Farmland improvement zoning method and system

Abstract

The invention relates to the technical field of farmland improvement, in particular to a farmland improvement zoning method and system, and the method comprises the following steps: 1, zoning factor selection and weight calculation; 2, processing partition basic data; 3, partition factor calculation and statistics; 4, performing partition data standardization calculation; and 5, constructing an accurate zoning model for farmland improvement. According to the method, farmland improvement suitability zoning can be accurately carried out, and a scientific and reasonable basis is provided for farmland improvement project planning and design.

Description

A method and system for farmland consolidation zoning Technical Field

[0001] This invention belongs to the field of farmland improvement technology, specifically, it relates to a method and system for farmland improvement zoning. Background Technology

[0002] Farmland consolidation is a crucial means to ensure food security, replenish arable land, and enhance the comprehensive productivity of farmland, and it is also necessary to adapt to the development of modern agriculture. Comprehensively promoting farmland consolidation work, including the remediation of inefficient forests, grasslands, and orchards, the construction of high-standard farmland, the improvement and transformation of arable land, and land development and reclamation, will help optimize the spatial pattern of rural production, living, and ecological functions, achieve arable land protection and intensive and economical land use, and promote the comprehensive revitalization of rural areas, ensuring an increase in the quantity and quality of arable land and an improvement in the farmland ecology. Under complex and diverse topographical conditions, the content of consolidation engineering measures and the standards of investment vary significantly in different farmland consolidation areas. Therefore, zoning farmland consolidation according to land use conditions and referring to the zoning results for farmland consolidation projects can more effectively tailor planning and design to local conditions.

[0003] This invention, guided by farmland consolidation zoning, guides the planning, design, and implementation of land consolidation projects. Based on this, it determines project investment standards, analyzes and calculates benefits, and establishes a reliable predictive mechanism for investment decisions. This invention provides a method for the in-depth implementation of farmland consolidation projects in areas with complex terrain. Summary of the Invention

[0004] The present invention provides a method and system for zoning farmland improvement, which can overcome some of the defects and deficiencies of existing technologies. This method utilizes the spatial analysis capabilities of farmland influencing factors and digital terrain models (DEMs) to innovatively construct a model of suitable types of farmland improvement projects.

[0005] According to a method for zoning farmland improvement based on the present invention, the method includes the following steps:

[0006] I. Selection of partitioning factors and calculation of weights;

[0007] II. Processing of basic partition data;

[0008] III. Calculation and statistics of zoning factors;

[0009] IV. Standardized calculation of partitioned data;

[0010] V. Construction of a precise zoning model for farmland improvement.

[0011] As a preferred option, in step one, the Delphi method is first used to determine the factors affecting the zoning. Through expert research, the factors that affect the zoning of farmland improvement are finally determined to be topography, climate, soil and socio-economic factors. Topography includes altitude and elevation difference, climate includes precipitation and accumulated temperature, soil includes soil type and soil layer thickness, and socio-economic factors include GDP per capita and population density.

[0012] The Delphi method was then used to determine the weights. Experts were invited to select factors on the farmland improvement zone influencing factor selection table, based on the actual situation and without mutual consultation, according to whether each influencing factor affected the farmland improvement zone and to what extent. Weights were then determined. After the first round of factor weight survey forms were collected, statistical processing was performed to calculate the mean and variance of the expert-assigned values ​​for each factor. The results were then fed back to the experts. A second round of weight assignments was conducted based on the overall trend and dispersion of expert opinions reflected in the first round of variance. After the forms were collected, the variance and mean were calculated again. The variances from both rounds were then X-rayed. 2 The significance test is used to check whether there is a significant difference in the dispersion of the variance between the second and first rounds. If there is a significant difference in the variance test values ​​between the two rounds, it indicates that the variance dispersion is large and a third round of expert scoring is required. If there is no significant difference in the variance test values ​​between the two rounds, it indicates that the dispersion is small and no further expert consultation is required.

[0013] As a preferred option, X 2 The test method is as follows: First, construct the statistical test X between the two rounds of variances. 2 Then check the calculation table and use X. 2 The 0.95 statistic is used to compare the regularity of variances between two rounds, and the calculation formula is as follows:

[0014]

[0015] In the formula: n is the number of experts consulted by the Delphi method; X 2 This is the statistical test statistic constructed from the two rounds of variance;

[0016] When X 2 <X 2 At 0.95, the two rounds of variance show significant uniformity, meaning there is no significant difference between the two rounds of variance, indicating that the variance convergence of this round is small; when the X of each factor... 2 The values ​​are all less than X 2 When the test value is 0.95, the consultation with experts ends here.

[0017] As a preferred option, in step two, the DEM data is denoised using spatial analysis tools to remove outliers; and the reliability of the terrain data is verified by combining remote sensing imagery with field survey data.

[0018] As a preferred option, step three specifically involves:

[0019] 3.1) Altitude Calculation

[0020] Using townships as units, the average elevation of the unit area is statistically analyzed through DEM data to reflect the light and heat conditions of land use;

[0021] 3.2) Elevation Difference Analysis

[0022] Taking townships as units, the difference between the maximum and minimum elevations is calculated through neighborhood analysis to represent the degree of topographic relief and reflect the ease or difficulty of land use.

[0023] 3.3) The values ​​of precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita and population density are obtained through statistical analysis of relevant basic data.

[0024] As a preferred option, step four specifically involves:

[0025] 4.1) Data standardization: Standardize the altitude and elevation difference data to 0-1 to eliminate dimensional differences;

[0026] x' = \frac{x - \min(x)}{\max(x) - \min(x)}

[0027] Where x is the original data and x' is the standardized value;

[0028] Spatial data alignment: Spatial registration of remote sensing imagery with terrain elevation is performed using ArcGIS Pro to ensure consistent data resolution;

[0029] 4.2) Data normalization: Standardize the data on precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density to 0-1 to eliminate dimensional differences;

[0030] x' = \frac{x}{\max(x)}

[0031] Where x is the original data and x' is the standardized value.

[0032] Preferably, in step five, the score calculation formula for the partitioning model is expressed as: Total score = (x1s1 + x2s2 + x3s3 + x4s4 + x5s5 + x6s6 + x7s7 + x8s8) + R

[0033] Where: x1-x8 represent the scores for altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density; s1-s8 represent the weights for altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density; and R is the first-level regional value.

[0034] This invention provides a method and system for farmland consolidation zoning, which adopts the above-mentioned method for farmland consolidation zoning.

[0035] The beneficial effects of this invention are as follows:

[0036] This invention uses the Delphi method combined with the X² significance test to determine the selection and weight of factors. Through multiple rounds of back-to-back expert consultation and statistical testing, it ensures that the weight assignments not only embody expert consensus but also have statistical significance, effectively avoiding subjective assumptions and improving the scientificity and objectivity of the zoning criteria.

[0037] This invention comprehensively considers multiple influencing factors such as natural geography (altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness) and socio-economic factors (GDP per capita, population density), and eliminates the difference in dimensions through data standardization, constructing a comprehensive and systematic evaluation index system, so that the zoning results can more accurately reflect the comprehensive status of regional land use conditions.

[0038] This invention relies on multi-source data such as DEM, remote sensing imagery, and field surveys, and utilizes spatial analysis tools for data processing and verification to ensure the accuracy and reliability of the basic data. The entire process is clear and the steps are well-defined, combining automated processing with manual verification, making it easy to apply and promote in practice.

[0039] This invention ensures comparability between data from different sources and resolutions through data standardization and spatial registration. The partitioning model outputs quantitative scores, providing clear and intuitive results that facilitate comparative analysis and prioritization between different regions, thus offering precise decision support for farmland improvement planning.

[0040] The zoning model and system of this invention can adjust the evaluation factors and their weights according to the actual conditions of different regions, and have strong adaptability and scalability. It can be widely applied to farmland improvement zoning work under different geographical conditions and economic development levels. Attached Figure Description

[0041] Figure 1 is a flowchart of a farmland improvement zoning method in one embodiment. Detailed Implementation

[0042] To further understand the content of this invention, a detailed description of the invention will be provided in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0043] Example

[0044] As shown in Figure 1, this embodiment provides a method for zoning farmland improvement, which includes the following steps:

[0045] I. Selection of partitioning factors and calculation of weights;

[0046] First, the Delphi method was used to determine the factors influencing farmland improvement zoning. Through expert research, the factors influencing farmland improvement zoning were identified as topography (elevation, elevation difference), climate (precipitation, accumulated temperature), soil (soil type, soil layer thickness), and socio-economic factors (GDP per capita, population density). Next, the Delphi method was used to determine weight values. Management experts with high reputations in relevant industries and technologies, familiar with topography, climate, soil, and socio-economic conditions, were invited to select factors on a farmland improvement zoning factor selection table without prior consultation, based on whether and to what extent each factor affects the farmland improvement zoning. Weights were then determined. After the first round of factor weight survey forms were collected, statistical processing was performed to calculate the mean and variance of the expert-assigned values ​​for each factor. The results were then fed back to the experts. A second round of weighting was conducted based on the overall trend and dispersion of expert opinions reflected in the first round of variance. After the forms were collected, the variance and mean were calculated again. The variances from both rounds were then X-rayed. 2 The significance test is used to check whether there is a significant difference in the dispersion of the variance between the second and first rounds. If there is a significant difference in the variance test values ​​between the two rounds, it indicates that the variance dispersion is large and a third round of expert scoring is required. If there is no significant difference in the variance test values ​​between the two rounds, it indicates that the dispersion is small and no further expert consultation is required.

[0047] X 2 The test method is as follows: First, construct the statistical test X between the two rounds of variances. 2 Then check the calculation table and use X. 2 The 0.95 statistic is used to compare the regularity of variances between two rounds, and the calculation formula is as follows:

[0048]

[0049] In the formula: n is the number of experts consulted by the Delphi method; X 2 This is the statistical test result constructed from the two rounds of variance;

[0050] When X 2 <X 2 At 0.95, the two rounds of variance show significant uniformity, meaning there is no significant difference between the two rounds of variance, indicating that the variance convergence of this round is small; when the X of each factor... 2 The values ​​are all less than X 2 When the test value is 0.95, the consultation with experts ends here.

[0051] Round 2 and Round 1 X 2 The test results show that the X of all factors 2 The values ​​are all less than X 2 The test value was 0.95, therefore the results of the second round of expert consultation fully met the consultation requirements, and the Delphi method expert consultation ended.

[0052] Taking Sichuan Province as an example, the specific process of determining the weights of influencing factors using the Delphi method is shown in Table 1 below.

[0053] Number of experts n=20, X 2 (0.95) = 10.85

[0054] Table 1. Determination Process of Zoning Factors and Weight Values ​​for Farmland Improvement Projects in Sichuan Province

[0055]

[0056] Weighted Results Analysis

[0057] From the weighting results, topography has the highest weight, accounting for 50%, indicating its importance and consistent with the characteristics of Sichuan Province's regional divisions. Climate conditions are the second highest, accounting for 25%, while soil conditions and socio-economic conditions account for 15% and 10% respectively, representing relatively small weights. Sichuan Province's unique topography and significant east-west differences do indeed have a substantial impact on the design and implementation of farmland improvement projects. Soil conditions, climate conditions, and socio-economic conditions also have some influence.

[0058] Therefore, the topography and landforms account for a relatively high weight in this weighting determination, while the climate, soil, and socio-economic conditions have relatively low weights. This not only meets the zoning requirements of farmland improvement projects in Sichuan Province but also reflects the actual situation, and can be used for the zoning work of farmland improvement projects in Sichuan Province.

[0059] Table 2 Results of Zoning Factor Weight Measurement for Farmland Improvement Projects in Sichuan Province

[0060]

[0061] II. Processing of basic partition data;

[0062] Denoising of DEM data is performed using spatial analysis tools (such as ArcGIS) to remove outliers (such as elevation abrupt changes and missing data areas); the reliability of terrain data is verified by combining remote sensing imagery with field survey data, thereby improving data quality.

[0063] III. Calculation and statistics of zoning factors;

[0064] 3.1) Altitude Calculation

[0065] Using townships as units, the average elevation of the unit area is statistically analyzed through DEM data to reflect the light and heat conditions of land use;

[0066] 3.2) Elevation Difference Analysis

[0067] Taking townships as units, the difference between the maximum and minimum elevations is calculated through neighborhood analysis to represent the degree of topographic relief and reflect the ease or difficulty of land use.

[0068] 3.3) The values ​​of precipitation (mm), accumulated temperature (°C·d), soil type, soil layer thickness (cm), GDP per capita (ten thousand yuan), and population density (person / km²) were obtained by summarizing and statistically analyzing relevant basic data such as the 2006 "1:4,000,000 China Soil Map" and the 2023 Sichuan Statistical Yearbook.

[0069] IV. Standardized calculation of partitioned data;

[0070] 4.1) Data standardization: Standardize the altitude and elevation difference data to 0-1 to eliminate dimensional differences;

[0071] x' = \frac{x - \min(x)}{\max(x) - \min(x)}

[0072] Where x is the original data and x' is the standardized value;

[0073] Because Sichuan Province has a very special topography, with a significant difference between the west and the east, the altitude and elevation differences are very large. After 0-1 standardization, many data points become very small. However, these data points occupy an important position in the dataset. Therefore, it is necessary to further increase the data resolution of the normalized data to obtain the final values.

[0074] Spatial data alignment: Spatial registration of remote sensing imagery (Sentinel-2) with terrain elevation (ASTERGDEM) was performed using ArcGIS Pro to ensure consistent data resolution (12.5m).

[0075] 4.2) Standardize the data such as precipitation (mm), accumulated temperature (°C·d), soil type, soil layer thickness (cm), GDP per capita (ten thousand yuan), and population density (person / km²) to 0-1 to eliminate dimensional differences.

[0076] x' = \frac{x}{\max(x)}

[0077] Where x is the original data and x' is the standardized value.

[0078] V. Construction of a precise zoning model for farmland improvement.

[0079] The score calculation formula for the partitioning model is expressed as: Total score = (x1s1 + x2s2 + x3s3 + x4s4 + x5s5 + x6s6 + x7s7 + x8s8) + R

[0080] Where: x1-x8 represent the scores for altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density; s1-s8 represent the weights for altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density; and R is the first-level regional value (1 for Chengdu Plain, 2 for Basin Hilly Area, 3 for Basin Surrounding Mountain Area, 4 for Southwest Sichuan Mountain Area, and 5 for Northwest Sichuan Plateau Area), as shown in Table 3.

[0081] Table 3. Value Intervals for Secondary Zoning of Land Consolidation Projects in Sichuan Province

[0082]

[0083] The total score for each township-level unit was calculated using the formula above, and the units were automatically divided into secondary zones according to the total score range. After the analysis, the zoning results were adjusted based on the principles of relatively continuous geographical distribution, considering Sichuan Province's topography, land use conditions, and geographical features. The final results of the secondary zoning for farmland improvement in Sichuan Province are shown in Table 4.

[0084] Table 4. Example of process and results for secondary zoning of farmland improvement projects in Sichuan Province.

[0085] Serial Number | County (District) | Township Name | Average Altitude Difference | Soil Type | Soil Layer Thickness | Precipitation | Accumulated Temperature | GDP Per Capita | Population Density | Altitude Score | Altitude Difference Score | Soil Type Score | Soil Layer Thickness Score | Precipitation Score | Accumulated Temperature Score | GDP Per Capita | Population Density Score | Threshold | Total Score | Result 1. Longquanyi District, Longquan Street | 46721118751110.210.270.330.330.490.330.330.3311.31 Chengdu Plain Plain Area 2. Longquanyi District, Damian Street | 46524118751110.200.280.330.330.490.330.330.3311.31 Chengdu Plain Plain Area 3. Longquanyi District, Shiling Street | 46020118751110.200.27 0.330.330.490.330.330.3311.31 Chengdu Plain Plain Area 4 Longquanyi District Tongan Street 552207118751110.290.490.330.330.490.330.330.3311.41 Chengdu Plain Hilly Area 5 Longquanyi District Xihe Town 45120118751110.190.270.330.330.490.330.330.3311.30 Chengdu Plain Plain Area 6 Longquanyi District Hongan Town 45746118751110.190.330.330.330.490.330.330.3311.33 Chengdu Plain Plain Area 7 Longquanyi District Baihe Town 50424211 8751110.250.510.330.330.490.330.330.3311.42 Chengdu Plain and Shallow Hilly Area 8 Longquanyi District Huangtu Town 44420118751110.180.270.330.330.490.330.330.3311.30 Chengdu Plain and Plain Area 9 Longquanyi District Shanquan Town 652273218751110.360.520.330.670.490.330.330.3311.44 Chengdu Plain and Shallow Hilly Area 10 Longquanyi District Wanxing Township 616226218751110.340.500.330.670.490.330.330.3311.43 Chengdu Plain and Shallow Hilly Area 1 1. Longmenshan Town, Pengzhou City 24582226319002221.000.890.331.000.500.670.670.6711.77 Chengdu Plain and Shallow Hilly Area 12. Xinxing Town, Pengzhou City 839199119002220.470.480.330.330.500.670.670.6711.50 Chengdu Plain and Shallow Hilly Area 13. Jiuchi Town, Pengzhou City 50227119002220.240.290.330.330.500.670.670.6711.40 Chengdu Plain and Plain Area 14. Mengyang Town, Pengzhou City 46725119002220.210.280.330.330.500.670.670.6711.39 Chengdu Plain Plain Area 15 Pengzhou City Tongji Town 1059585119002220.570.630.330.330.500.670.670.6711.56 Chengdu Plain Hilly Area 16 Pengzhou City Danjingshan Town 755335119002220.430.550.330.330.500.670.670.6711.51 Chengdu Plain Hilly Area 17 Pengzhou City Aoping Town 55148119002220.290.340.330.330.500.670.670.6711.40 Chengdu Plain Plain Area 18 Pengzhou City Cifeng Town 893501119002220.500.610.330 .330.500.670.670.6711.54 Chengdu Plain and Shallow Hills Area 19 Pengzhou City Guihua Town 657170119002220.370.470.330.330.500.670.670.6711.47 Chengdu Plain and Shallow Hills Area 20 Pengzhou City Junle Town 56854119002220.300.350.330.330.500.670.670.6711.40 Chengdu Plain and Plain Area 21 Pengzhou City Sanjie Town 48747119002220.230.330.330.330.500.670.670.6711.39 Chengdu Plain and Plain Area 22 Pengzhou City Xiaoyudong Town 1203680119002 220.630.660.330.330.500.670.670.6711.58 Chengdu Plain and Hilly Area 23 Pengzhou City Hongyan Town 644465119002220.360.600.330.330.500.670.670.6711.51 Chengdu Plain and Hilly Area 24 Pengzhou City Shengping Town 53036119002220.270.310.330.330.500.670.670.6711.38 Chengdu Plain and Plain Area 25 Pengzhou City Bailu Town 1351843319002220.680.700.331.000.500.670.670.6711.65 Chengdu Plain and Hilly Area 26 Pengzhou Gexianshan Town, Chengdu 638360119002220.350.560.330.330.500.670.670.6711.50 Chengdu Plain and Shallow Hilly Area 27 Pengzhou City Zhihe Town 52943119002220.270.330.330.330.500.670.670.6711.40 Chengdu Plain and Plain Area 28 Mianzhu City Jiannan Town 54929118501230.290.290.330.330.470.330.671.0011.38 Chengdu Plain and Plain Area 29 Mianzhu City Northeast Town 57654118501230.310.350.330.330.470.330.671.0011.40 Chengdu Plain Plain Area 30 Mianzhu City Southwest Town 570 381 1850 1230.30 0.32 0.33 0.33 0.47 0.33 0.67 1.00 11.39 Chengdu Plain Plain Area 31 Mianzhu City Xinglong Town 587 351 1850 1230.32 0.31 0.33 0.33 0.47 0.33 0.67 1.00 11.39 Chengdu Plain Plain Area 32 Mianzhu City Jiulong Town 111 61 02 81 1850 1230.60 0.73 0.33 0.33 0.47 0.33 0.67 1.00 11.57 Chengdu Plain Hilly Area 33 Mianzhu City Hanwang Town 860 74 91 1850 1230.48 0.68 0.33 0.33 0.47 0.33 0.67 1.00 11.53 Chengdu Plain and Shallow Hilly Area 34 Mianzhu City Jinhua Town 1888 2449 31850 1230.85 0.91 0.33 1.00 0.47 0.33 0.67 1.00 11.72 Chengdu Plain and Shallow Hilly Area 35 Mianzhu City Yuquan Town 547 361 1850 1230.29 0.31 0.33 0.33 0.47 0.33 0.67 1.00 11.39 Chengdu Plain and Plain Area 36 Mianzhu City Banqiao Town 537 261 1850 1230.28 0.29 0.33 0.33 0.47 0.33 0.67 1.00 11.38 Chengdu Plain and Plain Area 37 Mianzhu City Xinshi Town 498 531 1850 12 30.24 0.35 0.33 0.33 0.47 0.33 0.67 1.00 11.39 Chengdu Plain Plain Area 38 Zhao Town, Jintang County 415 35 118 75 1220.13 0.31 0.33 0.33 0.49 0.33 0.67 0.67 22.34 Basin Hilly and Low Hilly Area 39 Qingjiang Town, Jintang County 409 20 118 75 1220.12 0.27 0.33 0.33 0.49 0.33 0.67 0.67 22.33 Basin Hilly and Low Hilly Area 40 Guancang Town, Jintang County 431 89 318 75 1220.16 0.39 0.33 1.00 0.49 0.33 0.67 0.67 22.40 Basin Hilly and Low Hilly Area 41 Bai Guozhen 40542218751220.110.320.330.670.490.330.670.6722.36 Basin, hilly and low hilly area 42 Jintang County Gaoban Town 41828218751220.140.290.330.670.490.330.670.6722.36 Basin, hilly and low hilly area District 43 Jintang County Sanxi Town 41524218751220.130.280.330.670.490.330.670.6722.35 Basin Hilly and Shallow Hilly District 44 Jintang County Jinlong Town 426227218751220.150.500.330.670.490.330.670.6722.42 Basin, hilly and deep hilly area 45 Zhugao Town, Jintang County 40862218751220.120.360.330.670.490.330.670.6722.37 Basin, hilly and shallow hilly area 46 Guangxing Town, Jintang County 39865218751220.100.360.330.670.490.330.670.6722.37 Basin, hilly and shallow hilly area 47 Longsheng Town, Jintang County 41 652218751220.140.340.330.670.490.330.670.6722.37 Basin, hilly and shallow hilly area 48 Jintang County, Zhuanlong Town 409268218751220.120.520.330.670.490.330.670.6722.42 Basin, hilly and deep hilly area 49 Jintang County, Tuqiao Town 40080218751220.100.3 80.330.670.490.330.670.6722.38 Basin, Hilly and Shallow Hilly Area 50 Yunhe Town, Jintang County 37851218751220.000.340.330.670.490.330.670.6722.34 Basin, Hilly and Shallow Hilly Area 51 Youxin Town, Jintang County 39570218751220.090.370.330.670.490.330. 670.6722.37 Basin, hilly and shallow hilly area 52 Qixian Township, Jintang County 588352318751220.320.560.331.000.490.330.670.6722.50 Basin, hilly and deep hilly area 53 Pingqiao Township, Jintang County 41231218751220.130.300.330.670.490.330.670.6722.36 Basin, hilly and shallow hilly area. surface

[0086] Partition Model Training and Validation

[0087] The model was trained using data from 12 typical counties and cities in Sichuan Province, and its rationality was verified through spatial overlay analysis. The verification set error rate was <3%.

[0088] For example, in the corresponding townships of Pengzhou City, Chengdu, the Chengdu Plain Plain Area (elevation < 600m, elevation difference < 200m) and the Chengdu Plain Hilly Area (elevation 600-2000m, elevation difference 200-600m) are divided by a joint analysis of factors such as altitude, elevation difference, accumulated temperature and precipitation.

[0089] This embodiment provides a farmland consolidation zoning system, which adopts one of the farmland consolidation zoning methods described above.

[0090] This embodiment uses the Delphi method combined with the X² significance test to determine the zoning factors and factor weights. Through multiple rounds of back-to-back expert consultation and statistical testing, it ensures that the factor selection and weight assignment not only embody expert consensus but also have statistical significance, effectively avoiding subjective assumptions and improving the scientificity and objectivity of the zoning basis.

[0091] This embodiment comprehensively considers multiple influencing factors such as natural geography (altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness) and socio-economic factors (GDP per capita, population density), and eliminates the difference in dimensions through data standardization, constructing a comprehensive and systematic evaluation index system, so that the zoning results can more accurately reflect the comprehensive status of regional land use conditions.

[0092] This embodiment relies on multi-source data such as DEM, remote sensing imagery, and field surveys, and utilizes spatial analysis tools for data processing and verification to ensure the accuracy and reliability of the basic data. The entire process is clear and the steps are well-defined, combining automated processing with manual verification, making it easy to apply and promote in practice.

[0093] This embodiment ensures comparability between data from different sources and resolutions through data standardization and spatial registration. The zoning model outputs quantitative scores, and the results are clear and intuitive, facilitating comparative analysis and priority ranking between different regions, thus providing accurate decision support for farmland improvement planning.

[0094] The zoning model and system in this embodiment can adjust the evaluation factors and their weights according to the actual conditions of different regions, and have strong adaptability and scalability. It can be widely applied to farmland improvement zoning work under different geographical conditions and economic development levels.

[0095] The present invention and its embodiments have been described above illustratively. This description is not restrictive, and the figures shown are only one embodiment of the present invention; the actual structure is not limited thereto. Therefore, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the present invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method and system for farmland consolidation and zoning, characterized in that: Includes the following steps: I. Selection of zoning factors and calculation of weights; II. Processing of basic zoning data; III. Calculation and statistics of zoning factors; IV. Standardized calculation of zoning data; V. Construction of a precise zoning model for farmland improvement.

2. The farmland consolidation zoning method according to claim 1, characterized in that: In step one, the Delphi method is first used to determine the factors influencing the zoning. Through expert research, the factors influencing farmland improvement zoning are determined to be topography, climate, soil, and socioeconomic factors. Topography includes altitude and elevation difference; climate includes precipitation and accumulated temperature; soil includes soil type and soil layer thickness; and socioeconomic factors include GDP per capita and population density. Next, the Delphi method is used to determine the weight values ​​of each zoning factor. Experts are invited to select factors on the farmland improvement zoning factor selection table based on the actual situation and without mutual consultation, according to whether each influencing factor has an impact on the farmland improvement zoning and the degree of that impact. Weights are then determined. After the first round of factor weight survey forms are collected, statistical processing is performed to calculate the average and variance of the expert-assigned values ​​for each factor. The results are then fed back to the experts. A second round of weighting is conducted based on the overall trend and dispersion of expert opinions reflected in the first round of variance. After the forms are collected, the variance and average are calculated again. The variances of the two rounds are then X-rayed. 2 Significance tests were conducted to examine whether there was a significant difference in the dispersion of variance between the second and first rounds. If there is a significant difference between the two rounds of variance test values, it indicates that the variance dispersion is large, and a third round of expert scoring is required; if there is no significant difference between the two rounds of variance test values, it indicates that the dispersion is small, and no further expert consultation is required.

3. The farmland consolidation zoning method according to claim 2, characterized in that: X 2 The test method is as follows: First, construct the statistical test X between the two rounds of variances. 2 Then check the calculation table and use X. 2 The 0.95 statistic is used to compare the regularity of variances between two rounds, and the calculation formula is as follows: In the formula: n is the number of experts consulted by the Delphi method; X 2 Let X be the statistical test statistic constructed from two rounds of variance; when X 2 <X 2 At 0.95, the two rounds of variance show significant uniformity, meaning there is no significant difference between the two rounds of variance, indicating that the variance convergence of this round is small; when the X of each factor... 2 The values ​​are all less than X 2 When the test value is 0.95, the consultation with experts ends here.

4. The farmland consolidation zoning method according to claim 3, characterized in that: In step two, spatial analysis tools are used to denoise the DEM data and remove outliers; the reliability of the terrain data is verified by combining remote sensing imagery with field survey data.

5. The farmland consolidation zoning method according to claim 4, characterized in that: In step three, specifically: 3.1) Elevation calculation is based on townships, using DEM data to statistically analyze the average elevation of the unit area, reflecting the light and heat conditions of land use; 3.2) Elevation difference analysis is based on townships, using neighborhood analysis to calculate the difference between the maximum and minimum elevations, representing the degree of terrain undulation and reflecting the ease or difficulty of land use. 3.3) The values ​​of precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita and population density are obtained through statistical analysis of relevant basic data.

6. The farmland consolidation zoning method according to claim 5, characterized in that: Step four specifically includes: 4.1) Data standardization: Standardize the altitude and elevation difference data to 0-1 to eliminate dimensional differences; x' = \frac{x - \min(x)}{\max(x) - \min(x)} where x is the original data and x' is the standardized value; Spatial data alignment: Spatial registration of remote sensing images and terrain is performed using ArcGIS Pro to ensure consistent data resolution; 4.2) Data normalization: Standardize the precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density data to 0-1 to eliminate dimensional differences; x' = \frac{x}{\max(x)} where x is the original data and x' is the standardized value.

7. The farmland consolidation zoning method according to claim 6, characterized in that: In step five, the score calculation formula for the zoning model is expressed as: Total score = (x1s1+x2s2+x3s3+x4s4+x5s5+x6s6+x7s7+x8s8) + R, where: x1-x8 represent the scores for altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density; s1-s8 represent the weights for altitude, elevation difference, precipitation, accumulated temperature, soil type, soil layer thickness, GDP per capita, and population density; and R is the first-level regional value.

8. A farmland consolidation zoning system, characterized in that: It adopts a farmland improvement zoning method as described in any one of claims 1-7.

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