Natural resource asset checking data accounting processing method

Abstract

The invention provides an accounting processing method for natural resource asset checking data, and relates to the technical field of natural resource checking, and the method comprises the steps: obtaining the natural resource checking data of a to-be-checked region, extracting a resource pattern spot, and recognizing a neighborhood relation and a common boundary length; obtaining standard price system data; acquiring an accounting price by adopting a self-adaptive matching strategy based on a resource type, adopting self-adaptive weight matching of price heterogeneity perception for construction land types, and adopting layered degradation matching for non-construction land types; quantitative accounting is carried out according to the accounting price to obtain a basic accounting value, an abnormal pattern spot is identified by adopting a consistency verification strategy based on a neighborhood pattern spot and a common boundary length, and the abnormal pattern spot is corrected by adopting a self-adaptive interpolation correction strategy to obtain a final accounting value; and performing quality checking on the accounting result, and performing summary statistics according to classification conditions to generate a checking and accounting result report. According to the invention, high-precision and high-coverage natural resource checking data accounting is realized.

Description

Technical Field

[0001] This invention relates to the field of natural resource inventory technology, and in particular to a method for accounting and processing natural resource asset inventory data. Background Technology

[0002] The inventory and accounting of natural resource assets requires the physical quantity statistics and value indicators of various types of natural resources, including land, forests, grasslands, wetlands, water resources, and minerals. This includes spatial data integration, price calculation, and quality verification. In practice, price matching of resource patches is a crucial step in the accounting process, and its accuracy directly affects the final results. When resource patches span multiple price zones, how to reasonably allocate the weights of each price zone to obtain accurate calculated prices is a technical problem that needs to be solved. Furthermore, after batch accounting calculations on a large number of patches, effectively identifying and handling anomalies in the accounting results caused by data abnormalities, matching errors, or improper parameter settings is also an important aspect of ensuring accounting quality.

[0003] During the quantitative accounting process, spatial discontinuities or statistical outliers may occur in the accounting results. Spatial discontinuities manifest as significant differences in the accounting results of adjacent map features, even though these map features are geographically adjacent and have similar resource conditions, and theoretically, the accounting results should have a certain degree of continuity. Statistical outliers manifest as accounting values ​​for individual map features significantly deviating from the normal distribution range among map features of the same type. These anomalies may be caused by various reasons, such as errors in basic data, price matching mistakes, missing attribute information, or improper parameter settings in the accounting model. In large-scale inventory and accounting tasks, manually checking and correcting outlier map features one by one is a huge workload, and the coverage rate is difficult to guarantee, affecting the overall quality of the accounting results.

[0004] Chinese patent application CN113610622A discloses a land value assessment method based on multi-source data. This method acquires data from multiple sets of influencing indicators (including productivity, suitability, and risk indicators) for the land to be assessed. It determines the scores of these influencing indicator sets based on multiple influencing factors, assigns weights to each score using the analytic hierarchy process (AHP), calculates a land value index, and maps this index to a value regression model to obtain the land value of the land to be assessed. This method quantifies the impact of suitability and risk indicators on land value and uses the AHP to comprehensively assess multi-dimensional influencing factors. However, this method has shortcomings in areas such as cross-regional parcel price matching and quality control of calculation results, and it does not provide a quality verification and anomaly correction mechanism for the calculation results. Summary of the Invention

[0005] In view of this, the present invention provides a method for accounting and processing natural resource asset inventory data to solve the problem of inaccurate price acquisition when resource plots span multiple price areas, as well as the problem of the lack of effective quality verification and anomaly correction mechanism for accounting results.

[0006] The technical solution of this invention is implemented as follows: This invention provides a method for accounting and processing natural resource asset inventory data, including: S1. Under a unified reference time point, acquire natural resource inventory data of the area to be investigated, extract resource patches from them and classify them into construction land, agricultural land and forest land and trees according to resource type, extract the attribute information and geometric center coordinates of each resource patch, and identify the neighboring patches of each resource patch and the length of the common boundary between them through spatial topology analysis. S2. Obtain standard price system data, including price system parameter tables for various resources; S3. Match each resource patch with the standard price system data, and use an adaptive matching strategy based on resource type to obtain the accounting price; S4. Based on the calculated price and attribute information of each resource patch, the basic calculated value is obtained by quantitative calculation. An abnormal patch is identified by a consistency verification strategy based on the neighboring patches and the length of the common boundary. For the abnormal patches, an adaptive interpolation correction strategy is used to interpolate and correct the basic calculated value and the reference calculated value calculated based on the basic calculated value of the neighboring patches to obtain the final calculated value. S5. Conduct quality checks on the accounting results of all resource patches, summarize and statistically analyze them according to classification conditions, and generate an inventory and accounting result report.

[0007] Preferably, in step S3, an adaptive matching strategy based on resource type is used to obtain the accounting price, specifically including: a hierarchical degradation matching strategy for non-construction land resource patches, and an adaptive weight matching strategy based on price heterogeneity perception for construction land resource patches. Among them, the adaptive weight matching strategy for price heterogeneity perception includes: A topological overlay operation is performed between the resource patch vector layer and the standard price system spatial layer. The intersection area between the resource patch and each price region is obtained through spatial intersection operations. Let the intersection area between the m-th price region and the resource patch be denoted as... The total area of ​​the resource patch is S, where the subscript m represents the m-th price region; Extract the geometric center coordinates and calculate the price parameters for each price region. Let the geometric center coordinates of the m-th price region be denoted as... The calculation price parameters are as follows: ; Calculate the geometric center coordinates of resource patches Geometric center coordinates of each price zone Euclidean distance between ; The price difference is calculated based on the accounting price parameters of each price region. The weighting calculation mode is adaptively selected based on the magnitude of the price difference, and the accounting price parameters of each price region are... With corresponding weights The calculated price of resource patches is obtained by performing a weighted summation. .

[0008] Preferably, the step of calculating the price difference based on the calculated prices of each price region, and adaptively selecting the weight calculation mode according to the magnitude of the price difference, specifically includes: The price difference is calculated using the coefficient of variation: Where n is the total number of price regions that overlap with resource patches; and These represent the maximum value function and the minimum value function, respectively. Set low threshold and high threshold When price difference When the price difference is high, the area-weighted model is used; when the price difference is low, the area-weighted model is used. When the price difference is high, a position-strengthened weighting model is adopted; when the price difference is low... When the threshold is between low and high, linear interpolation is used to transition between area weight and position enhancement weight.

[0009] Preferably, the weight of the m-th price region in the area-weighted model is: The weight calculation process for the position-enhanced weighting mode is as follows: Calculate the average distance between all overlapping price regions and the geometric center of the resource patch: Calculate the distance weighting factor for the m-th price region: In the formula, It is a very small positive number, used to avoid the denominator being zero; Calculate the unnormalized composite weights: The unnormalized composite weights of all overlapping price regions are normalized to obtain the position enhancement weights: In the formula, This is a price range index.

[0010] Preferably, the hierarchical degradation matching strategy specifically includes: Based on the degree of influence of attribute conditions on price differences, matching conditions are divided into three levels: core layer conditions, important layer conditions, and auxiliary layer conditions. Construct a matching query sequence from strict to lenient, including a first-level query that requires all conditions of the core layer, important layer and auxiliary layer to be exactly matched, a second-level query that requires the conditions of the core layer and important layer to be exactly matched, a third-level query that only requires the conditions of the core layer to be exactly matched, and a fourth-level query that performs nearest neighbor matching within the same administrative division. Starting from the first-level query, the attribute information of the resource patch is used to perform conditional matching queries in the price system parameter table. If the query result is a unique record, the price parameter of that record is extracted as the accounting price parameter. If the query result is empty, the query is downgraded to the next level and continues to be executed. If the query result is multiple records, the attribute distance between each candidate record and the target resource patch is calculated and the record with the smallest attribute distance is selected as the matching result.

[0011] Preferably, step S4 employs a consistency verification strategy based on neighborhood patches and common boundary length to identify abnormal patches, specifically including: After calculating the basic values ​​of all resource patches, for the target resource patch, read its set of neighboring patches and the length of the common boundary with each neighboring patch. Let the target resource patch be patch i, its set of neighboring patches be N(i), and the length of the common boundary with neighboring patch j be... ; Calculate neighborhood weights based on the length of the common boundary: Calculate the local average accounting value: in The number of neighboring patches, This is the basic accounting value for patch i. This is the basic accounting value for the neighboring map patch j; Computational spatial consistency index: When the spatial consistency index exceeds a preset threshold and the statistical outlier exceeds a preset threshold, the resource patch is identified as an abnormal patch.

[0012] Preferably, the step of determining the resource patch as an abnormal patch when the spatial consistency index exceeds a preset threshold and the statistical outlier exceeds a preset threshold specifically includes: Set a consistency threshold ,like Then mark patch i as a suspected anomalous patch; Calculate the statistical outlier of patch i within the same administrative region and the same resource type: in This is the average value calculated for the same type of map features. The standard deviation of the calculated values ​​for the same type of map features; If we count outliers Then the suspected abnormal patches will be confirmed as abnormal patches.

[0013] Preferably, the step of using an adaptive interpolation correction strategy to interpolate and correct the basic calculated value and the reference calculated value based on the basic calculated value of the neighboring patches to obtain the final calculated value specifically includes: Calculate the reference accounting value based on neighborhood information: The corrected accounting value is calculated using an adaptive interpolation correction formula: in The adaptive interpolation coefficients are determined based on the spatial consistency index. In the formula, This is the consistency threshold.

[0014] Preferably, in step S4, the basic accounting value is obtained by quantitative calculation based on the accounting price parameters and attribute information of each resource patch, specifically including: For construction land resource patches, the basic accounting value calculation formula is as follows: Where A is the area of ​​the map patch. For calculating price parameters, r represents the plot ratio attribute, and L represents the location level based on the distance between the geometric center coordinates of the map patch and the administrative center. Let f be the function of plot ratio. This is a locational influence function; For agricultural land resource patches, the basic accounting value calculation formula is as follows: Where Q represents the quality grade attribute and G represents the slope grade attribute. The quality grade influence function, This is the slope grade influence function; For forest land and tree resource patches, the basic accounting value is: The accounting value for the forest land portion is: The accounting value for the timber portion is: Where M represents forest stock volume, T represents forest type code, C represents age group type code, and D represents canopy closure. For forest type influence function, Let be the age group influence function. This is the function that influences canopy closure.

[0015] Preferably, step S4 further includes boundary case handling when implementing adaptive interpolation correction: for isolated patches without neighboring patches, the spatial consistency verification step is skipped, and anomaly judgment is made only by statistical outlier; for patches with fewer than a preset number threshold, or where the proportion of patches marked as anomalous in the neighboring patches exceeds a preset proportion threshold, the adaptive interpolation coefficient is multiplied by a reduction coefficient to reduce the influence weight of neighborhood information; when calculating the reference accounting value of neighboring patches, only neighboring patches with the same resource type as the target patch are selected for calculation.

[0016] The present invention has the following advantages over the prior art: This invention improves the accuracy of cross-regional parcel price matching by employing an adaptive weight matching strategy based on price heterogeneity perception in the price matching process. It automatically selects the weight calculation mode based on the degree of price difference between price regions spanned by a parcel. When the price difference is large, the spatial distance between the geometric center of the parcel and the center of the price region is used to strengthen the influence of location weight; when the price difference is small, a simple area weight is used, thereby improving the accuracy of cross-regional parcel price matching. Furthermore, in the quantitative accounting process, a spatial consistency verification strategy based on neighboring parcels and the length of the common boundary is introduced. This strategy utilizes the spatial autocorrelation characteristics of a parcel and its neighboring parcels in the accounting results to identify abnormal parcels. An adaptive interpolation correction strategy is then used to dynamically adjust the interpolation coefficients between the original accounting value and the neighboring reference accounting value based on the degree of spatial consistency deviation. This achieves automated quality control of large-scale inventory accounting results, thus solving the technical problems of insufficient accuracy in cross-regional parcel price matching and insufficient quality control capabilities in existing technologies. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or 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 of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a diagram illustrating the technical implementation of the present invention; Figure 3 This is a flowchart of the adaptive weight matching strategy of the present invention; Figure 4 This is a flowchart of the hierarchical degradation matching strategy of the present invention; Figure 5 This is a flowchart of the spatial neighborhood consistency verification strategy of the present invention; Figure 6 This is a flowchart of the adaptive interpolation correction strategy of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] like Figure 1 and Figure 2 As shown, the present invention provides a method for accounting and processing natural resource asset inventory data, including: S1. Under a unified reference time point, acquire natural resource inventory data of the area to be investigated, extract resource patches from them and classify them into construction land, agricultural land and forest land and trees according to resource type, extract the attribute information and geometric center coordinates of each resource patch, and identify the neighboring patches of each resource patch and the length of the common boundary between them through spatial topology analysis. S2. Obtain standard price system data, including price system parameter tables for various resources; S3. Match each resource patch with the standard price system data, and use an adaptive matching strategy based on resource type to obtain accounting price parameters; S4. Quantify and calculate the basic accounting value based on the accounting price parameters and attribute information of each resource patch. Use a consistency verification strategy based on neighboring patches and common boundary length to identify abnormal patches. Use an adaptive interpolation correction strategy to interpolate and correct the basic accounting value and the reference accounting value calculated based on the basic accounting value of neighboring patches to obtain the final accounting value. S5. Summarize and statistically analyze the final accounting values ​​of all resource patches according to the classification conditions, and generate an inventory and accounting result report.

[0021] In one embodiment of the present invention, step S1 includes: First, the administrative boundaries and spatial scope of the area to be investigated are determined. Multi-source natural resource inventory data is collected within this area. The sources of this data include annual land survey results, cadastral survey results, natural resource ownership registration results, real estate registration results, mineral resource reserves databases, and forest, grassland, wetland, and desertification survey results. Among these, the annual land change survey data is continuously updated based on the results of the Third National Land Survey (completed in 2019). Using remote sensing, surveying, and geographic information technologies, and based on orthophoto maps, land use status information, land types, areas, and ownership information are obtained through field surveys, forming a land use vector covering the entire country. Databases include: Forest resource inventory data, sourced from the National Continuous Forest Resource Inventory System and forest resource planning and design surveys, containing information on forest distribution, forest type, stock volume, and canopy density; Grassland monitoring data, obtained through remote sensing monitoring and ground quadrat surveys, providing information on grassland resource types, area, and quality; Wetland resource survey data, recording the distribution range, wetland types, and ecological status of wetlands; Water resource census data, including information on the distribution and reserves of surface water and groundwater resources; and Mineral resource registration data, sourced from the mining rights management system, recording the type, reserves, and mining permit information of mineral resources. The data preprocessing includes overlay checking, format conversion, mathematical standardization, information extraction, data supplementation, and quality inspection. Overlay checking verifies the spatial location of data from different sources, identifying positional deviations and topological errors. Format conversion transforms various data types into a unified vector data format. Mathematical standardization converts all spatial data to the 2000 National Geodetic Coordinate System and the 1985 National Height Datum; for historical data using other coordinate systems, seven-parameter or four-parameter conversion methods are used. Information extraction extracts the geometric boundaries and attribute information of map features. Data supplementation fills in missing attribute information for map features by reviewing original survey records, conducting on-site verification, or logical deduction. Quality inspection performs a preliminary check on data completeness and logical consistency. The collected heterogeneous data underwent format parsing and coordinate system unification. Since data from different sources may use different data formats and coordinate systems, all spatial data should be uniformly converted to the 2000 National Geodetic Coordinate System and the 1985 National Elevation Datum. The format parsing process converts various types of data into a unified vector data format, extracting the geometric boundaries and attribute information of map features. Natural resource inventory data is automatically classified according to resource type. Based on the annual land survey classification system and the classification standards of various special resource surveys, resource map features are divided into three types: construction land, agricultural land, and forest land / tree land. Construction land includes land occupied by urban and rural construction, industrial and mining land, and transportation land; agricultural land includes cultivated land, orchards, and other agricultural land used for agricultural production; forest land / tree land includes arbor forests, shrub forests, sparse forests, and other land with trees and their forest resources. The classification process reads the land use code attribute field of the map features and automatically classifies them according to the coding rules. For cross-type map features, classification is performed according to the dominant land use type. Attribute fields were extracted for each resource patch object, and an attribute dataset was established. The extracted general attributes include patch number, resource type code, administrative division code, land category code, spatial area, geometric center coordinates of the patch, and rights restriction identifier. The rights restriction identifier records whether the patch is subject to mortgages, seizures, or other rights restrictions. For construction land resource patches, additional specific attributes such as plot ratio and land use nature were extracted. The plot ratio was obtained from construction land approval data or real estate registration data. For patches without a plot ratio, a value was calculated and assigned based on land use nature and building density. For agricultural land resource patches, additional specific attributes such as quality grade code, slope grade code, and homogeneous zone number were extracted. The quality grade code was determined based on agricultural land grading results, and the slope grade code was determined based on slope values ​​calculated using a digital elevation model. For forest land and forest trees resource patches, additional specific attributes such as forest type code, age group type code, unit volume, and canopy closure were extracted. These attributes mainly originated from sub-compartment attribute information in forest resource survey data. When extracting geometric attributes, the geometric center coordinates of each patch are calculated and recorded by taking the centroid coordinates of the patch polygon. Simultaneously, spatial topology analysis is used to identify and record the set of neighboring patches for each patch. Specifically, the spatial topology calculation function of the geographic information system software is used to determine whether there is a common boundary between patches. If the boundary lines of patch i and patch j overlap, patch j is considered a neighboring patch of patch i, and the length of the common boundary is calculated. The length of the common boundary is obtained by extracting the intersection line segment of the two patch boundaries and calculating its geographic length. For each patch, its set of neighboring patches, the corresponding common boundary length, and the basic attribute information of the neighboring patches are stored in the associated field of the patch attribute table.

[0022] In one embodiment of the present invention, step S2 includes: Obtain standard price system data corresponding to the areas to be investigated. The standard price system data is compiled based on national and industry technical specifications. For construction land resources, a construction land price calculation parameter system is established based on GB / T 18508-2014 "Urban Land Valuation Regulations" and GB / T18507-2014 "Urban Land Grading and Classification Regulations," as well as local benchmark land price results. For agricultural land resources, an agricultural land price calculation parameter system is formulated based on GB / T 28406-2012 "Agricultural Land Valuation Regulations" and GB / T 28405-2012 "Agricultural Land Grading Regulations." For forest land and timber resources, a forest land and timber price calculation parameter system is formulated based on LY / T 2407-2015 "Technical Specifications for Forest Resource Asset Appraisal" and relevant forestry industry standards. It should be noted that this invention uses construction land, agricultural land, and forest land / trees as the main accounting targets, but resource types also include grassland, wetlands, and water areas. This invention does not elaborate on the specific accounting processes for each type. In actual accounting, corresponding price accounting parameter systems can be established by referring to the accounting methods for the above three resource types. For example, grassland can refer to the agricultural land method, using grassland grade and vegetation coverage as influencing factors; water areas with aquaculture functions can refer to the agricultural land method, using water quality and water depth as influencing factors; wetlands with primarily ecological functions can establish a specialized parameter system based on wetland ecological value accounting standards. The adaptive matching strategy, spatial consistency verification, and anomaly correction core technologies of this invention are universal and applicable to the inventory and accounting of various natural resources. For construction land resources, the standard price system is provided in the form of a spatial price system layer. This spatial layer adopts a vector polygon format, usually Shapefile or GeoDatabase format, dividing the accounting area into several price zone polygons. Each price zone polygon is an independent spatial element, and its attribute table records fields such as price zone number, use type, grade identifier, and accounting price parameters. The division of price zones is based on the results of urban land grading, and different price zone systems are established according to commercial use, residential use, and industrial use. Within the same use, different levels are divided according to the quality of land conditions, and each level corresponds to a benchmark land price. The spatial layer also records the geometric boundary coordinate sequence of each price zone. The geometric center coordinates of the price zone can be extracted through a geographic information system. These coordinates are usually stored in the center point X coordinate and center point Y coordinate fields of the attribute table. If they are not stored, they are calculated in real time through geometric operations. For agricultural land resources, the standard price system is provided in the form of a price system parameter table, which is compiled based on the results of agricultural land grading and benchmark land price assessment. The parameter table adopts a multi-dimensional classification structure, establishing a price parameter index system according to classification dimensions such as administrative division, homogeneous zone number, land type code, quality grade, and slope grade. Each record in the parameter table corresponds to a benchmark land price parameter value under a specific combination of conditions, with the unit being yuan per square meter or yuan per mu. The parameter table also includes auxiliary information such as the scope of application, benchmark date, and correction coefficient. For forest land and timber resources, the standard price system is also provided in the form of parameter tables. The parameter tables are established separately for forest land assets and timber assets. The price parameters for forest land assets are classified according to administrative divisions, forest types, forest land categories, etc., while the price parameters for timber assets are classified according to administrative divisions, forest types, age groups, tree species groups, etc., and record the price per unit area of ​​forest land and the price per unit volume of timber respectively. In addition, the standard price system data also includes a plot ratio correction factor reference table and a period date correction factor reference table; the plot ratio correction factor table records the correction factor values ​​in segments according to the type of use and plot ratio range, which is used to convert land prices under different plot ratio conditions into prices under the benchmark plot ratio; the period date correction factor table records the average annual price change rate of different resource types in different time periods, which is used to make period date corrections when the accounting benchmark date is inconsistent with the price system benchmark date; The standard price system data also includes administrative division center point coordinate data, which is provided in the form of spatial point layers or coordinate tables, recording the coordinates of the administrative center points of administrative divisions at all levels (provincial, prefecture-level, county-level, and township-level). The administrative center point is generally selected from the coordinates of the government seat or the geometric center of the administrative division. This coordinate data is used to calculate the spatial distance between resource patches and administrative centers, and then classify the location level. The location level is determined according to the spatial distance between the center of the patch and the corresponding administrative division center point: when the distance is less than 5 kilometers, it is classified as a first-level location; when the distance is between 5 and 15 kilometers, it is classified as a second-level location; and when the distance is greater than 15 kilometers, it is classified as a third-level location. It is also necessary to obtain control layer data such as ecological protection red line layer and permanent basic farmland protection area layer; the ecological protection red line layer identifies the spatial range of strictly protected areas and general control areas, and is provided in the form of vector polygon layer, with information such as control type and control intensity marked in the attribute table; these control layers are used to determine whether resource patches are located within the control area in the accounting and correction process, and to apply the corresponding correction coefficients; The sources of the aforementioned standard price system data include benchmark land price results published by the natural resources authorities, price appraisal reports prepared by land appraisal agencies, and reference prices for forest land and timber asset appraisals published by the forestry authorities. When obtaining standard price system data, it is necessary to ensure that the benchmark date, scope of application, and technical standards of the data match the inventory and accounting tasks. If necessary, the price parameters should be adjusted by date or region to ensure the accuracy and timeliness of the accounting results.

[0023] In one embodiment of the present invention, step S3 includes: like Figure 3 As shown, for construction land resource patches, an adaptive weight matching strategy based on price heterogeneity perception is adopted. The resource patch vector layer is topologically overlaid with the standard price system spatial layer. Spatial intersection operations are used to obtain the set of intersection polygons between the patch and each price region. For each price region that spatially overlaps with the patch, its intersection area is denoted as... The total area of ​​the patch is denoted as S, and the geometric center coordinates of the m-th price region are extracted. and calculation price parameters ; Calculate the coordinates of the geometric center of the map patch Geometric center coordinates of each price zone Euclidean distance between them: in and The x and y coordinates of the center of the patch are: and Let x and y be the x and y coordinates of the center of the m-th price region; After extracting the spatial data, the price difference between price regions is calculated to determine whether the spatial location-enhanced weighting mechanism needs to be activated. The price difference is calculated using the coefficient of variation. Where n is the total number of price regions that overlap with the map patch; and These represent the maximum and minimum value functions, respectively. Price difference is calculated as the ratio of range to mean. Compared to the coefficient of variation based on standard deviation, this form has lower computational complexity and is suitable for small sample scenarios where the patch typically spans only 2-5 price regions. The range directly reflects the maximum difference between price regions. This indicator reflects the relative dispersion of prices between the price regions spanned by the patch. When the price difference is large, it indicates that the patch spans regions with significantly different price levels (e.g., simultaneously spanning primary and tertiary locations). In this case, the spatial location of the patch has a more significant impact on its calculated price. Different weighting calculation methods are used based on the magnitude of price differences. Two threshold parameters are set: a low threshold and a high threshold. and high threshold In this embodiment, The value is 0.1. Value 0.3; Low threshold This indicates that the price range is within 10% of the mean; high threshold. This indicates that the price range is more than 30% of the mean; this threshold is set with reference to the dispersion characteristics of price zones in land valuation practice, and can cover common cross-zone plot price matching scenarios; When price difference When it is assumed that the price levels of each price region are similar, a simple area-weighted model is adopted, and the weight of the m-th price region is: When price difference When price differences across price regions are considered significant, a location-strengthened weighting model is adopted. In this model, the average distance between all overlapping price regions and the center of the map patch is first calculated: Then calculate the distance weighting factor for the m-th price region: In the formula, It is a very small positive number, used to avoid the denominator being zero; the significance of this distance weighting factor is: the closer the center of a price area is to the center of the map patch, the better. The smaller, The larger the value, the more representative the price range is of the value of the patch, because the main part of the patch is closer to the price range. Calculate the unnormalized composite weights: The unnormalized composite weights of all overlapping price regions are normalized to obtain the position enhancement weights: In the formula, Price range index; When price difference When the threshold is between low and high, linear interpolation is used to transition between area weights and location enhancement weights. Interpolation coefficients are defined as follows: The final weights are: After completing the weight calculation, the accounting price parameters for each price region will be... With corresponding weights Multiply and sum to obtain the calculated price of the map patch. ; The core idea of ​​the aforementioned adaptive weight matching strategy for price heterogeneity perception is as follows: when there is a large price difference between price regions spanned by a patch, the spatial location of the patch has a more significant impact on its calculated price, thus strengthening the weight of spatial location; when the price difference is small, a simple area weight is sufficient to meet the accuracy requirements. This mechanism automatically judges and switches the weight calculation mode through the price difference index, improving the accuracy of price matching for cross-regional patches while maintaining simplicity and efficiency. like Figure 4 As shown in this embodiment, a layered degradation matching strategy is adopted for non-construction land resource patches. Based on the degree of influence of attribute conditions on price differences, the matching conditions are divided into three levels: core layer conditions, important layer conditions, and auxiliary layer conditions. For agricultural land resources, the core layer conditions include administrative division codes and homogeneous zone numbers; the important layer conditions include land type codes and quality grade codes; and the auxiliary layer conditions include slope grade codes. For forest land resources, the core layer conditions include administrative division codes and forest type codes; the important layer conditions include land type codes and age group type codes; and the auxiliary layer conditions include canopy closure grade codes. Construct a matching query sequence from strict to lenient, which includes four levels from strict to lenient: Level 1 requires all core layer, important layer, and auxiliary layer conditions to match exactly; Level 2 requires core layer and important layer conditions to match exactly, and relaxes auxiliary layer conditions; Level 3 only requires core layer conditions to match exactly; Level 4 serves as a fallback mechanism, performing nearest neighbor matching within the same administrative region. During the hierarchical query process, starting from Level 1, the attribute fields of the map features extracted in Step 1 are used to perform conditional matching queries in the standard price system parameter table of Step 2. If the query result is a unique record, the price parameters of that record are directly extracted. As a result of the matching, the matching process ends. If the query result is empty, the process is downgraded to the next level of query and continues. If the query result contains multiple records, the process enters the multiple result selection process. In the multi-result optimization process, the attribute distance between each candidate record and the target patch is calculated. The attribute distance is calculated using a weighted distance formula: Where J is the set of conditional attributes included in the current query level; The weight coefficient of condition j is the largest for the core layer conditions, followed by the important layer conditions, and the smallest for the auxiliary layer conditions. This weight allocation makes the contribution of the core conditions to the attribute distance dominant, ensuring that price matching prioritizes the attribute conditions with the greatest impact. This is an attribute dissimilarity function. For categorical attributes such as land cover codes and forest type, if the attribute value of the target patch is equal to the attribute value of the candidate record, then... ,otherwise For rank attributes such as quality rank and age group rank, ,in For the target patch attribute value, Let be the attribute value of the k-th candidate record; after calculating the attribute distances of all candidate records, select the record with the smallest attribute distance and extract its price parameter. As a matching result; If no match is found after Level 3 query, a Level 4 fallback query mechanism is initiated. Within the same administrative region, all records of the same resource type are extracted from the parameter table in Step 2. The total attribute distance between each record and the target patch (this distance includes the weighted difference of all matchable attributes) is calculated, and the record with the smallest total attribute distance is selected as the matching result. Through the above hierarchical filtering degenerate matching algorithm, accurate matching is guaranteed when the standard price system parameters are complete, while also providing degenerate fault tolerance when parameters are missing or conditions are not completely matched. For forest land and timber resource patches, since forest land and timber need to be calculated separately, forest land price parameters and timber price parameters need to be matched separately. During the matching process, the forest land attributes (administrative division, land use code, forest type, etc.) are first used as conditions to query the forest land calculation price parameters in the price system parameter table. Perform the aforementioned stratified degradation matching process; then, using forest attributes (administrative division, forest type, age group, etc.) as conditions, query the forest accounting price parameters in the price system parameter table. The same hierarchical degradation matching process is executed. The two matching processes are independent of each other, and each outputs its own calculated price. The core idea of ​​the aforementioned tiered degenerate matching strategy is that different attribute conditions have varying degrees of impact on prices, with core conditions having the greatest determining effect on price differences, while auxiliary conditions have a relatively smaller impact. When the standard price system parameter table lacks a perfectly matching record, the less influential auxiliary conditions are relaxed first, while the more influential core conditions are retained, thereby improving the matching success rate while ensuring matching accuracy.

[0024] In one embodiment of the present invention, step S4 includes: Select the appropriate accounting model based on the type of resource parcel to calculate the basic accounting value. For construction land resource parcels, the formula for calculating the basic accounting value is: Where A is the area of ​​the map patch. For calculating price parameters, r is the plot ratio attribute, and L is the location level classified according to the distance between the geometric center coordinates of the map patch and the administrative center; Floor area ratio influence function A piecewise linear model is used based on the floor area ratio: This function considers the non-linear impact of plot ratio on the land price per unit area of ​​construction land: when the plot ratio increases from 1.0 to 3.0, the increase in the correction coefficient due to the significant increase in development intensity is the most obvious. Setting the slope to 0.15 means that the correction range in this interval is about 30% (1.0 + 0.15 × 2 = 1.30); when the plot ratio increases from 3.0 to 5.0, although high-rise buildings further increase the building area, the marginal benefits decrease due to constraints such as sunlight, fire protection, and supporting facilities. The slope drops to 0.05, which means that the correction range in this interval is about 10% (1.3 + 0.05 × 2 = 1.40); the cap value of 1.4 indicates that the correction coefficient will no longer increase after the plot ratio exceeds 5.0, avoiding excessively high calculation results for ultra-high plot ratios; Location Influence Function Based on the location level values, the coefficient for Level 1 location (distance < 5km) is 1.1, reflecting the advantages of central location; the coefficient for Level 2 location (5-15km) is 1.0 as the benchmark; and the coefficient for Level 3 location (> 15km) is 0.9, reflecting the disadvantages of peripheral location. For agricultural land resource patches, the basic accounting value calculation formula is as follows: Where Q is the quality grade attribute and G is the slope grade attribute (Q and G are extracted in step S1). The quality grade influence function is: The quality grade Q ranges from 1 to 15, with grade 1 being the best. This results in a 14% value difference between grade 1 and grade 15 (1.14 vs. 1.00). This difference is based on empirical data from farmland grading practice, where the price difference between different quality grades of arable land in the same region is usually in the range of 10%-15%. The slope grade influence function is: The slope grade G ranges from 1 to 6, with grade 1 being the smallest. This results in a value difference of about 10% between grade 1 flat land and grade 6 steep slope (1.08 vs. 0.98). This difference is based on the impact of slope on the ease of cultivation: flat land is suitable for mechanized operations, while sloping farmland with a slope greater than 15° (corresponding to grades 5-6) has high labor intensity and a high risk of soil erosion. In agricultural land valuation practice, the correction range for the slope factor is usually within ±10%. The above formula for calculating agricultural land uses arable land as a typical example. For different types of agricultural land, appropriate influencing factors can be selected based on actual conditions: for arable land, quality grade and slope are the main considerations; for orchards, site conditions, irrigation conditions, and types of economic tree species are considered; and for aquaculture water surfaces, water quality, water depth, and suitability for aquaculture are considered. The structure of the influence function can be uniformly expressed as the product of the influence function of natural factor attributes and the influence function of socio-economic factor attributes. The function type and parameter values ​​are determined based on the dominant influencing factors of the specific land type. For forest land and tree resource patches, the basic accounting value is: The accounting value for the forest land portion is: The accounting value for the timber portion is: Where M is the forest stock volume, T is the forest type code, C is the age group type code, and D is the canopy closure (M, T, C, and D are all extracted in step S1). Forest type influence function The coefficients are determined based on forest type: timber forest coefficient is 1.05, protection forest coefficient is 1.0, special-purpose forest coefficient is 0.95, and other forest types coefficient is 0.9. The difference in these coefficients is based on the experience in forest asset accounting practice that there is usually a 5%-10% difference in market price parameters between timber forests and non-timber forests. Age group influence function Based on the age group type, the coefficient is 0.6 for young forests, 0.8 for middle-aged forests, 0.95 for near-mature forests, 1.0 for mature forests, and 0.85 for over-mature forests. This coefficient system refers to the forestry law that in forest resource asset accounting, the stock volume of young forests is about 60% of that of mature forests and that of middle-aged forests is about 80%. The influence function of canopy closure is The value of canopy closure D ranges from 0 to 1; the value variation range is 30% (from 0.70 to 1.00). This range takes into account that canopy closure reflects the positive correlation between forest density, stock volume and canopy closure. However, low canopy closure forest land (D<0.2) still has a basic accounting price, so a lower limit of 0.70 is set to avoid the accounting result being too low. like Figure 5 As shown, after calculating the basic values ​​of all patches, a spatial neighborhood consistency check is performed. For the target resource patch i, its set of neighboring patches N(i) and the length of the common boundary between it and each neighboring patch j are read. ; Calculate the neighborhood weights based on the common boundary length: This weight reflects the spatial correlation strength between neighboring patches and the target patch; the longer the common boundary, the stronger the correlation, and the greater the weight. Calculate the local average accounting value: in The number of neighboring patches, This is the basic accounting value for patch i. This is the basic accounting value for the neighboring map patch j; Computational spatial consistency index: This indicator reflects the relative difference between the calculation results of a patch and the calculation results of its neighboring patches; the larger the value, the greater the difference. Set a consistency threshold In this embodiment, Value 0.3; Consistency threshold This indicates that the relative difference between the calculated value of a map patch and the weighted average value of its neighborhood exceeds 30%. This threshold comprehensively considers the spatial autocorrelation characteristics of natural resource map patches and the reasonable fluctuation range in actual calculations. like Then mark patch i as a suspected anomalous patch; Further calculate the statistical outlier of patch i within the same administrative region and the same resource type: in This is the average value calculated for the same type of map features. The standard deviation of the calculated values ​​for the same type of map features; The outlier threshold is set to 3. If the statistical outlier count exceeds the outlier threshold... The suspected abnormal patches are then confirmed as abnormal patches; the statistical outlier threshold of 3 corresponds to the 3σ principle of the normal distribution, and the calculated values ​​exceeding this range can be regarded as extreme outliers in statistics; like Figure 6 As shown, for patches identified as anomalous, an adaptive correction mechanism based on neighborhood information is adopted. The idea behind this mechanism is that when the calculated result of a patch differs too much from the neighborhood value, there may be a matching error or data anomaly. However, the original calculated value cannot be simply replaced with the neighborhood average value (because the patch may indeed have special characteristics). Instead, an adaptive interpolation method should be used to make a compromise between the original calculated value and the neighborhood reference value according to the degree of difference. First, calculate the reference accounting value based on neighborhood information: This reference value is a weighted average of the basic accounting values ​​of the neighborhood patches, with the weights based on the length of the common boundary. The corrected accounting value is calculated using an adaptive interpolation correction formula: in The adaptive interpolation coefficients are determined based on the spatial consistency index. When the consistency index just exceeds the threshold When the value is relatively small, the revised accounting value is based on the original basic accounting value. When the consistency index is relatively large... The revised accounting value is increased, and it references neighborhood information more but does not completely replace the original accounting value, thus ensuring the rationality of the revision. When implementing adaptive interpolation correction, boundary cases need to be handled. For isolated patches without neighboring patches, the spatial consistency check is skipped, and anomaly detection is performed solely based on outlier count. If an outlier is identified, it is marked but not corrected. For patches with fewer than three neighboring patches, or where more than 50% of the neighboring patches are marked as outliers, the adaptive interpolation coefficient is multiplied by a reduction factor of 0.5 to reduce the influence weight of neighborhood information and prevent anomaly propagation. When calculating the neighborhood reference value, only neighboring patches of the same resource type as the target patch are selected for calculation. If the number of neighboring patches of the same type is zero, the spatial consistency check is skipped. For the corrected patch, the original basic value and the corrected value are recorded in its attribute data. The system includes spatial consistency index, statistical outlier, correction flag, and interpolation coefficients. Specific fields recorded include: Original base value field (recording the uncorrected original value); Corrected value field (recording the interpolated value); Spatial consistency index field (recording the degree of consistency deviation between the patch and its neighbors); Statistical outlier field (recording the statistical outlier degree of the patch among similar types); Interpolation coefficient field (recording the actual interpolation coefficients used); Number of neighboring patches field (recording the number of neighboring patches involved in the calculation); Correction flag field (marking whether the patch has been corrected); and Correction reason field (recording the anomaly type (spatial consistency anomaly, statistical outlier anomaly, or dual anomaly)). Abnormal map features can arise from two main causes. The first is data errors or matching mistakes, such as incorrect entry of map feature attribute information, selection of the wrong price range during price matching, or improper setting of calculation model parameters. In these cases, the original calculated value does indeed have a deviation and needs correction. The second is that the map feature itself has unique characteristics, such as special development conditions, ownership restrictions, or usage restrictions, leading to a significant difference in its calculation result compared to surrounding map features. In these cases, the original calculated value may be reasonable and should not be simply corrected. Since it is difficult to accurately distinguish between these two types of situations during the automated processing stage, this invention adopts an adaptive interpolation correction strategy, whose core features include four aspects. First, incomplete replacement, interpolation coefficients... The upper limit is set to 0.5, meaning that the corrected calculated value retains at least 50% of the original calculated value and will not completely adopt the neighboring reference value. Second, the degree of difference determines the correction strength. When the spatial consistency deviation is small (just exceeding the threshold), the interpolation coefficient is small, and the correction range is limited; when the deviation is large, the interpolation coefficient increases, but still does not exceed 0.5. Third, correction traceability information is retained. The corrected map features will record complete information such as the original calculated value, the corrected calculated value, spatial consistency index, statistical outlier, and interpolation coefficient in the attribute table, facilitating subsequent manual review. Fourth, neighborhood information quality control is implemented. When the number of neighboring map features is insufficient or the proportion of abnormal map features in the neighborhood is too high, the weight of neighborhood information will be reduced (interpolation coefficient reduction) to avoid the propagation of anomalies. Through the above mechanisms, adaptive interpolation correction achieves the goal of reasonable correction using spatial correlation information while retaining the original features of the map features. This improves the accuracy of batch processing and retains the judgment basis for subsequent quality checks.

[0025] After completing consistency verification and correction, the calculated values ​​of all map features are adjusted for date and special factors. Let the base date for calculation be... The price system base date is Calculate the time interval Query the average annual price change rate g based on resource type, and calculate the adjustment factor for the specified period: For rights restriction correction, check the rights restriction identifiers in the map patch attribute data. If the map patch has rights restrictions such as mortgage or seizure, then the rights restriction correction coefficient will be adjusted accordingly. Take the smaller value, 0.85, otherwise... For ecological control correction, the map patch is spatially overlaid with the ecological protection red line layer for judgment. If the map patch is located within a strictly protected area, the ecological control correction coefficient is determined. Take the smaller value; if it is located within a general control area, then... Take the average value of 0.7, otherwise... The final calculated value is: Write the basic calculation value, the calculation value after consistency verification, and the final calculation value into the corresponding fields of the map patch attribute table to complete the quantitative calculation and correction optimization process of the map patch.

[0026] In one embodiment of the present invention, step S5 includes: Before generating the inventory and accounting results report, a quality check is performed on the accounting results of all resource patches, with a focus on patches that have undergone anomaly identification and correction. Completeness check includes verifying the integrity of the inventory dataset and checking for empty values ​​in required attribute fields; verifying the consistency between the number of patches and the original inventory data, and checking for any omissions or duplications; checking the completeness of correction information records, and ensuring that all abnormal patches have their original values, corrected values, and correction coefficients recorded. Logical consistency check includes verifying the correctness of logical relationships between attribute values, such as whether the deviation between the patch area and the geometrically calculated area is within the allowable error range (not exceeding 5%); whether the calculated price parameters are within a reasonable range and whether there are any obvious outliers; and checking whether the correction magnitude is reasonable and whether the rate of change of the calculated values ​​before and after correction is within a reasonable range. Thematic quality check includes verifying the rationality of the accounting results by resource type, such as whether the calculated value per unit area of ​​construction land conforms to local price levels, and whether the calculated results of forest land and trees match the forest age and stock volume; statistically analyzing the distribution characteristics of various resource accounting values, and identifying abnormally distributed resource types or administrative regions. The review of the correction results includes a focused review of the patches corrected by adaptive interpolation, sorted by interpolation coefficient and correction magnitude, prioritizing patches with larger interpolation coefficients (close to 0.5) and correction magnitudes exceeding 30%; a sampling check of the corrected patches, with a check rate of no less than 10% of the total number of corrected patches; for dual abnormal patches where both spatial consistency index and statistical outlier significantly exceed the threshold, manual review is conducted one by one; during the review, the original inventory data, price matching records, and information on surrounding patches need to be reviewed to analyze the causes of anomalies. If it is confirmed that the data is incorrect or the matching is faulty, the corrected accounting value is adopted and explained in the remarks; if it is confirmed that the patch has special characteristics (such as special use, ownership restrictions, etc.), the original accounting value is restored and the reasons for the special characteristics are explained in the remarks; if it cannot be clearly determined, it is marked as requiring further investigation and listed separately in the report. A list of abnormal map features is generated, sorted by indicators such as anomaly severity, correction magnitude, and interpolation coefficient. The list includes fields such as feature number, resource type, original calculated value, corrected calculated value, percentage correction, spatial consistency index, statistical outlier, interpolation coefficient, number of neighboring map features, review conclusion, and remarks. After quality verification, a quality verification report is generated, recording the problems found, the confirmation status of the correction results, and the data quality evaluation conclusion. The calculated results confirmed by quality verification are used as the final output in the summary and statistics stage. For all resource patches that have completed quality verification, the physical quantity data fields and accounting result data fields are grouped and summarized statistically according to the following classification conditions: administrative division (provincial, prefecture-level, county-level, township-level), resource type (construction land, agricultural land, forest land and trees and their sub-types), and ownership nature (state-owned, collective). For each classification group, physical quantity indicators are statistically analyzed, including the number of patches, total area, and for forest land and trees, total stock volume; value indicators are statistically analyzed, including total accounting value, average accounting value, and accounting value per unit area; and quality indicators are statistically analyzed, including the number and proportion of abnormal patches, the number and proportion of corrected patches, correction confirmation rate, data completeness rate, and price matching success rate. Generate a comprehensive analysis report on the inventory and accounting, including data tables, distribution charts, and spatial thematic maps. The report content includes an overall overview (investigation area, total resources, total accounting value, etc.), categorized statistics (categorized statistical tables and charts by resource type, administrative division, and ownership nature), spatial distribution (thematic maps of resource distribution, heat maps of accounting value density), quality evaluation (data integrity evaluation, analysis of abnormal map features, explanation of corrections, quality risk warnings), and a list of attachments (detailed map feature attribute tables including correction information, a list of abnormal map features and review conclusions, and a quality verification report). Output a standard-format inventory and accounting results data package, including a map feature attribute data table (containing complete information such as original accounting value, corrected accounting value, correction identifier, and correction coefficient), a categorized summary statistical table, a comprehensive analysis report (Word or PDF format), a spatial data file (Shapefile or GeoDatabase format), a quality verification report and a list of abnormal map features (including manual review conclusions), and a correction traceability table (recording comparison information of all corrected map features before and after correction).

[0027] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for accounting and processing natural resource asset inventory data, characterized in that, include: S1. Under a unified reference time point, acquire natural resource inventory data of the area to be investigated, extract resource patches from them and classify them into construction land, agricultural land and forest land and trees according to resource type, extract the attribute information and geometric center coordinates of each resource patch, and identify the neighboring patches of each resource patch and the length of the common boundary between them through spatial topology analysis. S2. Obtain standard price system data, including price system parameter tables for various resources; S3. Match each resource patch with the standard price system data, and use an adaptive matching strategy based on resource type to obtain the accounting price; S4. Based on the calculated price and attribute information of each resource patch, the basic calculated value is obtained by quantitative calculation. An abnormal patch is identified by a consistency verification strategy based on the neighboring patches and the length of the common boundary. For the abnormal patches, an adaptive interpolation correction strategy is used to interpolate and correct the basic calculated value and the reference calculated value calculated based on the basic calculated value of the neighboring patches to obtain the final calculated value. S5. Conduct quality checks on the accounting results of all resource patches, summarize and statistically analyze them according to classification conditions, and generate an inventory and accounting result report.

2. The method for accounting and processing natural resource asset inventory data according to claim 1, characterized in that, In step S3, an adaptive matching strategy based on resource type is used to obtain the accounting price. Specifically, this includes: a hierarchical degradation matching strategy for non-construction land resource patches and an adaptive weight matching strategy based on price heterogeneity perception for construction land resource patches. Among them, the adaptive weight matching strategy for price heterogeneity perception includes: A topological overlay operation is performed between the resource patch vector layer and the standard price system spatial layer. The intersection area between the resource patch and each price region is obtained through spatial intersection operations. Let the intersection area between the m-th price region and the resource patch be denoted as... The total area of ​​the resource patch is S, where the subscript m represents the m-th price region; Extract the geometric center coordinates and calculate the price parameters for each price region. Let the geometric center coordinates of the m-th price region be denoted as... The calculation price parameters are as follows: ; Calculate the geometric center coordinates of resource patches Geometric center coordinates of each price zone Euclidean distance between ; The price difference is calculated based on the accounting price parameters of each price region. The weighting calculation mode is adaptively selected based on the magnitude of the price difference, and the accounting price parameters of each price region are... With corresponding weights The calculated price of resource patches is obtained by performing a weighted summation. .

3. The method for accounting and processing natural resource asset inventory data according to claim 2, characterized in that, The calculation of price differences based on the accounting price parameters of each price region, and the adaptive selection of a weighting calculation mode based on the magnitude of the price differences, specifically includes: The price difference is calculated using the coefficient of variation: Where n is the total number of price regions that overlap with resource patches; and These represent the maximum value function and the minimum value function, respectively. Set low threshold and high threshold When price difference When the price difference is high, the area-weighted model is used; when the price difference is low, the area-weighted model is used. When the price difference is high, a position-strengthened weighting model is adopted; when the price difference is low... When the threshold is between low and high, linear interpolation is used to transition between area weight and position enhancement weight.

4. The method for accounting and processing natural resource asset inventory data according to claim 3, characterized in that, The weight of the m-th price region in the area-weighted model is: The weight calculation process for the position-enhanced weighting mode is as follows: Calculate the average distance between all overlapping price regions and the geometric center of the resource patch: Calculate the distance weighting factor for the m-th price region: In the formula, It is a very small positive number, used to avoid the denominator being zero; Calculate the unnormalized composite weights: The unnormalized composite weights of all overlapping price regions are normalized to obtain the position enhancement weights: In the formula, This is a price range index.

5. The method for accounting and processing natural resource asset inventory data according to claim 2, characterized in that, The hierarchical degradation matching strategy specifically includes: Based on the degree of influence of attribute conditions on price differences, matching conditions are divided into three levels: core layer conditions, important layer conditions, and auxiliary layer conditions. Construct a matching query sequence from strict to lenient, including a first-level query that requires all conditions of the core layer, important layer and auxiliary layer to be exactly matched, a second-level query that requires the conditions of the core layer and important layer to be exactly matched, a third-level query that only requires the conditions of the core layer to be exactly matched, and a fourth-level query that performs nearest neighbor matching within the same administrative division. Starting from the first-level query, the attribute information of the resource patch is used to perform conditional matching queries in the price system parameter table. If the query result is a unique record, the price parameter of that record is extracted as the accounting price. If the query result is empty, the query is downgraded to the next level and continues to be executed. If the query result is multiple records, the attribute distance between each candidate record and the target resource patch is calculated and the record with the smallest attribute distance is selected as the matching result.

6. The method for accounting and processing natural resource asset inventory data according to claim 1, characterized in that, Step S4 employs a consistency check strategy based on neighborhood patches and common boundary length to identify abnormal patches, specifically including: After calculating the basic values ​​of all resource patches, for the target resource patch, read its set of neighboring patches and the length of the common boundary with each neighboring patch. Let the target resource patch be patch i, its set of neighboring patches be N(i), and the length of the common boundary with neighboring patch j be... ; Calculate neighborhood weights based on the length of the common boundary: Calculate the local average accounting value: in The number of neighboring patches, This is the basic accounting value for patch i. This is the basic accounting value for the neighboring map patch j; Computational spatial consistency index: When the spatial consistency index exceeds a preset threshold and the statistical outlier exceeds a preset threshold, the resource patch is identified as an abnormal patch.

7. The method for accounting and processing natural resource asset inventory data according to claim 6, characterized in that, The step of determining a resource patch as an abnormal patch when the spatial consistency index exceeds a preset threshold and the statistical outlier exceeds a preset threshold specifically includes: Set a consistency threshold ,like Then mark patch i as a suspected anomalous patch; Calculate the statistical outlier of patch i within the same administrative region and the same resource type: in This is the average value calculated for the same type of map features. The standard deviation of the calculated values ​​for the same type of map features; If we count outliers Then the suspected abnormal patches will be confirmed as abnormal patches.

8. The method for accounting and processing natural resource asset inventory data according to claim 6, characterized in that, The adaptive interpolation correction strategy for abnormal patches involves interpolating and correcting the basic calculated value with a reference calculated value based on the basic calculated value of neighboring patches to obtain the final calculated value. Specifically, this includes: Calculate the reference accounting value based on neighborhood information: The corrected accounting value is calculated using an adaptive interpolation correction formula: in The adaptive interpolation coefficients are determined based on the spatial consistency index. In the formula, This is the consistency threshold.

9. The method for accounting and processing natural resource asset inventory data according to claim 1, characterized in that, In step S4, the basic accounting value is obtained by quantitatively calculating the accounting price and attribute information of each resource patch, specifically including: For construction land resource patches, the basic accounting value calculation formula is as follows: Where A is the area of ​​the map patch. For price calculation, r represents the plot ratio attribute, and L represents the location level based on the distance between the geometric center coordinates of the map patch and the administrative center. Let f be the function of plot ratio. This is a locational influence function; For agricultural land resource patches, the basic accounting value calculation formula is as follows: Where Q represents the quality grade attribute and G represents the slope grade attribute. The quality grade influence function, This is the slope grade influence function; For forest land and tree resource patches, the basic accounting value is: The accounting value for the forest land portion is: The accounting value for the timber portion is: Where M represents forest stock volume, T represents forest type code, C represents age group type code, and D represents canopy closure. For forest type influence function, Let be the age group influence function. This is the function that influences canopy closure.

10. The method for accounting and processing natural resource asset inventory data according to claim 8, characterized in that, Step S4 also includes boundary case handling when implementing adaptive interpolation correction: for isolated patches without neighboring patches, the spatial consistency verification step is skipped, and anomaly judgment is made only by statistical outlier; for patches with fewer than a preset threshold of neighboring patches, or where the proportion of patches marked as anomalous in the neighboring patches exceeds a preset proportion threshold, the adaptive interpolation coefficient is multiplied by a reduction coefficient to reduce the influence weight of neighborhood information; when calculating the reference accounting value of neighboring patches, only neighboring patches with the same resource type as the target patch are selected for calculation.

Images