A natural resource right confirmation registration management system based on big data

Abstract

The application relates to the technical field of resource management, and particularly discloses a natural resource right confirmation registration management system based on big data, and particularly relates to the technical field of resource management. The system comprises an infrastructure layer, a data layer, a platform layer and an application layer. The infrastructure layer is used for providing IT resources for the natural resource right confirmation registration management system. The data layer takes natural resource right confirmation registration result data as the core, and uniformly manages and uploads the data. The platform layer is used for standardized service publishing and safety control. The application layer encapsulates two-dimensional and three-dimensional data services of the platform layer into a business interface, and integrates a level evaluation, a cross-channel diagram and city and county scheduling into a one-screen decision-making portal. The application considers standard unification, two-dimensional and three-dimensional integration, quality measurability and scheduling executability, reduces management cost caused by false positives / missed reports and inconsistent cross-period indicators, and improves overall efficiency, data reliability and decision-making timeliness.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, and more specifically, to a natural resource ownership registration and management system based on big data. Background Technology

[0002] Unified registration and confirmation of natural resource rights is a crucial foundation for clarifying resource property rights and advancing ecological civilization system reform and modern governance. The needs arising during implementation, such as "land registration survey results data, registration database, 3D display and statistical analysis," require the platform to achieve integrated 2D and 3D management, spatial analysis, and comprehensive display under unified standards, interfaces, and security, ultimately providing data support for management decisions. However, existing systems often remain at the "display and query" interface, lacking a hierarchical evaluation and scheduling dashboard that integrates progress and quality maturity, and cannot directly support unified coordination and process correction at multiple levels from "city-district / county-result package."

[0003] Therefore, there is an urgent need for a big data mining and analysis system that couples the "confirmation and registration database" with "multi-dimensional progress features" to form an operable Gantt chart progress view and risk warning, so as to achieve a closed loop for "assisting decision-making". Summary of the Invention

[0004] To overcome the aforementioned deficiencies in existing technologies, this invention provides a natural resource ownership registration and management system based on big data. This system employs a layered architecture of "infrastructure layer—data layer—platform layer—application layer," deeply coupling the ownership registration database with process characteristics such as progress and quality. Furthermore, by constructing a feature library and training a risk assessment model, it generates risk levels, providing auxiliary decision-making support for unified city-level management and scheduling, thereby addressing the problems raised in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A natural resource ownership registration and management system based on big data includes:

[0007] The infrastructure layer provides IT resources for the natural resource ownership registration and management system.

[0008] The data layer, with the data of natural resource ownership confirmation and registration as its core, will be uniformly managed and uploaded;

[0009] The platform layer is used for standardized service publishing and security management.

[0010] The application layer encapsulates the platform layer's 2D and 3D data services into a business interface, and integrates "level assessment + Gantt chart + city and county scheduling" into a single-screen decision entry point.

[0011] The application layer also includes a rights confirmation and assessment module, which is used to build a feature library, train a rights assessment model, and provide auxiliary decision-making for unified management and scheduling at the city level, including the following steps:

[0012] Y1 enables one-click quality inspection and report generation by pre-setting rules for various registration units. After completing standardized data entry and traceability review, the data is incorporated into resource services. Then, through integrated management, the registration units are associated with weight and adjustment attributes and statistical data. Finally, the data is aggregated at the result package level to build a feature library.

[0013] Y2, based on the feature library training level evaluation model, adopts two parallel approaches to verify each other, and forms a comprehensive score and a risk score, which are then unified as a composite score;

[0014] Y3, the composite score is mapped to three risk levels under a stable threshold;

[0015] Y4, based on the three risk levels mentioned above, in the unified management and scheduling view after logging in with the "city-level account", each result package of the city and each district and county is divided into horizontal bars. Figure 3 The timeline displays the planned, actual, and projected progress, and uses color coding to directly overlay registration and level information. Managers can filter and download abnormal nodes by administrative region and deliverable package.

[0016] As a further aspect of this invention, the infrastructure layer provides IT resources for the natural resource ownership registration and management system, including the following specific components: The infrastructure layer includes servers, switches, firewalls, routers, intrusion detectors, disk arrays, and other devices. The servers provide core computing and application support, serving as the operating platform for business and server-side components; the switches, as network aggregation devices, facilitate high-speed interconnection between servers and storage / security devices, forming the foundation of the "network resources"; the routers enable Layer 3 forwarding and egress connectivity between different network segments / security domains, supporting cross-network communication and external interconnection; the intrusion detectors monitor and alert on abnormal behavior and suspicious access, supporting rapid investigation of potential attacks and violations; the disk arrays, as the core of the "storage resources," provide centralized storage, capacity expansion, and backup / recovery linkage capabilities, supporting the reliable persistence of business and spatial data; the other devices provide necessary data center and maintenance support (such as monitoring and auditing) for the aforementioned computing, storage, and network operations, collectively constituting the IT support environment for system operation.

[0017] As a further aspect of the present invention, the data layer, with the natural resource ownership confirmation and registration results data as its core, uniformly manages and uploads it, including the following specific contents: the base data of the data layer consists of basic geographic information data, natural resource ownership confirmation results data, and regional oblique photography data, providing direct support and a unified source for the two-dimensional and three-dimensional integration of ownership confirmation and registration work.

[0018] The data layer also performs data aggregation and quality inspection before data is stored, specifically including the following steps:

[0019] S1 performs a data integrity check.

[0020] S2 performs data standardization checks, focusing on two aspects: "mathematical foundation" and "topological relationships".

[0021] S3 performs a data logic check, covering consistency in structure, code, numbering, charts, and results.

[0022] As a further aspect of this invention, the platform layer, used for standardized service publishing and security management, includes the following specific components: The platform layer comprises idesktopX, iserver, and a natural resource rights registration information management platform. idesktopX handles desktop-level tasks such as data editing, data import / export, data conversion, graphic editing, and attribute querying, producing 2D / 3D maps and data resources for subsequent service publishing and 3D scene display. iServer handles service publishing and standardized external provision: providing OGC-compliant APIs / services, 3D scene services, and data query services, allowing upper layers to access 2D / 3D maps and data via standard interfaces. The natural resource ownership registration information management platform includes: 1. Basic functions: data editing, import / export, data conversion, graphic editing, and attribute query; 2. Dynamic data entry: supports "one-click quality inspection," and creates and loads the ownership registration database after passing the quality inspection; 3. Data quality inspection: allows configuration of inspection rules for various registration unit data, covering consistency and compliance checks of the results data; 4. Integrated management: associates registration units with various ownership attribute data and statistical data, and provides spatial and attribute editing; 5. Map output: supports map display and one-click output of registration units, and allows for hierarchical setting of the visible range; 6. Data update: supports modification and editing of the space and attributes of registration units, and directly pushes the data to the database after approval, updating and overwriting the original data.

[0023] As a further aspect of this invention, the application layer encapsulates the 2D and 3D data services of the platform layer into a business interface, and integrates them into a single-screen decision-making entry point using "level assessment + Gantt chart + city / county scheduling," including the following specific contents: The application layer includes a layer directory, layer control, resource retrieval, spatial positioning, and multi-source data overlay; its overlay is compatible with OGC standards (WMTS / WMS) and 3D scene services, ensuring cross-source availability and scene consistency. The application layer provides 3D roaming, split-screen / roll-up comparison, attribute query, classification statistics, and supports analyses such as no duplication / no omission, view span, slope and aspect, visible area, and excavation.

[0024] The technical effects and advantages of this invention, a natural resource ownership registration and management system based on big data, are as follows: This invention employs a layered architecture of "infrastructure layer—data layer—platform layer—application layer," deeply coupling the ownership registration database with process characteristics such as progress and quality. The bottom layer provides highly reliable computing, storage, and security protection using IT resources such as servers, switches, routers, intrusion detection, and disk arrays. The data layer, under unified coordinate and projection standards, integrates multi-source heterogeneous data such as vector / raster, oblique photography, BIM, and point clouds, and provides one-click quality inspection and trace entry for integrity, standardization, and logic, significantly improving data consistency and availability. The platform layer publishes OGC standard 2D and 3D services using iDesktopX and iServer, supporting standardized provisioning and cross-source interoperability. The application layer encapsulates spatial analysis, 3D scenes, and statistical capabilities. The system provides a unified dashboard integrating "level assessment + Gantt chart + city / county scheduling": By constructing a feature library and training a level assessment model, quality, topology, and process items are quantified into continuous risk scores. Combining weighted error costs and monthly replay optimization stability thresholds, outcome packages are divided into three levels: A, B, and C, achieving a closed loop from "display and query" to "assessment—early warning—rectification—reassessment." The management end uses a multi-level system of city-district / county-outcome package with color-coded level identifiers linked by a three-track Gantt chart (planned / actual / predicted). For high-risk level C, rectification orders are automatically issued with full traceability, promoting problem localization, timeliness tracking, and result verification. Overall, the system balances standardization, two-dimensional and three-dimensional integration, measurable quality, and executable scheduling, significantly reducing management costs caused by false alarms / missed alarms and inconsistencies in cross-period reporting, and improving overall efficiency, data credibility, and decision-making timeliness. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the structure of a natural resource ownership registration and management system based on big data according to the present invention.

[0026] Figure 2 This is a schematic diagram of data quality inspection and warehousing in this invention.

[0027] Figure 3 This is a detailed diagram illustrating errors in the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1

[0030] See Figure 1The structural diagram shown illustrates a natural resource ownership registration and management system based on big data, comprising:

[0031] The infrastructure layer provides IT resources for the natural resource ownership registration and management system.

[0032] The data layer, with the data of natural resource ownership confirmation and registration as its core, will be uniformly managed and uploaded;

[0033] The platform layer is used for standardized service publishing and security management.

[0034] The application layer encapsulates the platform layer's 2D and 3D data services into a business interface, and integrates "level assessment + Gantt chart + city and county scheduling" into a single-screen decision entry point.

[0035] Furthermore, the infrastructure layer, used to provide IT resources for the natural resource ownership registration and management system, includes: servers, switches, firewalls, routers, intrusion detectors, disk arrays, and other devices. The servers provide core computing and application support (such as high-performance application servers and map caching strategies to improve the efficiency of complex map rendering and access), serving as the operating platform for business and server-side components; the switches, as aggregation network devices, facilitate high-speed interconnection between servers, storage, and security devices, forming the foundation of "network resources"; the routers enable Layer 3 forwarding and egress connectivity between different network segments / security domains, supporting cross-network communication and external interconnection; the intrusion detectors monitor and alert on abnormal behavior and suspicious access, supporting rapid investigation of potential attacks and violations; the disk arrays, as the core of "storage resources," provide centralized storage, capacity expansion, and backup / recovery linkage capabilities, supporting the reliable persistence of business and spatial data; the other devices provide necessary data center and maintenance support (such as monitoring and auditing) for the aforementioned computing, storage, and network operations, collectively constituting the IT support environment for system operation.

[0036] Furthermore, the data layer, centered on the natural resource ownership registration results data, centralizes and uploads this data. This includes: the base data of the data layer, composed of basic geographic information data, natural resource ownership registration results data, and regional oblique photogrammetry data, providing direct support and a unified source for the two-dimensional and three-dimensional integration of ownership registration work. In terms of data item organization, the data layer not only carries traditional vector / raster data but also incorporates multi-source heterogeneous data such as oblique photogrammetry, BIM, and laser point clouds, using an integrated two-dimensional and three-dimensional data model to support the integrated expression and analysis needs of outdoor / indoor and macro / micro perspectives.

[0037] The data layer also performs data aggregation, quality inspection, and data entry. Figure 2 The diagram shows the data quality inspection and entry process, which includes the following steps:

[0038] S1, Perform a data integrity check: Check whether the "electronic results, document results, map results" and other materials are complete, and also check whether the "spatial and non-spatial elements" in the electronic results are complete. Compare them item by item with the survey forms, maps, approval documents, and original materials to ensure that the materials are complete and meet the submission / database standards.

[0039] S2, perform data standardization checks, focusing on two aspects: "mathematical foundation" and "topological relationship": the plane coordinate system adopts the 2000 National Geodetic Coordinate System, the elevation system adopts the 1985 National Elevation Datum, and the projection method complies with the "Technical Regulations for Natural Resources Cadastral Survey"; at the same time, check whether there is overlap between point / line / area feature layers, whether they are closed, and whether the topological reference tolerance meets the standards, and review whether there is improper overlap between different natural resource patches and various layers (including disputed layers).

[0040] S3, perform data logic checks, covering structural consistency, code consistency, unique numbering, chart consistency, and result consistency: verify the length, decimal places, quantity, name, type, and field values ​​of attribute fields item by item; perform code table consistency verification for code-type fields (including "element codes"); perform uniqueness verification for each item of natural resource registration unit number, its branch number, public control zone number, and boundary point unified number; check the consistency of distribution range and attribute information according to "within layer - between layers - between charts", and verify whether the associated control information, resource type information, and original material list are consistent; also, based on the "registration unit number", compare the boundary description table, survey record table, result verification table, and post-entry database table information item by item to confirm that the submitted results are consistent at the three levels of "spatial - non-spatial - merged results".

[0041] In this embodiment, as Figure 3 As shown, right-clicking the error message will bring up a pop-up window where you can perform operations such as "Exception", "Cancel Exception", "Export Current Errors", "Export All Errors", or "Copy Error Messages". Selecting "Export Error Messages" will export the error message to Excel.

[0042] Furthermore, the platform layer, used for standardized service publishing and security management, includes: idesktopX, iserver, and the Natural Resources Rights Registration Information Management Platform. idesktopX handles desktop-level tasks such as data editing, data import / export, data conversion, graphic editing, and attribute querying, producing 2D / 3D maps and data resources for subsequent service publishing and 3D scene display. During the data preparation phase, idesktopX supports the fusion processing of integrated 2D / 3D data and multi-source heterogeneous data (such as oblique photography, BIM, and laser point clouds), providing compliant and publishable data results for 3D display and spatial analysis at the application layer. iServer handles service publishing and standardized external provision: providing OGC-compliant APIs / services (such as WMTS and WMS), 3D scene services, and data query services, allowing upper layers to access 2D / 3D maps and data via standard interfaces. The natural resource ownership registration information management platform includes: 1. Basic functions: data editing, import / export, data conversion, graphic editing, and attribute query; 2. Dynamic data entry: supports "one-click quality inspection," and creates and loads the ownership registration database after passing the quality inspection; 3. Data quality inspection: allows configuration of inspection rules for various registration unit data, covering consistency and compliance checks of the results data; 4. Integrated management: associates registration units with various ownership attribute data and statistical data, and provides spatial and attribute editing; 5. Map output: supports map display and one-click output of registration units, and allows for hierarchical setting of the visible range; 6. Data update: supports modification and editing of the space and attributes of registration units, and directly pushes the data to the database after approval, updating and overwriting the original data.

[0043] Furthermore, the application layer encapsulates the 2D and 3D data services of the platform layer into a business interface, and integrates them into a single-screen decision-making entry point using "level assessment + Gantt chart + city and county scheduling". This includes: a layer directory, layer control, resource retrieval, spatial positioning, and multi-source data overlay; its overlay is compatible with OGC standards (WMTS / WMS) and 3D scene services, ensuring cross-source availability and scene consistency. The application layer provides 3D roaming, split-screen / roll-down comparison, attribute query, and classification statistics, and supports analyses such as no duplication / no omission, view span, slope and aspect, visible area, and excavation.

[0044] The application layer also includes a rights confirmation and assessment module, which is used to build a feature library, train a rights assessment model, and provide auxiliary decision-making for unified management and scheduling at the city level, including the following steps:

[0045] Y1 pre-sets completeness, correctness, and consistency check rules for various registration units, triggering one-click quality inspection and automatically generating quality inspection reports. Upon passing inspection, it creates a target library and loads data in batches according to source and target data sources, achieving standardized data entry and traceable verification. The entered data is incorporated into data resource services, providing layer catalogs and control, resource retrieval and spatial positioning, and multi-source overlay. It completes roaming measurements and classification statistics by registration unit or administrative region in a 3D scene, performing analyses such as non-duplication, no omissions, visibility, slope and aspect, line-of-sight, and excavation, accumulating basic statistics and spatial diagnostic features. Based on mathematical and topological relationships, the system performs procedural checks on coordinate benchmarks, projection methods, point-line-plane closure, and topological tolerance. Simultaneously, it generates calculable entries such as defect counts and proportions according to logical items like field structure, code tables, unique numbering, and chart consistency. Through integrated management, it links registration units with weighting attributes and statistical data, and relies on a data update-to-database mechanism to continuously accumulate process characteristics such as editing time to database coverage and update frequency, maintaining consistency in the timeline of features as business evolves. Finally, it aggregates these various features at the deliverable package level to construct a feature library.

[0046] Y2, based on a feature library-trained rating model, first performs missing value imputation and extremum removal on quality, topology, and management items of different dimensions, and performs rolling statistics by time window to suppress short-term fluctuations. Then, it adopts two parallel approaches for mutual verification: one is a rule-based interpretable scorecard, which maps elements such as integrity defect rate, topology conflict rate, number of logical consistency problems, update cycle fluctuation, and missing cross-table associations into sub-item scores, and determines the comprehensive score by combining expert weights and historical regression weights; the other is a lightweight supervised model for anomaly scoring, which uses the structural features of graph mining and the matching strength of similarity connections to perform group division and anomaly measurement, forming a probabilistic risk score. The two scores, after being scaled and unified, are used as a composite score for searching candidate thresholds for grading. Subsequently, threshold optimization and stability verification are performed, with "providing data support for decision-making" as the objective function. The optimization focuses on "the trade-off between false positive and false negative rates" and "whether the sample ratio of each level under the three-level grading meets management expectations." Replay tests are conducted over time to verify the stability of the thresholds within multiple monthly windows. When there are obvious seasonal or structural changes caused by batch data entry, thresholds are set for each region and the criteria are recorded to ensure horizontal comparability.

[0047] To simultaneously optimize false positives and false negatives and ensure that the three-level results meet management expectations, the continuous risk scores output by the aforementioned level assessment model are first probabilistically calibrated. Then, with "weighted error cost" as the target, threshold optimization is performed on a dataset where historical quality inspection and acceptance conclusions can be replayed: misclassifying low-risk as high-risk is considered a false positive, and misclassifying high-risk as low-risk is considered a false negative. Higher weights are given based on the principle that the management side is more sensitive to false negatives. A combination of grid search and time window replay is used to calculate the weighted error rate and monthly stability index for each candidate threshold combination. At the same time, a "tiered proportion constraint" is introduced, that is, without sacrificing the key error cost, the sample proportions of level A, B, and C fall within a reasonable range set by management. If a structural change occurs in a certain month (such as centralized batch warehousing or seasonal operations causing distribution migration), thresholds are set separately by region or period and versions are recorded while maintaining traceability to ensure horizontal comparability and result consistency.

[0048] Y3, the composite score, is mapped to three risk levels under a stable threshold: Level A indicates that the core quality and topology items meet the standards and the schedule and process items are stable; Level B indicates that there are slight deviations but the overall situation is controllable; Level C indicates that there are significant risks in terms of quality or schedule, requiring immediate rectification and overall planning. The mapping process is as follows: First, using real-world processes supported by the platform, such as "one-click quality inspection, quality inspection reports, and batch warehousing," the verification items such as completeness, correctness, and consistency are precipitated as measurable features, serving as the quality baseline for scoring. Simultaneously, layer services, spatial positioning, 3D scene overlay, and statistical analysis capabilities are utilized to form quantitative evidence of the relationship between progress and space, serving as contextualized input for scoring. Then, these features are fed into a grade assessment model to obtain continuous risk scores, and monthly playback is performed using high-performance spatiotemporal data services and visualization support to optimize two stable thresholds, ensuring that the weighted cost of false alarms and missed alarms reaches the management objective, and that the graded proportion falls within the management expectation range. Finally, after determining the thresholds, deliverable packages with a comprehensive score greater than the upper threshold are labeled as Grade A, indicating stable quality and progress with satisfactory spatial consistency; those between the upper and lower thresholds are labeled as Grade B, indicating controllable deviations requiring monitoring; and those below the lower threshold are labeled as Grade C, indicating significant quality or progress risks requiring immediate rectification.

[0049] Y4, based on the three risk levels mentioned above, in the unified management and scheduling view after logging in with the "city-level account", each result package of the city and each district and county is divided into horizontal bars. Figure 3The system displays planned, actual, and projected progress on a timeline, using color coding to directly overlay registration and grading information. Grade A indicates on-time performance and stable quality; Grade B indicates controllable deviations; and Grade C indicates significant risk. Managers can filter and download abnormal nodes by administrative region deliverable packages. Once a package is classified as Grade C, the high-risk package is automatically added to the rectification list. The system generates a rectification order, assigning responsible units and deadlines. Simultaneously, the sidebar links to quality inspection points and spatial verification evidence to pinpoint the source of the problem. The entire rectification process, including the issuance time, response time, completion time, and review results, is fully tracked, and the Gantt chart updates in real-time with actual progress and review conclusions.

[0050] This invention employs a layered architecture of "infrastructure layer—data layer—platform layer—application layer," deeply coupling the land registration database with process characteristics such as progress and quality. The bottom layer provides highly reliable computing, storage, and security protection through IT resources such as servers, switches, routers, intrusion detection, and disk arrays. The data layer, under unified coordinate and projection standards, integrates multi-source heterogeneous data such as vector / raster, oblique photography, BIM, and point cloud, with one-click quality inspection and traceable data entry for integrity, standardization, and logical consistency, significantly improving data consistency and availability. The platform layer publishes OGC standard 2D and 3D services using iDesktopX and iServer, supporting standardized provisioning and cross-source interoperability. The application layer encapsulates spatial analysis, 3D scenes, and statistical capabilities into "level assessment + Gantt chart + city / county scheduling." The system features a one-screen dashboard: by building a feature library and training a level assessment model, it quantifies quality, topology, and process items into continuous risk scores. Combining weighted error costs and monthly replay optimization stability thresholds, it categorizes outcome packages into three levels: A, B, and C, achieving a closed loop from "display and query" to "assessment—early warning—rectification—reassessment." The management end uses a multi-level system—city, district / county, and outcome package—with a three-track Gantt chart linked to color coding for planned / actual / predicted risk levels. For high-risk level C, it automatically issues rectification orders and leaves a complete record, promoting problem identification, timeliness tracking, and result verification. Overall, the system balances standardization, two-dimensional and three-dimensional integration, measurable quality, and executable scheduling, significantly reducing management costs caused by false alarms / missed alarms and inconsistencies in reporting across periods, and improving overall efficiency, data credibility, and decision-making timeliness.

[0051] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0052] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A natural resource ownership registration and management system based on big data, characterized in that, include: The infrastructure layer provides IT resources for the natural resource ownership registration and management system. The data layer, with the data of natural resource ownership confirmation and registration as its core, will be uniformly managed and uploaded; The platform layer is used for standardized service publishing and security management. The application layer encapsulates the platform layer's two-dimensional and three-dimensional data services into a business interface, and integrates "level assessment + Gantt chart + city and county scheduling" into a single-screen decision entry point. The application layer also includes a rights confirmation and assessment module, which is used to build a feature library, train a rights assessment model, and provide auxiliary decision-making for unified management and scheduling at the city level, including the following steps: Y1 enables one-click quality inspection and report generation by pre-setting rules for various registration units. After completing standardized data entry and traceability review, the data is incorporated into resource services. Then, through integrated management, the registration units are associated with weight and adjustment attributes and statistical data. Finally, the data is aggregated at the result package level to build a feature library. Y2, based on a feature library-trained rating model, employs two parallel approaches for mutual verification, resulting in a comprehensive score and a risk score, which are then unified as a composite score. The first approach is a rule-based interpretable scorecard that maps elements such as integrity defect rate, topological conflict rate, number of logical consistency issues, update cycle fluctuations, and missing cross-table associations into sub-scores, and determines the comprehensive score through a combination of expert weights and historical regression weights. The second approach is a lightweight supervised model for anomaly scoring, which utilizes the structural features of graph mining and the matching strength of similarity connections to perform group division and anomaly measurement, forming a probabilistic risk score. Y3, the composite score is mapped to three risk levels under a stable threshold; Y4. Based on the three risk levels, in the unified management and scheduling view after logging in with the "city-level account", the planned, actual and predicted progress of each result package of the city and each district and county is displayed on the three time tracks of the Gantt chart, and the registration and level information are directly overlaid with color codes. Managers can filter and download abnormal nodes by administrative region and result package. In Y3, the mapping process is as follows: First, using the platform-supported "one-click quality inspection, quality inspection report, and batch warehousing" real process, the integrity, correctness, and consistency verification items are precipitated as measurable features, serving as the quality baseline for scoring. Simultaneously, layer services, spatial positioning, 3D scene overlay, and statistical analysis capabilities are used to form quantitative evidence of the relationship between progress and space, serving as the scenario-based input for scoring. Then, the above features are fed into the grade assessment model to obtain continuous risk scores, and monthly playback is performed using spatiotemporal data services and visualization support to optimize two stable thresholds, ensuring that the weighted cost of false alarms and missed alarms reaches the management target, and that the graded proportion falls within the management expectation range. Finally, after determining the thresholds, deliverable packages with a comprehensive score greater than the upper threshold are labeled as Grade A, indicating stable quality and progress with satisfactory spatial consistency; those between the upper and lower thresholds are labeled as Grade B, indicating controllable deviations requiring tracking; and those below the lower threshold are labeled as Grade C, indicating significant quality or progress risks requiring immediate rectification.

2. The natural resource ownership registration and management system based on big data as described in claim 1, characterized in that, The infrastructure layer includes servers, switches, firewalls, routers, intrusion detectors, disk arrays, and data center maintenance equipment; Servers provide computing and application support, switches enable high-speed interconnection of devices, routers support cross-network communication, intrusion detectors monitor abnormal access, disk arrays provide centralized storage and backup, and maintenance equipment ensures the operation of the IT environment.

3. The natural resource ownership registration and management system based on big data as described in claim 1, characterized in that, The base data of the data layer includes basic geographic information data, natural resource ownership confirmation results data, and regional oblique photography data. It integrates multi-source heterogeneous data such as vector / raster, oblique photography, BIM, and laser point cloud to support integrated two-dimensional and three-dimensional expression and analysis.

4. The natural resource ownership registration and management system based on big data as described in claim 1, characterized in that, The data quality inspection of the data layer supports error handling: after quality inspection, an error list is generated, and "exception", "cancel exception", "export error" and "copy error information" operations are performed on the errors. The exported error information is in Excel format.

5. The natural resource ownership registration and management system based on big data according to claim 1, characterized in that, The platform layer includes idesktopX, iServer, and the Natural Resources Ownership Registration Information Management Platform; idesktopX is responsible for data editing, import / export, conversion, graphic editing, attribute querying, and multi-source heterogeneous data fusion processing. The iServer publishes APIs / services, 3D scene services, and data query services that conform to OGC standards. The natural resource ownership registration information management platform provides basic functions, dynamic data entry, quality inspection, integrated management, map output, and data update functions.

6. The natural resource ownership registration and management system based on big data according to claim 1, characterized in that, The "one-click quality inspection" function of the natural resource ownership registration information management platform: configure inspection rules to cover the consistency and compliance of registration unit data, and create and load the ownership registration database after the quality inspection is passed; the "data update" function supports the modification of registration unit space and attributes, and pushes the data directly to the database to overwrite the original data after the review is approved.

7. The natural resource ownership registration and management system based on big data according to claim 1, characterized in that, The application layer includes a layer directory, layer control, resource retrieval, spatial positioning, and multi-source data overlay. It provides 3D roaming, split-screen / roll-up comparison, attribute query, and classification statistics, and supports non-duplication / non-omission, line-of-sight, slope and aspect, visible field, and excavation analysis.

8. The natural resource ownership registration and management system based on big data according to claim 1, characterized in that, The integrated two-dimensional and three-dimensional data model of the data layer supports integrated expression and analysis of outdoor / indoor and macro / micro views, meeting the spatial analysis and comprehensive display needs of property rights registration.

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