A method and device for checking the consistency of multi-source cultivated land grid data organization

Abstract

The application provides a kind of multi-source cultivated land grid data organization consistency inspection method and device, the method is first for the purpose of grid data organization, selects characteristic attribute;Then according to the characteristic attribute, the cultivated land original data and cultivated land grid data of research target area are classified respectively according to attribute level, and the area data with attribute level is obtained;According to the selected administrative division level, the area data with attribute level is divided in spatial range, and the regional data with administrative region spatial division and attribute level is obtained;The area of each region under each attribute level is calculated, and the area change dynamic degree of each region is determined;According to the area change dynamic degree of each region, the consistency of space and attribute is inspected. Using the application can carry out consistency inspection on cultivated land grid data from space and attribute.

Description

Technical Field

[0001] This invention belongs to the field of geospatial information system technology, and specifically relates to a method and apparatus for verifying the consistency of multi-source farmland grid data organization. Background Technology

[0002] With the development of precision agriculture and smart agriculture, data-driven agricultural decision-making and management have become increasingly important. Farmland gridding, against the backdrop of agricultural modernization and precision agriculture, has become a significant technological innovation. By dividing farmland into standardized, manageable units, and combining this with high-precision geographic information systems, remote sensing, and information technology, farmland gridding provides crucial agricultural information and spatial resolution for agricultural production. This is essential for farmland management, crop monitoring, and yield forecasting.

[0003] Against the backdrop of agricultural modernization and advancements in information technology, multimodal farmland data, including site conditions, profile characteristics, and soil health, is crucial for land quality assessment. Data organization and management methods based on partitioned data models have become the mainstream data fusion technology for processing such multimodal data. However, data gridding inevitably brings a series of challenges, such as loss of spatial accuracy, multi-valued attribute matching, and significant differences in semantic expression, necessitating conflict resolution during multimodal data fusion. The conflict-resolved data then needs to undergo consistency checks to verify the accuracy of data mapping and ensure high data quality standards. In the context of spatiotemporal big data applications, developing appropriate data organization consistency check techniques is crucial for improving data quality, maximizing the comprehensive application potential of spatiotemporal big data, and promoting the advancement of agricultural informatization.

[0004] Developing appropriate data organization consistency testing techniques can provide more comprehensive and accurate information on arable land quality, obtain more precise spatiotemporal distribution of arable land quality, gain a deeper understanding of the changing trends in different regions and periods, and provide reliable spatiotemporal data support for agricultural decision-making.

[0005] Currently, there are two main methods for verifying the consistency of multi-source farmland grid data:

[0006] (1) Constructing a consistency test model

[0007] Zheng Jingyuan et al. (see Gridding Method for Cultivated Land Data in Traditional Agricultural Areas of China in 1820. Journal of Geographical Sciences, 2009: 36-48. Lin Shanshan, Zheng Jingyun, He Fanneng) constructed a consistency test model to calculate the original proportion of cultivated land data, the percentage of cultivated land grids to the total cultivated land area, and the post-grid value. They judged whether there were significant differences between the two to ensure the effectiveness of data grid conflict resolution and guarantee data consistency.

[0008] (2) Grid-based reconstruction of historical cultivated land data in China using the hierarchical and zoning method

[0009] This type of method mainly employs new parameters. The allocation index used, the "arable land suitability index," consists of four parameters: soil pH, soil organic carbon content, effective accumulated temperature, and humidity index. This index, which indicates the distribution trend of arable land allocation based on natural conditions over a long time scale, is divided into ten levels from 0 to 9. Data consistency is verified by comparing the arable land suitability indices in the original data and gridded data. See "Gridded Reconstruction of Historical Arable Land Data in China Based on Hierarchical Zoning Method," Acta Geographica Sinica, 2016: 1144-1156. Wei Xiwen, Miao Lijuan, Jiang Yuan, et al.

[0010] However, the above scheme cannot perform consistency checks on both spatial and attribute aspects for farmland grid data. Summary of the Invention

[0011] In view of this, the present invention provides a method for verifying the consistency of multi-source farmland grid data organization, which solves the problem that farmland grid data is difficult to verify in terms of both spatial and attribute aspects in the prior art. Based on the characteristics of grid data, a data verification scheme for judging the organizational consistency of farmland grid data based on the dynamic degree of area change is proposed.

[0012] To solve the above-mentioned technical problems, the present invention is implemented as follows.

[0013] A method for verifying the consistency of multi-source farmland grid data organization includes:

[0014] Step 1: The purpose of organizing grid data is to select representative attributes;

[0015] Step 2: Based on the described characterization attributes, classify the original farmland data and farmland grid data of the target area according to their attribute levels to obtain surface data with attribute levels;

[0016] Step 3: Based on the selected administrative division level, divide the spatial range of the surface data with attribute levels to obtain regional data with administrative division spatial division and attribute levels;

[0017] Step 4: Calculate the area of ​​each region under each attribute level and determine the dynamic degree of area change of each region; the dynamic degree of area change is the difference between the original data of cultivated land of the same attribute level in the same region and the area occupied after gridding, and the result is the standardized result of the area corresponding to the original data of cultivated land.

[0018] Step 5: Perform a consistency check on the spatial and attribute properties based on the dynamic degree of area change in each region; the smaller the dynamic degree of area change, the better the consistency.

[0019] Preferably, step 3 specifically includes:

[0020] Step 301: Based on the attribute level classification results, merge the faceted data with the same attribute level into one faceted data;

[0021] Step 302: Select an administrative division level as the regional segmentation standard according to the purpose of organizing the grid data; perform regional segmentation on the fused surface data according to the administrative division data to obtain regional data; each regional data corresponds to an administrative region, that is, obtain the regional data with administrative region spatial division and attribute level.

[0022] Preferably, after step 302, the segmented regional data is further merged into a new layer of data, and spatially connected with the administrative division data to obtain the administrative region attributes corresponding to the regional data, which are used for displaying the administrative region attributes of the new layer of data.

[0023] Preferably, step 4 specifically includes:

[0024] Step 401: Calculate the area of ​​each region under each attribute level;

[0025] Step 402: Calculate the dynamic degree of area change for each region as follows:

[0026]

[0027] Where K is the total number of attribute levels; U ij V represents the area of ​​the i-th region at the j-th attribute level, obtained from processing raw farmland data. ij This represents the area of ​​the i-th region at the j-th attribute level, obtained from processing farmland grid data.

[0028] Preferably, step 5 is as follows:

[0029] Set a consistency threshold, compare the dynamic degree of area change with the threshold, and determine whether the consistency requirements are met.

[0030] Alternatively, a range of consistency levels can be set, and the consistency level can be determined based on the dynamics of area changes.

[0031] Preferably, the method further includes: spatially linking the area change dynamics of each region with administrative division data, assigning an area change dynamics attribute to each region after matching the administrative division names one by one, and visually presenting the distribution map of regional dynamics.

[0032] Preferably, if the purpose is to evaluate the quality of cultivated land, the characteristic attribute is selected as soil fertility, and the selected administrative division level is county.

[0033] Preferably, using soil fertility as the characterizing attribute, the soil is divided into 5 levels, with higher levels indicating stronger soil fertility:

[0034] Level 0 includes: water areas;

[0035] The first category includes: fixed grassland sandy soil, aeolian calcareous meadow soil, semi-fixed meadow sandy soil, semi-fixed grassland sandy soil, and fixed meadow sandy soil.

[0036] The second grade includes: moderately loess-type soda salinized black calcareous soil, soda salinized meadow soil-soda meadow alkaline soil, soda salinized meadow soil, soda meadow saline soil, soda meadow alkaline soil, slightly sandy loess-type soda salinized black calcareous soil, moderately sandy loess-type soda salinized black calcareous soil, slightly loess-type soda salinized black calcareous soil, soda salinized meadow soil-soda meadow alkaline soil, and heavily salinized meadow marsh soil.

[0037] The third category includes: non-calcareous alluvial soil, medium-humic sandy loess light black calcareous soil, thin-humic sandy loess light black calcareous soil, non-calcareous meadow marsh soil, thin-humic bottom-concealed light black calcareous soil, thin-humic sandy loess light black calcareous soil, medium-humic bottom-concealed light black calcareous soil, non-calcareous new deposited soil, thick-humic sandy light black calcareous soil, and medium-humic bottom-concealed light black calcareous soil - non-calcareous new deposited soil.

[0038] Grade 4 includes: non-calcareous alluvial soil, medium-humic sandy loess light black calcareous soil, thin-humic sandy loess light black calcareous soil, non-calcareous meadow marsh soil, thin-humic bottom-concealed light black calcareous soil, thin-humic sandy loess light black calcareous soil, medium-humic bottom-concealed light black calcareous soil, non-calcareous new deposited soil, thick-humic sandy light black calcareous soil, and medium-humic bottom-concealed light black calcareous soil - non-calcareous new deposited soil.

[0039] The present invention also provides a multi-source farmland grid data organization consistency verification device, including a grade classification module, a region division module, a dynamic degree acquisition module, and a consistency verification module;

[0040] The graded classification module is used to classify the original farmland data and farmland grid data of the research target area according to the characterization attributes selected for the purpose of grid-based data organization, so as to obtain surface data with attribute grades.

[0041] The region division module is used to divide the spatial range of surface data with attribute levels according to the selected administrative division level, so as to obtain regional data with administrative spatial division and attribute level.

[0042] The dynamic degree acquisition module is used to calculate the area of ​​each region under each attribute level and determine the dynamic degree of area change of each region. The dynamic degree of area change is the difference between the original data of cultivated land of the same attribute level in the same region and the area occupied by the gridded area, and the result is the result after standardization of the area corresponding to the original data of cultivated land.

[0043] The consistency verification module is used to check the consistency of space and attributes based on the dynamic degree of area change in each region; the smaller the dynamic degree of area change, the better the consistency.

[0044] Preferably, the method by which the region division module obtains region data with administrative spatial division and attribute levels is as follows:

[0045] Based on the attribute level classification results, faceted data with the same attribute level are merged into one faceted data.

[0046] Based on the purpose of organizing the grid data, an administrative division level is selected as the standard for regional segmentation; based on the administrative division data, the merged surface data is segmented into regions to obtain regional data; each regional data corresponds to an administrative region.

[0047] Beneficial effects:

[0048] (1) Based on the principle that the proportion of land of the same grade within the same region should be approximately the same before and after gridding, this invention designs an area change dynamic degree as a measure of grid dynamic degree. The original cultivated land data and cultivated land grid data are reorganized by selecting representative attributes and administrative division levels, so that the regional data have the same area standard and attribute levels. Then, the area change dynamic degree of the region before and after gridding is calculated. Since the reorganized data has both spatial and attribute information, consistency verification in both spatial and attribute aspects can be achieved. At the same time, to avoid accuracy problems and differences in verification standards caused by scale issues, this invention also standardizes the area difference, making the data comparison benchmark more consistent and scientific.

[0049] (2) A preferred embodiment of the present invention provides a specific calculation formula for dynamic degree. The formula adopts the summation method, which sums up the area differences of different levels in the same area and standardizes them to obtain the dynamic degree of the area. The dynamic degree index of this method has a unified scale, can integrate multi-level information, simplify complex data, and is simple to calculate and easy to operate, which facilitates comparative analysis between regions.

[0050] (3) In a preferred embodiment of the present invention, the segmented regional data is further merged into new layer data and the administrative division data is spatially connected, which can realize the display of the administrative region attributes of the new layer data, so that users can obtain an intuitive image of the segmentation results.

[0051] (4) In a preferred embodiment of the present invention, after obtaining the dynamic degree, the present invention further performs matching of administrative region names and visualizes the distribution map of regional dynamic degree, so that users can easily observe which region's dynamic degree does not meet the dynamic degree requirements, and then adjust the gridding scheme, such as modifying the conflict detection scheme in the gridding process.

[0052] (5) When the present invention is applied to the evaluation of cultivated land quality, soil fertility can be selected as the characterization attribute, the county can be selected as the administrative division level, and a large number of studies have given a soil grade classification method suitable for the dynamic degree calculation and consistency detection of the present invention. By using this grade and attribute selection, spatial and attribute consistency detection can be effectively carried out. Attached Figure Description

[0053] Figure 1 This is a flowchart of the method for verifying the consistency of multi-source farmland grid data organization according to the present invention.

[0054] Figure 2 This is a flowchart of a method for verifying the consistency of multi-source farmland grid data organization according to Embodiment 1 of the present invention.

[0055] Figure 3 This is a map showing the soil type classification of a certain city.

[0056] Figure 4 This is a block diagram of the multi-source farmland grid data organization consistency verification device of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0058] During the data consistency verification process, the proportion of land of the same grade in the same region should be roughly the same before and after gridding. Based on this principle, this invention designs a grid dynamic degree, which calculates the difference between the original data of the same grade in the same region and the area after gridding. Due to the scale problem, the area difference must be compared within the same county area to be scientific. Therefore, the ratio of the area difference to the area of ​​the corresponding county area is used as the grid dynamic degree.

[0059] Figure 1 A flowchart of the method for verifying the consistency of multi-source farmland grid data organization according to the present invention is shown, as follows: Figure 1 As shown, it includes the following steps:

[0060] Step 1: The purpose of organizing grid data is to select representative attributes.

[0061] This step involves selecting representative attributes based on the intended use of the grid data. For example, for the purpose of evaluating arable land quality, the standard attribute for arable land quality could be soil fertility; the stronger the soil fertility, the better the arable land quality.

[0062] Step 2: Based on the selected representation attributes, classify the original farmland data and farmland grid data of the research target area according to attribute levels to obtain surface data with attribute levels.

[0063] Raw farmland data is organized using plots as the recording unit. Each plot corresponds to multiple data types, and each data type has specific attributes. For raw farmland data, the data types can include soil data, farmland data, organic matter data, etc. For soil data, the attributes specifically include plot ID, area, perimeter, soil number, soil type, etc.

[0064] Farmland grid data is a data organization method that uses grids as the recording unit. It requires gridding the raw farmland data, mapping its attributes to the grid. The existence of multiple plots within a grid can cause data conflicts; therefore, conflict resolution principles must be established in the gridding process to ensure correct mapping of plot attributes. Data consistency verifies the accuracy of the spatial and attribute mapping of the data.

[0065] Both raw farmland data and farmland grid data can be vector data.

[0066] Step 3: Based on the selected administrative division level, divide the surface data with attribute levels into spatial ranges to obtain regional data with administrative division spatial division and attribute levels.

[0067] In this step, the administrative division level can be selected based on the size of the target research area. Selecting the "county" level can accommodate the range of cultivated land area and has good analytical accuracy and consistency detection effect.

[0068] The specific implementation process for this step is as follows:

[0069] Step 301: Based on the attribute level classification results, merge the faceted data with the same attribute level into one faceted data;

[0070] Step 302: Select an administrative division level as the regional segmentation standard according to the purpose of organizing the grid data; perform regional segmentation on the fused surface data according to the administrative division data to obtain regional data; each regional data corresponds to an administrative region, that is, obtain the regional data with administrative region spatial division and attribute level.

[0071] Preferably, the segmented regional data can be merged into a new layer of data and spatially connected with the administrative division data to obtain the administrative region attributes corresponding to the regional data, which are used for displaying the administrative region attributes of the new layer of data.

[0072] Step 4: Calculate the area of ​​each region under each attribute level and determine the dynamic degree of area change for each region; the dynamic degree of area change is the difference between the original data of cultivated land of the same attribute level in the same region and the area occupied after gridding, and the result is the standardized result of the area corresponding to the original data of cultivated land.

[0073] In this step, the area of ​​each region under each attribute level is calculated, and then the dynamic degree of area change of each region is calculated using the following formula:

[0074]

[0075] Where K is the total number of attribute levels; U ij V represents the area of ​​the i-th region at the j-th attribute level, obtained from processing raw farmland data. ij This represents the area of ​​the i-th region at the j-th attribute level, obtained from processing farmland grid data.

[0076] This embodiment uses an additive approach, summing up the area differences at different levels within the same region and standardizing them to obtain the dynamic degree. In practice, the dynamic degree components for each level can also be calculated within the same region, and these dynamic degrees can then be organized together using methods such as weighting or averaging to obtain a comprehensive dynamic degree measurement result.

[0077] Step 5: Perform a consistency check on spatial and attribute properties based on the dynamic degree of area change in each region; the smaller the dynamic degree of area change, the better the consistency.

[0078] This step allows setting a consistency threshold, comparing the dynamic degree of area change with the threshold, and if it is less than the threshold, the consistency requirement is considered met; otherwise, it is considered not to meet the requirement.

[0079] You can also set a consistency level range and determine the consistency level based on the dynamics of area changes. Setting the level facilitates image display and hierarchical optimization.

[0080] The following section describes in detail the method for verifying the consistency of multi-source farmland grid data organization in this invention, with the purpose of farmland quality evaluation and grid data organization.

[0081] Figure 2 This is a flowchart of the method for verifying the consistency of multi-source farmland grid data organization in Embodiment 1 of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0082] Step 1: The purpose of organizing grid data is to select representative attributes.

[0083] In this embodiment, soil fertility is selected as the characterization attribute. Both the original farmland data and the generated farmland grid data contain the attribute of soil type, which includes 40 soil types such as non-calcareous alluvial soil, broken-skinned yellow sandy loess, and light black calcareous soil. Since the purpose of this embodiment is farmland quality evaluation, through extensive research, the 40 soil types were ranked into five levels according to their soil fertility, generating a new level attribute. A higher level indicates stronger soil fertility and better farmland quality. The classification is shown in Table 1. Based on this classification, the data after classifying the land in a certain city is visualized as follows: Figure 3 As shown.

[0084] Table 1. Correspondence between Soil Type Grades

[0085]

[0086] Step 2: Based on the representational attributes, classify the original farmland data and farmland grid data into attribute-level data to obtain area data with attribute levels. Perform steps 3-7 below on the attribute-level classification results of the original farmland data and farmland grid data respectively.

[0087] In practice, you can use PyCharm to write Python programs to add and assign level attributes to the raw farmland data and farmland grid data.

[0088] Step 3: Based on the attribute level classification results, merge faceted data with the same attribute level into one faceted data.

[0089] In this step, fragmented areas (plots or grids) with the same attribute level are merged into a larger area. The resulting new layer contains data with attribute level 1, attribute level 2, and so on. This is represented as D. j , meaning face data with attribute level j.

[0090] In practice, the fusion tool in the data management tools of ArcGIS software is used to merge the hierarchical fields in the original data and grid data. The hierarchical field is selected as the fusion field to generate the merged data.

[0091] Step 4: Select an administrative division level as the regional division standard based on the purpose of organizing the grid data; according to the regional division unit and administrative division data, segment the merged surface data to obtain regional data. Each regional data corresponds to an "administrative region" and has its own attribute level. This can be expressed as D' ij This means data with region i and attribute level j.

[0092] In this embodiment, "province" is selected as the regional division standard to avoid scale issues in dynamic degree calculation. Administrative division operations are then performed on the surface data of each attribute level after fusion.

[0093] In practice, you can use the segmentation tool in the analysis tools of ArcGIS software. Select the merged file from the previous step as the input feature, select the input range (i.e., the administrative boundary files of each district and county) as the segmentation feature, and select the field of each city name as the segmentation field. This will result in a vector file of land use for each district and county.

[0094] Step 5: Merge all the segmented polygon data to generate a new layer. The newly generated layer merges the data from each administrative region for subsequent grid dynamics visualization.

[0095] In practice, this step can utilize the merge tool in the data management tools of ArcGIS software. Select all the files split from the previous step as the input dataset, and fill in the storage location and name as the output dataset to generate a new layer of data, which is beneficial for subsequent visualization operations.

[0096] Step 6: Based on the administrative division data, perform spatial connections on the segmented regional data to obtain the administrative division attributes corresponding to the regional data.

[0097] The newly merged layer data did not carry administrative region attributes during software processing; that is, the data did not display the administrative region to which it belonged. Spatial joining operations are required to display these administrative region attributes. Spatial joining is a method for matching and associating data from different data sources based on geospatial location relationships. When performing grid mapping operations, the selected spatial relationship usually depends on the specific task objectives and data characteristics. Based on the purpose of this farmland quality assessment, intersection is one of the most commonly chosen spatial relationships. Intersection ensures the accuracy and representativeness of the data, effectively optimizes spatial analysis, supports more refined decision-making, and promotes consistency in unified analysis.

[0098] In practice, you can use the Spatial Join tool in the Analysis Tools of ArcGIS software. Select the merged file as the target feature, select the administrative boundary file as the join feature, select the storage location and name for the output feature set, select JOIN_ONE_TO_ONE for the join operation, select rank field, BSM, etc. for the fields to retain, select HAVE THEIR CENTERIN for the matching option, add a new field named Area and set the precision to double, and assign the administrative attributes to the merged layer data.

[0099] Step 7: Calculate the area of ​​each region under each level.

[0100] The data obtained in the previous step is in the format of a certain level of an administrative region. We can use the geometric tools in ArcGIS software to calculate the land area of ​​the j-th level of the i-th county and display the data in the area field.

[0101] Step 8: Calculate the dynamic degree of area change for each region based on the area data of each region and each level. The dynamic degree of area change is shown in formula (1).

[0102] The calculated raw farmland data and farmland grid data are stored in the same Excel spreadsheet according to the one-to-one correspondence between county i and level j. Using the built-in tools of Excel, the grid dynamic degree is calculated according to the grid dynamic degree formula. The dynamic degree can be positive or negative. The absolute value is processed to obtain the grid dynamic degree of each county.

[0103] Step 9: Determine whether the consistency inspection standard is met based on the dynamic degree of area change.

[0104] The criteria for determining grid dynamics are as follows: According to the formula for grid dynamics, the smaller the data change (i.e., the smaller the dynamics), the lower the grid dynamics, and the better the data consistency within that range. Specific evaluation criteria are shown in Table 2 below.

[0105] Table 2. Criteria for Judging the Consistency Level of Area Change Dynamics

[0106] Mesh dynamic range Consistency results 0-0.2 very good 0.2-0.4 good 0.4-0.6 generally 0.6-0.8 Poor 0.8-1.0 Difference

[0107] Import the calculated table into ArcGIS software, open the administrative boundary file and spatially connect it, assign regional grid dynamics by matching administrative district names one by one, and finally visualize the distribution map of regional grid dynamics.

[0108] This concludes the process.

[0109] Based on the above method, the present invention also provides a device for verifying the consistency of multi-source farmland grid data organization, such as... Figure 4 As shown, it includes a classification module, a region division module, a dynamic degree acquisition module, and a consistency verification module. Among them,

[0110] The grading module is used to classify the original farmland data and farmland grid data of the research target area according to the characteristic attributes selected for the purpose of grid-based data organization, so as to obtain surface data with attribute grades.

[0111] The region division module is used to divide the spatial range of polygon data with attribute levels according to the selected administrative division level, so as to obtain regional data with administrative spatial division and attribute levels.

[0112] The dynamic degree acquisition module is used to calculate the area of ​​each region under each attribute level and determine the dynamic degree of area change of each region. The dynamic degree of area change is the difference between the original data of cultivated land of the same attribute level in the same region and the area occupied by the gridded area, and is the result after standardization of the area corresponding to the original data of cultivated land.

[0113] The consistency verification module is used to check the consistency of space and attributes based on the dynamic degree of area change in each region; the smaller the dynamic degree of area change, the better the consistency.

[0114] The regional division module obtains regional data with administrative spatial division and attribute levels in the following way:

[0115] Based on the attribute level classification results, faceted data with the same attribute level are merged into one faceted data.

[0116] Based on the purpose of organizing the grid data, an administrative division level is selected as the standard for regional segmentation; based on the administrative division data, the merged surface data is segmented into regions to obtain regional data; each regional data corresponds to an administrative region.

[0117] This invention proposes a data organization consistency detection principle, in which grid dynamics is an innovative indicator used to quantify the degree of change in data within a grid. This indicator verifies the consistency of grid data by classifying and statistically analyzing the area of ​​cultivated land data before and after gridding, thereby classifying the data by type. This scheme can verify the consistency of gridded cultivated land data from both spatial and attribute perspectives, and combines the generated dynamics data with the study area to visualize the degree of change. The grid dynamics verification method can effectively handle multi-source data and supports consistency verification in complex data environments, especially in the process of data fusion and integration.

[0118] The specific embodiments described above only illustrate the design principles of the present invention. The shapes and names of the components in this description may differ and are not limited. Therefore, those skilled in the art can modify or make equivalent substitutions to the technical solutions described in the foregoing embodiments; and these modifications and substitutions do not depart from the inventive spirit and technical solutions of the present invention, and should all fall within the protection scope of the present invention.

Claims

1. A method for verifying the consistency of multi-source farmland grid data organization, characterized in that, include: Step 1: Select representational attributes based on the purpose of organizing grid data; Step 2: Based on the described characterization attributes, classify the original farmland data and farmland grid data of the target area according to their attribute levels to obtain surface data with attribute levels; Step 3: Based on the selected administrative division level, divide the spatial range of the surface data with attribute levels to obtain regional data with administrative division spatial division and attribute levels; Step 4: Calculate the area of ​​each region under each attribute level and determine the dynamic degree of area change of each region; the dynamic degree of area change is the difference between the original data of cultivated land of the same attribute level in the same region and the area occupied after gridding, and the result is the standardized result of the area corresponding to the original data of cultivated land. Step 5: Perform a consistency check on spatial and attribute properties based on the dynamic degree of area change in each region; The smaller the dynamic range of area changes, the better the consistency; Step 3 specifically includes: Step 301: Based on the attribute level classification results, merge the faceted data with the same attribute level into one faceted data; Step 302: Select an administrative division level as the regional segmentation standard according to the purpose of organizing the grid data; perform regional segmentation on the fused surface data according to the administrative division data to obtain regional data; each regional data corresponds to an administrative region, that is, obtain the regional data with administrative region spatial division and attribute level; The segmented regional data is further merged into a new layer of data and spatially connected with the administrative division data to obtain the administrative region attributes corresponding to the regional data, which are used to display the administrative region attributes of the new layer of data. Step 4 specifically includes: Step 401: Calculate the area of ​​each region under each attribute level; Step 402: Calculate the dynamic degree of area change for each region as follows: ； Where K is the total number of attribute levels; U ij V represents the area of ​​the i-th region at the j-th attribute level, obtained from processing raw farmland data. ij This represents the area of ​​the i-th region at the j-th attribute level, obtained from processing farmland grid data. Step 5 is to set a consistency threshold, compare the dynamic degree of area change with the threshold, and determine whether the consistency requirements are met; or set a consistency level range and determine the consistency level based on the dynamic degree of area change.

2. The method as described in claim 1, characterized in that, The method further includes: spatially linking the area change dynamics of each region with administrative division data, assigning an area change dynamics attribute to each region after matching the administrative division names one by one, and visually presenting the distribution map of regional dynamics.

3. The method as described in claim 1, characterized in that, If the purpose is to evaluate the quality of arable land, the characteristic attribute is selected as soil fertility, and the selected administrative division level is county.

4. The method as described in claim 3, characterized in that, Based on soil fertility as a characteristic attribute, soils are divided into 5 levels, with higher levels indicating stronger soil fertility: Level 0 includes: water areas; The first category includes: fixed grassland sandy soil, aeolian calcareous meadow soil, semi-fixed meadow sandy soil, semi-fixed grassland sandy soil, and fixed meadow sandy soil. The second grade includes: moderately loess-type soda salinized black calcareous soil, soda salinized meadow soil-soda meadow alkaline soil, soda salinized meadow soil, soda meadow saline soil, soda meadow alkaline soil, slightly sandy loess-type soda salinized black calcareous soil, moderately sandy loess-type soda salinized black calcareous soil, slightly loess-type soda salinized black calcareous soil, soda salinized meadow soil-soda meadow alkaline soil, and heavily salinized meadow marsh soil. The third category includes: non-calcareous alluvial soil, medium-humic sandy loess light black calcareous soil, thin-humic sandy loess light black calcareous soil, non-calcareous meadow marsh soil, thin-humic bottom-concealed light black calcareous soil, thin-humic sandy loess light black calcareous soil, medium-humic bottom-concealed light black calcareous soil, non-calcareous new deposited soil, thick-humic sandy light black calcareous soil, and medium-humic bottom-concealed light black calcareous soil - non-calcareous new deposited soil. Grade 4 includes: non-calcareous alluvial soil, medium-humic sandy loess light black calcareous soil, thin-humic sandy loess light black calcareous soil, non-calcareous meadow marsh soil, thin-humic bottom-concealed light black calcareous soil, thin-humic sandy loess light black calcareous soil, medium-humic bottom-concealed light black calcareous soil, non-calcareous new deposited soil, thick-humic sandy light black calcareous soil, and medium-humic bottom-concealed light black calcareous soil - non-calcareous new deposited soil.

5. A device for verifying the consistency of multi-source farmland grid data organization, characterized in that, The method for verifying the consistency of multi-source farmland grid data organization described in any one of steps 1-4 is executed; the device includes a hierarchical classification module, a regional division module, a dynamic degree acquisition module, and a consistency verification module. The graded classification module is used to classify the original farmland data and farmland grid data of the research target area according to the characterization attributes selected for the purpose of grid-based data organization, so as to obtain surface data with attribute grades. The region division module is used to divide the spatial range of surface data with attribute levels according to the selected administrative division level, so as to obtain regional data with administrative spatial division and attribute level. The dynamic degree acquisition module is used to calculate the area of ​​each region under each attribute level and determine the dynamic degree of area change of each region. The dynamic degree of area change is the difference between the original data of cultivated land of the same attribute level in the same region and the area occupied by the gridded area, and the result is the result after standardization of the area corresponding to the original data of cultivated land. The consistency verification module is used to check the consistency of space and attributes based on the dynamic degree of area change in each region; the smaller the dynamic degree of area change, the better the consistency.

6. The apparatus as claimed in claim 5, characterized in that, The method by which the region division module obtains region data with administrative spatial division and attribute levels is as follows: Based on the attribute level classification results, faceted data with the same attribute level are merged into one faceted data. Based on the purpose of organizing the grid data, an administrative division level is selected as the standard for regional segmentation; based on the administrative division data, the merged surface data is segmented into regions to obtain regional data; each regional data corresponds to an administrative region.

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