Cultivated land layout planning system and method based on data map

Abstract

The invention discloses a cultivated land layout planning system and method based on a data map, and relates to the technical field of cultivated land planning, and the technical scheme is characterized in that the method comprises the following steps: dividing the cultivated land of a target area according to the adaptation level to obtain cultivated land blocks; collecting land block coding associated data, remote sensing monitoring adaptive data and intelligent irrigation linkage data of each cultivated land block; according to the linkage range value, obtaining a first cultivated land area value, which actually needs layout optimization, of the auxiliary centralized block, and according to the first cultivated land area value and the auxiliary optimization parameter value, making a first cultivated land layout planning scheme; and performing differencing processing between the main optimization parameter value and the original layout parameter of the main concentrated block to obtain a deviation parameter value, obtaining a second cultivated land area value of the main concentrated block, which actually needs layout optimization, according to the deviation parameter value, and making a second cultivated land layout planning scheme according to the second cultivated land area value and the main optimization parameter value. The method has the effects of ensuring that planning can land on the ground and improving the comprehensive benefits of cultivated land.

Description

Technical Field

[0001] This invention relates to the field of farmland planning technology, and more specifically, to a farmland layout planning system and method based on data maps. Background Technology

[0002] Existing machine learning applications in farmland layout planning typically rely on feature mining and model prediction from multi-source data to achieve intelligent and precise planning. These methods first collect multi-dimensional data on farmland soil properties, topography, climate conditions, and resource availability, while integrating historical planting structures, yield data, and optimization cases to construct a dataset encompassing both natural and production characteristics of the farmland. Subsequently, through data preprocessing (such as missing value imputation and feature standardization), features related to soil fertility, irrigation accessibility, and crop suitability are extracted. Machine learning models (such as random forests, neural networks, and support vector machines) are then used to perform correlation analysis on these features. For example, classification models can be used to identify suitable planting types for different farmlands, regression models can be used to predict yield and resource consumption indicators under different layouts, or clustering models can be used to divide farmland plots with similar optimization needs.

[0003] Traditional farmland layout planning methods lack the integration of multi-source information on farmland. For example, some plans divide farmland solely based on area or soil fertility, ignoring dynamic soil moisture data from remote sensing and supporting data from intelligent irrigation systems. This results in coarse classification of farmland plots, failing to identify areas truly needing optimization. Furthermore, they lack a collaborative system between core and supporting plots, neglecting spatial connections and resource sharing between different farmlands. Consequently, optimized farmland struggles to generate synergistic benefits and adapt to diverse farmland conditions and development needs within the region. Some areas possess both farmland requiring improvement and supporting farmland reliant on core area resources. However, a single planning scheme cannot address the differentiated needs of different plots, easily leading to an overemphasis on core areas while neglecting supporting areas, thus limiting the plan's feasibility and adaptability. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a farmland layout planning system and method based on data maps.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for farmland layout planning based on data maps, comprising the following steps:

[0007] The cultivated land in the target area is divided into cultivated land blocks according to the adaptation level; the land block code association data, remote sensing monitoring adaptation data and intelligent irrigation linkage data of each cultivated land block are collected;

[0008] Based on the plot coding association data, remote sensing monitoring adaptation data, and intelligent irrigation linkage data, target cultivated plots that need to be optimized in layout are selected from cultivated plots.

[0009] Based on the conditional correlation characteristics, the main concentrated area and the auxiliary concentrated area are selected from the target cultivated area.

[0010] The auxiliary optimization parameter values ​​are obtained by processing and analyzing historical farmland optimization feature data, main concentrated blocks and auxiliary concentrated blocks;

[0011] Based on the linkage range value, the first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained, and the first cultivated land layout planning scheme is formulated based on the first cultivated land area value and the auxiliary optimization parameter value.

[0012] The deviation parameter value is obtained by subtracting the main optimization parameter value from the original layout parameters of the main concentrated block. Based on the deviation parameter value, the second cultivated land area value that actually needs to be optimized in the layout of the main concentrated block is obtained. Based on the second cultivated land area value and the main optimization parameter value, the second cultivated land layout planning scheme is formulated.

[0013] Preferably, the target cultivated land parcels requiring layout optimization are selected from the cultivated land parcels based on parcel coding association data, remote sensing monitoring adaptation data, and intelligent irrigation linkage data. This process specifically includes the following steps:

[0014] The data on land parcel coding association, remote sensing monitoring adaptation data, and smart irrigation linkage data are integrated to form a farmland characteristic dataset.

[0015] Obtain the threshold values ​​for layout optimization in each cultivated area;

[0016] The key indicator data in the cultivated land feature dataset are compared with the compliance threshold, and the target cultivated land plots whose indicator data meet the compliance threshold are extracted from the cultivated land plots.

[0017] Preferably, the main concentrated area and the auxiliary concentrated area are selected from the target cultivated area based on the conditional association characteristics, specifically including the following steps:

[0018] If the target cultivated area is a conditionally associated concentrated area, then the main concentrated area is selected from the target cultivated area.

[0019] Auxiliary concentrated blocks are extracted from the target cultivated area blocks based on the radiation and linkage range of the main concentrated blocks;

[0020] Among them, the main centralized block and the auxiliary centralized block are conditionally related.

[0021] Preferably, the auxiliary optimization parameter values ​​are obtained by processing and analyzing historical farmland optimization feature data, main concentrated blocks, and auxiliary concentrated blocks, specifically including the following steps:

[0022] Based on historical farmland optimization characteristic data and main concentrated area characteristic data, the layout optimization parameters of the main concentrated area are determined to obtain the main optimization parameter values;

[0023] Based on historical farmland optimization characteristic data and related impact data, the radiation linkage range of the main concentrated block after layout optimization is determined to obtain the linkage range value.

[0024] Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized in the layout of the auxiliary centralized blocks are determined to obtain the auxiliary optimization parameter values.

[0025] Preferably, based on historical farmland optimization characteristic data and characteristic data of the main concentrated blocks, the layout optimization parameters of the main concentrated blocks are determined to obtain the main optimization parameter values, specifically including the following steps:

[0026] Extract layout optimization data of cultivated land plots from historical cultivated land optimization feature data;

[0027] The correlation coefficient between the layout optimization data and the optimized layout parameters is extracted to obtain the optimization correlation coefficient;

[0028] Based on the optimized correlation coefficient and the characteristic data of the main centralized block, the layout optimization parameters of the main centralized block are determined to obtain the main optimization parameter value.

[0029] Preferably, the method further includes the following steps:

[0030] The main difference value data is obtained by subtracting the main optimization parameter value from the original layout parameters of the auxiliary centralized block;

[0031] Obtain the main region area data by acquiring the actual cultivated land area of ​​the main centralized block;

[0032] Combine the main difference data and the main area data into correlation impact data.

[0033] Preferably, based on historical farmland optimization characteristic data and related impact data, the radiation linkage range of the main concentrated area after layout optimization is determined to obtain the linkage range value, specifically including the following steps:

[0034] Historical difference data and historical optimization area data were extracted from historical cultivated land optimization characteristic data based on the correlation impact data.

[0035] Historical difference data and historical optimized area data are combined into historical correlation feature data;

[0036] Data on the historical linkage range of cultivated land blocks after optimization were extracted from historical cultivated land optimization feature data.

[0037] The linkage correlation coefficient is obtained by extracting the correlation coefficient between historical correlation feature data and historical linkage range data.

[0038] The linkage range value is obtained by judging the radiation linkage range of the main centralized block layout optimization based on the associated impact data and linkage linkage coefficient.

[0039] Preferably, the area of ​​the first cultivated land that actually needs to be optimized in the layout of the auxiliary concentrated area is obtained based on the linkage range value. A layout planning scheme for the first cultivated land is then formulated based on the first cultivated land area value and the auxiliary optimization parameter values, specifically including the following steps:

[0040] Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized for the layout of the auxiliary centralized blocks are determined, and the auxiliary optimization parameter values ​​are obtained.

[0041] The preprocessed auxiliary difference value is obtained by subtracting the auxiliary optimization parameter value from the original layout parameters of the auxiliary centralized block;

[0042] The first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained based on the preprocessed auxiliary difference value and the linkage correlation coefficient.

[0043] The first auxiliary centralized block planning scheme is obtained by laying out the auxiliary centralized block based on the first cultivated land area value and the auxiliary optimization parameter value. The first main centralized block planning scheme is obtained by laying out the main centralized block based on the main area area data and the main optimization parameter value.

[0044] Among them, the combination of the first auxiliary centralized block planning scheme and the first main centralized block planning scheme constitutes the first cultivated land layout planning scheme.

[0045] Preferably, the second cultivated land area value that actually needs to be optimized in the layout of the main concentrated area is obtained based on the deviation parameter value. A second cultivated land layout planning scheme is then formulated based on the second cultivated land area value and the main optimization parameter value, specifically including the following steps:

[0046] Based on the main area area data, deviation parameter values, and linkage coefficients, the second cultivated land area value that actually needs to be optimized for the layout of the main concentrated block is obtained.

[0047] Based on the cultivated land area of ​​the auxiliary centralized block, the pre-processed auxiliary difference value, and the linkage correlation coefficient, the actual pre-processed cultivated land area of ​​the auxiliary centralized block that needs to be optimized for layout is obtained.

[0048] The second auxiliary centralized block planning scheme is obtained by planning the layout of the auxiliary centralized block based on the pre-processed cultivated land area and auxiliary optimization parameter values.

[0049] The layout planning scheme for the second main concentrated block is obtained by planning the layout of the main concentrated block based on the second cultivated land area value and the main optimization parameter value;

[0050] Among them, the second auxiliary centralized block planning scheme and the second main centralized block planning scheme are combined to form the second cultivated land layout planning scheme.

[0051] A farmland layout planning system based on data maps, comprising:

[0052] Data Acquisition Module: Divides the cultivated land in the target area into cultivated land plots according to the adaptation level; collects the plot code association data, remote sensing monitoring adaptation data and intelligent irrigation linkage data of each cultivated land plot;

[0053] The filtering module filters out target farmland plots that need to be optimized for layout based on plot code association data, remote sensing monitoring adaptation data, and smart irrigation linkage data.

[0054] Selection module: Selects the main concentrated area and the secondary concentrated area from the target cultivated area based on the conditional association characteristics;

[0055] Processing module: Processes and analyzes historical farmland optimization feature data, main centralized blocks, and auxiliary centralized blocks to obtain auxiliary optimization parameter values;

[0056] The first planning module: Based on the linkage range value, the first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained, and the first cultivated land layout planning scheme is formulated based on the first cultivated land area value and the auxiliary optimization parameter value;

[0057] The second planning module calculates the difference between the main optimization parameter value and the original layout parameters of the main centralized block to obtain the deviation parameter value. Based on the deviation parameter value, it obtains the second cultivated land area value that actually needs to be optimized in the layout of the main centralized block. Based on the second cultivated land area value and the main optimization parameter value, it formulates the second cultivated land layout planning scheme.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] This invention divides arable land into plots according to suitability levels, and combines plot coding, remote sensing monitoring, and intelligent irrigation screening to obtain target arable land plots. This avoids indiscriminate planning for all arable land, allowing optimization resources to be concentrated on areas requiring adjustment, significantly improving the accuracy of layout planning and resource utilization efficiency. By formulating a first arable land layout plan and a second arable land layout plan, a collaborative plan can be formed based on the characteristics of the secondary concentrated plot and the radiation range of the primary concentrated plot, or a targeted plan can be formed based on the deviation parameters of the primary concentrated plot, adapting to different regional needs and resource conditions. This enhances the feasibility of the planning scheme, ensuring that the plan can be implemented and improving the overall benefits of arable land. Attached Figure Description

[0060] Figure 1This is a schematic diagram illustrating the steps of a farmland layout planning method based on a data map, as provided in an embodiment of the present invention.

[0061] Figure 2 This is a schematic diagram of a farmland layout planning system based on a data map, provided in an embodiment of the present invention. Detailed Implementation

[0062] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0063] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0064] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0065] Reference Figures 1-2 As shown.

[0066] The embodiments further illustrate the farmland layout planning system and method based on data maps proposed in this invention.

[0067] A method for farmland layout planning based on data maps, comprising the following steps:

[0068] The cultivated land in the target area is divided into cultivated land blocks according to the suitability level.

[0069] The target area's arable land is divided into arable plots according to suitability levels. Soil fertility data, including organic matter content and pH, is obtained; topographic parameters, such as slope and altitude, are measured; and the distances between the target area and irrigation facilities and transportation networks are determined. This data serves as the basis for suitability level assessment, ensuring that the categorization dimensions align with actual agricultural needs.

[0070] Each indicator is assigned a grading standard and corresponding score. For example, high soil fertility, medium soil fertility, and low soil fertility correspond to 8-10 points, 5-7 points, and 0-4 points, respectively. A terrain slope of less than or equal to 5° is assigned 10 points, a terrain slope greater than 5° and less than or equal to 15° is assigned 6 points, and a terrain slope greater than 15° is assigned 2 points. Irrigation convenience of less than or equal to 500 meters is assigned 10 points, irrigation convenience of greater than 500 meters and less than or equal to 1500 meters is assigned 6 points, and irrigation convenience of greater than 1000 meters is assigned 2 points.

[0071] If high-yield planting is the core, the weight of soil fertility is set to 0.35, the weight of irrigation facilities is set to 0.25, and the weight of topography is set to 0.4; the comprehensive suitability score is calculated as: soil fertility score × weight 1 + topography slope score × weight 2 + irrigation facilities score × weight 3, thus obtaining the comprehensive score of each piece of farmland.

[0072] Areas scoring 8-10 are designated as high-fitness zones, suitable for concentrated planting of high-value-added crops; areas scoring 5-7 are designated as medium-fitness zones, suitable for conventional grain planting areas; and areas scoring 0-4 are designated as low-fitness zones, suitable for ecological restoration or adjusting planting structure.

[0073] Collect plot coding association data, remote sensing monitoring adaptation data, and smart irrigation linkage data for each cultivated area;

[0074] Based on the plot coding association data, remote sensing monitoring adaptation data, and intelligent irrigation linkage data, target cultivated plots that need to be optimized in layout are selected from cultivated plots.

[0075] Based on the conditional correlation characteristics, the main concentrated area and the auxiliary concentrated area are selected from the target cultivated area.

[0076] The auxiliary optimization parameter values ​​are obtained by processing and analyzing historical farmland optimization feature data, main concentrated blocks and auxiliary concentrated blocks;

[0077] Based on the linkage range value, the first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained, and the first cultivated land layout planning scheme is formulated based on the first cultivated land area value and the auxiliary optimization parameter value.

[0078] The deviation parameter value is obtained by subtracting the main optimization parameter value from the original layout parameters of the main concentrated block. Based on the deviation parameter value, the second cultivated land area value that actually needs to be optimized in the layout of the main concentrated block is obtained. Based on the second cultivated land area value and the main optimization parameter value, the second cultivated land layout planning scheme is formulated.

[0079] Based on land parcel coding association data, remote sensing monitoring adaptation data, and smart irrigation linkage data, target farmland parcels requiring layout optimization are selected from the farmland parcels. The specific steps include:

[0080] The data on land parcel coding association, remote sensing monitoring adaptation data, and smart irrigation linkage data are integrated to form a farmland characteristic dataset.

[0081] Obtain the threshold values ​​for layout optimization in each cultivated area;

[0082] The key indicator data in the cultivated land feature dataset are compared with the compliance threshold, and the target cultivated land plots whose indicator data meet the compliance threshold are extracted from the cultivated land plots.

[0083] Land parcel coding and association data forms the basic identity information of cultivated land parcels, including ownership, area, and historical planting records. For example, the coding and association data of a certain cultivated land parcel records its area as 20 mu (approximately 3.3 hectares) and the crop planted in the past three years as wheat. Remote sensing monitoring and adaptation data is dynamic information about cultivated land obtained through satellite or UAV remote sensing, including soil moisture, crop growth, and changes in parcel boundaries. For example, remote sensing data shows that the current soil moisture content of this parcel is 18%, and the crop coverage is 75%. Smart irrigation linkage data is operational data associated with the irrigation system, including irrigation duration, water consumption, and irrigation equipment malfunction records. For example, the most recent irrigation in this parcel lasted 2 hours, with a single water consumption of 30 cubic meters. By mapping these three types of data to each cultivated land parcel, a complete cultivated land feature dataset containing identity, dynamic status, and operational status of supporting facilities is formed.

[0084] The target thresholds for optimizing the layout of each cultivated land plot are determined. These thresholds are set based on the goals of cultivated land optimization in the region, with different indicators corresponding to different threshold standards. For example, the target threshold for soil moisture is set at 15%-22% (to ensure suitable soil moisture for crop growth), the target threshold for irrigation water consumption is set at ≤35 cubic meters per irrigation (to control irrigation costs and water resource consumption), and the target threshold for crop cover is set at ≥80% (to ensure good crop growth). These thresholds are adjusted based on the actual needs of regional agricultural production to ensure they meet the expected goals of layout optimization.

[0085] Compare the key indicators in the cultivated land feature dataset with the compliance thresholds to screen for target blocks, and each indicator data needs to be matched with the corresponding threshold one by one. The soil moisture content of 18% is within the threshold range of 15% - 22%, and the single irrigation water consumption of 30 cubic meters meets the threshold of ≤ 35 cubic meters, but the crop coverage of 75% does not reach the threshold of ≥ 80%. At this time, the screening rules for indicators need to be clarified. If it is set that all key indicators need to meet the thresholds to be included in the target block, then this block is excluded due to the non-compliance of the crop coverage; if it is set that the core indicators only need to be met, and the crop coverage is a core indicator, then this block is marked as the target cultivated land block that needs layout optimization. Through such comparison, all cultivated land blocks whose key indicator data meet the compliance thresholds are extracted as the target objects for layout optimization, ensuring that the subsequent optimization work focuses on the blocks that actually have room for improvement.

[0086] Select the main concentrated blocks and auxiliary concentrated blocks from the target cultivated land blocks according to the condition-related features. The specific steps are as follows:

[0087] If the target cultivated land block is a concentrated block related to conditions, select the main concentrated block from the target cultivated land block;

[0088] Extract the auxiliary concentrated blocks from the target cultivated land blocks according to the radiation linkage range of the main concentrated blocks;

[0089] Among them, there is a conditional relationship between the main concentrated blocks and the auxiliary concentrated blocks.

[0090] The concentrated block related to conditions refers to the contiguous area in the target cultivated land block that has related features. The related features usually include the spatial connectivity of the plots and the sharing of resource facilities. For example, in a certain area of the target cultivated land blocks, there is a contiguous block with an area of 50 mu. All the plots inside share the same set of intelligent irrigation systems, and the soil adaptation grades are all high adaptation. At the same time, the types of crops planted in adjacent plots are the same. Such a contiguous area with spatial, resource, and planting feature associations is selected as the main concentrated block.

[0091] The extraction of the auxiliary concentrated blocks needs to be determined according to the radiation linkage range of the main concentrated blocks. The radiation linkage range is usually related to the resource coverage ability of the main concentrated blocks. For example, for the intelligent irrigation system supporting the main concentrated block, its effective radiation coverage range is the plots within 100 meters around. Then this 100 meters is the radiation linkage range of this main concentrated block. At this time, it is necessary to screen out the plots in the radiation linkage range of the main concentrated block and having a conditional relationship with the main concentrated block from the target cultivated land blocks. Here, the conditional relationship can be the dependence relationship of irrigation resources or the collaborative relationship of planting structures.

[0092] If a main concentrated area is a contiguous 50-mu (approximately 3.3 hectares) high-suitability wheat planting area, and the supporting intelligent irrigation system has a radiation range of 100 meters, and there are several plots of land with a total area of ​​20 mu (approximately 1.3 hectares) within 100 meters of the target cultivated area, and these plots are also suitable for wheat planting and rely on the irrigation system of the main concentrated area for water supply, then these 20-mu plots of land are extracted as auxiliary concentrated areas.

[0093] The process involves analyzing historical farmland optimization feature data, main concentrated blocks, and auxiliary concentrated blocks to obtain auxiliary optimization parameter values. This includes the following steps:

[0094] Based on historical farmland optimization characteristic data and main concentrated area characteristic data, the layout optimization parameters of the main concentrated area are determined to obtain the main optimization parameter values;

[0095] Based on historical farmland optimization characteristic data and related impact data, the radiation linkage range of the main concentrated block after layout optimization is determined to obtain the linkage range value.

[0096] Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized in the layout of the auxiliary centralized blocks are determined to obtain the auxiliary optimization parameter values.

[0097] Historical farmland optimization feature data includes layout optimization records of similar plots in the past, such as optimization parameters for wheat planting plots with the same high adaptability level, including adjusted planting density and optimized irrigation frequency. The feature data for the main concentrated plots is their current actual state, such as an existing planting density of 400,000 plants per mu and a current irrigation frequency of once every 5 days. The correlation coefficients between the optimization parameters and feature data of similar plots in the historical data are extracted. For example, historical data shows that the correlation coefficient between the optimized planting density value and the initial density of high-adaptability wheat plots is 0.1, and the optimized density = initial density × (1 + 0.1). If the initial planting density of the main concentrated plot is 400,000 plants / mu, then the optimized planting density value of the main optimization parameter is 400,000 × (1 + 0.1) = 440,000 plants / mu. If the optimization correlation coefficient for irrigation frequency in the historical data is -0.2, and the current irrigation frequency is 5 days / time, then the optimized irrigation frequency is 5 × (1 - 0.2) = 4 days / time. These specific optimization values ​​together constitute the main optimization parameter value.

[0098] The correlation impact data consists of the difference between the main optimization parameter value and the original parameters of the auxiliary centralized block, as well as the area data of the main centralized block. For example, if the irrigation frequency in the main optimization parameters is 4 days / time, and the original irrigation frequency of the auxiliary centralized block is 6 days / time, the difference is 2 days / time. The area of ​​the main centralized block is 50 mu (approximately 3.3 hectares). These two data points together constitute the correlation impact data. The radiation linkage range corresponding to similar correlation impact data is extracted from historical data. For example, if the difference in irrigation frequency is 2 days / time and the main block area is 50 mu (approximately 3.3 hectares), the corresponding radiation linkage range is 120 meters. The linkage coefficient between the two is also calculated. If the correlation coefficient between this correlation impact data and the linkage range in the historical data is 0.8, then based on the current correlation impact data, the optimized linkage range value of the main centralized block is derived to be 120 meters × 0.8 = 96 meters.

[0099] Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized for the layout of the auxiliary centralized blocks are determined to obtain the auxiliary optimization parameter values. For example, if the current planting density of the auxiliary centralized block is 380,000 plants / acre and it relies on the irrigation system of the main centralized block, combined with the optimized planting density value of 440,000 plants / acre in the main optimization parameters, and taking into account the soil suitability level (medium suitability) of the auxiliary centralized block, the correlation coefficient is adjusted to 0.08. Then, the optimized planting density value in the auxiliary optimization parameters is 380,000 × (1 + 0.08) = 410,400 plants / acre. At the same time, based on the optimized irrigation frequency value of 4 days / time in the main block, the optimized irrigation frequency value of the auxiliary centralized block is also adjusted to 4 days / time. These optimized values ​​adapted to the characteristics of the auxiliary centralized block together constitute the auxiliary optimization parameter values.

[0100] Based on historical farmland optimization characteristic data and characteristic data of the main concentrated blocks, the layout optimization parameters of the main concentrated blocks are determined to obtain the main optimization parameter values. The specific steps include:

[0101] Extract layout optimization data of cultivated land plots from historical cultivated land optimization feature data;

[0102] The correlation coefficient between the layout optimization data and the optimized layout parameters is extracted to obtain the optimization correlation coefficient;

[0103] Based on the optimized correlation coefficient and the characteristic data of the main centralized block, the layout optimization parameters of the main centralized block are determined to obtain the main optimization parameter value.

[0104] First, we extract the layout optimization data of cultivated land plots from the historical cultivated land optimization feature data. This historical cultivated land optimization feature data covers all records of different types of cultivated land plots after layout optimization, including the original features of the plots (such as initial planting density, irrigation frequency, and soil fertility level) and the corresponding optimized parameters (such as adjusted planting density and optimized irrigation frequency). For example, the historical data records that the original planting density of a highly adapted wheat plot was 380,000 plants / mu, and the optimized planting density was 420,000 plants / mu; the original irrigation frequency of a moderately adapted maize plot was 7 days / time, and the optimized irrigation frequency was 5 days / time. These original features and the corresponding optimized parameters are the layout optimization data that needs to be extracted.

[0105] The correlation coefficient between the optimized layout data and the optimized layout parameters is extracted, and the correlation between the changes in the original features and the changes in the optimized parameters in the historical data is calculated to obtain the optimization correlation coefficient. Taking planting density as an example, multiple sets of historical layout optimization data for highly adapted wheat blocks are selected. For example, the original density of block A is 380,000 plants / mu, and the optimized density is 420,000 plants / mu, with a change of +40,000 plants / mu; the original density of block B is 400,000 plants / mu, and the optimized density is 440,000 plants / mu, with a change of +40,000 plants / mu. The optimization correlation coefficient = (optimized parameters - original parameters) / original parameters. The optimization correlation coefficient of block A = (420,000 - 380,000) / 380,000 ≈ 0.105; the optimization correlation coefficient of block B = (440,000 - 400,000) / 400,000 = 0.1. Taking the average of multiple sets of data, the optimization correlation coefficient of planting density of highly adapted wheat blocks is approximately 0.1. If the original value of the irrigation frequency of the high-fit block in the historical data is 5 days / time, and the optimized value is 4 days / time, the optimization correlation coefficient is (4-5) / 5=-0.2.

[0106] The main optimization parameter values ​​are obtained by combining the optimized correlation coefficient and the feature data of the main centralized block. Assuming the current main centralized block is a highly adapted wheat block, with an original planting density of 400,000 plants / mu and an original irrigation frequency of 5 days / time in its feature data, the main optimization parameter value = original feature data of the main centralized block × (1 + optimized correlation coefficient). The main optimization parameter value for planting density is 40 × (1 + 0.1) = 440,000 plants / mu; the main optimization parameter value for irrigation frequency is 5 × (1 - 0.2) = 4 days / time.

[0107] It also includes the following steps:

[0108] The main difference value data is obtained by subtracting the main optimization parameter value from the original layout parameters of the auxiliary centralized block;

[0109] Obtain the main region area data by acquiring the actual cultivated land area of ​​the main centralized block;

[0110] Combine the main difference data and the main area data into correlation impact data.

[0111] The primary difference data is obtained by subtracting the primary optimization parameter values ​​from the original layout parameters of the secondary centralized blocks. The primary optimization parameter values ​​are the target parameters after layout optimization in the primary centralized block, such as a wheat planting density of 440,000 plants / acre and an irrigation frequency of 4 days / time in the primary centralized block. The original layout parameters of the secondary centralized blocks represent their current unoptimized state; for example, the original planting density of a certain secondary centralized block is 380,000 plants / acre and the original irrigation frequency is 6 days / time. The primary difference data is calculated as: Primary difference data = Primary optimization parameter value - Original layout parameters of the secondary centralized block. Therefore, the primary difference data for planting density is 440,000 - 380,000 = 60,000 plants / acre; the primary difference data for irrigation frequency is 40,000 - 60,000 = -2 days / time. These differences reflect the parameter gap between the optimized primary centralized block and the current state of the secondary centralized block.

[0112] The main area area data represents the actual cultivated land area of ​​the main concentrated block, serving as a fundamental indicator of the main block's resource radiation capacity. For example, if the current main concentrated block is a contiguous 50-mu (approximately 3.3 hectares) highly adaptable cultivated land area, then its actual cultivated land area is 50 mu (approximately 3.3 hectares), and the corresponding main area area data is 50 mu (approximately 3.3 hectares).

[0113] The main difference data includes a planting density difference of 60,000 plants / mu and an irrigation frequency difference of -2 days / time. The main area data is 50 mu. Combining these two types of data forms the correlation impact data, specifically manifested as a planting density difference of 60,000 plants / mu, an irrigation frequency difference of -2 days / time, and a main area of ​​50 mu. This dataset reflects both the parameter differences between the optimized main centralized block and the auxiliary centralized block, and also the scale basis of the main block.

[0114] Based on historical farmland optimization characteristic data and related impact data, the radiation and linkage range of the main concentrated area after layout optimization is determined to obtain the linkage range value. The specific steps include:

[0115] Historical difference data and historical optimization area data were extracted from historical cultivated land optimization characteristic data based on the correlation impact data.

[0116] Historical difference data and historical optimized area data are combined into historical correlation feature data;

[0117] Data on the historical linkage range of cultivated land blocks after optimization were extracted from historical cultivated land optimization feature data.

[0118] The linkage correlation coefficient is obtained by extracting the correlation coefficient between historical correlation feature data and historical linkage range data.

[0119] The linkage range value is obtained by judging the radiation linkage range of the main centralized block layout optimization based on the associated impact data and linkage linkage coefficient.

[0120] Based on the current correlation impact data, similar records are selected from the historical farmland optimization characteristic data. For example, if the current correlation impact data includes a planting density difference of 60,000 plants / mu, an irrigation frequency difference of -2 days / time, and a main area of ​​50 mu, then cases with the same difference data and area are searched in the historical data. For example, a historical record is found with a planting density difference of 58,000 plants / mu, an irrigation frequency difference of -1.9 days / time, and a historical optimized area of ​​48 mu. These data are similar in characteristics to the current correlation impact data and are extracted as analysis samples.

[0121] The extracted historical difference data and historical optimized area data are integrated into a dataset. Taking the above historical case as an example, the difference in planting density of 58,000 plants / mu and the difference in irrigation frequency of -1.9 days / time are combined with the historical optimized area of ​​48 mu to form historical correlation feature data. This dataset fully reflects the parameter differences and scale characteristics of the main concentrated area in the historical case.

[0122] The actual radiating and synergistic range generated after the optimization of cultivated land blocks in corresponding historical cases is obtained from historical cultivated land optimization feature data. For example, in the above historical case, the optimization of the main block actually radiated and influenced the auxiliary concentrated blocks within a 100-meter radius. This 100-meter radius is the corresponding historical synergistic range data.

[0123] The linkage coefficient is calculated as follows: Historical linkage range data ÷ (Historical optimized area data × Average historical difference data). The average historical difference data is (5.8 + 1.9) ÷ 2 = 3.85. The historical optimized area data is 48 mu (approximately 3.5 hectares), and the historical linkage range data is 100 meters. Therefore, the linkage coefficient is 100 ÷ (48 × 3.85) ≈ 100 ÷ 184.8 ≈ 0.541. In practice, multiple similar historical cases are selected to calculate the average linkage coefficient to improve accuracy.

[0124] The linkage range value = linkage correlation coefficient × (main area data × mean of main difference data). The mean of the main difference data is (6+2)÷2=4, the main area data is 50 mu, and the linkage correlation coefficient is the average of multiple cases, which is 0.55. Therefore, the linkage range value is 0.55×(50×4)=0.55×200=110 meters.

[0125] Based on the linkage range value, the actual area of ​​the first cultivated land that needs to be optimized in the auxiliary centralized area is obtained. Based on the first cultivated land area value and the auxiliary optimization parameter value, a layout planning scheme for the first cultivated land is formulated, which includes the following steps:

[0126] Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized for the layout of the auxiliary centralized blocks are determined, and the auxiliary optimization parameter values ​​are obtained.

[0127] The preprocessed auxiliary difference value is obtained by subtracting the auxiliary optimization parameter value from the original layout parameters of the auxiliary centralized block;

[0128] The first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained based on the preprocessed auxiliary difference value and the linkage correlation coefficient.

[0129] The first auxiliary centralized block planning scheme is obtained by laying out the auxiliary centralized block based on the first cultivated land area value and the auxiliary optimization parameter value. The first main centralized block planning scheme is obtained by laying out the main centralized block based on the main area area data and the main optimization parameter value.

[0130] Among them, the combination of the planning scheme for the first auxiliary centralized block and the planning scheme for the first main centralized block constitutes the first farmland layout planning scheme.

[0131] Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized for the layout of the auxiliary centralized blocks are determined to obtain the auxiliary optimization parameter values. For example, the characteristic data of a certain auxiliary centralized block are that it is a medium-suitable wheat planting area, the original planting density is 380,000 plants / mu, and the original irrigation frequency is 6 days / time. Combining its conditional correlation with the main centralized block (depending on the main block's irrigation system), and referring to the historical optimization patterns of similar blocks, its auxiliary optimization parameter values ​​are determined. The planting density is optimized to 410,000 plants / mu, and the irrigation frequency is optimized to 4 days / time, in sync with the main centralized block.

[0132] Pre-treatment auxiliary difference value = auxiliary optimization parameter value - original layout parameters of auxiliary centralized block. Taking the above auxiliary block as an example, the pre-treatment auxiliary difference value of planting density is 41-38=30,000 plants / acre, and the pre-treatment auxiliary difference value of irrigation frequency is 4-6=-2 days / time. These differences reflect the range of parameter adjustments required for the auxiliary block.

[0133] The first cultivated land area value = total area of ​​the auxiliary concentrated block × (mean value of pre-treated auxiliary difference × linkage coefficient). Assuming the total area of ​​the auxiliary concentrated block is 20 mu, the mean value of pre-treated auxiliary difference is (3+2)÷2=2.5, and the linkage coefficient is 0.55 obtained earlier, then the first cultivated land area value is 20×(2.5×0.55)=20×1.375=27.5 mu.

[0134] For the auxiliary concentrated area, based on the first cultivated land area value of 27.5 mu and the auxiliary optimization parameter values ​​(planting density of 410,000 plants / mu and irrigation frequency of 4 days / time), the division of its planting area and the connection of irrigation nodes are planned to form the first auxiliary concentrated area planning scheme; for the main concentrated area, based on the main area area data of 50 mu and the main optimization parameter values ​​(planting density of 440,000 plants / mu and irrigation frequency of 4 days / time), its planting layout and resource matching upgrades are planned to form the first main concentrated area planning scheme.

[0135] The planning scheme of the first auxiliary centralized block is combined with the planning scheme of the first main centralized block to obtain the first farmland layout planning scheme covering the main and auxiliary blocks. This scheme not only ensures the optimization effect of the main centralized block, but also realizes the synergistic adaptation between the auxiliary and main centralized blocks.

[0136] Based on the deviation parameter value, the actual area of ​​the second cultivated land that needs to be optimized in the layout of the main centralized block is obtained. Based on the second cultivated land area value and the main optimization parameter value, a second cultivated land layout planning scheme is formulated, which includes the following steps:

[0137] Based on the main area area data, deviation parameter values, and linkage coefficients, the second cultivated land area value that actually needs to be optimized for the layout of the main concentrated block is obtained.

[0138] Based on the cultivated land area of ​​the auxiliary centralized block, the pre-processed auxiliary difference value, and the linkage correlation coefficient, the actual pre-processed cultivated land area of ​​the auxiliary centralized block that needs to be optimized for layout is obtained.

[0139] The second auxiliary centralized block planning scheme is obtained by planning the layout of the auxiliary centralized block based on the pre-processed cultivated land area and auxiliary optimization parameter values.

[0140] The layout planning scheme for the second main concentrated block is obtained by planning the layout of the main concentrated block based on the second cultivated land area value and the main optimization parameter value;

[0141] Among them, the second auxiliary centralized block planning scheme and the second main centralized block planning scheme are combined to form the second cultivated land layout planning scheme.

[0142] Based on the main area area data, deviation parameter values, and linkage coefficients, the actual second cultivated land area value requiring layout optimization in the main concentrated block is obtained. The deviation parameter value is the difference between the main optimization parameter value and the original layout parameters of the main concentrated block. For example, if the original planting density of the main concentrated block is 400,000 plants / mu, the main optimization parameter value is 440,000 plants / mu, and the corresponding deviation parameter value is 40,000 plants / mu; if the original irrigation frequency is 5 days / time, the main optimization parameter value is 4 days / time, and the deviation parameter value is -1 day / time. The second cultivated land area value = main area area data × (mean deviation parameter value × linkage coefficient). Assuming the main area area data is 50 mu, the mean deviation parameter value is (4+1)÷2=2.5, and the linkage coefficient is 0.55, then the second cultivated land area value is 50×(2.5×0.55)=50×1.375=68.75 mu (the actual area needs to be adjusted according to the actual range of the main block to ensure reasonableness).

[0143] For example, the cultivated land area of ​​the auxiliary concentrated block is 20 mu, the mean of the pretreatment auxiliary difference value (planting density difference of 30,000 plants / mu, irrigation frequency difference of -2 days / time) is 2.5, the linkage correlation coefficient is 0.55, the pretreated cultivated land area = cultivated land area of ​​auxiliary concentrated block × (mean of pretreatment auxiliary difference value × linkage correlation coefficient), that is, the pretreated cultivated land area is 20 × (2.5 × 0.55) = 27.5 mu.

[0144] Based on the pre-treated cultivated land area of ​​27.5 mu and the auxiliary optimization parameters (planting density of 410,000 plants / mu and irrigation frequency of 4 days / time), the distribution of its planting area and its linkage with the irrigation system of the main block are planned to form the second auxiliary concentrated block planning scheme; based on the second cultivated land area of ​​68.75 mu and the main optimization parameters (planting density of 440,000 plants / mu and irrigation frequency of 4 days / time), the adjustment of its planting layout and the expansion and upgrading of resource supporting facilities are planned to form the second main concentrated block planning scheme.

[0145] The second auxiliary centralized block planning scheme is combined with the second main centralized block planning scheme to obtain the second cultivated land layout planning scheme covering the main and auxiliary blocks. The second cultivated land layout planning scheme is another set of optimization schemes based on the deviation parameters and linkage relationship of the main blocks. It can be used in conjunction with the first cultivated land layout planning scheme to provide more flexible choices for cultivated land layout.

[0146] A farmland layout planning system based on data maps, comprising:

[0147] Data Acquisition Module: Divides the cultivated land in the target area into cultivated land plots according to the adaptation level; collects the plot code association data, remote sensing monitoring adaptation data and intelligent irrigation linkage data of each cultivated land plot;

[0148] The filtering module filters out target farmland plots that need to be optimized for layout based on plot code association data, remote sensing monitoring adaptation data, and smart irrigation linkage data.

[0149] Selection module: Selects the main concentrated area and the secondary concentrated area from the target cultivated area based on the conditional association characteristics;

[0150] Processing module: Processes and analyzes historical farmland optimization feature data, main centralized blocks, and auxiliary centralized blocks to obtain auxiliary optimization parameter values;

[0151] The first planning module: Based on the linkage range value, the first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained, and the first cultivated land layout planning scheme is formulated based on the first cultivated land area value and the auxiliary optimization parameter value;

[0152] The second planning module calculates the difference between the main optimization parameter value and the original layout parameters of the main centralized block to obtain the deviation parameter value. Based on the deviation parameter value, it obtains the second cultivated land area value that actually needs to be optimized in the layout of the main centralized block. Based on the second cultivated land area value and the main optimization parameter value, it formulates the second cultivated land layout planning scheme.

[0153] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data map-based farmland layout planning method, characterized by, The method includes the following steps: The cultivated land in the target area is divided into cultivated land blocks according to the adaptation level; the land block code association data, remote sensing monitoring adaptation data and intelligent irrigation linkage data of each cultivated land block are collected; Based on the plot coding association data, remote sensing monitoring adaptation data, and intelligent irrigation linkage data, target cultivated plots that need to be optimized in layout are selected from cultivated plots. Based on the conditional correlation characteristics, the main concentrated area and the auxiliary concentrated area are selected from the target cultivated area. The auxiliary optimization parameter values ​​are obtained by processing and analyzing historical farmland optimization feature data, main concentrated blocks and auxiliary concentrated blocks; Based on the linkage range value, the first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained, and the first cultivated land layout planning scheme is formulated based on the first cultivated land area value and the auxiliary optimization parameter value. The deviation parameter value is obtained by subtracting the main optimization parameter value from the original layout parameters of the main concentrated block. Based on the deviation parameter value, the second cultivated land area value that actually needs to be optimized in the layout of the main concentrated block is obtained. Based on the second cultivated land area value and the main optimization parameter value, the second cultivated land layout planning scheme is formulated.

2. The method for farmland layout planning based on data maps according to claim 1, characterized in that, Based on land parcel coding association data, remote sensing monitoring adaptation data, and smart irrigation linkage data, target farmland parcels requiring layout optimization are selected from the farmland parcels. The specific steps include: The data on land parcel coding association, remote sensing monitoring adaptation data, and smart irrigation linkage data are integrated to form a farmland characteristic dataset. Obtain the threshold values ​​for layout optimization in each cultivated area; The key indicator data in the cultivated land feature dataset are compared with the compliance threshold, and the target cultivated land plots whose indicator data meet the compliance threshold are extracted from the cultivated land plots.

3. The method for farmland layout planning based on data maps according to claim 1, characterized in that, Based on conditional association characteristics, primary and secondary concentrated areas are selected from the target cultivated area, specifically including the following steps: If the target cultivated area is a conditionally associated concentrated area, then the main concentrated area is selected from the target cultivated area. Auxiliary concentrated blocks are extracted from the target cultivated area blocks based on the radiation and linkage range of the main concentrated blocks; Among them, the main centralized block and the auxiliary centralized block are conditionally related.

4. The method for farmland layout planning based on data maps according to claim 1, characterized in that, The process involves analyzing historical farmland optimization feature data, main concentrated blocks, and auxiliary concentrated blocks to obtain auxiliary optimization parameter values. This includes the following steps: Based on historical farmland optimization characteristic data and main concentrated area characteristic data, the layout optimization parameters of the main concentrated area are determined to obtain the main optimization parameter values; Based on historical farmland optimization characteristic data and related impact data, the radiation linkage range of the main concentrated block after layout optimization is determined to obtain the linkage range value. Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized in the layout of the auxiliary centralized blocks are determined to obtain the auxiliary optimization parameter values.

5. The method for farmland layout planning based on data maps according to claim 4, characterized in that, Based on historical farmland optimization characteristic data and characteristic data of the main concentrated blocks, the layout optimization parameters of the main concentrated blocks are determined to obtain the main optimization parameter values. The specific steps include: Extract layout optimization data of cultivated land plots from historical cultivated land optimization feature data; The correlation coefficient between the layout optimization data and the optimized layout parameters is extracted to obtain the optimization correlation coefficient; Based on the optimized correlation coefficient and the characteristic data of the main centralized block, the layout optimization parameters of the main centralized block are determined to obtain the main optimization parameter value.

6. The method for farmland layout planning based on data maps according to claim 5, characterized in that, It also includes the following steps: The main difference value data is obtained by subtracting the main optimization parameter value from the original layout parameters of the auxiliary centralized block; Obtain the main region area data by acquiring the actual cultivated land area of ​​the main centralized block; Combine the main difference data and the main area data into correlation impact data.

7. The method for farmland layout planning based on data maps according to claim 6, characterized in that, Based on historical farmland optimization characteristic data and related impact data, the radiation and linkage range of the main concentrated area after layout optimization is determined to obtain the linkage range value. The specific steps include: Historical difference data and historical optimization area data were extracted from historical cultivated land optimization characteristic data based on the correlation impact data. Historical difference data and historical optimized area data are combined into historical correlation feature data; Data on the historical linkage range of cultivated land blocks after optimization were extracted from historical cultivated land optimization feature data. The linkage correlation coefficient is obtained by extracting the correlation coefficient between historical correlation feature data and historical linkage range data. The linkage range value is obtained by judging the radiation linkage range of the main centralized block layout optimization based on the associated impact data and linkage linkage coefficient.

8. The method for farmland layout planning based on data maps according to claim 7, characterized in that, Based on the linkage range value, the actual area of ​​the first cultivated land that needs to be optimized in the auxiliary centralized area is obtained. Based on the first cultivated land area value and the auxiliary optimization parameter value, a layout planning scheme for the first cultivated land is formulated, which includes the following steps: Based on the characteristic data of the auxiliary centralized blocks, the parameters that need to be optimized for the layout of the auxiliary centralized blocks are determined, and the auxiliary optimization parameter values ​​are obtained. The preprocessed auxiliary difference value is obtained by subtracting the auxiliary optimization parameter value from the original layout parameters of the auxiliary centralized block; The first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained based on the preprocessed auxiliary difference value and the linkage correlation coefficient. The first auxiliary centralized block planning scheme is obtained by laying out the auxiliary centralized block based on the first cultivated land area value and the auxiliary optimization parameter value. The first main centralized block planning scheme is obtained by laying out the main centralized block based on the main area area data and the main optimization parameter value. Among them, the combination of the first auxiliary centralized block planning scheme and the first main centralized block planning scheme constitutes the first cultivated land layout planning scheme.

9. A method for farmland layout planning based on data maps according to claim 8, characterized in that, Based on the deviation parameter value, the actual area of ​​the second cultivated land that needs to be optimized in the layout of the main centralized block is obtained. Based on the second cultivated land area value and the main optimization parameter value, a second cultivated land layout planning scheme is formulated, which includes the following steps: Based on the main area area data, deviation parameter values, and linkage coefficients, the second cultivated land area value that actually needs to be optimized for the layout of the main concentrated block is obtained. Based on the cultivated land area of ​​the auxiliary centralized block, the pre-processed auxiliary difference value, and the linkage correlation coefficient, the actual pre-processed cultivated land area of ​​the auxiliary centralized block that needs to be optimized for layout is obtained. The second auxiliary centralized block planning scheme is obtained by planning the layout of the auxiliary centralized block based on the pre-processed cultivated land area and auxiliary optimization parameter values. The layout planning scheme for the second main concentrated block is obtained by planning the layout of the main concentrated block based on the second cultivated land area value and the main optimization parameter value; Among them, the second auxiliary centralized block planning scheme and the second main centralized block planning scheme are combined to form the second cultivated land layout planning scheme.

10. A farmland layout planning system based on a data map, applied to the farmland layout planning method based on a data map as described in any one of claims 1-9, characterized in that, include: Data Acquisition Module: Divides the cultivated land in the target area into cultivated land plots according to the adaptation level; collects the plot code association data, remote sensing monitoring adaptation data and intelligent irrigation linkage data of each cultivated land plot; The filtering module filters out target farmland plots that need to be optimized for layout based on plot code association data, remote sensing monitoring adaptation data, and smart irrigation linkage data. Selection module: Selects the main concentrated area and the secondary concentrated area from the target cultivated area based on the conditional association characteristics; Processing module: Processes and analyzes historical farmland optimization feature data, main centralized blocks, and auxiliary centralized blocks to obtain auxiliary optimization parameter values; The first planning module: Based on the linkage range value, the first cultivated land area value that actually needs to be optimized in the layout of the auxiliary centralized block is obtained, and the first cultivated land layout planning scheme is formulated based on the first cultivated land area value and the auxiliary optimization parameter value; The second planning module calculates the difference between the main optimization parameter value and the original layout parameters of the main centralized block to obtain the deviation parameter value. Based on the deviation parameter value, it obtains the second cultivated land area value that actually needs to be optimized in the layout of the main centralized block. Based on the second cultivated land area value and the main optimization parameter value, it formulates the second cultivated land layout planning scheme.

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