Methods, devices, terminals, and media for data entry of natural resource asset inventory results.

Abstract

This invention provides a method, apparatus, terminal, and medium for storing natural resource asset inventory results data. The method includes: acquiring resource asset inventory results data and basic element data; processing the resource asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results; performing full-chain quality inspection on the standard dataset of asset inventory results to obtain a quality inspection standard dataset; and storing the quality inspection standard dataset in a hierarchical manner to obtain an asset inventory results database. This improves the digital governance capabilities of natural resource asset inventory results data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method, apparatus, terminal, and medium for storing data from a natural resource asset inventory. Background Technology

[0002] The natural resource asset inventory is a fundamental and systematic project based on the unified registration and confirmation of natural resource rights. Through systematic investigation, monitoring, and accounting, it comprehensively ascertains the core attributes of various natural resource assets, such as quantity, quality, ownership, distribution, and value, and establishes a dynamic updating mechanism. Its essence is to transform natural resources from "physical resources" into quantifiable, manageable, and tradable "asset data," providing a scientific basis for natural resource management, ecological protection and restoration, and asset property rights system reform.

[0003] Mapping and storing the results of natural resource asset inventory is a core step in building a unified natural resource management system. However, in practice, it often faces several systemic challenges, including data fragmentation, lack of standards, lagging dynamic updates, and low application efficiency. On the one hand, the data sources of natural resource asset inventory are heterogeneous, with mixed formats including remote sensing imagery, vector data, and tabular data. Furthermore, inconsistencies in coordinate systems and attribute fields lead to low data fusion efficiency. On the other hand, data quality is flawed, including typical topological errors and logical contradictions in attributes, resulting in a trust crisis in data applications after storage and an inability to meet refined requirements. In addition, the data storage and management model is relatively outdated, leading to inefficient data application and the lack of a dynamic data update mechanism, resulting in data becoming "zombie" data.

[0004] To effectively, systematically, and uniformly manage the results of the inventory of natural resource assets, provide a foundational data source for natural resource asset management, and support the construction of a natural resource management system, thereby enhancing digital governance capabilities, establishing an accurate, efficient, standardized, and regulated natural resource asset inventory database has become an essential requirement for the current digital management of natural resources. Therefore, it is necessary to propose a method for mapping and storing the results of the inventory of all state-owned natural resource assets, standardizing the data entry process, unifying the management of the asset inventory data, and visualizing it through digital means. This will facilitate direct use by management departments, improve efficiency, expand data application scenarios, and support digital construction.

[0005] This technology, by constructing a standardized method and system for mapping and storing the results of the national natural resource asset inventory, can effectively, quickly, accurately, and systematically realize the standardized mapping and storage of the national natural resource asset inventory results. It provides technical support for establishing an asset inventory database that meets the needs of natural resource management and greatly improves the efficiency and scientific nature of mapping and storing the national natural resource asset inventory results. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a method for storing natural resource asset inventory data, aiming to improve the digital governance capabilities of natural resource asset inventory data.

[0007] Firstly, embodiments of this application provide a method for storing natural resource asset inventory results data, including:

[0008] Obtain data on the results of the resource and asset inventory and basic element data;

[0009] Data processing is performed on the resource and asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results;

[0010] A full-chain quality inspection was performed on the asset inventory results standard dataset to obtain the quality inspection results standard dataset.

[0011] The quality inspection results standard dataset is stored in the database in a hierarchical manner to obtain the asset inventory results database.

[0012] Optionally, the resource asset inventory results data and the basic element data are processed to obtain a standard dataset of asset inventory results, including:

[0013] The resource asset inventory results data are transformed, coordinate corrected, and classified to obtain preprocessed results data.

[0014] The preprocessed data and the basic element data are integrated to obtain a standard dataset of asset inventory results.

[0015] Optionally, the resource asset inventory results data are subjected to data transformation, coordinate correction, and data classification to obtain preprocessed results data, including:

[0016] The resource and asset inventory results data are converted from multiple data formats to obtain converted inventory results data;

[0017] The coordinates of the converted inventory results data are corrected by error compensation using a residual neural network model to obtain corrected inventory results data;

[0018] The data from the correction and verification results are classified to obtain preprocessed data.

[0019] Optionally, the resource asset inventory results data are converted from multiple sources to obtain converted inventory results data, including: converting the conventional format resource inventory results data from multiple sources to obtain conventional converted inventory results data;

[0020] Non-standard resource inventory results data are converted from multi-source data formats using a format conversion model to obtain non-standard converted inventory results data. The format conversion model is a deep learning model built using the Transformer architecture and trained on historical non-standard format resource inventory results data.

[0021] The routine conversion and inventory results data and the non-standard conversion and inventory results data are combined to obtain the conversion and inventory results data;

[0022] Optionally, the coordinates of the transformed inventory results data are corrected through an error compensation process using a residual neural network model to obtain corrected inventory results data, including:

[0023] Based on the transformed and cleared data, determine the original coordinate system type parameters, projection method parameters, and elevation datum parameters;

[0024] The original coordinate system type parameters, projection method parameters, and elevation datum parameters are input into a preset residual neural network model, and the obtained survey control point data are fused into the residual neural network model to obtain the corrected and cleared result data. The residual neural network model is used to perform error compensation correction on the coordinates of the transformed and cleared result data. The residual neural network model is a coordinate error compensation correction model constructed by fitting the transformation model parameters by least squares method and using Kalman filtering to achieve dynamic correction of the residuals.

[0025] Optionally, the corrected and verified data is classified to obtain preprocessed data, including:

[0026] A natural resource asset ontology database is constructed using dynamic knowledge graph technology;

[0027] The semantic features of the corrected inventory results data are extracted from the natural resource asset ontology using a named entity recognition model, resulting in preprocessed data after data classification. The named entity recognition model is a BiLSTM+CRF model.

[0028] Optionally, the preprocessed data and the basic element data are integrated to obtain a standard dataset of asset inventory results, including:

[0029] Obtain the time range, spatial range, and preset type classification for the investigation;

[0030] The preprocessed data that are within the time and spatial scope of the survey are coded to obtain spatial element identification codes and type data identification codes;

[0031] The basic element data is segmented according to the time and space scope of the investigation to obtain the time and space requirement data;

[0032] The preprocessed data is organized according to the preset type classification, spatial element identification code, type data identification code, and time and space requirement data to obtain the asset inventory result standard dataset.

[0033] Optionally, a full-chain quality inspection is performed on the asset inventory results standard dataset to obtain a quality inspection results standard dataset, including:

[0034] A quality inspection rule engine was designed using SQL technology, and data standardization rules and logical verification matrices were constructed.

[0035] A quality inspection database for asset inventory results will be constructed based on data standardization rules and logical verification matrices.

[0036] The data in the asset inventory results standard dataset is called by a Python script in the asset inventory results data quality inspection library, and the data in the asset inventory results standard dataset is extracted after quality inspection to obtain the quality inspection results standard dataset.

[0037] Optionally, the quality inspection results standard dataset can be stored in a hierarchical manner to obtain an asset inventory results database, including:

[0038] Establish an asset inventory results database that includes a data storage layer, a data indexing layer, a quality assessment layer, and a comprehensive analysis layer;

[0039] The data in the quality inspection result standard dataset is identified and classified according to the spatial element identification code and type data identification code, and the data in the quality inspection result standard dataset is stored in the data storage layer using distributed object storage technology.

[0040] Obtain the attribute table data corresponding to the data in the quality inspection result standard dataset, and store the attribute table data corresponding to the data in the quality inspection result standard dataset in the data index layer;

[0041] The quality inspection results standard dataset is used to conduct a quality assessment of the centralized data through a data quality scoring model to obtain quality inspection report data, and the quality inspection report data is stored in the quality assessment layer;

[0042] If a comprehensive analysis instruction is received, the analysis report data is obtained through geographic weighted principal component analysis based on the information in the instruction, and the analysis report data is stored in the comprehensive analysis layer.

[0043] Secondly, embodiments of this application provide a device for storing natural resource asset inventory results data, including:

[0044] The data acquisition module is used to acquire resource and asset inventory results data and basic element data;

[0045] The data processing module is used to process the resource asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results.

[0046] The data quality inspection module is used to perform full-chain quality inspection on the asset inventory results standard dataset to obtain the quality inspection results standard dataset.

[0047] The data entry module is used to enter the quality inspection results standard dataset into the database in a hierarchical manner, thereby obtaining the asset inventory results database.

[0048] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for storing natural resource asset inventory results data as described in any of the first aspects above.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for storing natural resource asset inventory results data as described in any one of the first aspects above.

[0050] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the natural resource asset inventory results data entry method described in any of the first aspects above.

[0051] In this embodiment, resource asset inventory results data and basic element data are acquired; the resource asset inventory results data and the basic element data are processed to obtain a standard dataset of asset inventory results; the standard dataset of asset inventory results undergoes full-chain quality inspection to obtain a quality inspection result standard dataset; and the quality inspection result standard dataset is stored in a hierarchical manner to obtain an asset inventory results database. This improves the digital governance capabilities of natural resource asset inventory results data.

[0052] This application, by establishing a multi-source data conversion engine, intelligent coordinate system correction, and semantic mapping toolbox, has completed data format conversion, unified coordinate system, and automatic data classification. It has achieved automated preprocessing of the survey results data of various categories of state-owned natural resource assets, solved the problem of heterogeneous multi-source data, formed a unified data foundation, and provided a standard data processing paradigm for the data to be entered into the database.

[0053] This application utilizes Python technology to call SQL technology to construct data standardization rules and logical verification matrices for the results of the national natural resource asset inventory. The constructed standard asset inventory data quality inspection library realizes automated inspection of the national natural resource asset inventory data, improves data quality inspection efficiency, and reduces data errors caused by manual quality inspection.

[0054] This application utilizes Python technology and distributed object storage for data storage. The data indexing layer employs R-Tree spatial indexing. In the comprehensive analysis layer, local constraint learning is introduced to reduce the dimensionality of the data and improve the data response rate. A four-layer architecture is adopted to complete the mapping and database entry of asset inventory results data, which greatly improves the efficiency and standardization of data entry, saves memory after data entry, increases the scientificity and reliability of the database, and improves the response efficiency after data entry. It provides a new template for mapping and database entry of the results of the national natural resource asset inventory. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0056] Figure 1 This is a flowchart illustrating a method for storing natural resource asset inventory results data according to an embodiment of this application;

[0057] Figure 2 This is a flowchart illustrating the second embodiment of the method for storing natural resource asset inventory results data provided in this application;

[0058] Figure 3 This is a schematic diagram of the method for storing the natural resource asset inventory results data in the database provided in this application, using geographic unit data;

[0059] Figure 4 This is a schematic diagram of the method for storing the natural resource asset inventory results data provided in this application, which includes land cover data.

[0060] Figure 5This is a schematic diagram of the natural resource asset inventory results data after data classification according to the data entry method of the natural resource asset inventory results data provided in this application;

[0061] Figure 6 This is a schematic diagram of the structure of the data entry device for the natural resource asset inventory results provided in this application embodiment;

[0062] Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0063] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0064] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0065] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0066] The method for storing natural resource asset inventory results data provided in this application can be implemented by a natural resource asset inventory results data storage device. The device acquires the natural resource asset inventory results; integrates the natural resource asset inventory results to obtain integrated result data; and uses the RETE algorithm to match the integrated result data with rules in a quality inspection rule database to obtain data quality inspection results. The quality inspection rule database is constructed based on the result data quality inspection rules.

[0067] Figure 1 This illustration shows a schematic flowchart of the data entry process for natural resource asset inventory results provided in this application embodiment. It is intended as an example and not a limitation. This method can be applied to the aforementioned data entry device for natural resource asset inventory results, or it can be a method for users or operators to operate and make judgments on the device. Figure 1 As shown, the method may include:

[0068] S10, Obtain resource and asset inventory results data and basic element data;

[0069] To improve the digital governance capabilities of natural resource asset inventory results data, the natural resource asset inventory results data storage device acquires natural resource asset inventory results data and basic element data.

[0070] The resource asset inventory results include resource category data and corresponding resource asset data. The resource category data includes: state-owned land resource assets, state-owned forest resource assets, state-owned grassland resource assets, state-owned wetland resource assets, mineral resource assets, state-owned water resource assets, and marine resource assets. The resource asset data includes resource quantity data, asset value data, and usage right information. Each resource category data contains corresponding resource quantity data, asset value data, and usage right information. The basic element data includes: geographic unit data, land cover data, and "three zones and three lines" data.

[0071] S20, perform data processing on the resource asset inventory results data and the basic element data to obtain the asset inventory results standard dataset;

[0072] After acquiring the natural resource asset inventory results data and basic element data, the natural resource asset inventory results data storage device processes the natural resource asset inventory results data and basic element data to obtain a standard dataset of natural resource asset inventory results.

[0073] Furthermore, referring to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the method for storing natural resource asset inventory results data according to the present invention. Based on the above... Figure 2 The illustrated embodiment processes the resource asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results, specifically including:

[0074] S21, perform data conversion, coordinate correction and data classification on the resource asset inventory results data to obtain preprocessed results data;

[0075] After acquiring the natural resource asset inventory results data and basic element data, the natural resource asset inventory results data storage device performs data conversion, coordinate correction and data classification on the resource asset inventory results data to obtain preprocessed results data.

[0076] To address the issue of heterogeneous multi-source data and establish a unified data foundation, providing a standardized data source for the final database, the acquired multi-source data from the national inventory of all-people-owned natural resource assets undergoes automated preprocessing. This preprocessing primarily includes multi-source data format conversion, coordinate system correction, and data classification.

[0077] As one implementation method, the resource asset inventory results data undergoes data conversion, coordinate correction, and data classification to obtain preprocessed results data. This may include: converting the resource asset inventory results data into a multi-source data format to obtain converted inventory results data; correcting the coordinates of the converted inventory results data using a residual neural network model to obtain corrected inventory results data; and classifying the corrected inventory results data to obtain preprocessed results data.

[0078] As one implementation method, the resource asset inventory results data undergoes multi-source data format conversion to obtain converted inventory results data. This can include: performing multi-source data format conversion on conventionally formatted resource inventory results data to obtain conventionally converted inventory results data; performing multi-source data format conversion on non-standardly formatted resource inventory results data using a format conversion model, where the format conversion model is a deep learning model built using the Transformer architecture and trained on historical non-standardly formatted resource inventory results data; and merging the conventionally converted inventory results data and the non-standardly converted inventory results data to obtain the converted inventory results data.

[0079] First, a multi-source data conversion engine is established, integrating over 100 format conversion methods to form an automatic data conversion workflow, such as converting Excel to GeoJSON, solving nearly 90% of the multi-source data conversion challenges. For the remaining data conversion challenges, a Transformer architecture is introduced to build a deep learning-based format conversion model. This model is trained on historical data to automatically identify field mapping relationships in non-standard formats, resolving the remaining conversion difficulties. By integrating existing methods to form a conversion chain and building a deep learning-based format conversion model, fully automated processing of multi-source data formats is achieved.

[0080] As one implementation method, the coordinates of the transformed inventory data are corrected for errors using a residual neural network model to obtain corrected inventory data. This can include: determining the original coordinate system type parameters, projection method parameters, and elevation datum parameters from the transformed inventory data; inputting the original coordinate system type parameters, projection method parameters, and elevation datum parameters into a preset residual neural network model; and fusing the obtained surveying control point data into the residual neural network model to obtain corrected inventory data. The residual neural network model is used to correct the coordinates of the transformed inventory data for errors. The residual neural network model is a coordinate error compensation and correction model constructed by fitting the transformation model parameters using the least squares method and using Kalman filtering to achieve dynamic correction of the residuals.

[0081] Secondly, an intelligent coordinate system correction tool is built. This is achieved through the development of an error-compensated coordinate transformation system. This system, based on a residual neural network-based coordinate offset prediction model, achieves millimeter-level coordinate transformation accuracy. It unifies the mathematical foundation of the acquired data from the national inventory of all natural resource assets, such as the conventional "2000 National Geodetic Coordinate System" (CGCS 2000) and "1985 National Elevation Datum." The residual neural network-based coordinate offset prediction model requires three parameters as input: the original coordinate system type, projection method, and elevation datum. It also integrates surveying control point data (GNSS reference station data) to complete the coordinate prediction. For millimeter-level coordinate errors, a dual-loop feedback supplementation algorithm is designed for adjustment. The first loop (outer loop) fits the transformation model parameters using the least squares method, while the second loop (inner loop) uses Kalman filtering to dynamically correct the residuals.

[0082] As one implementation method, classifying the calibration and inventory results data to obtain preprocessed results data may include: constructing a natural resource asset ontology using dynamic knowledge graph technology; and extracting semantic features from the calibration and inventory results data based on the natural resource asset ontology using a named entity recognition model, where the named entity recognition model is a BiLSTM+CRF model. Before extracting semantic features from the calibration and inventory results data based on the natural resource asset ontology using the named entity recognition model to obtain the preprocessed results data, the process may include: constructing a semantic similarity matrix based on the natural resource asset ontology; detecting the calibration and inventory results data based on the semantic similarity matrix; if a new data source's field naming rule is detected, expanding the nodes of the natural resource asset ontology; if no new data source's field naming rule is detected, not expanding the nodes of the natural resource asset ontology.

[0083] Further semantic knowledge mapping is constructed by introducing dynamic knowledge graph technology into the semantic mapping toolbox to establish an ontology database for the natural resource asset domain (containing attribute semantic rule trees for resource types such as land, minerals, forests, grasslands, and wetlands). First, a BiLSTM+CRF (a classic named entity recognition) model is used to extract semantic features from multi-source data fields. Then, a semantic similarity matrix is ​​constructed based on the ontology database (formula: Sim=α*A+β*B+γ*C, where A represents structural similarity; B represents semantic relevance; C represents resource domain weight; model coefficients α, β, and γ are dynamically adjusted through machine learning). An adaptive mapping engine is developed, which automatically expands the ontology database nodes when new data source field naming rules are detected, ultimately achieving intelligent mapping. By constructing the ontology database and using the BiLSTM+CRF model to achieve automated discrimination of data of the same resource type, asset inventory results data categorized by resource type are finally obtained.

[0084] The automated block classification and preprocessing of the data from the national inventory of natural resource assets can be truly achieved through multi-source data transformation engines, intelligent coordinate transformation systems, and semantic mapping tools.

[0085] This application, by establishing a multi-source data conversion engine, intelligent coordinate system correction, and semantic mapping toolbox, has completed data format conversion, unified coordinate system, and automatic data classification. It has achieved automated preprocessing of the survey results data of various categories of state-owned natural resource assets, solved the problem of heterogeneous multi-source data, formed a unified data foundation, and provided a standard data processing paradigm for the data to be entered into the database.

[0086] S22, the preprocessed result data and the basic element data are integrated to obtain the asset inventory result standard dataset.

[0087] After receiving the preprocessed data, the natural resource asset inventory results data entry device integrates the preprocessed data and the basic element data to obtain a standard dataset of asset inventory results.

[0088] In other words, the preprocessed datasets of the national natural resource asset inventory results are integrated to generate a standard dataset of national natural resource asset inventory results.

[0089] As one implementation method, the preprocessed result data and the basic element data are integrated to obtain a standard dataset of asset inventory results, including: obtaining the inventory time range, inventory spatial range, and preset type classification; encoding the preprocessed result data that is within the inventory time range and inventory spatial range to obtain spatial element identification codes and type data identification codes; segmenting the basic element data according to the inventory time range and inventory spatial range to obtain time and space requirement data; and organizing the preprocessed result data according to the preset type classification, spatial element identification codes, type data identification codes, and time and space requirement data to obtain the standard dataset of asset inventory results.

[0090] First, a unified time and spatial scope for the inventory is established. Based on the time requirements for data entry, inventory data of all state-owned natural resource assets that meet the timeframes are collected. Then, based on the spatial scope requirements, inventory data of various categories of resource assets within the spatial scope are organized. Subsequently, each piece of spatial data related to state-owned natural resource assets is coded to determine a unique identifier for the spatial element (denoted as kjwybsm). Simultaneously, the corresponding tabular, document, and image data are also coded, with unique element codes denoted as kjwybsm+bg, kjwybsm+wd, and kjwybsm+tx, respectively. At the same time, the basic element data is segmented according to time and spatial requirements, forming geographic unit data, land cover data, and "three zones and three lines" data that meet both time and spatial needs. Among these, geographic unit data includes, for example... Figure 3 As shown; the main data contained in land cover are as follows Figure 4 As shown.

[0091] Subsequently, the data from the inventory of all state-owned natural resource assets were integrated based on the time and spatial scope of the inventory, such as... Figure 5 As shown, a standard dataset of natural resource asset inventory results is generated by classifying and integrating data into 10 major categories: state-owned agricultural land, state-owned construction land, state-owned unused land, state-owned construction land with undetermined users, minerals, state-owned forests, state-owned grasslands, state-owned wetlands, state-owned water resources, and marine resources. Each type of resource asset inventory dataset consists of data on the physical quantity of resources, asset value, and ownership of usage rights.

[0092] S30, perform full-chain quality inspection on the asset inventory results standard dataset to obtain the quality inspection results standard dataset;

[0093] After obtaining the standard dataset of the asset inventory results, the natural resource asset inventory results data entry device performs a full-chain quality inspection on the standard dataset of the asset inventory results to obtain the quality inspection results standard dataset.

[0094] As one implementation method, performing full-chain quality inspection on the asset inventory results standard dataset to obtain a quality inspection results standard dataset may include: designing a quality inspection rule engine using SQL technology, constructing data specification rules and logical verification matrices; constructing an asset inventory results data quality inspection library based on the data specification rules and logical verification matrices; calling data from the asset inventory results standard dataset through Python scripts in the asset inventory results data quality inspection library, and extracting the data from the asset inventory results standard dataset after quality inspections for data integrity, logical consistency, coordinate system consistency, attribute standardization, value range correctness, spatial reference, and time validity to obtain the quality inspection results standard dataset.

[0095] As another implementation method, after extracting the data from the asset inventory result standard dataset through quality inspections such as data integrity, logical consistency, coordinate system consistency, attribute standardization, value range correctness, spatial reference, and time validity, to obtain the quality inspection result standard dataset, the following steps may be included: using a risk-weighted sampling algorithm to determine a dynamic sampling ratio based on historical data quality records; sending the data in the quality inspection result standard dataset to the quality inspection terminal according to the dynamic sampling ratio; and receiving the manual review record returned by the quality inspection terminal based on the data in the quality inspection result standard dataset.

[0096] The constructed standard dataset of the results of the national natural resource asset inventory to be included in the database will be subjected to a full-chain quality inspection of the data by a combination of automated inspection and manual review.

[0097] The automated quality inspection utilizes Python technology to develop batch inspection scripts, performing batch checks categorized by resource type. Key checks include data integrity, logical consistency, coordinate system consistency, attribute standardization, value range correctness, spatial reference, and time validity. A quality inspection rule engine is designed using SQL technology to construct data standardization rules and logical verification matrices for the national natural resource asset inventory results. A standard asset inventory data quality inspection database is established, and Python scripts are used to call this database, completing the automated inspection of the national natural resource asset inventory results data. If the automated inspection fails, steps S21 and S22 are executed for data processing, re-preprocessing and data integration, until all data to be entered into the database passes the automated inspection.

[0098] After completing automated data checks, manual review is used to conduct a final check before data is entered into the database, achieving end-to-end quality control of the data before entry. Manual review employs random sampling, using a Python script to randomly sample data on all state-owned natural resource assets. A risk-weighted sampling algorithm is used, dynamically adjusting the sampling ratio based on historical data quality records for each resource type (e.g., 5% for minerals, 3% for forests). This rule is followed by automatic sampling to improve quality control efficiency. The sampled data is then handed over to professional data quality control personnel for individual quality checks.

[0099] The data quality inspection process (including automated results and manual review records) is stored on the blockchain to ensure that the quality inspection process is traceable and tamper-proof, thus meeting the needs of natural resource data auditing.

[0100] This application utilizes Python technology to call SQL technology to construct data standardization rules and logical verification matrices for the results of the national natural resource asset inventory. The constructed standard asset inventory data quality inspection library realizes automated inspection of the national natural resource asset inventory data, improves data quality inspection efficiency, and reduces data errors caused by manual quality inspection.

[0101] S40: The quality inspection results standard dataset is stored in the database in a hierarchical manner to obtain the asset inventory results database.

[0102] After obtaining the quality inspection result standard dataset of spatial element identification code and type data identification code, the natural resource asset inventory result data entry device enters the quality inspection result standard dataset into the database in a hierarchical manner, thus obtaining the asset inventory result database.

[0103] One implementation method involves storing the quality inspection result standard dataset in a hierarchical manner to obtain an asset inventory result database. This can include: establishing an asset inventory result database comprising a data storage layer, a data index layer, a quality assessment layer, and a comprehensive analysis layer; identifying and classifying the data in the quality inspection result standard dataset based on the spatial element identifier code and type data identifier code, and storing the data in the quality inspection result standard dataset in the data storage layer using distributed object storage technology; obtaining the attribute table data corresponding to the data in the quality inspection result standard dataset and storing the attribute table data corresponding to the data in the data index layer; performing quality assessment on the data in the quality inspection result standard dataset using a data quality scoring model to obtain quality inspection report data, and storing the quality inspection report data in the quality assessment layer. The data quality scoring model is a multi-dimensional quality assessment indicator system, including scores for data completeness, accuracy, timeliness, consistency, and traceability; and if a comprehensive analysis instruction is received, obtaining analysis report data through geographic weighted principal component analysis based on the information in the comprehensive analysis instruction, and storing the analysis report data in the comprehensive analysis layer.

[0104] The standard dataset of the national natural resource asset inventory results to be entered into the database is layered after quality inspection. The data from the national natural resource asset inventory results is characterized by its wide range of sources, diverse types, and large volume; therefore, a layered database entry method is adopted to complete the data entry. The layered database entry mainly includes a four-layer architecture: data storage layer, data indexing layer, quality assessment layer, and comprehensive analysis layer.

[0105] The data storage layer, serving as the core data foundation of the database after data entry, uses unique identifiers for data identification and classification. Utilizing Python technology and distributed object storage, it ultimately forms a distributed spatial database capable of supporting petabyte-level data storage, solving the challenge of massive data storage. Considering data updates, data is stored in a hot / cold tier. Hot data (the latest survey results or updated data) uses an in-memory database for millimeter-level response, while cold data (historical versions of survey results) uses distributed columnar storage with compression to reduce petabyte-level storage costs. Simultaneously, decoupling is achieved through physical and logical data layering. Data is categorized by result type into spatial data, tabular data, document data, and image data. Tabular, document, and image data establish connections with spatial data through mapping rules, serving as supplementary information for spatial data. Spatial data is physically isolated and decomposed into a basic layer (including geographic unit data, land cover data, and "three zones and three lines" data) and a thematic layer (i.e., data from the national natural resource asset survey, stored in partitions according to resource category). By using the GeoMesa framework, spatial data and attribute data are uniformly encoded into a spatiotemporal cube, realizing integrated spatial-attribute storage and improving efficiency for subsequent relational queries.

[0106] The data indexing layer creates a composite spatial index, utilizing a combination of R-Tree and Geohash technologies to build a two-layer spatial index structure. This indexes the inventory results data after it has been stored. User-adaptive indexes can be customized according to the needs of natural resource management, or indexes can be based on resource catalogs, enabling rapid response to TB-level data queries. R-Tree is a self-balancing tree-like data structure used to store spatial objects with multi-dimensional coordinates. It uses the minimum bounding box (MBR) as the basis for spatial indexing, organizing data through a hierarchical tree structure to ensure that irrelevant objects are quickly filtered out during queries. Through R-Tree, the storage of all nationally owned natural resource asset inventory results data is based on its bounding rectangle, with intermediate nodes aggregating the MBRs of lower-level nodes to form higher-dimensional spatial index areas. This structure achieves efficient retrieval through hierarchical nesting of bounding boxes. During queries, only the bounding boxes need to be compared to locate the target data, eliminating the need to search through each underlying record, significantly reducing the computational workload of retrieving nationally owned natural resource asset inventory results data. Furthermore, based on spatial indexing using R-Tree technology, a resource type weight factor is introduced to prioritize indexing frequently queried resources, such as construction land, which can reduce the retrieval time in overlapping areas of MBR and further improve indexing efficiency.

[0107] The quality assessment layer constructs a multi-dimensional quality assessment indicator system, including data completeness, accuracy, timeliness, consistency, and traceability, thereby designing a data quality scoring model, as shown in the following formula:

[0108]

[0109] In the formula: a1, a2, a3, a4, and a5 are the coefficients of the data quality scoring model. The model is dynamically adjusted adaptively through machine learning to reflect the data quality priorities of different resource types; W represents data integrity; Z represents data accuracy; S represents data timeliness; Y represents data consistency; and K represents data traceability. Based on the data quality scoring model, a data quality evaluation matrix is ​​established for the results of the national natural resource asset inventory, and a data quality evaluation report is generated. Furthermore, the quality inspection process (including automated results and manual review records) is stored to ensure that the data quality inspection process is traceable and tamper-proof, meeting the needs of natural resource data auditing, and establishing a data blockchain for evidence storage and traceability.

[0110] The comprehensive analysis layer performs a comprehensive analysis of the data from the national inventory of all-people-owned natural resource assets after it has been entered into the database, providing guidance for the application of this data. The data from the national inventory of all-people-owned natural resource assets comprises multiple resource categories and has high dimensionality. To improve the efficiency of comprehensive data analysis, dimensionality reduction is needed for the multi-resource-type data. However, existing dimensionality reduction methods do not consider the spatial heterogeneity (spatial autocorrelation) of the natural resource asset inventory data. Therefore, this layer employs Geographically Weighted Principal Component Analysis (GWPCA) to reduce the dimensionality of the spatial data, minimizing the impact of high dimensionality (multiple resource categories) and ensuring that resource data from adjacent regions maintain clustering characteristics in the low-dimensional space. This improves the stability and reliability of the database, thereby enhancing the accuracy and efficiency of big data comprehensive analysis. The difference between GWPCA and ordinary principal component analysis (PCA) lies in the inclusion of a geographic weight matrix when calculating the covariance matrix. PCA generates new variables through linear combinations of the original variables, significantly reducing the number of variables included in the model, thus achieving data dimensionality reduction while retaining most of the information. GWPCA uses kernel weighting and geographic weighting to find localized principal components at the target location. At the target location, neighboring observations are weighted using a distance decay weighting function, and then standard principal component analysis is locally applied to its specific weighted subset of data. The specific principle is as follows:

[0111] 1. Geographically weighted variance-covariance matrix at the target location: ,in It is a diagonal matrix of geographic weights generated by the selected kernel weighting function, where X represents the asset inventory results data matrix of the target area.

[0112] 2. The GWPCA calculation for a spatial location i is as follows: Where L is an n×m dimensional matrix of eigenvectors, and V is a diagonal matrix of eigenvalues.

[0113] 3. The score matrix at the same position i: ;

[0114] 4. Divide each local eigenvalue by , "Trace" is an abbreviation for the trace of a matrix. The trace of a matrix is ​​the sum of the elements on the main diagonal of a square matrix. It can find a localized version of the proportion of each component to the total variance in the original data.

[0115] The window size for this localized GWPCA application is controlled by the kernel bandwidth, i.e., the geographically weighted window size. Smaller bandwidth results in more rapid spatial variation of the results, while larger bandwidth produces results that increasingly resemble global principal component analysis.

[0116] As another implementation, after storing the quality inspection result standard dataset in a hierarchical manner to obtain the asset inventory result database, the process can include: receiving result data update instructions and updating the asset inventory result database according to the result data update instructions. The result data update instructions include data modification instructions and data deletion instructions. When a data modification instruction is received, the questionable data corresponding to the data modification instruction is found through the data index layer, and the correct data in the data modification instruction replaces the data at the corresponding position in the asset inventory result database, inheriting the unique identifier of the original data. When a data deletion instruction is received, the questionable data corresponding to the data modification instruction is found through the data index layer, and the data at the corresponding position in the asset inventory result database is deleted in the data deletion instruction.

[0117] In other words, according to the requirements of the national task of inventorying all state-owned natural resource assets, the corresponding asset inventory results data will be updated and entered into the database according to the time task nodes. This also includes modifying and deleting data already in the database. The updating and entry of asset inventory results data into the database is carried out sequentially according to steps S21, S22, and S30, ultimately completing the updating and entry of the inventory results data for all state-owned natural resource assets at the new time node. Simultaneously, the entered asset inventory results data is stored in blocks according to annual time nodes. The modification and deletion of already entered data first involves finding the questionable data through the data index layer, then replacing it with the correct data (assigning null values ​​to data to be deleted), inheriting the unique identifier of the original data, thus completing the modification and deletion of the questionable data.

[0118] In summary, the process involves: acquiring resource and asset inventory data and basic element data; processing this data to obtain a standard dataset for the asset inventory results; conducting full-chain quality inspection on the standard dataset to obtain a quality inspection standard dataset; and then storing the quality inspection standard dataset in a hierarchical manner to create an asset inventory database. This improves the digital governance capabilities of natural resource asset inventory data.

[0119] This application utilizes Python technology and distributed object storage for data storage. The data indexing layer employs R-Tree spatial indexing. In the comprehensive analysis layer, local constraint learning is introduced to reduce the dimensionality of the data and improve the data response rate. A four-layer architecture is adopted to complete the mapping and database entry of asset inventory results data, which greatly improves the efficiency and standardization of data entry, saves memory after data entry, increases the scientificity and reliability of the database, and improves the response efficiency after data entry. It provides a new template for mapping and database entry of the results of the national natural resource asset inventory.

[0120] For those consistent with the above, please refer to Figure 6 , Figure 6 This application provides a schematic diagram of a device for storing natural resource asset inventory results data. (See attached diagram.) Figure 6 As shown, the device includes:

[0121] Data acquisition module 601 is used to acquire resource and asset inventory results data and basic element data;

[0122] Data processing module 602 is used to process the resource asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results.

[0123] The data quality inspection module 603 is used to perform full-chain quality inspection on the asset inventory results standard dataset to obtain the quality inspection results standard dataset.

[0124] The data entry module 604 is used to enter the quality inspection result standard dataset into the database in a hierarchical manner to obtain the asset inventory result database.

[0125] This application also provides a terminal device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor. When the processor executes the computer program, it implements the steps in the embodiment of the method for storing natural resource asset inventory results data.

[0126] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the natural resource asset inventory results data entry methods described in the above method embodiments.

[0127] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the natural resource asset inventory results data entry methods described in the above method embodiments.

[0128] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.

[0129] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0131] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0132] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for storing data from a natural resource asset inventory, characterized in that, include: Obtain data on the results of the resource and asset inventory and basic element data; Data processing is performed on the resource and asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results; A full-chain quality inspection was performed on the asset inventory results standard dataset to obtain the quality inspection results standard dataset. The quality inspection results standard dataset is stored in the database in a hierarchical manner to obtain the asset inventory results database; A full-chain quality inspection was performed on the asset inventory results standard dataset to obtain the quality inspection results standard dataset, including: A quality inspection rule engine was designed using SQL technology, and data standardization rules and logical verification matrices were constructed. A quality inspection database for asset inventory results will be constructed based on data standardization rules and logical verification matrices. The data in the asset inventory results standard dataset is called by a Python script in the asset inventory results data quality inspection library, and the data in the asset inventory results standard dataset is extracted after quality inspection to obtain the quality inspection results standard dataset.

2. The method for storing natural resource asset inventory results data according to claim 1, characterized in that, Data processing is performed on the resource and asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results, including: The resource asset inventory results data are transformed, coordinate corrected, and classified to obtain preprocessed results data. The preprocessed data and the basic element data are integrated to obtain a standard dataset of asset inventory results.

3. The method for storing natural resource asset inventory results data according to claim 2, characterized in that, The resource asset inventory data undergoes data transformation, coordinate correction, and data classification to obtain preprocessed result data, including: The resource and asset inventory results data are converted from multiple data formats to obtain converted inventory results data; The coordinates of the converted inventory results data are corrected by error compensation using a residual neural network model to obtain corrected inventory results data; The data from the correction and verification results are classified to obtain preprocessed data.

4. The method for storing natural resource asset inventory results data according to claim 3, characterized in that, The resource and asset inventory results data are converted from multiple sources to obtain converted inventory results data, including: converting the conventional format resource inventory results data from multiple sources to obtain conventional converted inventory results data; Non-standard resource inventory results data are converted from multi-source data formats using a format conversion model to obtain non-standard converted inventory results data. The format conversion model is a deep learning model built using the Transformer architecture and trained on historical non-standard format resource inventory results data. The routine conversion and inventory results data and the non-standard conversion and inventory results data are combined to obtain the conversion and inventory results data; Alternatively, the coordinates of the transformed inventory results data may be corrected through an error compensation process using a residual neural network model to obtain corrected inventory results data, including: Based on the transformed and cleared data, determine the original coordinate system type parameters, projection method parameters, and elevation datum parameters; The original coordinate system type parameters, projection method parameters, and elevation datum parameters are input into a preset residual neural network model, and the obtained survey control point data are fused into the residual neural network model to obtain the corrected and cleared result data. The residual neural network model is used to perform error compensation correction on the coordinates of the transformed and cleared result data. The residual neural network model is a coordinate error compensation correction model constructed by fitting the transformation model parameters by least squares method and using Kalman filtering to achieve dynamic correction of the residuals. Alternatively, the corrected and verified data can be classified to obtain preprocessed data, including: A natural resource asset ontology database is constructed using dynamic knowledge graph technology; The semantic features of the corrected inventory results data are extracted from the natural resource asset ontology using a named entity recognition model, resulting in preprocessed data after data classification. The named entity recognition model is a BiLSTM+CRF model.

5. The method for storing natural resource asset inventory results data according to any one of claims 2 to 4, characterized in that, The preprocessed data and the basic element data are integrated to obtain a standard dataset of asset inventory results, including: Obtain the time range, spatial range, and preset type classification for the investigation; The preprocessed data that are within the time and spatial scope of the survey are coded to obtain spatial element identification codes and type data identification codes; The basic element data is segmented according to the time and space scope of the investigation to obtain the time and space requirement data; The preprocessed data is organized according to the preset type classification, spatial element identification code, type data identification code, and time and space requirement data to obtain the asset inventory result standard dataset.

6. The method for storing natural resource asset inventory results data according to claim 5, characterized in that, The quality inspection results standard dataset is stored in a hierarchical manner to obtain the asset inventory results database, which includes: Establish an asset inventory results database that includes a data storage layer, a data indexing layer, a quality assessment layer, and a comprehensive analysis layer; The data in the quality inspection result standard dataset is identified and classified according to the spatial element identification code and type data identification code, and the data in the quality inspection result standard dataset is stored in the data storage layer using distributed object storage technology. Obtain the attribute table data corresponding to the data in the quality inspection result standard dataset, and store the attribute table data corresponding to the data in the quality inspection result standard dataset in the data index layer; The quality inspection results standard dataset is used to conduct a quality assessment of the centralized data through a data quality scoring model to obtain quality inspection report data, and the quality inspection report data is stored in the quality assessment layer; If a comprehensive analysis instruction is received, the analysis report data is obtained through geographic weighted principal component analysis based on the information in the instruction, and the analysis report data is stored in the comprehensive analysis layer.

7. A device for storing data from a natural resource asset inventory, characterized in that, include: The data acquisition module is used to acquire resource and asset inventory results data and basic element data; The data processing module is used to process the resource asset inventory results data and the basic element data to obtain a standard dataset of asset inventory results. The data quality inspection module is used to design a quality inspection rule engine using SQL technology, construct data standardization rules and logical verification matrices; construct an asset inventory results data quality inspection library based on the data standardization rules and logical verification matrices; call data from the asset inventory results standard dataset through Python scripts in the asset inventory results data quality inspection library, and extract the asset inventory results standard dataset from the quality-inspected data to obtain the quality inspection results standard dataset. The data entry module is used to enter the quality inspection results standard dataset into the database in a hierarchical manner, thereby obtaining the asset inventory results database.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for storing natural resource asset inventory results data as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for storing natural resource asset inventory results data as described in any one of claims 1 to 6.

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