Natural resource right confirmation registration method and system

Abstract

The application provides a natural resource right registration method and system, wherein the method comprises the following steps: obtaining a file, identifying the data format of the file by executing a signature algorithm, parsing a vector image and text content according to the data format to obtain structured information; performing format unification and field cleaning on the structured information, generating a plan structure according to geographical elements embodied by the vector image, performing topological automatic checking based on the plan structure, repairing topological errors by adopting node capture based on geometric tolerance, and obtaining standardized information; storing the standardized information in a database in batches and completing consistency checking; performing business logic automatic processing based on the standardized information, clipping all vector images corresponding to the original space by using a registration unit to obtain a natural resource patch map, performing area calculation, attribute information assignment and boundary point generation based on the natural resource patch map, and obtaining analysis results; and collecting the analysis results and the standardized information to form registration results.

Description

Technical Field

[0001] This invention relates to the field of cross-management technology of natural resources, and in particular to a method and system for confirming and registering natural resource rights. Background Technology

[0002] Unified registration of natural resource ownership is a fundamental task for clarifying the ownership of natural resource assets and establishing a property rights system. Its core lies in the precise definition and registration of ownership of natural ecological spaces such as watercourses, forests, wetlands, and grasslands. This work is characterized by its diverse data sources, wide range of origins, and strict standards. It involves the integration, processing, and analysis of heterogeneous data from multiple sources, including remote sensing imagery, survey information, and registration attributes, ultimately generating registration information that conforms to unified standards and serves as the sole basis for legal registration, certification, and property rights management.

[0003] In related technologies, the actual business of natural resource ownership registration generally adopts a semi-automated operation mode. During data preprocessing and coordinate transformation, operators manually unify the coordinates of vector image data from different sources using GIS software such as ArcGIS and QGIS. This requires manual setting of transformation parameters, has weak batch processing capabilities, and is prone to accuracy loss due to parameter setting errors. In other format conversion and data processing, GIS software or CAD data is used for data format conversion, and tools such as Excel are used to organize attribute tables to extract attribute information. The database involves complex layer structures, field types, and value ranges, and the data import process requires… In comparison with multiple mapping relationships, manual operation is prone to omissions or deviations, and related errors are difficult to detect and correct in a timely manner. In the actual business logic application process, it is necessary to combine and overlay registration units, land category patches, ownership zones, etc. When calculating the area of ​​natural resource types within a registration unit, geometric calculation methods are often used. Geometric calculation methods have problems such as rounding errors and differences in data accuracy, which often result in a closure difference between the sum of each natural resource patch and the total area of ​​the registration unit. The operators distribute the difference among each patch using the area ratio allocation method. The adjustment principles and execution scale vary from person to person, resulting in the area data processed by different projects or different personnel not being completely consistent, which seriously affects the authority of the data.

[0004] Based on the above analysis of the development status of this technology field, the existing technology lacks the ability to construct an automated data processing workflow that includes data access, processing and output of results, and to set standards to eliminate subjective errors in data integration, attribute linking, area adjustment and map production. In particular, the adjustment process solves the optimal solution by establishing a set of constraint equations. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for confirming and registering natural resource rights, in order to solve the above-mentioned problems in the prior art.

[0006] According to a first aspect of the present invention, a method for confirming and registering natural resource rights is provided, comprising: The system acquires files including registration units and original space, executes a signature algorithm to identify the file's data format, and parses vector images and text content based on the data format to obtain structured information. The structured information is formatted and cleaned, and a planar map structure is generated based on the geographic features represented by the vector image. The planar map structure is used for automated topology checking, and node capture based on geometric tolerance is used to repair topology errors, resulting in standardized information. Standardized information is stored in the database in batches and consistency checks are performed. Based on standardized information, business logic is automatically processed. The registration unit is used to crop all vector images corresponding to the original space to obtain cropped images. Based on the cropped images, area calculation, attribute information assignment and boundary point generation are performed to obtain analysis results. The analysis results and standardized information are collected to form the registration results.

[0007] According to a second aspect of the present invention, a natural resource ownership registration system is provided, comprising: The convergence sensing module is used to acquire files including registration units and original space, execute signature algorithms to identify the data format of the files, and parse vector images and text content according to the data format to obtain structured information; The standardization module is used to unify the format and clean the fields of structured information, generate a planar map structure based on the geographic features represented by the vector image, perform automated topology checks based on the planar map structure, and use node capture based on geometric tolerance to repair topology errors to obtain standardized information. The data writing module is used to store standardized information into the database in batches and perform consistency checks. The business logic execution module is used to automatically process business logic based on standardized information. It uses the registration unit to crop all vector images corresponding to the original space to obtain cropped images. Based on the cropped images, it performs area calculation, attribute information assignment, and boundary point generation to obtain analysis results. The results generation module is used to collect analysis results and standardized information to form registration results.

[0008] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the natural resource ownership registration method provided in the first aspect of the present disclosure.

[0009] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which an information transmission implementation program is stored, which, when executed by a processor, implements the steps of the natural resource ownership registration method provided in the first aspect of the present disclosure.

[0010] The technical solution provided by this invention has the following beneficial effects: it constructs an automated data processing workflow that includes data access, processing, and output, shortening the project cycle from several weeks to several days or hours, fundamentally solving the bottleneck of generation efficiency; it eliminates subjective errors in data integration, attribute linking, area adjustment, and map production by setting standards, ensuring the consistency and compliance of the output quality; and it creates a standardized and reusable technical process framework that can meet the changing needs of different regions and projects, ensuring the comparability of different project results, and realizing version control and traceability in the processing process, thereby improving the standardization and normalization of project management. It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of the natural resource ownership registration method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of data aggregation sensing according to an embodiment of the present invention; Figure 3 This is a schematic diagram of standardized cleaning according to an embodiment of the present invention; Figure 4 This is a schematic diagram of data entry in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the automation of business logic according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the rights registration framework according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the natural resource ownership registration system according to an embodiment of the present invention; Figure 8 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0014] Method Example 1 According to embodiments of the present invention, a method for confirming and registering natural resource rights is provided. Figure 1 This is a flowchart of the natural resource ownership registration method according to an embodiment of the present invention, such as... Figure 1 As shown, the natural resource ownership registration method according to an embodiment of the present invention specifically includes: In step S110, a file including the registration unit and the original space is acquired, a signature algorithm is executed to identify the file's data format, and vector images and text content are parsed according to the data format to obtain structured information, specifically including: This step enables automated information extraction without requiring prior knowledge of the specific details of the data.

[0015] The documents include information about the registration unit and its original, intact space, and the information types include vector images and text content.

[0016] (1) Data format recognition Use a binary signature algorithm as the signature algorithm; The signature algorithm extracts the first preset length bytes of the file, usually 4-512 bytes. These preset length bytes are then matched with a pre-defined format rule database, which is a file magic number database, to determine the underlying container format. For example, 0x4949 or 0x4D4D is identified as the TIFF base format. Preferably, more complex structure analysis using existing technology confirms that it is GeoTIFF, and 0x504B0304 is identified as the ZIP compression format. This leads to the inference that it may be SHP, GDB, or DWG, etc. The binary signature result is obtained as the data format, i.e., the recognition result described above; Preferably, file extension auxiliary verification is performed, and the file extensions such as .shp, .tif, and .dwg are cross-validated with the binary signature result. If they are inconsistent, the binary signature result shall prevail, and a warning message shall be recorded.

[0017] If a binary signature result cannot be obtained, the data format is obtained through regular expression parsing. For example, for text formats without a significant binary signature, such as CSV and GML, regular expression parsing is performed, for example, by checking whether the file contains... <gml:> 、 <featurecollection>We use tags to identify GML; we analyze whether the first row contains comma-separated column headers to identify CSV.

[0018] (2) Coordinate system identification Extract other content after parsing the data format; Obtain vector images of labeled land parcel layers, ownership boundary layers, special feature layers, and administrative boundary layers, as well as text content; Land cover maps represent land cover, which will be further classified and identified as resource types; ownership boundaries are the legal boundaries of land ownership or use rights; special elements are the divisions of special management settings or preset methods; administrative boundaries are the boundaries of the jurisdiction of governments at all levels. Each layer describes the same area from different perspectives.

[0019] After parsing the data format, attempt to parse the coordinate system information in the vector image, i.e., the coordinate system type, and prioritize reading the coordinate system information from standard locations such as GeoKey in GeoTIFF and .prj files in SHP. When coordinate system information is missing or damaged, the coordinate system information is inferred from the range of coordinate values. The inference is mainly based on the data content. The range of data coordinate values ​​is analyzed. If the X coordinate value is in the range of [-180, 180] and the Y coordinate value is in the range of [-90, 90], it is inferred to be the geographic coordinate system (GCS). If the X / Y coordinate values ​​are large numbers of 6-8 digits (such as 500000, 4000000), it is inferred to be the projected coordinate system (PCS).

[0020] If the coordinate system information obtained through inference is a projected coordinate system, such as UTM or Gauss-Kruger, the coordinate value range is matched with the common typical zone range of the projected coordinate system, and the zone range corresponding to the projected coordinate system and the corresponding prediction confidence value are output, providing the most likely candidate list of coordinate systems. It should be noted that when a coordinate system conjecture with confidence is used as output, an additional threshold needs to be set or a manual intervention interface needs to be provided.

[0021] (3) Character encoding recognition Based on text-based content, such as DBF attribute tables like CSV or GML, an n-gram statistical model is used to predict the probability of the text appearing under different character encodings. In this embodiment of the invention, n takes the value 2 or 3, with a default value of n=2. It can be dynamically adjusted according to the data complexity. Multi-encoding classification is performed in combination with byte frequency distribution to obtain character encoding recognition results. The n-gram statistical model can be represented using Equation 4: Formula 4; in, This indicates character encoding types such as UTF-8 and GBK. This represents the byte sequence to be detected. This represents the posterior probability of the encoding given the data. The likelihood, or probability, represents the probability of this byte sequence appearing under this encoding. It is the result calculated by the n-gram model, for example, the probability of this byte sequence appearing under UTF-8 encoding. This represents the prior probability of the encoding. For example, the probability of GBK encoding may be higher in a Chinese environment. The denominator is the normalization factor of all possible encodings.

[0022] (4) Attribute information recognition For most vector data, the schema of the attribute table in the text information is extracted and parsed based on the character encoding recognition results, and used as the attribute information. If there is no attribute table, such as in non-standard structured text, the text information is sampled and parsed line by line, and the attribute fields and corresponding values ​​are inferred based on the structural characteristics, which are then used as the attribute information. The specific process is as follows: For each column, samples are taken, and existing technologies are used to parse the values ​​into types such as Integer, Float, and Date. The structural characteristics are inferred using regular expressions and statistical distributions. Examples are as follows: Integer: ^[+-]?\d+$, indicating that it may contain positive or negative signs, and is a string of numbers.

[0023] Floating-point type: ^[+-]?\d+(\.\d+)?([eE][+-]?\d+)?$, which indicates that it may have a plus or minus sign, is a string of numbers, and can optionally include a decimal part and a scientific notation part.

[0024] Date type: Matching is performed using date patterns, such as YYYY-MM-DD, YYYY / MM / DD, MM-DD-YYYY, MM / DD / YYYY, combined with range validation.

[0025] The preferred approach is to infer business semantics based on the content of attribute fields using keyword matching and rule base matching, which can be used together as a mapping of attribute content.

[0026] (5) Structured information storage The obtained coordinate system information, character encoding recognition results and attribute information are encapsulated into a metadata object. In this embodiment of the invention, it is encapsulated into a structured JSON or XML format metadata object. In this embodiment of the invention, metadata objects are associated with the original files, that is, with the corresponding vector images and text. Two methods are used to associate metadata objects with files: First, they are stored as independent files in the project metadata directory, with the file name consistent with the original file, and associated with the index through "data ID"; Second, they are written as hidden attributes in the extended fields of the original file, such as GeoKey in GeoTIFF and metadata fields in SHP, to ensure that metadata and data entities are transmitted and managed synchronously. This process is used to structurally encapsulate the metadata output from the preceding process and establish a connection with the original data, serving as the data foundation for subsequent applications. Preferably, based on the recognition results of the structured information, a temporary standardized intermediate data view is automatically created, which can be in memory or cache, to directly decode the structured information for subsequent steps to call directly. This intermediate data view retains a reference pointer to the original file, realizing the synchronous flow of data combined with metadata.

[0027] Figure 2 This is a schematic diagram of data aggregation sensing according to an embodiment of the present invention, such as... Figure 2 As shown, this demonstrates the automated and accurate identification of structured data.

[0028] In step S120, the structured information is formatted and cleaned, and a planar map structure is generated based on the geographic features represented by the vector image. Automated topology checking is performed based on the planar map structure, and topology errors are corrected using node capture based on geometric tolerance to obtain standardized information. Specifically, this includes: (1) Standardized format and coordinate transformation Heterogeneous vector images in structured information are converted into a unified data format. Native spatial data formats such as SHP, GDB, and CAD are converted into standards to define unified geometric types and memory storage structures. For example, the unified data format stipulates that all coordinates are stored in double-precision floating-point type. Since the attribute information has been unified after the information is extracted, it does not need to be processed in this step. Perform batch coordinate transformation on coordinate system information in structured information; In this embodiment of the invention, Formula 5 is used to perform batch coordinate transformation on non-CGCS2000 coordinate system data using a seven-parameter Bursa model: Formula 5; in, This indicates the coordinate system information after transformation. Indicates the translation parameter. Indicates the scale factor. Represents the rotation matrix. Indicates the rotation angle parameter. This indicates the original coordinate system information.

[0029] (2) Cleaning of irrelevant field information and normalization of attributes The process iterates through the attribute fields in the structured information and compares them with a preset whitelist of fields. The whitelist is based on experience in the field. Any attribute field not in the whitelist is removed. This process cleans the source data of temporary fields, intermediate status identifiers, software-specific metadata, and other redundant information that is not related to the business logic of rights registration. This ensures the purity of the data, reduces storage space usage, and avoids irrelevant fields from potentially interfering with subsequent analysis, charting, and database construction processes. Preferably, before or after the cleaning process, dictionary lookup and replacement are performed based on a predefined attribute mapping rule table to achieve automated mapping and semantic unification of complete fields. The attribute mapping rule table specifies the mapping relationship between source data field names and target standard fields and defines the necessary value domain conversion rules, which solves the heterogeneity problem of different metadata in terms of structure or semantics, and provides a unified and clean data foundation for the standardized processing of subsequent processes.

[0030] (3) Topological and logical consistency verification Based on a pre-built topology rule base, a dynamic topology relationship checking and repair algorithm is performed on vector data. Spatial relationships between features are constructed using planar graph theory knowledge. Features refer to entities on the map that have location and attributes, i.e., the identification status under different methods. Further checks are then performed on rules such as "polygon features cannot overlap" and "boundaries must be closed." The specific process is as follows: Planar graph theory is used to validate the core model and generate planar graph structures. ,in, It represents a vertex set consisting of endpoints, intersections, and independent points, and is a discretized representation of spatial coordinates; Represents a continuous boundary line segment without self-intersection, where each edge Connecting two vertices corresponds to the boundary line segment of a line feature or a polygon feature; This represents a set of faces that are connected regions defined by closed loops formed by edges. A connected region defined by a closed loop formed by edges corresponds to the interior of a face feature. It should be noted that in the scenario of natural resource ownership registration, the registration unit is often composed of regular line segments. In a few cases, arcs and equilateral lines are involved, which can still be approximated as line segments. Preferably, according to Euler's formula for a planar diagram, the number of its elements satisfies... ,in, This represents the number of connected components, providing a mathematical basis for verifying topological consistency.

[0031] Automated topology checks are performed using graph constraints, identifying topology error nodes by ensuring that polygon features do not overlap and that boundaries are closed. Mapping and detection of the "face features cannot overlap" rule: This rule is mapped to "any two faces ( The boundary loops of two faces must not intersect at non-vertices, and their internal regions must be mutually exclusive. The system detects this by constructing a plane subdivision or calculating a dual graph and using edge-face relationships. If the boundary loops of two faces intersect at non-vertices or a vertex of one face is located inside another face, it is judged as an overlap violation. Mapping and detection of the "boundary must be closed" rule: This rule is mapped to "any edge sequence representing a face boundary must form a closed loop, and all edges are shared by two faces, except for infinite faces outside the map"; The system detects this by checking the degree of the edge and the connectivity of the face: if an edge is referenced by only one face, i.e., its degree is 1, it forms a "dangling line", violating the closure rule; if the edge sequence of a face boundary cannot form a closed loop, it is determined to be unclosed; if it is not referenced by any face and its degree is 0, it is an "isolated redundant point". The endpoints of redundant points and dangling lines are both topological error nodes.

[0032] Use Formula 1 to filter boundary line segments whose vertical distance relative to the current topological error node is within the preset tolerance range as target line segments: Formula 1; in, This indicates the node currently identified as having a topological error. and These represent the two endpoints of the boundary line segment. This indicates the preset tolerance, which can be set to 0.001-0.01 meters. Instead of deleting, move and capture the topological error node to the target line segment with the lowest vertical distance. Update the planar graph structure after moving, and iterate through the judgment and move capture until all topological error nodes are eliminated or the iteration limit is reached. Nodes are not only spatial representations but also attribute information. Randomly deleting them will result in the loss of important attributes. Therefore, in this embodiment of the invention, a capture method is used instead of deletion.

[0033] The purpose of this algorithm is to attract and move nodes close to the line segment to achieve precise overlap. It deals with the disordered geometry of nodes and line segments across the entire network and uses an iterative convergence strategy instead of the original single recursion to handle the chain topological reactions that may be caused by node movement, ensuring that the network eventually reaches a globally consistent state.

[0034] Figure 3 This is a schematic diagram of standardized cleaning according to an embodiment of the present invention, as shown below. Figure 3 The diagram illustrates the process of obtaining standardized information.

[0035] In step S130, the standardized information is stored in the database in batches and a consistency check is performed, specifically including: (1) Dynamic generation and optimization of database schema Based on standard definitions, such as XML Schema or specific JSON format definitions, data definition language (DDL) scripts are dynamically generated. Databases are created in database management systems (such as PostGIS or File Geodatabase) through custom language scripts. All necessary feature classes, tables, attribute fields (including type, length, precision, etc.), domains (value range), subtypes, and complex relational rules and topological rules are automatically created. (2) Transactional batch write Standardized information in the form of large-scale streams is divided into fixed-size batches and written to the database, for example, each batch processes 1000-5000 data points. Preferably, before each batch enters the database, geometric verification, attribute constraint checks, and spatial reference matching are performed sequentially. Geometric verification mainly includes whether the coordinates are within a reasonable range, attribute constraint checks mainly determine whether the attribute values ​​are within the range, and spatial reference matching determines whether the coordinate system transformation is correct. The above checks are completed in a database transaction. This mechanism ensures the atomicity of the operation. The failure of a single batch will not affect the committed data, and rollback and retry can be implemented, which greatly improves the robustness and efficiency of large-scale data processing.

[0036] (3) Consistency verification and automated recording Once standardized information is stored in the database, a predefined verification SQL script is executed to compare the consistency between the stored information and the information in the original file. The main comparison objects include the consistency between the stored data and the source data in terms of the number of elements, key attribute statistics, spatial range, etc., and a data integrity report is generated. Preferably, key information is extracted from the database, such as data source, coordinate system, processing time, and processing flow version number, to achieve data traceability and full lifecycle management.

[0037] Figure 4 This is a schematic diagram of data entry in an embodiment of the present invention, as shown below. Figure 4 As shown, this illustrates the process of forming a standard database based on standardized information and after verification.

[0038] In step S140, business logic is automatically processed based on standardized information. The registration unit crops all vector images corresponding to the original space to obtain a natural resource patch map. Based on the natural resource patch map, area calculation, attribute information assignment, and boundary point generation are performed to obtain analysis results, specifically including: This step is the core of the business logic, innovatively transforming complex business operations that rely on human experience and multi-step interactions into a series of automated processes powered by spatial analysis algorithms and rule engines.

[0039] (1) Intelligent extraction and spatial overlay of data range An R-Tree spatial index is constructed using registration units, and the index network in which the registration units are located is recorded. Based on the corresponding positions of the land parcel layer, ownership boundary layer, special element layer, and administrative boundary layer of the registration unit located by the R-Tree spatial index, each layer describes the same area. Therefore, a parallel spatial clipping operation is performed to clip the layers within the registration unit range to obtain the clipped image. For example, if the registration unit is in the area where index grid 2 is located, it will also be located in the same index network in other layers. Subsequent data processing is strictly limited to the scope of the registration unit to eliminate interference from irrelevant data.

[0040] Supervised classification algorithms are used to identify the resource types of land cover patches in the cropped image, such as watercourses, forests, wetlands, and grasslands. The classification results of all land cover patches are aggregated and labeled in the land cover patch layer to obtain a natural resource patch layer, which provides a foundation for subsequent accurate spatial analysis. In this embodiment of the invention, a knowledge graph of "land category code - natural resource type" is constructed through domain information to summarize all resource types, and rule-based decision tree or support vector machine is used as a supervised classification algorithm.

[0041] (2) Multi-level area automatic calculation and intelligent adjustment In the natural resource patch layer of the cropped image, if a natural resource patch is not cropped through the registration unit boundary, its area directly inherits the authoritative ellipsoid area of ​​the original patch. For example, if wetland 1 is completely contained in the registration unit, the area of ​​the wetland in the registration unit is the same as the original area of ​​wetland 1. If wetland 1 is demarcated by a unit boundary, it means that a portion of wetland 1's area is outside the registration unit. This portion of the area cannot be included in the statistics within the registration unit. Therefore, Formula 2 is used to calculate the geometric segmentation ratio to calculate the area of ​​each natural resource patch. : Formula 2; in, The authoritative ellipsoidal area representing the original feature. This represents the planar geometric area of ​​a sub-part within a natural resource patch after it has been clipped and divided. This represents the sum of the geometric areas of all the divided parts, which is theoretically equal to... Ensure that the sum of the ellipsoidal areas of each sub-part after segmentation is strictly equal to the authoritative ellipsoidal area of ​​the original patch. The planar geometric area is calculated using the shoelace formula algorithm, and all calculations are performed in a unified projected coordinate system to ensure proportional geometric consistency. vertices For a simple polygon, with vertices given in clockwise or counterclockwise order, use Formula 6 to perform a high-precision shoelace calculation to calculate the area. : Formula 6; Among them, the definition and .

[0042] Based on the calculated area of ​​natural resource patches and the cropped image after spatial overlay, the area of ​​registration units and the area of ​​ownership zones are statistically analyzed. In practical applications, the areas of special zones and administrative zones are often directly defined by the government, but theoretically, the areas of special zones and administrative zones can still be statistically analyzed. At this point, the area of ​​each natural resource patch has been calculated. The area of ​​the registration unit is calculated by summing the areas of each natural resource patch. After overlaying with others, the results of other division methods can be parsed. For example, the registration unit consists of Forest 1 and Wetland 1. Forest 1 is element 1 and Wetland 1 is element 2. The area of ​​Forest 1 is 500㎡ and the area of ​​Wetland 1 is 195㎡. After overlaying, it can be seen that the base natural resources are 610㎡ and the state-owned natural resources are 85㎡, etc. Other division methods are similar. The superimposed area grouping and aggregation can be obtained automatically by simulation system or by conventional methods in the field, ensuring the accuracy of area statistics under different levels of spatial relationships.

[0043] Using the sum of the areas of each partition as the total area of ​​the registered units recorded in the file as a constraint, a set of equations is constructed. Preferably, it can first determine whether the calculated area of ​​the registered units is equal to the standard recorded in the file, and whether the sum of the areas of different levels is equal to the standard to trigger intelligent constraint adjustment. With the goal of minimizing the target adjustment amount, the optimal adjustment solution is obtained to adjust the area calculation result. The constraint equations are in the form of, for example: + Taking the ownership boundary layer as an example, a system of linear constraint equations is established. The goal is to minimize the area adjustment, solve for the optimal adjustment solution, and reasonably assign the minute differences to relevant polygons. This ensures that all area data, from within the layer to different charts, achieves absolute mathematical consistency. Formula 7 represents the least squares adjustment. Formula 7; in, This represents the design matrix, also known as the coefficient matrix, where each row corresponds to an observation equation and each column corresponds to a parameter to be determined. This represents the vector of parameter corrections to be determined. The difference between the observed value and the approximate value is represented by a closed difference vector. The weight matrix, usually a diagonal matrix, has diagonal elements that represent the weights of each observation, which are inversely proportional to the variance. The objective of adjustment is to minimize the sum of squared residuals of the adjusted observations. ,in, .

[0044] The general information in the attribute information is uniquely encoded as an identifier. In this embodiment of the invention, the unique encoding can be ensured by using a hash algorithm or sequence generator according to the rule of "administrative division code + element code + sequence number".

[0045] (3) Automatic assignment of complex attributes Linking some attribute information with natural resource patches; For descriptive and categorical attributes in attribute information, when multiple different values ​​appear, a preset priority or voting algorithm is used to determine the assignment. For example, the category of the same location in Forest 1 cannot be both "public welfare forest" and "commercial forest". For both quantitative and continuous attributes in the attribute information, the area-weighted average method is used to determine the assigned value, and the area-weighted average is calculated using Formula 8: Formula 8; in, and They represent the first within the superimposed range. i The attribute values ​​and area of ​​each specific element. This represents the total area of ​​the feature map, and the calculation result retains the same number of decimal places as the source data; For example, if Forest 1 has an area of ​​500㎡, public welfare forest has 200㎡ and a stock volume of 120m³ / ha, and commercial forest has 300㎡ and a stock volume of 100m³ / ha, then the average stock volume = 120×(200 / 500) + 100×(300 / 500). Using the "proportional calculation and formatted output algorithm," the area percentage of each type is automatically calculated and a string is generated in the format "Type 1 (percentage%), Type 2 (percentage%)...". Data on missing parts of the special survey results within the patch are not extrapolated, for example, "Public welfare forest 25.0%, commercial forest 70.0%, pending confirmation 5.0%".

[0046] (4) Generation of boundary point lines and spatial relationship description All nodes in the boundary segment of the registration unit are extracted, and the inflection points are identified using a variant of the Douglas-Puk algorithm. Since the registration unit is a closed loop, the traditional end-to-end connection method cannot generate a baseline. Therefore, all nodes are segmented and judged independently. That is, the closed loop is divided into multiple segments for processing. The connection between the beginning and end of the current segment is used as the reference. The nodes with a dynamic tolerance greater than the reference are identified as inflection points, which are significant points that must be retained. Compared with the traditional method, they are retained instead of being eliminated. Formula 3 is used to represent the dynamic tolerance range: Formula 3; in, This represents the dynamic tolerance of the k-th iteration. "Number of retained points" is the number of points retained after compression under the current tolerance in the current segment. "Number of original points" is the total number of points on the original boundary line in the current segment. The iteration ends when the number of retained points in the current segment meets the preset requirement, which is the number of boundary points required for the current segment.

[0047] It also obtains the intersection points of the boundary line segments of the registration unit with various partitions, merges the turning points and intersection points into a set of boundary points, and automatically numbers them in sequence; In this embodiment of the invention, a clockwise numbering method based on geometric sorting can be adopted to calculate the minimum outer matrix of all boundary points, select the upper left corner as the starting reference point, i.e., the northwest corner, calculate the polar angle of each boundary point relative to the boundary polygon, sort them in a clockwise direction from largest to smallest, number them from 1 according to the sorting result, and automatically add the "T" or "J" prefix according to the source of the points as shown in the diagram or analysis. Based on the sequence of numbered boundary points, the original registration unit boundary line is precisely broken at the boundary points to form continuous boundary line segments; Preferably, based on the spatial coordinates of the boundary points and the spatial relationships of adjacent features, the spatial relationship reasoning and natural language generation (NLG) algorithm automatically generates standard "boundary point location descriptions" and "boundary line direction descriptions". This algorithm combines a pre-set description template, a directional vocabulary such as "northeast", "along...west line", and distance calculation. The distance is calculated using the Euclidean algorithm, generating standardized description text such as "T1 is located southwest of T236, at the point where the curve and straight line change, and the vertical and horizontal distance from this point to the west line of XX Road to the east is X m".

[0048] Figure 5 This is a schematic diagram illustrating the automation of business logic in an embodiment of the present invention, such as... Figure 5 As shown, the process includes extracting overlay layers, calculating and adjusting area, automatically assigning attributes, generating boundary points and lines, and generating business information.

[0049] In step S150, the analysis results and standardized information are collected to form the registration results, which specifically include: generating registration forms and charts. At this point, cadastral survey data results that can be used for registration are formed. After the subsequent approval process is executed, the registration can be completed.

[0050] This step is the business encapsulation layer that connects data processing and legally binding results, and specifically includes the following parts: (1) Survey form Based on the generated results, the system encapsulates and automatically extracts and summarizes information from vector data and attributes, populates and generates standardized spreadsheets, mainly including: Unit Information Table: Automatically collects and fills in structured information such as the basic status, ownership status, natural status, and related information of the registered units; Boundary description table and boundary labeling table: The corresponding tables are automatically filled based on the generated boundary point and line data and natural language descriptions.

[0051] (2) Registration process form The aggregated business information is automatically filled into the corresponding fields of process forms such as the survey record form, survey result verification form, and result review form of the cadastral survey, completing the one-time entry of information and providing a structured basic document for subsequent manual review and confirmation.

[0052] It has enabled the automated conversion from "geographic data" to "business information" and "process forms".

[0053] (3) Automatic mapping of thematic maps It calls a predefined map style rule library that conforms to map legend specifications, such as the SLD / SE standard style description language, to automatically perform symbolic rendering, label avoidance and map frame finishing on spatial data, and output print-quality PDF or PNG format thematic maps. The label avoidance uses a force-directed graph algorithm to avoid overlay.

[0054] (4) Automatic generation of core registration forms The system calls upon the various survey forms and data generated in the "Cadastral Survey and Registration of Land Rights Business Information Generation" step, and automatically arranges and synthesizes complete forms and data documents that can be directly printed or electronically signed in accordance with legal format and layout requirements; based on the area summary results of the "Multi-level Area Automatic Calculation and Intelligent Adjustment" step, it generates corresponding statistical analysis reports in accordance with statistical system requirements.

[0055] The above technical solutions of the embodiments of the present invention will be illustrated with reference to the following accompanying drawings.

[0056] Figure 6 This is a schematic diagram of the rights registration framework according to an embodiment of the present invention, as shown below. Figure 6 As shown, a complete solution for multi-source data processing and result generation for natural resource ownership registration is presented, which mainly includes data aggregation and perception, data processing, database writing, core business logic, and result generation.

[0057] Method Example 2 In this embodiment of the invention, after identifying the data format, coordinate system, character encoding, and attribute structure, the identification results are stored in a structured manner and a stable association is established with the original data. This ensures that subsequent processing steps can efficiently and accurately call the structured data. An example of metadata encapsulation for structured information is shown below, using JSON format as an example: { "data_id": "unique_identifier", "original_filename": "example.shp", "format": "Shapefile", "coordinate_system": { "epsg": 4490, "name": "CGCS2000_GK_Zone_20", "confidence": 0.98 }, "encoding": "GBK", "attribute_schema": [ { "field_name": "DLBM", "data_type": "String", "semantic_label": "Land Category Code", "sample_values": ["0101", "0301"] } ], "detection_timestamp": "2025-12-08T10:00:00Z" } Independent storage saves the aforementioned metadata JSON file in the / metadata / subdirectory of the project directory, with the filename corresponding to the original data file; hidden storage writes the key metadata into user-defined fields or metadata segments of the file. During the process of format standardization and coordinate transformation, seven parameters It is derived from authoritative coordinate system transformation parameter tables issued by the state or local authorities and is built into the system parameter library, and is automatically called according to the source coordinate system code; The attribute mapping rule table maps "land type code" to "DLBM" and "paddy field" to code "0101"; it automatically completes the search, replacement and value conversion.

[0058] The general information in the attribute information is uniquely encoded as an identifier. In this embodiment of the invention, the unique encoding can be ensured by using a hash algorithm or sequence generator according to the rule of "administrative division code (6 digits) + element code (4 digits) + sequence number (10 digits)".

[0059] In the generation of boundary points, the process is iterated until the number of retained points meets the preset boundary point density requirement, such as one point every 50-100 meters. At the same time, the intersections of the boundary line with the ownership line and the administrative boundary line are detected, and the points are merged and deduplicated to form the final boundary point set.

[0060] The analysis results and standardized information are collected to form registration results. The registration forms are automatically formatted according to the nationally prescribed format, font and paragraph format, generating DOCX and PDF documents that can be directly printed or electronically signed.

[0061] In summary, addressing the existing problems, this invention provides a method for natural resource rights confirmation and registration. It constructs an automated data processing workflow encompassing data access, processing, and output, shortening the project cycle from weeks to days or even hours, fundamentally resolving the bottleneck in generation efficiency. During data processing, topological checks are performed by generating planar structures, enabling the formalization of vector images and facilitating topological rule mapping. Atomized data flow processing improves the robustness and efficiency of handling large volumes of data. Standardized procedures eliminate subjective errors in data integration, attribute linking, area adjustment, and map creation, ensuring consistency and compliance of the results. Area calculations utilize authoritative ellipsoids to avoid inaccuracies in planar calculations, and a multi-constraint adjustment algorithm based on the least squares principle eliminates area closure errors, ensuring absolute consistency across all area data. Overall, a standardized and reusable technical workflow framework is created, capable of adapting to varying needs across different regions and projects, ensuring comparability of project results, and enabling version control and traceability during processing, thereby improving the standardization and normalization of project management.

[0062] System Implementation Examples According to embodiments of the present invention, a natural resource ownership registration system is provided. Figure 7 This is a schematic diagram of the natural resource ownership registration system according to an embodiment of the present invention, such as... Figure 7 As shown, the natural resource ownership registration system according to an embodiment of the present invention specifically includes: The convergence sensing module 70 is used to acquire files including registration units and the original space, execute a signature algorithm to identify the file's data format, and parse vector images and text content according to the data format to obtain structured information. Specifically, it is used for: Use a binary signature algorithm as the signature algorithm; The signature algorithm extracts the first preset length bytes from the file, matches the preset length bytes with a pre-defined format rule database, and obtains a binary signature result as the data format. If a binary signature result cannot be obtained, the data format is obtained through regular expression parsing.

[0063] Obtain vector images of labeled land parcel layers, ownership boundary layers, special feature layers, and administrative boundary layers, as well as text content; The coordinate system information in the vector image is analyzed. When the coordinate system information is missing or damaged, the coordinate system information is inferred from the coordinate value range. If the inferred coordinate system information is a projected coordinate system, the corresponding zone range and the corresponding prediction confidence value are output. Based on text-type content, an n-gram statistical model is used to predict the probability of the text type appearing under different character encodings, thus obtaining the character encoding recognition results; The attribute table in the text information is extracted and parsed based on the character encoding recognition results, and is used as the attribute information; if the attribute table does not exist, the text information is sampled and parsed line by line, and the attribute fields and corresponding values ​​are inferred based on the structural characteristics, and are used as the attribute information. The obtained coordinate system information, character encoding recognition results, and attribute information are encapsulated into metadata objects, and the metadata objects are associated with the original files to obtain structured information.

[0064] The standardization module 72 is used to standardize the format and clean the fields of structured information, generate a planar map structure based on the geographic features represented by the vector image, perform automated topology checks based on the planar map structure, and use node capture based on geometric tolerance to repair topology errors, thereby obtaining standardized information. Specifically, it is used for: Heterogeneous vector images in structured information are converted into a unified data format, and coordinate system information in structured information is transformed in batches. Iterate through the attribute fields in the structured information, compare the attribute fields with the preset whitelist fields, and remove any attribute fields that do not appear in the preset whitelist fields for cleaning.

[0065] Generate planar structure ,in, This represents a vertex set consisting of endpoints, intersections, and independent points. This represents a continuous boundary line segment that does not intersect itself. This represents the set of faces that form a closed loop defined by edges; Automated topology checks are performed using graph constraints, identifying topology error nodes by ensuring that polygon features do not overlap and that boundaries are closed. Use Formula 1 to filter boundary line segments whose vertical distance relative to the current topological error node is within the preset tolerance range as target line segments: Formula 1; in, This indicates the node currently identified as having a topological error. and These represent the two endpoints of the boundary line segment. Indicates the preset tolerance; Instead of deleting, move and capture topologically incorrect nodes to the target line segment with the closest vertical distance. Update the planar graph structure after moving, and iterate through the judgment and move capture until all topologically incorrect nodes are eliminated or the iteration limit is reached.

[0066] Data writing module 74 is used to batch store standardized information into the database and perform consistency checks, specifically for: Create databases in a database management system using custom language scripts; Standardized information in stream form is divided into fixed-size batches and written to the database; After standardized information is stored in the database, a predefined verification SQL script is executed to compare the consistency between the stored information and the information in the original file.

[0067] Business logic execution module 76 is used for automatic business logic processing based on standardized information. It uses the registration unit to crop all vector images corresponding to the original space to obtain a natural resource patch map. Based on the natural resource patch map, it performs area calculation, attribute information assignment, and boundary point generation to obtain analysis results. Specifically, it is used for: An R-Tree spatial index is constructed using registration units, and the index network in which the registration units are located is recorded. Based on the corresponding positions of the land parcel layer, ownership boundary layer, special element layer and administrative boundary layer corresponding to the registration unit in the R-Tree spatial index, a parallel spatial clipping operation is performed to clip the layers within the registration unit range to obtain the clipped image. A supervised classification algorithm is used to identify the resource types of land cover patches in the cropped image. The classification results of all land cover patches are aggregated and labeled in the land cover patch layer to obtain the natural resource patch layer.

[0068] In the natural resource patch layer of the cropped image, if a natural resource patch was not cropped through the registration unit boundary, its area directly inherits the authoritative ellipsoid area of ​​the original patch; otherwise, the area of ​​the natural resource patch is calculated using Formula 2 to calculate the geometric segmentation ratio. : Formula 2; in, The authoritative ellipsoidal area representing the original feature. This represents the planar geometric area of ​​a sub-part of a natural resource patch after it has been clipped and segmented. The planar geometric area is calculated using the shoelace formula algorithm. Based on the calculated area of ​​natural resource patches and the cropped image after spatial overlay, the areas of registration units, ownership zones, special zones, and administrative zones are statistically analyzed. The total area of ​​the registration units recorded in the document is used as a constraint to construct a set of equations. With the goal of minimizing the target adjustment amount, the optimal adjustment solution is obtained to calculate the area. The general information in the attribute information is uniquely encoded as an identifier; For descriptive and categorical attributes in attribute information, when multiple different values ​​appear, a preset priority or voting algorithm is used to determine the value. For quantitative and continuous attributes in attribute information, an area-weighted average method is used to determine the value. Extract all nodes from the boundary segment of the registration unit, segment all nodes and judge them independently. Use the connection between the beginning and end of the current segment as the benchmark. Using a variant of the Douglas-Puk algorithm, identify nodes whose dynamic tolerance is greater than the benchmark as inflection points. Use Formula 3 to represent the dynamic tolerance range: Formula 3; in, This represents the dynamic tolerance for the k-th iteration, which continues until the number of retained points in the current segment meets the preset requirement, at which point the iteration ends. The results generation module 78 is used to collect analysis results and standardized information to form registration results, specifically for generating registration forms and charts.

[0069] In summary, addressing the existing problems, this invention, the Natural Resource Rights Confirmation and Registration System, constructs an automated data processing workflow encompassing data access, processing, and output, shortening the project cycle from weeks to days or even hours, fundamentally resolving the bottleneck in generation efficiency. During data processing, topological checks are performed by generating planar map structures, enabling the formalization of vector images for easier mapping of topological rules. Atomized data flow processing improves the robustness and efficiency of handling large volumes of data. Standardized procedures eliminate subjective errors in data integration, attribute linking, area adjustment, and map creation, ensuring consistency and compliance of the results. Area calculations utilize authoritative ellipsoids to avoid inaccuracies in planar calculations, and a multi-constraint adjustment algorithm based on the least squares principle eliminates area closure errors, ensuring absolute consistency across all area data. Overall, a standardized and reusable technical workflow framework is created, capable of adapting to varying needs across different regions and projects, ensuring comparability of project results, and enabling version control and traceability during processing, thereby improving the standardization and normalization of project management.

[0070] Electronic device examples Figure 8 This is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 800 may include at least one processor 810 and a memory 820. The processor 810 can execute instructions stored in the memory 820. The processor 810 is communicatively connected to the memory 820 via a data bus. In addition to the memory 820, the processor 810 can also be communicatively connected to an input device 830, an output device 840, and a communication device 850 via the data bus.

[0071] The processor 810 can be any conventional processor, such as a commercially available CPU. The processor may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems on chips (SOCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0072] The memory 820 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0073] In this embodiment of the present disclosure, the memory 820 stores executable instructions, and the processor 810 can read the executable instructions from the memory 820 and execute the instructions to implement all or part of the steps of any of the natural resource ownership registration methods in the above exemplary embodiments.

[0074] Computer-readable storage medium embodiments In addition to the methods and systems described above, exemplary embodiments of this disclosure may also be a computer program product or a computer-readable storage medium storing the computer program product, the computer product including computer program instructions that can be executed by a processor to implement all or part of the steps described in any of the natural resource ownership registration methods in the exemplary embodiments described above.

[0075] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. Programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages, and scripting languages ​​(e.g., Python). The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0076] Computer-readable storage media may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media include: static random access memory (SRAM) having one or more electrically connected wires; electrically erasable programmable read-only memory (EEPROM); erasable programmable read-only memory (EPROM); programmable read-only memory (PROM); read-only memory (ROM); magnetic storage; flash memory; magnetic disk or optical disk; or any suitable combination thereof.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.< / featurecollection> < / gml:>

Claims

1. A method for confirming and registering natural resource rights, characterized in that, include: The system acquires a file including a registration unit and the original space, executes a signature algorithm to identify the data format of the file, and parses the vector image and text content according to the data format to obtain structured information. The structured information is formatted and cleaned, and a planar map structure is generated based on the geographic features represented by the vector image. Based on the planar map structure, an automated topology check is performed, and node capture based on geometric tolerance is used to repair topology errors, thereby obtaining standardized information. The standardized information is stored in the database in batches and a consistency check is performed. Based on the standardized information, business logic is automatically processed. The registration unit is used to crop all vector images corresponding to the original space to obtain cropped images. Based on the cropped images, area calculation, attribute information assignment and boundary point generation are performed to obtain analysis results. The analysis results and the standardized information are combined to form the registration results.

2. The method according to claim 1, characterized in that, The specific steps of executing the signature algorithm to identify the data format of the file include: A binary signature algorithm is used as the signature algorithm. The signature algorithm extracts the first preset length bytes from the file, matches the preset length bytes with a pre-defined format rule database, and obtains a binary signature result as the data format. If the binary signature result cannot be obtained, the data format is obtained through regular expression parsing.

3. The method according to claim 1, characterized in that, The step of parsing vector images and text content according to the data format to obtain structured information specifically includes: Obtain vector images of labeled land parcel layers, ownership boundary layers, special feature layers, and administrative boundary layers, as well as text content; The coordinate system information in the vector image is analyzed. When the coordinate system information is missing or damaged, the coordinate system information is inferred from the coordinate value range. If the inferred coordinate system information is a projected coordinate system, the zone range corresponding to the projected coordinate system and the corresponding prediction confidence value are output. Based on the text content, the probability of the text appearing under different character encodings is predicted using an n-gram statistical model, and the character encoding recognition result is obtained. The attribute table in the text content is extracted and parsed based on the character encoding recognition result, and used as attribute information; if the attribute table does not exist, the text content is sampled and parsed line by line, and the attribute fields and corresponding values ​​are inferred based on the structural characteristics, and used as the attribute information. The obtained coordinate system information, character encoding recognition results, and attribute information are encapsulated into a metadata object, and the metadata object is associated with the original file to obtain the structured information.

4. The method according to claim 3, characterized in that, The process of format standardization and field cleaning of the structured information specifically includes: The heterogeneous vector images in the structured information are converted into a unified data format, and the coordinate system information in the structured information is subjected to batch coordinate transformation; The attribute fields in the attribute information of the structured information are traversed, and the attribute fields are compared with the preset whitelist fields. Any attribute field that does not appear in the preset whitelist fields is removed for cleaning.

5. The method according to claim 1, characterized in that, The process of generating a planar map structure based on the geographic features represented by the vector image, performing automated topology checks based on the planar map structure, and using node capture based on geometric tolerance to repair topology errors to obtain standardized information specifically includes: Generate planar structure ,in, This represents a vertex set consisting of endpoints, intersections, and independent points. This represents a continuous boundary line segment that does not intersect itself. This represents the set of faces that form a closed loop defined by edges; Automated topology checks are performed using graph constraints. The constraints, such as the requirement that face features cannot overlap and that boundaries must be closed, are used to identify topology error nodes. Use Formula 1 to filter boundary line segments whose vertical distance relative to the current topological error node is within the preset tolerance range as target line segments: Official 1; in, This indicates the node currently identified as having a topological error. and These represent the two endpoints of the boundary line segment. Indicates the preset tolerance; Instead of deleting, move and capture the topologically incorrect node to the target line segment with the closest vertical distance. After moving, update the planar graph structure. Iterate through the judgment and move capture until all topologically incorrect nodes are eliminated or the iteration limit is reached.

6. The method according to claim 1, characterized in that, The step of storing the standardized information in batches into the database and performing consistency verification specifically includes: Create databases in a database management system using custom language scripts; Standardized information in stream form is divided into fixed-size batches and written into the database; Once the standardized information is stored in the database, a predefined verification SQL script is executed to compare the consistency between the stored information and the information in the original file.

7. The method according to claim 3, characterized in that, The step of using the registration unit to crop all vector images corresponding to the original space to obtain the cropped image specifically includes: The registration unit is used to construct an R-Tree spatial index, and the index network in which the registration unit is located is recorded; Based on the corresponding positions of the R-Tree spatial index location registration unit to the land parcel layer, the ownership boundary layer, the special element layer and the administrative boundary layer, a parallel spatial clipping operation is performed to clip the layers within the registration unit range to obtain the clipped image; The resource types of land parcels in the cropped image are identified using a supervised classification algorithm. The classification results of all land parcels are aggregated and labeled in the land parcel layer to obtain a natural resource parcel layer.

8. The method according to claim 7, characterized in that, The specific analysis results obtained by performing area calculation, attribute information assignment, and boundary point generation based on the cropped image include: In the natural resource patch layer of the cropped image, if a natural resource patch is not cropped through the registration unit boundary, its area directly inherits the authoritative ellipsoid area of ​​the original patch; otherwise, the area of ​​the natural resource patch is calculated using Formula 2 to calculate the geometric segmentation ratio. : Official 2; in, The authoritative ellipsoidal area representing the original feature. This represents the planar geometric area of ​​a sub-part in a natural resource patch after being clipped and segmented, wherein the planar geometric area is calculated using the shoelace formula algorithm; Based on the calculated area of ​​natural resource patches and the cropped image after spatial overlay, the areas of registration units, ownership zones, special zones, and administrative zones are statistically analyzed. The total area of ​​the registration units recorded in the document is used as a constraint to construct a set of equations. With the goal of minimizing the target adjustment amount, the optimal adjustment solution is obtained to calculate the area. The general information in the attribute information is uniquely encoded as an identifier; For descriptive and categorical attributes in the attribute information, when multiple different values ​​appear, a preset priority or voting algorithm is used to determine the assigned value. For quantitative and continuous attributes in the attribute information, an area-weighted average method is used to determine the assigned value. All nodes in the boundary segment of the registration unit are extracted, and inflection points are identified using a variant of the Douglas-Puk algorithm. Each node is segmented and judged independently. The connection between the beginning and end of the current segment is used as a reference. The Douglas-Puk algorithm variant is then used to determine if the connection to the reference exceeds the dynamic tolerance. The node is used as the turning point, and the dynamic tolerance range is represented by Formula 3: Formula 3; in, This represents the dynamic tolerance for the k-th iteration, which continues until the number of points retained in the current segment meets the preset requirement and the iteration ends. Obtain the intersection points of the boundary line segments of the registration unit with various partitions, merge the turning points and the intersection points into a set of boundary points, and automatically number them in sequence.

9. The method according to claim 1, characterized in that, The process of compiling the analysis results and the standardized information to form the registration results specifically includes generating registration forms and charts.

10. A natural resource ownership registration system, characterized in that, include: The convergence sensing module is used to acquire files including registration units and original space, execute a signature algorithm to identify the data format of the files, and parse vector images and text content according to the data format to obtain structured information; The standardization module is used to unify the format and clean the fields of the structured information, generate a planar map structure based on the geographic features represented by the vector image, perform automated topology checks based on the planar map structure, and use node capture based on geometric tolerance to repair topology errors to obtain standardized information. The data writing module is used to store the standardized information into the database in batches and complete the consistency check; The business logic execution module is used to automatically process business logic based on the standardized information, and to use the registration unit to crop all vector images corresponding to the original space to obtain cropped images. Based on the cropped images, the module performs area calculation, attribute information assignment and boundary point generation to obtain analysis results. The results generation module is used to collect the analysis results and the standardized information to form the registration results.

Images