Method and device for spatial matching and information migration of agriculture-related data and right-confirmed data

Abstract

The application provides a method and device for spatial fitting and information migration of agricultural data and right-confirmed data, comprising: first, taking the right-confirmed data as a spatial reference, projecting and transforming multi-source agricultural data, spatial matching, resampling, and checking geometric precision and integrity to form a standardized agricultural data set; then, for grid-based agricultural data, taking the right-confirmed plot as a statistical unit, within the range of the grid pixel covered by the plot, the mode, mean, maximum or median value is used for feature extraction and information migration according to the data dispersion or numerical type; for vector-based agricultural data, the centroid method is used to migrate the attributes of most elements to the right-confirmed data, and the maximum intersection area method is used to complete the remaining information migration for unmatched elements; finally, the spatial fitting and attribute association of multi-element agricultural data and right-confirmed data are realized. The method significantly improves the data integration efficiency and decision support capability, and provides a new paradigm for agricultural digital management.

Description

Technical Field

[0001] This invention belongs to the field of agricultural remote sensing application and geographic information technology, and particularly relates to a method and apparatus for spatial overlay and information migration of agricultural data and land rights confirmation data. Background Technology

[0002] Traditional land registration and confirmation primarily relies on manual on-site surveying and paper-based archives. The recorded information only includes basic ownership details such as land location, area, and contractor, lacking productive data such as crop type, yield, land use changes, type of "two zones" (grain production functional zone and important agricultural product protection zone), type and year of high-standard farmland construction, arable land quality grade, soil suitability, and soil macro- and micro-elements. Once land rights are confirmed and certificates are issued, this data remains frozen indefinitely, causing the confirmation results to "sleep" in the archives and failing to release their potential value. Furthermore, it cannot dynamically reflect information such as land cultivation, changes in land use, and soil elements, easily leading to discrepancies between the confirmed and actual data.

[0003] Currently, agricultural and rural data is scattered across multiple departments, including natural resources, agriculture and rural affairs, water resources, and meteorology, resulting in significant issues such as inconsistent formats and asynchronous updates. This method uses land ownership confirmation data as a foundation, employing standardized governance methods such as projection transformation, spatial matching, geometric validity, and data integrity checks to form a unified spatial benchmark "data chassis." Then, by utilizing algorithms such as attribute migration and spatial statistics to integrate diverse agricultural data, a dynamic, updatable, and applicable spatial data asset system is constructed, enabling cross-system data linkage and sharing. This not only solves the data silo problem but also promotes the transformation of land ownership confirmation results from "static registration" to "dynamic empowerment." This method effectively addresses the issues of traditional land ownership confirmation data being information-single and lacking support from diverse agricultural data, improving the completeness and accuracy of land ownership confirmation data and providing a more reliable data foundation for agricultural management, financial services, and policy formulation.

[0004] In short, the release of the value of land ownership confirmation data is hindered, governance effectiveness is limited, and industrial development lags behind. The agricultural sector urgently needs to integrate diverse agricultural data with land ownership confirmation data to facilitate information migration and support the transformation of agricultural land ownership confirmation data from "registration results" to "production factors." This is precisely the core pain point that current agricultural geographic information applications urgently need to overcome.

[0005] Existing technologies have significant limitations in integrating diverse agricultural data with land ownership confirmation data, failing to effectively meet the complex needs of current rural land management and data governance. Specifically, the lack of unified data standards and interface specifications hinders seamless data integration between different sources and departments; and relatively lagging standardization processes and spatial data overlay and migration methods make it difficult to address the challenges of integrating massive amounts of heterogeneous data, thus restricting the refined management and digital transformation of rural land resources. This method primarily addresses the inconsistencies and difficulties in integrating diverse agricultural data with land ownership confirmation data. By establishing a unified data standardization and information migration process, it achieves efficient integration and spatial matching of diverse agricultural data from different sources and in different formats with land ownership confirmation data, improving data accuracy, consistency, and usability, and providing reliable data support for rural land management, policy formulation, and resource allocation. Summary of the Invention

[0006] To address the above technical problems, this invention provides a method and apparatus for spatial overlay and information migration of agricultural data and land rights confirmation data. The specific technical solution is as follows:

[0007] The spatial overlay and information migration method for agricultural data and land ownership confirmation data includes the following steps: First, using land ownership confirmation data as a spatial reference, projection transformation, spatial matching, resampling, and geometric accuracy and integrity verification are performed on multi-source agricultural data to form a standardized agricultural dataset; then, for raster-type agricultural data, using land ownership confirmation plots as statistical units, feature extraction and information migration are performed within the raster pixels they cover, based on the data discreteness or numerical type, using mode, mean, maximum / minimum, or median respectively; for vector-type agricultural data, the centroid method is first used to migrate most element attributes to the land ownership confirmation data, and then the maximum intersection area method is used to complete the remaining information migration for unmatched elements; finally, spatial overlay and attribute association between multi-source agricultural data and land ownership confirmation data are achieved.

[0008] The spatial overlay and information migration device for agricultural data and land ownership confirmation data includes the following modules: a standardized agricultural dataset formation module, which uses land ownership confirmation data as a spatial reference to perform projection transformation, spatial matching, resampling, and geometric accuracy and integrity verification on multi-source agricultural data to form a standardized agricultural dataset; an information migration module, which, for raster-type agricultural data, uses land ownership confirmation plots as statistical units and performs feature extraction and information migration within the raster pixels they cover, based on the data's discreteness or numerical type, using mode, mean, maximum / minimum, or median respectively; for vector-type agricultural data, it first uses the centroid method to migrate most element attributes to the land ownership confirmation data, and then uses the maximum intersection area method to complete the remaining information migration for unmatched elements; and an association module, which realizes the spatial overlay and attribute association of multi-source agricultural data and land ownership confirmation data.

[0009] An electronic device includes: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method.

[0010] A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method described thereon.

[0011] The present invention has the following beneficial effects:

[0012] (1) To address the information fragmentation problem caused by the traditional application of agricultural data being limited to a single data source, a spatial overlay and information migration method for combining agricultural data with rights confirmation data is proposed. This method achieves precise overlay and information migration of diverse agricultural data and rights confirmation data, ultimately achieving deep integration of diverse agricultural data and rights confirmation data. This method breaks down data barriers, significantly improves data integration efficiency and decision support capabilities, and provides a new paradigm for digital governance of agriculture;

[0013] (2) For vector-based agricultural data, this study proposes a step-by-step matching method that balances matching efficiency and accuracy. It prioritizes the centroid method for rapid matching of most elements, avoiding the slow matching speed caused by using only the maximum intersection area method. For elements that fail to match, the maximum intersection area method is used for precise matching, addressing the omissions that can easily occur when using only the centroid method. This method, through a combination of "rapid matching + precise matching," effectively improves the matching accuracy between vector-based agricultural data and land rights data while ensuring matching efficiency.

[0014] (3) The types of multi-dimensional agricultural spatial data are mainly divided into two categories: raster data and vector data. This invention explores the overlay methods and implementation processes for the two types of data. Attached Figure Description

[0015] Figure 1 A flowchart illustrating the spatial overlay and information migration technology for diverse agricultural data and land ownership confirmation data;

[0016] Figure 2 A flowchart for standardized processing of diverse agricultural data;

[0017] Figure 3 A flowchart illustrating the spatial overlay and information migration process between raster-based agricultural data and land ownership confirmation data;

[0018] Figure 4 This is a flowchart illustrating the spatial overlay and information migration process between vector-based agricultural data and land ownership confirmation data. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.

[0020] This invention proposes a method for spatial overlay and information migration of agricultural data and land rights confirmation data. It integrates key agricultural data such as basic geographic information data, soil survey and analysis results, remote sensing monitoring data, land use change survey data, data on the delineation of "two zones" (grain production functional zones and important agricultural product production protection zones), high-standard farmland data, and non-grain monitoring data. This data is then used to perform spatial feature statistical analysis and precise location overlay with agricultural land rights confirmation data, enabling the association and migration of key information from diverse agricultural data sources. First, projection transformation, spatial matching, and geometric validity verification of elements are performed on agricultural data from multiple sources and in multiple formats to construct a standardized agricultural dataset with a unified spatial coordinate system and valid and complete geometric elements. Then, using the land rights confirmation data as the overlay base, for vector-type agricultural data, the first step uses the centroid method to migrate key information from diverse agricultural data to the land rights confirmation data. The second step, for elements in the land rights confirmation data that were not overlaid with the diverse agricultural data in the first step, uses the maximum intersection area method to complete the data overlay if the land rights confirmation data intersects with the diverse agricultural data. Finally, for raster-based agricultural data, spatial statistics were performed in two cases, and the results were transferred to the land ownership data: (1) For Discrete Raster Data, the mode was used to extract statistical features; (2) For Numerical Raster Data, the mean, maximum, minimum, and median were used to extract statistical features. Figure 1 As shown, the method includes the following steps:

[0021] Step 1: Standardize and process diverse agricultural data to generate a standardized agricultural dataset;

[0022] First, slope and aspect maps are generated from the digital elevation model (DEM) of the study area; simultaneously, administrative division attribute verification is conducted, with intermediate checks verifying the standardization and uniqueness of administrative division codes. Second, agricultural data, including basic geographic information data, soil survey and analysis results data, remote sensing monitoring data, land change survey data, "two zones" delineation data, high-standard farmland data, and non-grain monitoring data, may have geometric deviations. Using the land ownership data as a spatial reference, projection transformation is performed to ensure accurate overlay and analysis of spatial data from different sources within the same coordinate system. Third, spatial matching of diverse agricultural data is performed. This process requires not only precise spatial correspondence of data but also consistency in time, attributes, and logic, thus providing high-quality spatial analysis for subsequent overlay. It involves several key steps and technical points. The first step requires resampling the original raster-based agricultural data (such as DEM, slope, aspect, and soil nutrient data) to unify their spatial resolution and pixel size, ensuring that data from different sources have the same raster structure. This step typically employs resampling methods such as Nearest Neighbor, Bilinear Interpolation, or Cubic Convolution. The appropriate algorithm is selected based on the data type and analytical requirements to maintain data accuracy and integrity. The second step, spatial matching based on the land rights data, means precisely aligning all processed raster and vector data with the geographic coordinates of the land rights data to achieve spatial consistency across multiple sources. Finally, verifying the geometric validity and integrity of elements in the multi-source agricultural data is crucial for ensuring the quality of multi-source agricultural spatial data. This process involves rigorously reviewing the geometric structure of vector data elements, including checking for topological relationships, self-intersections, dangling nodes, and overlapping areas of points, lines, and polygons. Simultaneously, it's necessary to verify the integrity of data attributes, ensuring that each element has the necessary attribute field values ​​without missing or outliers. Raster data checks primarily focus on pixel-level numerical accuracy, edge alignment precision, data structure standardization, and timeliness. (1) Pixel-level numerical accuracy: Verify whether the values ​​of raster pixels conform to the preset logic or data specifications; (2) Edge matching accuracy: Check whether the transition of raster pixel values ​​between adjacent map sheets or data blocks is smooth and reasonable, ensuring that there are no obvious misalignments, gaps or value abrupt changes at the splicing point; (3) Data structure standardization: Check whether the file format, data organization method and storage method of raster data meet the standard requirements, ensuring that the data can be read and processed correctly; (4) Agricultural data is often subject to dynamic monitoring, so it is necessary to check the data production date, update frequency and metadata timestamp to ensure that it has sufficient timeliness. Through a systematic verification process, errors in the data can be identified and corrected, improving the accuracy and reliability of the data, and providing a reliable standardized agricultural dataset for subsequent overlay.like. Figure 2 As shown.

[0023] Step 2: Spatial overlay and information migration of raster-based agricultural data and land ownership confirmation data

[0024] This is the core link of information migration. By taking the land parcels with confirmed ownership as the target area, statistical operations are performed on the raster pixels within its range to extract the agricultural indicator attributes corresponding to each land parcel. Specifically, this technology uses a spatial feature statistical algorithm to spatially overlay the vector land ownership boundary with the raster agricultural data, and calculates the statistical value for each vector element (land parcel) within the range of the raster pixels it covers. On the one hand, for discretized raster data (crop type data, cultivated land quality grade, etc.), the mode (the raster value with the highest frequency) is used for statistics; on the other hand, for numerical raster data, the following four methods are used to statistically calculate the raster feature values. (1) Average value, calculation formula: , The average value is calculated by summing all valid raster pixels within the area of ​​the land parcel to be registered. Represents the attribute value of the i-th raster cell within the land parcel, where N is the total number of raster cells within the area of ​​each land parcel with confirmed ownership; (2) Maximum value, calculated using the formula: , The maximum value of all raster cell attributes within the area of ​​each land parcel with confirmed ownership. This represents the attribute value of the i-th raster cell within the land parcel, where n represents the total number of valid raster cells within the area of ​​each land parcel with confirmed ownership; (3) Minimum value, calculated using the formula: , The minimum value of all raster cell attributes within the scope of each land parcel with confirmed ownership. The value of the i-th raster cell in the plot is represented by n, which represents the total number of valid raster cells within the area of ​​each plot with confirmed ownership. (4) Median, the calculation method is divided into three steps. First step, data extraction and sorting: extract the values ​​of all valid cells (non-null or non-NODATA values) in the raster and sort them in ascending order; Second step, parity judgment: determine whether the total number of valid cells (n) is odd or even; Third step, calculate the median: when n is odd, directly take the value of the middle raster cell, the calculation formula is: , The median of raster cells within the area of ​​each land parcel with confirmed ownership. This represents the value of the middle raster cell after sorting from smallest to largest; when n is even, the average of the two middle raster cells is taken, calculated using the following formula: , The median of raster cells within the area of ​​each land parcel with confirmed ownership. and These are the values ​​of the middle two raster cells after sorting from smallest to largest. For example... Figure 3 As shown.

[0025] Step 3: Spatial overlay and information migration of vector-based agricultural data and land ownership confirmation data

[0026] This step is a key technical aspect of the invention. Its core principle is to spatially match and attribute-associate vector geographic data (such as remote sensing monitoring data, land use change data, high-standard farmland data, soil survey sample data, etc.) from different channels and formats with land ownership confirmation data (including ownership information, plot boundaries, area, etc.). First, calculating the centroid of multivariate agricultural data requires... The formula is as follows: = , = , Let x be the x-coordinate of the i-th control point. Let y be the ordinate of the control point of the i-th element, and n be the total number of control points for that element. Next, overlay the corresponding range of ownership data. If the ownership data element contains the centroid of multi-dimensional agricultural data, match the corresponding element and perform attribute association. Then, find the elements whose centroids of multi-dimensional agricultural data elements do not spatially match the ownership data. Iterate through each ownership element and use the maximum intersection area method to find the corresponding multi-dimensional agricultural data element. The algorithm for finding the maximum intersection area method is expressed as: , To iterate through each data element's ownership rights, let m be the number of data elements in the multivariate agricultural data set that have not yet been matched in the spatial data set, and n be the total number of multivariate agricultural data elements. For the i-th data element of the property rights confirmation, For the j-th multi-dimensional agricultural data element, This represents solving for the area of ​​intersection function. If... If no intersecting multivariate agricultural data elements can be found, the result is empty. For each data element used for rights confirmation, the multivariate agricultural data element with the largest intersecting area is found, and the specific attribute value of that multivariate agricultural data element is assigned to the data element used for rights confirmation. Finally, the confidence level (CL) of the maximum intersecting area method is calculated using the following formula: Based on the calculation results, the confidence level is divided into four levels: 1) 0.6 < CL ≤ 1.0, i.e., accurate; 2) 0.3 < CL ≤ 0.6, i.e., general; 3) 0 ≤ CL ≤ 0.3, i.e., unreliable; 4) CL < 0 or CL > 1, i.e., incorrect.

[0027] like Figure 4 As shown.

[0028] The present invention has the following alternatives:

[0029] (1) In step 3, the spatial overlay and information migration of vector-type agricultural data and land rights confirmation data can be completed in one step using the maximum intersection area method for spatial matching.

[0030] (2) The confidence level grading standard in step 3 can also adopt different standards, such as a five-level grading standard. 1) When 0≤CL<0.3, it is unreliable; 2) When 0.3≤CL<0.6, it is average; 3) When 0.6≤CL<0.8, it is accurate; 4) When 0.8≤CL≤1, it is precise; 5) When CL<0 or CL>1, it is wrong.

[0031] The device for spatial overlay and information migration of agricultural data and land ownership confirmation data includes the following modules: a standardized agricultural dataset formation module, which uses land ownership confirmation data as a spatial reference to perform projection transformation, spatial matching, resampling, and geometric accuracy and integrity verification on multi-source agricultural data to form a standardized agricultural dataset; an information migration module, which, for raster-type agricultural data, uses land ownership confirmation plots as statistical units and performs feature extraction and information migration within the raster pixels they cover, based on the data's discreteness or numerical type, using mode, mean, maximum / minimum, or median respectively; for vector-type agricultural data, it first uses the centroid method to migrate most element attributes to the land ownership confirmation data, and then uses the maximum intersection area method to complete the remaining information migration for unmatched elements; and an association module, which realizes spatial overlay and attribute association between multi-source agricultural data and land ownership confirmation data.

[0032] An electronic device includes: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method.

[0033] A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method described thereon.

[0034] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0035] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for spatial overlay and information migration of agricultural data and land ownership confirmation data, characterized in that, Includes the following steps: First, using land ownership confirmation data as a spatial benchmark, projection transformation, spatial matching, resampling, and geometric accuracy and integrity verification are performed on multi-source agricultural data to form a standardized agricultural dataset. Then, for raster-based agricultural data, using land ownership confirmation plots as statistical units, feature extraction and information transfer are performed within the raster pixels they cover, based on the data's discrete or numerical type, using mode, mean, maximum / minimum, or median respectively. For vector-based agricultural data, the centroid method is first used to transfer most element attributes to the land ownership confirmation data, and then the maximum intersection area method is used to complete the remaining information transfer for unmatched elements. Finally, spatial overlay and attribute association between multi-source agricultural data and land ownership confirmation data are achieved.

2. The method according to claim 1, characterized in that, The standardization process specifically includes: performing projection transformation with the confirmed data as a reference; resampling raster-type agricultural data using the nearest neighbor method, bilinear interpolation, or cubic convolution method to achieve unified spatial resolution; performing topological, self-intersection, dangling node, and geometric validity checks on overlapping regions of vector data, and verifying attribute integrity; and performing checks on the logicality of pixel values, edge-joining accuracy, data structure standardization, and timeliness of raster-type agricultural data.

3. The method according to claim 1, characterized in that, In the migration of raster-based agricultural data, for discrete raster data, the mode is used to count the raster value that appears most frequently within the area of ​​each land parcel with confirmed ownership; for numerical raster data, the average, maximum, minimum or median of the raster pixels within the area of ​​each land parcel with confirmed ownership is calculated respectively. The calculation of the median includes extracting valid pixels, sorting them and then taking the middle value or the average of the two middle values ​​according to the parity of the total number of pixels.

4. The method according to claim 1, characterized in that, In the vector-based agricultural data information migration, the centroid method first calculates the geometric centroid of each multivariate agricultural data element, and then associates the ownership data element containing the centroid with the corresponding multivariate element in terms of attributes.

5. The method according to claim 4, characterized in that, For data elements whose ownership cannot be matched by the centroid method, the method of maximizing the intersection area is adopted: traverse each unmatched data element, calculate its intersection area with all multivariate agricultural data elements, and select the multivariate element with the largest intersection area for attribute migration. If no intersection exists, return null.

6. The method according to claim 5, characterized in that, The maximum intersection area method also includes confidence calculation and grading steps: the confidence level is equal to the intersection area of ​​the confirmed element and the matched multi-element divided by the total area of ​​the confirmed element; the matching results are divided into four levels: accurate, general, unreliable and erroneous according to the confidence level.

7. The method according to claim 6, characterized in that, The confidence level grading standard is: 0.6 < CL ≤ 1.0 is considered accurate; 0.3 < CL ≤ 0.6 is considered normal; 0 ≤ CL ≤ 0.3 is considered unreliable; CL < 0 or CL > 1 is considered erroneous data.

8. A device for spatial overlay and information migration of agricultural data and land ownership data, characterized in that, It includes the following modules: a standardized agricultural dataset formation module, which uses the land ownership data as a spatial reference to perform projection transformation, spatial matching, resampling, and geometric accuracy and integrity verification on multi-source agricultural data to form a standardized agricultural dataset; The information migration module, for raster-based agricultural data, uses land parcels with confirmed ownership as statistical units. Within the raster pixels they cover, it performs feature extraction and information migration using mode, mean, maximum / minimum, or median, depending on whether the data is discrete or numerical. For vector-based agricultural data, it first uses the centroid method to migrate most feature attributes to the land parcels with confirmed ownership, and then uses the maximum intersection area method to complete the remaining information migration for unmatched features. The association module enables spatial overlay and attribute association between multi-dimensional agricultural data and land parcels with confirmed ownership.

9. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, cause the processor to perform the method described in any one of claims 1 to 7.

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