A method and system for upgrading a high-standard farmland monitoring network system based on existing three-census sampling points

Abstract

A method for upgrading a high-standard farmland monitoring network system based on existing three-national census sampling points includes the following steps: S1, determining existing cultivated land quality monitoring points n; determining three-national census sampling points m; obtaining high-standard farmland distribution vector data FD; obtaining typical crop rotation and multiple cropping pattern distribution CD; obtaining administrative division vector data AD; S2, screening existing cultivated land quality monitoring points to obtain the screened existing cultivated land quality monitoring point n1; S3, calculating the maximum number of high-standard farmland monitoring points to be built based on the high-standard farmland distribution vector data FD and the existing cultivated land quality monitoring point n1. The present invention also proposes a corresponding system. The method and system of the present invention utilize existing three-national census sampling points to avoid duplication, help assess soil quality, farmland infrastructure, and planting utilization conditions, provide more scientific and comprehensive monitoring results, improve and optimize the existing monitoring network, increase the accuracy and reliability of monitoring results, and further ensure food security.

Description

Technical Field

[0001] The present invention relates to the field of farmland monitoring technology, and more specifically, to a method and system for upgrading a high-standard farmland monitoring network system based on existing three-census sampling points. Background Art

[0002] Farmland monitoring involves deploying a sufficient number of representative monitoring points to reflect overall farmland infrastructure, arable land quality, cropping uses, and other characteristics in a "point-to-surface" manner. Therefore, point placement must adhere to principles such as representativeness and spatial balance, taking into account historical monitoring points to ensure cost-effectiveness and continuity in monitoring efforts and results. A smaller number of points, coupled with greater spatial and temporal representation, can reflect farmland attributes. Lack of representativeness or irrational use of monitoring points can negatively impact the accuracy of monitoring results.

[0003] Common point placement methods currently include traditional random placement, systematic placement (grid placement), and zoning and intensified placement methods developed from traditional point placement methods. The survey area is partitioned using the initial grid placement, and grid placement is repeated. Areas of soil environmental interference are removed from the soil distribution map, and appropriate grid spacing is determined based on regional characteristics. Based on the survey area and soil property objectives, the survey area is divided into several independent sampling zones, and sampling points are then randomly and evenly distributed.

[0004] In evaluating the representativeness of zoning and sample point placement, some studies have used statistical methods to reflect the overall representativeness of the monitoring network using statistical indicators such as standard deviation, variance, skewness, and kurtosis. Some studies have used the quality of cultivated land as the dominant factor, combined with road accessibility and the suitability of monitoring points. In terms of zoning methods and minimum unit acquisition, some have used DEM elevation data, slope, vegetation index, and yield estimation data extracted from remote sensing images to divide samples. Some have combined the four indicators of terrain slope, effective soil thickness, irrigation guarantee rate, and soil organic matter content. Some have used the spatial distribution of cultivated land natural quality as the basis, combined with cultivated land utilization level, income level, and location data of the area where the cultivated land is located.

[0005] In summary, existing monitoring network deployment methods primarily improve upon traditional sampling methods, such as the grid method (systematic sampling) and stratified sampling. These methods suffer from inefficient data generation, poor sample representativeness, and inadequate reflection of the scale differences within high-standard farmland spatial systems. Furthermore, the spatial location of monitoring points is typically the centroid of the patch or the center of the grid, lacking a comprehensive quantitative assessment of the representativeness and importance of each point.

[0006] Furthermore, current farmland monitoring sites primarily focus on arable land quality, emphasizing soil properties. While existing methods consider the impact of farmland infrastructure and land consolidation projects on arable land use, limited by the technology used to collect farmland infrastructure information, most methods directly reflect arable land use using crop yields, without specifically identifying and setting representative indicators for farmland infrastructure. Summary of the Invention

[0007] In response to the problems in the background technology, the present invention proposes a method for upgrading the high-standard farmland monitoring network system based on the existing three-general census sample points, including: S1, data collection and preparation: determining the existing cultivated land quality monitoring point n; determining the three-general census sample point m; obtaining high-standard farmland distribution vector data FD; obtaining the typical rotation and multiple cropping pattern distribution CD; obtaining administrative division vector data AD; S2, screening the existing cultivated land quality monitoring points based on the data collected in step S1, and obtaining the screened existing cultivated land quality monitoring point n1; S3, calculating the maximum number of high-standard farmland monitoring points to be built based on the high-standard farmland distribution vector data FD collected in S1 and the screened existing cultivated land quality monitoring point n1 obtained in S2.

[0008] The present invention also proposes a high-standard farmland monitoring network system upgrade system based on the existing three-census sampling points, including computer executable code, which, when executed, implements the method described in claims 1-9.

[0009] The present invention aims to solve the problem that historical arable land quality monitoring points do not correspond to existing monitoring needs. The two indicators of farmland infrastructure and farmland utilization are introduced. This innovation not only fills the gaps in the existing monitoring system in infrastructure assessment and planting use monitoring, but also provides a more comprehensive and accurate monitoring method for the shortcomings of the existing arable land quality monitoring network system. The wide distribution of the three census sample points provides more comprehensive and representative monitoring data, covering different regions and soil types. By utilizing the existing three census sample points, it can not only save a lot of manpower, material and financial resources and avoid duplicate construction, but also help evaluate soil quality, farmland infrastructure and planting utilization status, provide more scientific and comprehensive monitoring results, improve and optimize the existing monitoring network, improve the accuracy and reliability of monitoring results, and further ensure food security.

[0010] 1. The present invention comprehensively considers multiple factors such as soil type, farmland infrastructure, land use type, and spatial balance, and is more in line with the monitoring needs of high-standard farmland.

[0011] 2. The present invention is based on the representativeness of monitoring points. By screening highly representative points and using fewer supplementary monitoring points, a high-standard farmland monitoring network system with economy, representativeness and high coverage is formed from point to surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to make the present invention more easily understood, the present invention will be described in more detail with reference to the specific embodiments shown in the accompanying drawings. These drawings only depict typical embodiments of the present invention and should not be considered as limiting the scope of protection of the present invention.

[0013] Figure 1 It is a technical roadmap of the method of the present invention.

[0014] Figure 2 The present invention is a flowchart of an embodiment of the method.

[0015] Figure 3 The present invention is a flowchart of another embodiment of the method.

[0016] Figure 4 The present invention is a flowchart of another embodiment of the method.

[0017] Figure 5 The present invention is a flowchart of another embodiment of the method.

[0018] Figure 6 The present invention is a flowchart of another embodiment of the method.

[0019] Figure 7 The present invention is a flowchart of another embodiment of the method.

[0020] Figure 8 This is a map showing the distribution of existing cultivated land quality monitoring points and their utilization types.

[0021] Figure 9 This is a map showing the distribution of the three census sampling points.

[0022] Figure 10 This is a map of the distribution of the three census sample points to be screened and the surface types.

[0023] Figure 11 This is a map showing the distribution of monitoring points to be built and the surface types.

[0024] Figure 12 This is a map showing the distribution and types of high-standard farmland monitoring points. DETAILED DESCRIPTION

[0025] The following describes the embodiments of the present invention with reference to the accompanying drawings so that those skilled in the art can better understand the present invention and implement it. However, the enumerated embodiments are not intended to limit the present invention. Unless there is a conflict, the following embodiments and the technical features in the embodiments can be combined with each other, wherein the same components are represented by the same figure marks.

[0026] like Figure 1The method of the present invention is shown. Existing national, provincial, municipal, and county cultivated land quality monitoring points are screened, and a comprehensive assessment of the representativeness and importance of soil survey sample points is conducted to screen and generate the final list of supplementary high-standard farmland cultivated land quality monitoring points to be built, thereby improving and optimizing the existing monitoring network and enhancing the accuracy and reliability of monitoring results. The method mainly includes: S1, data collection and preparation; S2, screening existing cultivated land quality monitoring points; S3, calculating the number of monitoring points to be built; S4, screening the additional three-survey sample points; and S5, spatial distribution of high-standard farmland monitoring points.

[0027] In one embodiment, step S1 includes steps S11 - S15 .

[0028] S11: Determine existing cultivated land quality monitoring points n. Cultivated land quality monitoring points are established within basic farmland protection zones, in accordance with relevant requirements, based on different cultivated land grades and major soil types. In addition to their original geographic location, these points are determined through long-term, targeted, and effective monitoring to obtain information on soil type (soil texture, soil structure, soil pH, etc.), land use type (cultivated land type, cropping system, crop type, etc.), and nutrient status (soil nutrient content and nutrient balance at the monitoring point).

[0029] S12: Determine the third national soil survey sampling point m. The third national soil survey sampling point refers to the sampling point of the third national soil survey. Each third national soil survey sampling point includes the geographical location of the sampling point, the soil type represented by the sampling point, and the land use status of the sampling point.

[0030] S13: Obtain high-standard farmland distribution vector data FD.

[0031] High-standard farmland distribution vector data is a type of geographic information data, the main content of which includes the location, area, type, construction status and other information of high-standard farmland. It is used to represent the distribution of high-standard farmland in geographic space. It is usually stored in vector format and can generally be obtained from the geographic information system platform.

[0032] S14: Obtain the distribution CD of typical crop rotation and multiple cropping patterns.

[0033] Typical crop rotation and multiple cropping pattern distribution data refers to the distribution of various typical crop rotation and multiple cropping patterns within different geographical regions. This data typically includes the planting ratios of different crops in different years, crop rotation cycles, and multiple cropping indices, and can generally be obtained from geographic information system platforms.

[0034] S15: Obtain administrative division vector data AD.

[0035] Administrative division vector data is a type of digital data that uses graphic elements such as points, lines, and surfaces to represent administrative division boundaries and geographic information. It contains information such as the name, code, and boundary lines of the administrative division and can generally be obtained from a geographic information system platform.

[0036] In one embodiment, S2: screening existing farmland quality monitoring points based on the data collected in step S1 to obtain screened existing farmland quality monitoring points n1.

[0037] In principle, high-standard farmland quality monitoring points should be built on high-standard farmland. Figure 2 In the embodiment shown, the pre-screening method constructed includes:

[0038] S21: Perform spatial overlay analysis on the existing cultivated land quality monitoring points n and the high-standard farmland distribution vector data FD.

[0039] S22: Determine whether each cultivated land quality monitoring point is located on high-standard farmland. If it is located on high-standard farmland, it is retained; if it is located outside high-standard farmland, it is discarded.

[0040] S23: With each existing cultivated land quality monitoring point n as the center, in order to reduce the spatial coverage overlap rate of the monitoring points, the maximum area of ​​50,000 mu (radius of about 3.25 km) is used as the standard, and within the circle with a radius of 3.5 km, the distribution of high-standard farmland F is determined. n , the proportion of high-standard farmland is A n .

[0041] S24: When the area accounts for A n When the value is less than the first threshold a1, the corresponding existing cultivated land quality monitoring point is discarded; when the value is greater than the first threshold a1, the corresponding cultivated land quality monitoring point is retained.

[0042] After this step, the selected existing cultivated land quality monitoring points n1 are obtained. The selected existing cultivated land quality monitoring points refer to the cultivated land quality monitoring points on high-standard farmland. These monitoring points will be upgraded and constructed based on the existing cultivated land quality monitoring points according to the principle of economy. Existing monitoring points will be abolished or upgraded as appropriate.

[0043] S3: Calculate the maximum number of high-standard farmland monitoring points to be built based on the high-standard farmland distribution vector data FD collected by S1 and the existing cultivated land quality monitoring points n1 obtained after screening by S2.

[0044] In combination with the "High-standard Farmland Construction Plan (2021-2030)", the maximum number of high-standard farmland monitoring points to be built is calculated based on the density requirement of setting up no less than one monitoring point for every 35,000 to 50,000 mu, and the existing cultivated land quality points n1 after screening.

[0045] exist Figure 3In the embodiment shown, step S3 includes:

[0046] S31: Calculate the area of ​​high-standard farmland Area.

[0047] The vector area of ​​high-standard farmland refers to the vector area of ​​high-standard farmland in a region calculated by using GIS (geographic information system) and output in units of 10,000 mu.

[0048] S32: Calculate the maximum number N of high-standard farmland monitoring points to be built.

[0049] Calculation method for the maximum number of high-standard farmland monitoring points to be built (Formula 1):

[0050] N (number) = Area 10,000 mu / 35,000 mu – n1 (number) (1),

[0051] Where N is the maximum number of high-standard farmland monitoring points to be built; Area is the high-standard farmland area Area calculated in step S31; and n1 is the number of existing cultivated land quality points n1 after output screening in step S2.

[0052] S4: Screen and supplement the three national census sampling points.

[0053] According to the monitoring needs of high-standard farmland and the characteristics of the three national census sample data, the representativeness of the sample points is evaluated by scoring them according to five dimensions: the proportion of high-standard farmland area, soil type, quality standard of cultivated land for high-standard farmland construction, land use type, and high-standard farmland infrastructure. The spatial locations of the high-standard farmland cultivated land quality monitoring points to be built are arranged according to the principles of high scores and spatial balance.

[0054] Step S4 includes: S41 preliminary screening of the three national census sample points; S42 evaluation of the three national census sample points; S43 manual verification and adjustment.

[0055] exist Figure 4 In the embodiment shown, S41: performing preliminary screening of the three census sampling points. Step S41 includes S411-S414.

[0056] Specifically, S411: consistent with the operation of the aforementioned step S21, for the three-national census sample point m obtained in S12, the high-standard farmland distribution vector FD obtained in S13, and the screened existing cultivated land quality monitoring point n1 obtained in S2, the three-national census sample point m and the high-standard farmland distribution vector data FD are spatially overlaid and analyzed.

[0057] S412: Determine whether each three-level census sampling point m is located on high-standard farmland. If so, retain it; if not, discard it.

[0058] S413: Calculate the straight-line distance between each three-level census sampling point m and the selected existing cultivated land quality monitoring point n1. Allowing for slight overlap in the coverage area of ​​the two monitoring points (3.5km radius), if the distance is less than 6km, it is discarded; if the distance is greater than 6km, it is retained.

[0059] S414: With each three-level census sampling point m as the center, within a circle with a radius of 3.5 km, calculate the distribution of high-standard farmland as F m Calculate the proportion of high-standard farmland area as A m .

[0060] S415: When the area accounts for A m When it is less than the second threshold a2, the corresponding three-channel sample point is discarded; when it is greater than the second threshold a2, it is retained.

[0061] After steps S41 to S44, the post-screening three-level census sampling point m1 is obtained. The post-screening three-level census sampling point m1 is located on concentrated and contiguous high-standard farmland and, based on the principle of differentiated monitoring point density, does not overlap with the monitoring range of the post-screening cultivated land quality monitoring point in S23. In subsequent steps, a representative score is calculated for each post-screening three-level census sampling point.

[0062] Step S42: Evaluate the three sampling points. Figure 5 In the illustrated embodiment, step S42 includes S421 - S425 .

[0063] S421: Calculate the soil genus weight T based on the distribution CD of typical crop rotation and multiple cropping patterns obtained in S14 and the post-screening three-level census sampling point m1 obtained in S46. Specifically, calculate the percentage of each soil genus type at the post-screening three-level census sampling point, and calculate the soil genus type weight of the three-level census sampling point based on the percentage (Formula 2).

[0064] T = Sqrt(m s / m) (2),

[0065] Where, T is the weight of soil species and genus type, m s is the number of soil genus types s, and m is the three sampling points after screening.

[0066] S422: Weight Q of cultivated land quality construction standards.

[0067] By comparing the high-standard farmland arable land quality construction standards, the weights of the arable land quality construction standards at the three census sampling points were obtained.

[0068] Table 1 Details of cultivated land quality weights Q at the three census sampling points

[0069]

[0070] Note: ○ indicates that the standard is met, and × indicates that the standard is not met.

[0071] S423: High-standard farmland infrastructure weight E.

[0072] Comparing the high-standard farmland infrastructure construction standards, and according to the number of infrastructure and distance density, the infrastructure weight E of the three census sampling points is obtained. The calculation steps are as follows:

[0073] (1) High-standard farmland infrastructure projects include farmland irrigation and drainage pumping stations, channels and supporting buildings, farmland forest networks, roads, etc. The following four indicators are used to evaluate the infrastructure projects at the points and their corresponding high-standard farmland distribution F (Formula 3-6):

[0074] Pumping station: E1 = number of pumping stations in F × average distance from the point to the pumping station (3)

[0075] Channel: E2 = length of channel within F × average distance from point to channel (4)

[0076] Road accessibility: E3 = the shortest straight-line distance between the point and the road (5)

[0077] Farmland forest network: E4 = length of farmland forest network within F (6)

[0078] (2) Normalize the scores of the four indicators, taking the pump station indicator score E1 as an example (Formula 7):

[0079] (7),

[0080] Where, is the normalized pump station index score, The original pump station index score, It is the maximum value of the pump station indicator scores of all three survey sampling points. It is the minimum value of the pump station indicator scores of all three survey sampling points.

[0081] (3) Infrastructure construction weight (Formula 8).

[0082] (8),

[0083] S424: Land use type weight U.

[0084] After statistical screening, the proportion of each major crop type covered by the three census sampling points was calculated, and the land use type weights of the three census sampling points were obtained based on the proportion of the number (Formula 9).

[0085] U = Sqrt(m u / m) (9),

[0086] Where U is the weight of land use type, m u is the number of land use type u, and m is the sampling points of the three censuses after screening.

[0087] S426: Score of the three census sample points after screening.

[0088] Score=A*T*Q*E*U (10),

[0089] Where Score is the score of the three national census sampling points, A is the proportion of high-standard farmland area, T is the weight of soil type, Q is the weight of cultivated land quality standard for high-standard farmland construction, E is the weight of high-standard farmland infrastructure, and U is the weight of land use type.

[0090] Output: Select Nn three-level census sample points m2 from highest to lowest score. After calculating the score of each three-level census sample point in step S41, select Nn three-level census sample points m2 from highest to lowest score. Generally, selecting sample points with higher scores can better represent the entire dataset.

[0091] Step S43: Perform manual verification and adjustment. Step S43 includes steps S431-S433.

[0092] Input: S42 selects Nn three-level census sample points m2 from high to low according to the score Score, and S15 administrative division vector data AD.

[0093] S431: Considering the spatial balance between points according to actual conditions, the administrative division vector data AD is superimposed. If the same village or town already has cultivated land quality points or selected high-scoring three-national survey sampling points to be screened, the scores of other three-national survey sampling points in the same village or town will be w times the original scores.

[0094] S432: Count the spatial distances between high-scoring points. The closer the distance, the worse the spatial balance. Delete low-scoring points with a distance of less than 6 km between them and add new points.

[0095] S433: Make appropriate adjustments based on the overall situation of sample layout to avoid large blank areas without points within the high-standard farmland project area, so as to increase the overall spatial balance.

[0096] Output: m3 of cultivated land quality monitoring points to be built.

[0097] S5: Generate a spatial distribution map of high-standard farmland monitoring points.

[0098] The existing cultivated land quality monitoring points n1 after screening outputted by the aforementioned step S2 and the cultivated land quality monitoring points m2 to be built outputted by the step S4 are merged, and a spatial distribution map of the monitoring points is generated using GIS software, including the number, location and spatial distribution of the monitoring points to be built, the boundaries, area and spatial distribution of the plots, the area and spatial distribution of high-standard farmland, etc.

[0099] The present invention also proposes a high-standard farmland monitoring network system upgrade system based on the existing three-census sampling points, which

[0100] The method and system of the present invention are verified. Taking a county in Jiangsu Province as an example, the results of upgrading the existing high-standard farmland monitoring network are as follows:

[0101] 1. Existing cultivated land quality monitoring points: A county has 32 cultivated land quality monitoring points, and 12 existing cultivated land quality monitoring points are retained after screening ( Figure 8 ).

[0102] 2. Number of monitoring points to be established: Of the 880,300 mu (approximately 1,000 acres) of cultivated land under the jurisdiction of a certain county, 714,600 mu (approximately 81.17%) of the land had been developed into high-standard farmland as of January 2, 2023. Based on a density requirement of at least one monitoring point for every 35,000 to 50,000 mu (approximately 1,000 acres), the maximum number of monitoring points required is 17, including 12 existing monitoring points and 5 new monitoring points to be established.

[0103] 3. Screen the three national census sample points.

[0104] (1) Analysis of the distribution and spatial overlap of the three census sampling points

[0105] A county has 636 sampling points for the three national censuses ( Figure 9 ), after spatial overlay analysis, there are 234 sampling points for the three national censuses to be screened located on high-standard farmland ( Figure 10 ), the cultivated land types are mainly paddy fields and irrigated land, and the soil types (subtypes) are mainly gray-humid soil.

[0106] (2) Calculate the scores of the three census sampling points to be screened

[0107] The proportion of the main crop types covered in the three national census sampling points to be screened was counted, and the land use weight U of the three national census sampling points was obtained based on the proportion of the number; the quality weight Q of the cultivated land of the high-standard farmland was obtained by comparing the construction standards; the infrastructure construction standards of high-standard farmland were compared, and the infrastructure weight E of the three national census sampling points was obtained based on the number and distance density of infrastructure; the proportion of the number of each soil type and genus in the three national census sampling points to be screened was counted, and the soil type and genus weight T of the three national census sampling points was obtained based on the proportion of the number; the score of the three national census sampling points to be screened was calculated (Table 1).

[0108] Table 2 takes 5 three-point census sample points as an example to illustrate the point score

[0109]

[0110] The three census sample points were selected in descending order of scores and manually adjusted to obtain the distribution of monitoring points to be built ( Figure 11 ).

[0111] 4. Results of the deployment of high-standard farmland monitoring points

[0112] The existing cultivated land quality monitoring points and the high-standard farmland monitoring points to be built were merged, and the spatial distribution result map of the monitoring points was generated using GIS software ( Figure 12 ).

[0113] The embodiments described above are merely preferred embodiments of the present invention. The phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments" used in this specification may refer to one or more of the same or different embodiments of the present disclosure. Any common changes and substitutions made by those skilled in the art within the scope of the present invention are intended to be encompassed within the scope of protection of the present invention.

Claims

1. A method for upgrading a high-standard farmland monitoring network system based on existing three census sampling points, characterized in that: include: S1, data collection and preparation: determine the existing cultivated land quality monitoring points n; Determine the three census sample points m; Obtain high-standard farmland distribution vector data FD; Obtain the distribution CD of typical crop rotation and multiple cropping patterns; Obtain administrative division vector data AD; S2, screening existing cultivated land quality monitoring points based on the data collected in step S1, and obtaining the screened existing cultivated land quality monitoring point n1; S3, based on the high-standard farmland distribution vector data FD collected by S1 and the existing cultivated land quality monitoring points n1 obtained after screening by S2, calculates the maximum number of high-standard farmland monitoring points to be built; S4. Screening and supplementing sampling points for the three national censuses: Based on the monitoring needs of high-standard farmland and the characteristics of the three national censuses sampling points, the representativeness scores of the sampling points are evaluated based on the proportion of high-standard farmland area, soil type, quality standards for high-standard farmland construction, land use type, and high-standard farmland infrastructure corresponding to the sampling points. The spatial locations of the high-standard farmland quality monitoring points to be built are arranged according to the scores and spatial balance principles; Wherein, step S2 includes: S21: Perform spatial overlay analysis on the existing cultivated land quality monitoring points n and the high-standard farmland distribution vector data FD; S22: Determine whether each cultivated land quality monitoring point is located on high-standard farmland. If it is located on high-standard farmland, it is retained; if it is located outside high-standard farmland, it is discarded. S23: With each existing cultivated land quality monitoring point n as the center, determine the distribution of high-standard farmland F within the range of a circle with a radius equal to the set threshold. n , the proportion of high-standard farmland area is A n ; S24: When the area accounts for A n If the value is less than the first threshold a1, the corresponding existing cultivated land quality monitoring point will be discarded; if the value is greater than the first threshold a1, the corresponding cultivated land quality monitoring point will be retained; Step S3 includes: S31, calculating the area of ​​high-standard farmland Area; S32, calculating the maximum number N of high-standard farmland monitoring points to be built: N = Area 10,000 mu / 35,000 mu – n1; Wherein, step S4 further includes: S411: Initial screening of the three national census sampling points: For the three national census sampling point m obtained in S1, the high-standard farmland distribution vector FD, and the existing cultivated land quality monitoring point n1 after screening obtained in S2, perform spatial overlay analysis on the three national census sampling point m and the high-standard farmland distribution vector data FD; S412: Determine whether each three-level census sampling point m is located on high-standard farmland. If so, retain it; if not, discard it. S413: Calculate the straight-line distance between each three-level census sampling point m and the selected existing cultivated land quality monitoring point n1; if the distance is less than the set threshold, discard it; otherwise, retain it; S414: With each three-level census sampling point m as the center, within the range of a perfect circle with a radius of the set threshold, calculate the distribution of high-standard farmland as F m Calculate the proportion of high-standard farmland area as A m ; S415: When the area of ​​high-standard farmland accounts for A m When it is less than the second threshold a2, the corresponding three-channel sample point is discarded; when it is greater than the second threshold a2, it is retained. Wherein, step S4 also includes: evaluating the three-general census sampling points, including: S421: based on the typical crop rotation and multiple cropping pattern distribution CD obtained in S1 and the three-general census sampling point m1 obtained in S4, the proportion of each soil species and genus type in the three-general census sampling points after screening is counted, and the weight T of the soil species and genus type of the three-general census sampling point is obtained according to the proportion of the number: T = Sqrt (m s / m1), where T is the weight of soil species and genus type, m s is the number of soil genus types s; Among them, step S4 also includes: calculating the weight Q of the cultivated land quality construction standard: comparing the high-standard farmland cultivated land quality construction standard to obtain the weight of the cultivated land quality construction standard of the three national census sampling points; statistically analyzing the proportion of the number of major crop types covered by the three national census sampling points after screening, and obtaining the land use type weight U of the three national census sampling points according to the proportion of the number = Sqrt (m u / m1), where U is the weight of land use type, m u is the number of land use types u; Step S4 further includes: comparing the high-standard farmland infrastructure construction standards and obtaining the infrastructure weight E of the three-census sampling points based on the number of infrastructure and distance density: 1) evaluating the infrastructure projects at the points and their corresponding high-standard farmland distribution F based on four indicators: pump stations, channels, road accessibility, and farmland forest network; 2) normalizing the scores of the four indicators; 3) multiplying the four normalized indicators to obtain the infrastructure construction weight E; Among them, step S4 also includes: the score of the three national census sample points after screening: Score=A*T*Q*E*U, where Score is the score of the three national census sample point, A is the proportion of high-standard farmland area, T is the soil type weight, Q is the weight of the arable land quality standard for high-standard farmland construction, E is the weight of high-standard farmland infrastructure, and U is the land use type weight; N three national census sample points m2 are selected from high to low according to the score S.

2. A high-standard farmland monitoring network system based on existing three census sampling points, characterized by: The invention comprises computer executable codes, which, when executed, implement the method according to claim 1.

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