Method and device for checking organization consistency of multi-source cultivated land grid data

Abstract

The invention provides a multi-source cultivated land grid data organization consistency checking method and device, and the method comprises the steps: firstly, selecting a representation attribute for the purpose of grid data organization; according to the characterization attributes, attribute grade classification is carried out on the cultivated land original data and the cultivated land grid data of the research target area, and surface data with attribute grades are obtained; performing spatial range division on the surface data with the attribute level according to the selected administrative division level to obtain regional data with administrative region spatial division and attribute level; calculating the area of each region under each attribute level, and determining the area change dynamic attitude of each region; and according to the area change dynamic attitude of each region, checking the consistency of the space and the attribute. By using the method, the consistency check of the cultivated land grid data in two aspects of space and attribute can be carried out.

Description

Technical Field

[0001] The invention belongs to the technical field of geographic space information systems, and in particular relates to a method and device for checking the consistency of multi-source farmland grid data organization. Background Art

[0002] With the development of precision agriculture and smart agriculture, it is becoming increasingly important to use data for agricultural decision-making and management. Farmland gridding has become an important technological innovation in the context of agricultural modernization and precision agriculture. Farmland gridding provides important agricultural information and spatial resolution for agricultural production by dividing farmland into standardized and manageable units, combined with high-precision geographic information systems, remote sensing technology and information technology, which is crucial for farmland management, crop monitoring and yield prediction.

[0003] In the context of agricultural modernization and information technology progress, multimodal cultivated land data such as site conditions, profile characteristics and soil health are crucial in land quality assessment. Data organization and management methods based on the partitioning data model 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 phenomena in attribute matching, and significant differences in semantic expression, which makes it necessary to perform conflict resolution operations in the multimodal data fusion process. The data after conflict resolution must also pass the consistency test to verify the accuracy of data mapping and ensure high standards of data quality. In the context of spatiotemporal big data applications, the development of appropriate data organization consistency verification technology is crucial to improving data quality, maximizing the comprehensive application potential of spatiotemporal big data, and promoting the improvement of agricultural informatization.

[0004] Developing appropriate data organization consistency verification technology can provide more comprehensive and accurate arable land quality information, obtain more precise spatiotemporal distribution of arable land quality, enable in-depth understanding of changing trends in different regions and periods, and provide reliable spatiotemporal data support for agricultural decision-making.

[0005] At present, there are mainly two methods for checking the consistency of multi-source farmland grid data organization:

[0006] (1) Constructing a consistency verification 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 and the percentage of cultivated land grids in the total cultivated land area, and the original grid value, and determine whether there is a significant difference between the two to ensure the effect of data grid conflict resolution and data consistency.

[0008] (2) Grid reconstruction of China’s historical cultivated land data using the hierarchical zoning method

[0009] This type of method mainly adopts new parameters. The allocation index "cultivated land suitability index" used is composed of four parameters: soil pH, soil organic carbon content, effective accumulated temperature and humidity index. The index that indicates the distribution trend of cultivated land allocation according to natural conditions over a long time scale, that is, the cultivated land suitability index is divided into ten levels from 0 to 9. The cultivated land suitability index in the original data and grid data is used to compare the data consistency test. See Grid reconstruction of China's historical cultivated land data based on the 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 the spatial and attribute aspects of cultivated land 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 it is difficult to conduct consistency verification of farmland grid data in terms of space and attributes in the prior art. According to the characteristics of grid data, a data verification scheme is proposed to determine the consistency of farmland grid data organization based on the dynamic degree of area change.

[0012] In order to solve the above technical problems, the present invention is implemented as follows.

[0013] A method for checking consistency of multi-source farmland grid data organization, comprising:

[0014] Step 1: Purpose of grid data organization, select characterization attributes;

[0015] Step 2: According to the characterization attributes, the original data of cultivated land and the cultivated land grid data of the research target area are classified by attribute level to obtain surface data with attribute levels;

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

[0017] Step 4: Calculate the area of ​​each region under each attribute level and determine the area change dynamics of each region; the area change dynamics 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 grid, and the result is normalized by the area corresponding to the original data of cultivated land;

[0018] Step 5: Perform spatial and attribute consistency checks based on the dynamics of area change in each region; the smaller the dynamics of area change, the better the consistency.

[0019] Preferably, the step 3 specifically includes:

[0020] Step 301: According to the attribute level classification result, the surface data with the same attribute level are merged into one surface data;

[0021] Step 302: Select an administrative division level as the regional segmentation standard according to the purpose of grid data organization; 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 district, that is, the regional data with administrative district spatial division and attribute level is obtained.

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

[0023] Preferably, the step 4 specifically includes:

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

[0025] Step 402: Calculate the area change dynamics of each region as:

[0026]

[0027] Among them, K is the total number of attribute levels; U ij V represents the area of ​​the jth attribute level of the ith region obtained based on the original cultivated land data processing. ij It represents the area of ​​the jth attribute level of the ith region obtained based on the processing of the cultivated land grid data.

[0028] Preferably, step 5 is:

[0029] Set a consistency threshold, compare the area change dynamics with the threshold, and determine whether the consistency requirement is met;

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

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

[0032] Preferably, for the purpose of evaluating arable land quality, the selected characterization attribute is soil fertility, and the selected administrative level is county.

[0033] Preferably, soil fertility is used as the characteristic attribute, and the soil is divided into 5 levels, the higher the level, the stronger the soil fertility:

[0034] Level 0 includes: water areas;

[0035] The first level includes: fixed grassland aeolian sandy soil, aeolian calcareous meadow soil, semi-fixed meadow aeolian sandy soil, semi-fixed grassland aeolian sandy soil, fixed meadow aeolian sandy soil;

[0036] The second level includes: moderate loess soda salinized chernozem, soda salinized meadow soil-soda meadow alkaline soil, soda salinized meadow soil, soda meadow saline soil, soda meadow alkaline soil, light sandy loess soda salinized chernozem, moderate sandy loess soda salinized chernozem, light loess soda salinized chernozem, soda salinized meadow soil-soda meadow alkaline soil, soda salinized meadow swamp soil;

[0037] The third level includes: non-calcareous alluvial soil, medium-humus sandy loess light chernozem soil, thin-humus sandy loess light chernozem soil, non-calcareous meadow swamp soil, thin-humus bottom-potential light chernozem soil, thin-humus sandy loess light chernozem soil, medium-humus bottom-potential light chernozem soil, non-calcareous new accumulation soil, thick-humus sandy light chernozem soil, medium-humus bottom-potential light chernozem soil-non-calcareous new accumulation soil;

[0038] The fourth level includes: non-calcareous alluvial soil, medium-humus sandy loess light chernozem soil, thin-humus sandy loess light chernozem soil, non-calcareous meadow bog soil, thin-humus bottom-potential light chernozem soil, thin-humus sandy loess light chernozem soil, medium-humus bottom-potential light chernozem soil, non-calcareous new accumulation soil, thick-humus sandy light chernozem soil, and medium-humus bottom-potential light chernozem soil-non-calcareous new accumulation 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 hierarchical classification module is used to classify the original cultivated land data and cultivated land grid data of the research target area according to the characterization attributes selected based on the purpose of grid data organization, and obtain surface data with attribute levels;

[0041] The region division module is used to divide the spatial range of the surface data with attribute levels according to the selected administrative division level, and obtain regional data with administrative area 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 area change dynamic degree of each region; the area change dynamic degree 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 normalized by the area corresponding to the original data of cultivated land;

[0043] The consistency verification module is used to perform spatial and attribute consistency verification based on the dynamic degree of area change of each region; the smaller the dynamic degree of area change, the better the consistency.

[0044] Preferably, the region division module obtains the region data with administrative region spatial division and attribute level in the following manner:

[0045] According to the attribute level classification results, the surface data with the same attribute level are merged into one surface data;

[0046] According to the purpose of grid data organization, an administrative division level is selected as the regional segmentation standard; according to the administrative division data, the fused surface data is segmented to obtain regional data; each regional data corresponds to an administrative district.

[0047] Beneficial effects:

[0048] (1) Based on the principle that the proportion of the area occupied by the same grade of land in the same region should be roughly the same before and after gridding, the present invention designs the dynamic degree of area change as a measure of grid dynamic degree. Characterization attributes and administrative division levels are selected to reorganize the original cultivated land data and cultivated land grid data so that the regional data have the same area standard and attribute level, and then the dynamic degree of area change of the region before and after gridding is calculated. Since the reorganized data has both spatial information and attribute information, consistency checks in both space and attributes can be implemented. At the same time, in order to avoid accuracy problems and differences in inspection standards caused by scale problems, the present invention also standardizes the area difference so that the data comparison benchmark is more consistent and scientific.

[0049] (2) A preferred embodiment of the present invention provides a specific calculation formula for dynamics. The formula adopts an addition method to add together the area differences of different levels in the same region and standardize them to obtain the dynamics of the region. The dynamics 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 is convenient for comparative analysis between regions.

[0050] (3) In a preferred embodiment of the present invention, the segmented area data are further merged into new layer data and the administrative division data are spatially connected, so that the administrative area attributes of the new layer data can be displayed, so that the user can obtain an intuitive image of the segmentation result.

[0051] (4) In a preferred embodiment of the present invention, after obtaining the dynamic degree, the present invention further matches the administrative district name and visualizes the regional dynamic degree distribution map, so that the user can easily observe which area's dynamic degree does not meet the dynamic degree requirement, 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 arable land quality, soil fertility can be selected as the characterization attribute, and the county can be selected as the administrative division level. Through a large amount of research, a soil grade classification method suitable for the dynamic degree calculation and consistency detection of the present invention is given. By using this grade and attribute selection, spatial and attribute consistency detection can be effectively performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention is a flow chart of a method for consistency checking of multi-source farmland grid data organization.

[0054] Figure 2 This is a flow chart of a method for checking consistency of multi-source farmland grid data organization according to an embodiment of the present invention.

[0055] Figure 3 This is a soil type grade map for a certain city.

[0056] Figure 4 It is a block diagram of a device for verifying consistency of multi-source farmland grid data organization according to the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0058] In the process of data consistency verification, the proportion of the area occupied by the same grade of land in the same region should be roughly the same before and after gridding. The present invention designs the grid dynamic degree based on this principle, that is, calculating the difference in area occupied by the original data of the same grade in the same region and 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 corresponding county area is taken as the grid dynamic degree.

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

[0060] Step 1: Purpose of grid data organization, select characterization attributes.

[0061] In this step, representative attributes are selected according to the purpose of using the grid data. For example, for the purpose of evaluating the quality of cultivated land, the standard attribute of cultivated land quality can be selected as soil fertility. The stronger the soil fertility, the better the cultivated land quality.

[0062] Step 2: According to the selected characterization attributes, the original cultivated land data and cultivated land grid data of the research target area are classified by attribute level to obtain surface data with attribute levels.

[0063] The original data of cultivated land is organized in a data recording mode with plots as the recording unit. Each plot corresponds to multiple types of data, and each type of data has specific attributes. For the original data of cultivated land, its types of data may include soil data, cultivated land data, organic matter data, etc.; for the data of soil data, its attributes include the ID, area, perimeter, soil number, soil type, etc. of the plot.

[0064] Farmland grid data is a data organization method that uses grids as recording units. The original farmland data needs to be gridded to map the attributes of the original farmland data to the grid. If there are multiple plots in the grid, data conflicts will occur in the grid. Therefore, it is necessary to formulate conflict resolution principles in the grid processing solution to correctly map the plot attributes. Data consistency verifies the correctness of data mapping in space and attributes.

[0065] Both original cultivated land data and cultivated land grid data can adopt vector data.

[0066] Step 3: According to the selected administrative division level, the spatial range of the surface data with attribute level is divided to obtain regional data with administrative spatial division and attribute level.

[0067] In this step, the administrative division level can be selected according to the size of the research target area, and the selection of the "county" level can adapt to the range of cultivated land area and has good analysis accuracy and consistency detection effect.

[0068] The specific implementation process of this step is:

[0069] Step 301: According to the attribute level classification result, the surface data with the same attribute level are merged into one surface data;

[0070] Step 302: Select an administrative division level as the regional segmentation standard according to the purpose of grid data organization; 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 district, that is, the regional data with administrative district spatial division and attribute level is obtained.

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

[0072] Step 4: Calculate the area of ​​each region at 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 at the same attribute level in the same region and the area occupied after gridding, and the result is normalized using the area of ​​the corresponding region of the original cultivated land data.

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

[0074]

[0075] Among them, K is the total number of attribute levels; U ij V represents the area of ​​the jth attribute level of the ith region obtained based on the original cultivated land data processing. ij It represents the area of ​​the jth attribute level of the ith region obtained based on the processing of the cultivated land grid data.

[0076] This embodiment adopts a summing method to sum up the area differences of different levels in the same region and standardize them to obtain the dynamic degree. In practice, the dynamic degree components of each level can also be calculated in the same region, and then these dynamic degrees are organized together by weighting or averaging to obtain a comprehensive dynamic degree measurement result.

[0077] Step 5: Perform spatial and attribute consistency checks based on the dynamics of area change in each region; the smaller the dynamics of area change, the better the consistency.

[0078] In this step, a consistency threshold can be set, and the dynamic degree of area change is compared with the threshold. If it is less than the threshold, it is considered that the consistency meets the requirement, otherwise it is considered that the requirement is not met.

[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 grading optimization.

[0080] The following describes in detail the method for checking the consistency of multi-source farmland grid data organization of the present invention, taking farmland quality evaluation as the purpose of grid data organization.

[0081] Figure 2 Flow chart of the method for checking consistency of multi-source farmland grid data organization in the first embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0082] Step 1: Select the characterization attributes for the purpose of grid data organization.

[0083] In this embodiment, soil fertility is selected as the characterization attribute. The soil type attribute exists in both the original cultivated land data and the generated cultivated land grid data. There are a total of 40 soil types, such as non-calcareous alluvial soil, broken yellow sand loess and light black calcium soil. Since the purpose of this embodiment is to evaluate the quality of cultivated land, through a large number of studies, the 40 types of soil are ranked according to the size of soil fertility and divided into five levels to generate new grade attributes. The higher the grade, the stronger the soil fertility and the better the quality of the cultivated land. The classification is described in Table 1, and the data after the land classification of a certain city based on this classification is visualized as shown in the figure. Figure 3 shown.

[0084] Table 1 Soil type and grade correspondence table

[0085]

[0086] Step 2: According to the characterization attributes, the original cultivated land data and the cultivated land grid data are classified by attribute level to obtain the surface data with attribute level. The following steps 3 to 7 are performed on the attribute level classification results of the original cultivated land data and the cultivated land grid data respectively.

[0087] In practice, you can use pycharm to write a python program to add level attributes and assign values ​​to the original cultivated land data and cultivated land grid data.

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

[0089] In this step, the fragmented areas (plots or grids) with the same attribute level are merged into a larger area. At this time, the new layer data content obtained is data with attribute level 1, data with attribute level 2, etc. It is represented by D j , which means the face data with attribute level j.

[0090] In practice, the fusion tool in the data management tool in ArcGis software is used to fuse the elements of the level fields in the original data and grid data, and the level fields are selected as the fusion fields to generate fused data.

[0091] Step 4: Select an administrative division level as the regional division standard according to the purpose of grid data organization; perform regional segmentation on the fused surface data according to the regional division unit and administrative division data to obtain regional data. Each regional data corresponds to an "administrative district" and has its own attribute level. It can be expressed as D' ij , which means data with area i and attribute level j.

[0092] In this embodiment, "province" is selected as the regional division standard to avoid the scale problem of dynamic degree calculation. The administrative division operation is performed on the fused surface data of each attribute level.

[0093] In practice, you can use the segmentation tool in the analysis tool in ArcGis software, select the fused file in the previous step as the input feature, select the input range, that is, the administrative boundary file of each district and county as the segmentation feature, and select the field of each city name as the segmentation field. The vector file storage of the land use of each district and county will be obtained.

[0094] Step 5: Merge all the segmented surface data to generate a new layer data. The newly generated layer data merges the data of each administrative district for the subsequent grid dynamic visualization.

[0095] In practice, this step can utilize the merge tool in the data management tool in the ArcGis software. The input dataset selects all the files split in the previous step, and the output dataset fills in the storage location and name to generate a new layer data to facilitate subsequent visualization operations.

[0096] Step 6: Based on the administrative division data, spatially connect the segmented regional data to obtain the administrative district attributes corresponding to the regional data.

[0097] The newly merged layer data does not carry the administrative district attributes during the software processing, that is, the administrative district to which it belongs is not displayed in the data. A spatial join operation is required to display its administrative area attributes. Spatial join is a method of matching and associating data from different data sources based on geographic spatial location relationships. When performing grid mapping operations, the selected spatial relationship usually depends on the specific task objectives and data characteristics. According to the purpose of this arable land quality evaluation, intersection is one of the most commonly used spatial relationships. Intersection can ensure the accuracy and representativeness of the data, effectively optimize spatial analysis, support more sophisticated decision-making, and promote the consistency of unified analysis.

[0098] In practice, you can use the spatial connection tool in the analysis tool in ArcGis software, select the merged file as the target feature, select the administrative boundary file as the connection feature, select the storage location and name for the output feature set, select JOIN_ONE_TO_ONE for the connection operation, select the level field, BSM, etc. for the retained field, select HAVE THEIR CENTERIN for the matching option, add a new field named Area, set the precision to double, and assign the administrative attributes to the merged layer data.

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

[0100] The data obtained in the previous step is in the format of a certain level of a certain administrative region. The geometry tools in the ArcGis software can be used to calculate the land area size of the jth level in the ith county and display the data in the area field.

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

[0102] The calculated original cultivated land data and cultivated land grid data are stored in the same EXCEL table according to the correspondence between county i and level j. The grid dynamic degree formula is used to calculate using the tools provided in the EXCEL table. The dynamic degrees obtained are both positive and negative. The absolute values ​​are processed to obtain the grid dynamic degrees of each county.

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

[0104] The rules for judging the grid dynamics are as follows. According to the formula for grid dynamics, the smaller the data change, the smaller the dynamics. The smaller the grid dynamics, the better the data consistency within this range. The specific judging criteria are shown in Table 2 below:

[0105] Table 2 Area change dynamic consistency level judgment standard

[0106] Grid 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] The calculated table was imported into ArcGis software, and the administrative boundary file was opened to connect it spatially. The regional grid dynamics was assigned after matching the administrative district names one by one, and finally the regional grid dynamics distribution map was visualized.

[0108] This is the end of the process.

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

[0110] The hierarchical classification module is used to classify the original cultivated land data and cultivated land grid data of the research target area according to the characterization attributes selected based on the purpose of grid data organization, and obtain surface data with attribute levels.

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

[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 after gridding, and the result is normalized using the area of ​​the corresponding region of the original data of cultivated land.

[0113] The consistency verification module is used to perform spatial and attribute consistency checks based on the dynamics of area changes in each region; the smaller the dynamics of area changes, the better the consistency.

[0114] Among them, the regional division module obtains regional data with administrative district spatial division and attribute levels in the following way:

[0115] According to the attribute level classification results, the surface data with the same attribute level are merged into one surface data;

[0116] According to the purpose of grid data organization, an administrative division level is selected as the regional segmentation standard; according to the administrative division data, the fused surface data is segmented to obtain regional data; each regional data corresponds to an administrative district.

[0117] The data organization consistency detection principle proposed in the present invention, in which grid dynamics is an innovative indicator, is used to quantify the degree of change of data in the grid. This indicator verifies the consistency of grid data by classifying the cultivated land data before and after gridding and counting the area by type. The scheme can perform data consistency verification on the gridded cultivated land data from both spatial and attribute levels, and combine the generated dynamic data with the study area to display the degree of change in a visual form. The grid dynamics verification method can effectively process multi-source data and support consistency verification in complex data environments, especially consistency verification in data fusion and data integration processes.

[0118] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A method for checking consistency of multi-source farmland grid data organization, characterized in that: include: Step 1: Purpose of grid data organization, select characterization attributes; Step 2: According to the characterization attributes, the original data of cultivated land and the cultivated land grid data of the research target area are classified by attribute level to obtain surface data with attribute levels; Step 3: According to the selected administrative division level, the spatial range of the surface data with attribute levels is divided to obtain regional data with administrative spatial division and attribute levels; Step 4: Calculate the area of ​​each region under each attribute level and determine the area change dynamics of each region; the area change dynamics 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 grid, and the result is normalized by the area corresponding to the original data of cultivated land; Step 5: Perform spatial and attribute consistency checks based on the dynamics of area change in each region; the smaller the dynamics of area change, the better the consistency.

2. The method according to claim 1, characterized in that The step 3 specifically includes: Step 301: According to the attribute level classification result, the surface data with the same attribute level are merged into one surface data; Step 302: Select an administrative division level as the regional segmentation standard according to the purpose of grid data organization; 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 district, that is, the regional data with administrative district spatial division and attribute level is obtained.

3. The method according to claim 2, characterized in that After step 302, the segmented area data are further merged into a new layer data, and spatially connected with the administrative division data to obtain the administrative area attributes corresponding to the area data, which are used for displaying the administrative area attributes of the new layer data.

4. The method according to claim 1, characterized in that The step 4 specifically includes: Step 401: Calculate the area of ​​each region at each attribute level; Step 402: Calculate the area change dynamics of each region as: Among them, K is the total number of attribute levels; U ij V represents the area of ​​the jth attribute level of the ith region obtained based on the original cultivated land data processing. ij It represents the area of ​​the jth attribute level of the ith region obtained based on the processing of the cultivated land grid data.

5. The method according to claim 1, characterized in that Step 5 is: Set a consistency threshold, compare the area change dynamics with the threshold, and determine whether the consistency requirement is met; Alternatively, a consistency level range can be set and the consistency level can be determined based on the dynamics of area changes.

6. The method according to claim 1, characterized in that The method further includes: spatially connecting the area change dynamics of each region with administrative division data, assigning area change dynamics attributes to each region after one-to-one matching of administrative district names, and visually presenting a regional dynamics distribution map.

7. The method according to claim 1, characterized in that For the purpose of evaluating the quality of cultivated land, the selected characteristic attribute is soil fertility, and the selected administrative level is county.

8. The method according to claim 7, characterized in that Taking soil fertility as the characteristic attribute, the soil is divided into 5 levels. The higher the level, the stronger the soil fertility: Level 0 includes: water areas; The first level includes: fixed grassland aeolian sandy soil, aeolian calcareous meadow soil, semi-fixed meadow aeolian sandy soil, semi-fixed grassland aeolian sandy soil, fixed meadow aeolian sandy soil; The second level includes: moderate loess soda salinized chernozem, soda salinized meadow soil-soda meadow alkaline soil, soda salinized meadow soil, soda meadow saline soil, soda meadow alkaline soil, light sandy loess soda salinized chernozem, moderate sandy loess soda salinized chernozem, light loess soda salinized chernozem, soda salinized meadow soil-soda meadow alkaline soil, soda salinized meadow swamp soil; The third level includes: non-calcareous alluvial soil, medium-humus sandy loess light chernozem soil, thin-humus sandy loess light chernozem soil, non-calcareous meadow swamp soil, thin-humus bottom-potential light chernozem soil, thin-humus sandy loess light chernozem soil, medium-humus bottom-potential light chernozem soil, non-calcareous new accumulation soil, thick-humus sandy light chernozem soil, medium-humus bottom-potential light chernozem soil-non-calcareous new accumulation soil; The fourth level includes: non-calcareous alluvial soil, medium-humus sandy loess light chernozem soil, thin-humus sandy loess light chernozem soil, non-calcareous meadow bog soil, thin-humus bottom-potential light chernozem soil, thin-humus sandy loess light chernozem soil, medium-humus bottom-potential light chernozem soil, non-calcareous new accumulation soil, thick-humus sandy light chernozem soil, and medium-humus bottom-potential light chernozem soil-non-calcareous new accumulation soil.

9. A device for checking consistency of multi-source farmland grid data organization, characterized in that: It includes level classification module, area division module, dynamic degree acquisition module and consistency verification module; The hierarchical classification module is used to classify the original cultivated land data and cultivated land grid data of the research target area according to the characterization attributes selected based on the purpose of grid data organization, and obtain surface data with attribute levels; The region division module is used to divide the spatial range of the surface data with attribute levels according to the selected administrative division level, and obtain regional data with administrative area 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 area change dynamic degree of each region; the area change dynamic degree 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 normalized by the area corresponding to the original data of cultivated land; The consistency verification module is used to perform spatial and attribute consistency verification based on the dynamic degree of area change of each region; the smaller the dynamic degree of area change, the better the consistency.

10. The device according to claim 9, characterized in that The regional division module obtains regional data with administrative district spatial division and attribute levels in the following manner: According to the attribute level classification results, the surface data with the same attribute level are merged into one surface data; According to the purpose of grid data organization, an administrative division level is selected as the regional segmentation standard; according to the administrative division data, the fused surface data is segmented to obtain regional data; each regional data corresponds to an administrative district.

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