint ownership confirmation is generated; Establish a two-way mutual recognition mechanism for registration of the place of entry and confirmation of the place of exit, so as to complete the cross-provincial collaborative verification and unified filing of the registration information and form a benchmark registration information database; Based on the benchmark registration information database, by combining multi-temporal satellite remote sensing images and dynamic monitoring data of ecological restoration, a dynamic monitoring system for natural resources is constructed, and a mechanism for detecting and early warning of changes in resource status is established. By using a resource status change detection and early warning mechanism, the dynamic changes in natural resources can be captured in real time, triggering a linkage update process for property rights registration information, so as to achieve continuous optimization and maintenance of the benchmark registration information database.

7. A natural resource ownership registration information processing system, wherein the system implements the method as described in any one of claims 1 to 6, characterized in that, include: The acquisition module is used to acquire complex, multi-source, heterogeneous natural resource data spanning provincial administrative regions. The processing module is used to perform coordinate benchmark unification and attribute structure standardization processing on the multi-source heterogeneous data to obtain a standardized data set with a unified spatiotemporal benchmark. The identification module is used to identify and extract areas of spatial conflict in natural resource ownership based on a standardized dataset with a unified spatiotemporal benchmark. The determination module is used to determine, based on the spatial conflict area, the topological intersection node of the administrative jurisdiction boundary of the cross-provincial enclave and the boundary of the natural resource type, the centroid of the disputed overlapping area of ​​the historical forest tenure certificate record boundary and the latest delineation results of the ecological protection red line, and the characteristic area center of the resource type that has undergone substantial changes before and after the implementation of the ecological restoration project. The optimization module is used to construct a spatial topology analysis unit based on three spatial reference control points: the topological intersection node, the centroid of the disputed overlapping area, and the center of the feature area. The spatial topology analysis unit is divided into several circular arc sub-regions. By performing curve fitting calculation and adjustment on each circular arc sub-region, an adjustment coefficient is obtained. Based on the adjustment coefficient, boundary accuracy correction parameters are generated. The boundary geometric features of the spatial conflict area are optimized by the boundary accuracy correction parameters to obtain the optimized conflict area. The adjudication module is used to automatically adjudicate the optimized conflict areas by constructing an intelligent model for resolving ownership conflicts, so as to obtain the adjudication results; A module is established to generate registration information for the ownership of complex natural resource units based on the adjudication results; Establish a mechanism for the dynamic monitoring of natural resources and the linkage and updating of information on property rights registration.

8. A natural resource ownership registration information processing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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