Method and system for upgrading high-standard farmland monitoring network system based on existing three-common sampling points

Abstract

The invention discloses a high-standard farmland monitoring network system upgrading method based on existing three-general sampling points. The method comprises the following steps: S1, determining an existing cultivated land quality monitoring point n; determining a three-general sample point m; obtaining high-standard farmland distribution vector data FD; obtaining typical crop rotation and multiple cropping planting mode distribution CD; obtaining administrative division vector data AD; s2, screening existing cultivated land quality monitoring points to obtain screened existing cultivated land quality monitoring points n1; and S3, for the high-standard farmland distribution vector data FD and the existing cultivated land quality monitoring point n1, calculating the maximum number of to-be-built high-standard farmland monitoring points. The invention also correspondingly provides a system. According to the method and the system, by utilizing the existing three-general sampling points, repeated construction is avoided, evaluation of soil quality, farmland infrastructure and planting utilization conditions is facilitated, a more scientific and comprehensive monitoring result is provided, an existing monitoring network is improved and optimized, the accuracy and the reliability of the monitoring result are improved, and the food safety is further guaranteed.

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 is done by setting up a certain number of representative monitoring points to reflect the overall farmland infrastructure, arable land quality, planting purposes and other characteristics in the form of "points leading to the whole". Therefore, the point layout must follow the principles of characteristic representativeness and spatial balance, taking into account the historical monitoring points, so that the monitoring work and results are economical and continuous, and the attribute characteristics of farmland are reflected with fewer points and better spatial and temporal representativeness. If the monitoring points lack representativeness and are unreasonable, it will have an adverse effect on the accuracy of the monitoring results.

[0003] At present, the common point layout methods include the traditional random point layout method, the systematic point layout method (grid method), and the zoning and densification point layout methods developed on the basis of the traditional point layout method. Use the initial grid layout sample points to partition the survey area and layout the grid sample points again. Eliminate the soil environmental interference area on the soil distribution map, and determine the appropriate grid distance based on the regional characteristics. According to the survey area and soil attribute targets, divide the survey area into several independent sample layout areas, and then randomly and evenly layout the sample points.

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

[0005] In summary, the main method of existing monitoring network deployment is to improve on the traditional sampling method, such as grid method (systematic sampling method), stratified sampling method, etc. This method has problems such as inefficient data, poor representativeness of sample points, and failure to fully reflect the scale differences of high-standard farmland spatial systems. In addition, the spatial location of the monitoring sample points is usually the centroid of the map or the center of the grid, lacking a comprehensive quantitative evaluation of the representativeness and importance of each point.

[0006] In addition, the current farmland monitoring samples are mostly focused on farmland quality monitoring, emphasizing soil properties. Although existing methods have taken into account that farmland infrastructure and land consolidation projects will affect the level of farmland utilization, they are mostly based on the yield of planted crops to directly reflect the level of farmland utilization, but are limited by farmland infrastructure information collection technology, and do not directly extract and set 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-census sample points, including: S1, data collection and preparation: determining the existing cultivated land quality monitoring point n; determining the three-census sample point m; obtaining the high-standard farmland distribution vector data FD; obtaining the typical rotation and multiple cropping pattern distribution CD; obtaining the administrative division vector data AD; S2, screening the existing cultivated land quality monitoring points according to 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 according to 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 farmland 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 purpose monitoring, but also provides a more comprehensive and accurate monitoring method for the shortcomings of the existing farmland 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, 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 by referring 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 The technical roadmap of the method of the present invention.

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

[0015] Figure 3 The present invention is a flow chart 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 farmland quality monitoring points and their utilization types.

[0021] Fig. 9 This is a diagram showing the distribution of the three census sample points.

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

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

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

[0025] The following describes the implementation modes 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. In the absence of conflict, the following embodiments and the technical features in the embodiments may 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. The established national, provincial, municipal and county cultivated land quality monitoring points are screened, and the representativeness and importance of the soil census sample points are comprehensively evaluated to screen and generate the final high-standard farmland cultivated land quality supplementary monitoring points to be built, improve and optimize the existing monitoring network, and improve the accuracy and reliability of the monitoring results. 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 and supplementing the three census sample points; S5, spatial distribution of high-standard farmland monitoring points.

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

[0028] S11: Determine the existing cultivated land quality monitoring point n. The cultivated land quality monitoring point refers to the monitoring point established in the basic farmland protection area according to the relevant requirements and based on different cultivated land grades and main soil types of different cultivated lands. In addition to its original geographical location information, it is also determined through long-term, positioning and effective monitoring to obtain information such as soil type (soil texture, soil structure, soil acidity, etc.), land use type (cultivated land type, planting system, crop type, etc.), nutrient status (soil nutrient content at the monitoring point, nutrient balance status, etc.).

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

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

[0031] The high-standard farmland distribution vector data is a kind of geographic information data. Its main content 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 in different geographical areas. These data usually include the planting ratio of different crops in different years, crop rotation cycle, multiple cropping index, etc., which can generally be obtained from the geographic information system platform.

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

[0035] Administrative division vector data is a digital data that uses graphic elements such as points, lines, and surfaces to express administrative division boundaries and geographic information. It contains information such as the name, code, and boundary line of the administrative division, and can generally be obtained from the 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 the screened existing farmland quality monitoring point 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 built on high-standard farmland, it will be retained; if it is built outside high-standard farmland, it will be discarded.

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

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

[0042] After this step, the existing cultivated land quality monitoring point n1 is obtained after screening. The existing cultivated land quality monitoring points after screening refer to the cultivated land quality monitoring points on high-standard farmland. These monitoring points will be upgraded and constructed on the existing cultivated land quality monitoring points according to the principle of economy, and the existing monitoring points will be abolished or transformed and 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 screened existing cultivated land quality monitoring points n1 obtained 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 screening of existing cultivated land quality points n1, with a density requirement of setting up no less than one monitoring point for every 35,000 to 50,000 mu.

[0045] exist Figure 3In the illustrated embodiment, step S3 comprises:

[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 the region calculated by using GIS (geographic information system) and output in units of ten thousand 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] Wherein, 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 census sampling points.

[0053] According to the monitoring needs of high-standard farmland and the characteristics of the three-census sample data, the representative scoring evaluation of the sample points was carried out through five dimensions, including the proportion of high-standard farmland area, soil type, quality standard of arable land for high-standard farmland construction, land use type, and high-standard farmland infrastructure. The spatial locations of the high-standard farmland arable land quality monitoring points to be built were arranged according to the principles of high scores and spatial balance.

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

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

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

[0057] S412: Determine whether each three-census sampling point m is on high-standard farmland. If it is on high-standard farmland, it is retained; if it is outside high-standard farmland, it is discarded.

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

[0059] S414: With each three-level census sampling point m as the center, within the range of a perfect 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, and when it is greater than the second threshold a2, it is retained.

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

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

[0063] S421: Based on the distribution CD of typical crop rotation and multiple cropping patterns obtained in S14 and the location m1 of the three-general survey sample points after screening obtained in S46, the weight T of soil genus type is calculated. Specifically, the proportion of the number of each soil type and genus type in the three-general survey sample points after screening is counted, and the weight of the soil type and genus type of the three-general survey sample point is obtained according to the proportion of the number (Formula 2).

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

[0065] Where T is the weight of soil type, m s is the number of soil genus types s, and m is the three census sample 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 sample points were obtained.

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

[0069]

[0070] Note: ○ indicates compliance, and × indicates noncompliance

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

[0072] Compare the construction standards of high-standard farmland infrastructure, and obtain the infrastructure weight E of the three census sample points based on the number of infrastructure and distance density. 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 in F × average distance from point to channel (4)

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

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

[0078] (2) The four index scores are normalized, taking the pump station index score E1 as an example (Formula 7):

[0079]

[0080] Where ρ1 is the normalized pump station index score, E1 is the original pump station index score, max(E1) is the maximum value of the pump station index scores of all three census sample points, and min(E1) is the minimum value of the pump station index scores of all three census sample points.

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

[0082] E=ρ1×ρ2×ρ3×ρ4 (8)

[0083] S424: Land use type weight U.

[0084] After statistical screening, the proportion of each major crop type covered by the three census sample points was calculated, and the land use type weights of the three census sample 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 sample 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] In the formula, Score is the score of the three census sample points, 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.

[0090] Output: Select Nn three-census sample points m2 from high to low according to the score S. After calculating the score of each three-census sample point in the above step S41, Nn three-census sample points m2 can be selected from high to low according to the score S. Generally, selecting a sample point with a higher score can better represent the entire data set.

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

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

[0093] S431: Consider the spatial balance between points according to the actual situation, and superimpose the administrative division vector data AD. If the same village or town has built arable land quality points or has selected high-scoring three-census sampling points to be screened, the score values ​​of other three-census sampling points in the same village or town will be w times the original ones.

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

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

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

[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 as a soil survey sample point 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 built: A county has 880,300 mu of cultivated land, and as of January 2, 2023, 714,600 mu of high-standard farmland has been built, accounting for 81.17%. According to the density requirement of setting up one monitoring point for every 35,000 to 50,000 mu, the maximum number of monitoring points to be built is 17, including 12 existing monitoring points and 5 new monitoring points to be built.

[0103] 3. Select sampling points for the three censuses.

[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 censuses ( Fig. 9 ), after spatial overlay analysis, there are 234 sampling points to be screened for the three census surveys located on high-standard farmland ( Fig.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 sample points to be screened

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

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

[0109] Sample number A U Q E T Point score Score 1 0.411 1 0.5 0.045 0.217 0.002 2 0.318 1 0.6 1.991 0.543 0.198 3 0.272 1 0.8 0.895 0.811 0.158 4 0.324 1 0.5 1.476 0.811 0.194 5 0.394 1 0.6 1.106 0.543 0.142 … … … … … … …

[0110] The three census sample points were selected in order from high to low according to the scores, and manual adjustments were made to finally obtain the distribution of monitoring points to be built ( Fig.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 ( Fig.12 ).

[0113] The embodiments described above are only preferred specific implementations of the present invention. This specification uses the phrases "in one embodiment", "in another embodiment", "in yet another embodiment" or "in other embodiments" to refer to one or more of the same or different embodiments according to the present disclosure. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope 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 the existing cultivated land quality monitoring points according to the data collected in step S1, and obtaining the screened existing cultivated land quality monitoring point n1; S3 calculates 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 screened existing cultivated land quality monitoring points n1 obtained by S2.

2. The method according to claim 1, characterized in that 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 will be retained; if it is located outside high-standard farmland, it will be discarded; S23: With each existing cultivated land quality monitoring point n as the center, determine the high-standard farmland distribution F within the range of a perfect circle with a radius equal to the set threshold. n , the proportion of high-standard farmland is A n ; S24: When the area accounts for A n When it is less than the first threshold a1, the corresponding existing cultivated land quality monitoring point is discarded, and when it is greater than the first threshold a1, it is retained.

3. The method according to claim 2, characterized in that Step S3 includes: S31, calculate the area of ​​high-standard farmland Area; S32, calculate the maximum number of high-standard farmland monitoring points to be built N: N = Area 10,000 mu / 35,000 mu – n1.

4. The method according to claim 1, characterized in that: Also includes: S4, screening and supplementing the three national census sample points: according to the monitoring needs of high-standard farmland and the characteristics of the three national census sample point data, the representative scoring evaluation of the points is carried out through five dimensions: the proportion of high-standard farmland area corresponding to the sample points, soil type, quality standards for high-standard farmland construction, land use type and high-standard farmland infrastructure, and the spatial locations of the high-standard farmland arable land quality monitoring points to be built are arranged according to the principles of high scores and spatial balance.

5. The method according to claim 4, characterized in that Step S4 also includes: S411: Preliminary screening of the three census sample points: For the three census sample point m obtained by S1, the high-standard farmland distribution vector FD, and the screened existing cultivated land quality monitoring point n1 obtained by S2, the three census sample point m and the high-standard farmland distribution vector data FD are spatially overlapped; S412: Determine whether each three-census sampling point m is on high-standard farmland. If so, keep it; if not, discard it. S413: Calculate the straight-line distance between each three-census sampling point m and the existing cultivated land quality monitoring point n1 after screening. If the distance is less than the set threshold, it is discarded; otherwise, it is retained; S414: With each three-census sampling point m as the center, within the range of a perfect circle with a radius equal to 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 proportion of high-standard farmland area is A m When it is less than the second threshold a2, the corresponding three-channel sample point is discarded, and when it is greater than the second threshold a2, it is retained.

6. The method according to claim 5, characterized in that Step S4 also includes: evaluating the three census sample points, including: S421: Based on the distribution CD of typical crop rotation and multiple cropping patterns obtained in S1 and the location m1 of the three-census sample points after screening obtained in S4, the proportion of each soil type and genus type in the three-census sample points after screening is calculated, and the weight T of the soil type and genus type of the three-census sample point is obtained according to the proportion of the number: T = Sqrt(m s / m), where T is the weight of soil type, m s is the number of soil genus types s, and m is the three census sample points after screening.

7. The method according to claim 6, characterized in that Step S4 also includes: Calculate the weight Q of the cultivated land quality construction standard: Compare the high-standard farmland cultivated land quality construction standard to obtain the weight of the cultivated land quality construction standard at the three census sampling points; The percentage of the main crop types covered by the three census sampling points after statistical screening, and the land use type weights of the three census sampling points were obtained according to the percentage of the number. u / m), where U is the weight of land use type, m u is the number of land use type u, and m is the sample points of the three censuses after screening.

8. The method according to claim 7, characterized in that Step S4 also includes: comparing the high-standard farmland infrastructure construction standards, and obtaining the infrastructure weights E of the three census sample points according to the number of infrastructure and distance density, etc.: 1) Evaluate the infrastructure projects at the points and their corresponding high-standard farmland distribution F based on four indicators: pumping stations, channels, road accessibility, and farmland forest network; 2) performing normalization operation on the scores of the four indicators; 3) Multiply the four normalized indicators to obtain the infrastructure construction weight E.

9. The method according to claim 8, characterized in that Step S4 also includes: 1) Score of the three census sample points after screening: Score = A*T*Q*E*U, where Score is the score of the three census sample points, A is the proportion of high-standard farmland area, T is the weight of soil type, Q is the weight of the quality standard of cultivated land for high-standard farmland construction, E is the weight of high-standard farmland infrastructure, and U is the weight of land use type; According to the scores Score, Nn three-level sampling points m2 are selected from high to low.

10. A high-standard farmland monitoring network system based on the existing three census sampling points, characterized in that: The invention comprises computer executable codes, and when the codes are executed, the methods according to claims 1 to 9 are implemented.

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