Intelligent irrigation control method and system for high-standard farmland

Abstract

The invention provides an intelligent irrigation control method and system for a high-standard farmland. The method comprises the following steps: acquiring crop canopy image data and farmland terrain elevation data of different crop planting areas in the high-standard farmland; determining a crop growth stage based on morphological characteristic data in the crop canopy image data, and constructing a soil hydrodynamic simulation model according to a root distribution depth interval and a theoretical water absorption rate coefficient corresponding to the crop growth stage; and based on the farmland terrain elevation data and the soil porosity parameter, generating a soil moisture migration parameter by using a soil hydrodynamic simulation model, generating a pipe network pressure regulation and control instruction in combination with the soil moisture migration parameter and a preset irrigation strategy, and adjusting the water outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation and control instruction. Generating an irrigation control instruction; accurate space-time matching of irrigation of the multi-crop rotation area is achieved, the water utilization efficiency is improved, and the problem of irrigation uniformity at the terrain difference and the rotation boundary is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural intelligent irrigation, and particularly to an intelligent irrigation control method and system for high-standard farmland. Background Art

[0002] In the multi-crop rotation planting mode of high-standard farmland, there are significant differences in the growth cycles and water demand laws of different crops. It is necessary to dynamically adjust the irrigation strategy according to the water absorption patterns and the depths of the active root zones in different growth periods. For example, shallow-rooted crops rely on surface soil moisture in the seedling stage, while deep-rooted crops require continuous deep soil water supply in the mature stage. At the same time, the undulating terrain of the farmland leads to uneven infiltration paths and distributions of surface water. Especially at the junction of rotation areas, the differences in the depths of the active root zones of adjacent crops and the slope changes in the terrain transition zones may cause ineffective infiltration or local waterlogging of irrigation water. In addition, under the rotation mode, it is necessary to quickly respond to the water demand changes after crop replacement. Especially when switching between adjacent crops, it is necessary to reduce water competition and resource conflicts in the boundary areas through refined irrigation control.

[0003] Currently, some intelligent irrigation solutions achieve automated irrigation control based on soil surface moisture thresholds by deploying a soil moisture sensor network and meteorological monitoring equipment, combined with a preset crop water demand model. The existing solutions fail to fully consider the dynamic changes in the root distributions and water absorption characteristics of different crops in the crop rotation area. Especially during the rotation switching stage, they cannot accurately match the root water absorption requirements of newly planted crops. Only by correcting the irrigation duration based on the terrain elevation, the deep soil moisture distribution in the undulating terrain area is uneven. Especially at the boundary of the rotation area, it is easy to cause ineffective infiltration or local waterlogging of irrigation water, resulting in low irrigation efficiency and crop yields. Summary of the Invention

[0004] The present invention provides an intelligent irrigation control method and system for high-standard farmland to solve the problems in the prior art that it is impossible to accurately match the root water absorption requirements of newly planted crops, it is easy to cause ineffective infiltration or local waterlogging of irrigation water at the boundary of the rotation area, resulting in low irrigation efficiency and crop yields, etc.

[0005] In the first aspect, the present invention provides an intelligent irrigation control method for high-standard farmland, including: Obtaining crop canopy image data and farmland terrain elevation data of different crop planting areas in high-standard farmland; Based on the morphological feature data in the crop canopy image data, determining the crop growth stage, and constructing a soil hydrodynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage; Based on the farmland terrain elevation data and soil porosity parameters, generating soil moisture migration parameters by using the soil hydrodynamics simulation model; Generate a pipe network pressure regulation instruction based on the soil moisture migration parameters and a preset irrigation strategy; According to the pipe network pressure regulation instruction, adjust the outlet pressure distribution of the intelligent irrigation pipe network to obtain an adjusted outlet pressure distribution, and generate an irrigation control instruction at the boundary of the crop rotation area according to the adjusted outlet pressure distribution.

[0006] Optionally, based on the morphological feature data in the crop canopy image data, determine the crop growth stage, and construct a soil hydrodynamics simulation model according to the root distribution depth interval and the theoretical water absorption rate coefficient corresponding to the crop growth stage, including: Extract the color distribution matrix, texture density parameter, and structural contour parameter of the crop canopy from each image frame of the crop canopy image data to generate morphological feature data of the crop canopy; Match a preset crop growth stage mapping relationship according to the expansion rate of the structural contour parameter and the main wavelength offset of the color distribution matrix in the morphological feature data to determine the crop growth stage; Select the root distribution depth interval and the theoretical water absorption rate coefficient per unit root length corresponding to the crop growth stage from a pre-stored crop root parameter database; Divide the farmland terrain elevation data into multiple grid cells, and bind the root distribution depth interval and elevation value to the corresponding grid cells to generate a geographical feature association table; Adjust the theoretical water absorption rate coefficient according to the geographical feature association table and a preset slope water absorption rate correction coefficient to generate an actual water absorption rate coefficient; Construct a soil hydrodynamics simulation model in a three-dimensional space coordinate system according to the actual water absorption rate coefficient.

[0007] Optionally, adjust the theoretical water absorption rate coefficient according to the geographical feature association table and a preset slope water absorption rate correction coefficient to generate an actual water absorption rate coefficient, including: Extract the slope angle corresponding to each grid cell from the geographical feature association table; Define correction coefficients corresponding to different slope angles according to a preset slope water absorption rate correction coefficient; Generate a first adjusted water absorption rate parameter based on the correction coefficient and the theoretical water absorption rate coefficient; If the parameter difference between adjacent soil layers within the same grid cell exceeds a preset layer difference threshold, reallocate the first adjusted water absorption rate parameter according to the root distribution depth interval to obtain a second adjusted water absorption rate parameter; If the difference in slope angles between adjacent grid cells exceeds the preset angle mutation threshold, recalculate the water absorption rate parameter after the second adjustment based on the average of the correction coefficients of the adjacent grid cells to obtain the actual water absorption rate coefficient corresponding to each grid cell.

[0008] Optionally, based on the farmland terrain elevation data and soil porosity parameters, use the soil hydrodynamics simulation model to generate soil water migration parameters, including: Define the pore connectivity parameters of each soil layer in the high-standard farmland according to the soil porosity parameters; Calculate the elevation difference between each grid cell and its adjacent grid cells to obtain a set of elevation differences, and use the direction of the maximum elevation difference in the set of elevation differences as the initial slope direction; Calculate the angular difference between the initial slope direction and the actual slope direction of the adjacent grid cells. If the angular difference exceeds the preset threshold, adjust the initial slope direction to the arithmetic mean of the actual slope directions to generate a set of slope parameters; Based on the set of slope parameters, assign slope direction weight coefficients to each soil layer, and superimpose the slope direction weight coefficients with the pore connectivity parameters of the corresponding soil layer to generate a water flow direction priority parameter corresponding to each grid cell; Calculate the seepage resistance coefficient corresponding to each soil layer in each grid cell according to the reciprocal relationship between the pore connectivity parameter and the preset resistance base value; Input the water flow direction priority parameter and the seepage resistance coefficient into the soil hydrodynamics simulation model to calculate the water migration amount and migration direction data between adjacent grid cells, and combine the water migration amount and migration direction data to generate soil water migration parameters.

[0009] Optionally, based on the set of slope parameters, assign slope direction weight coefficients to each soil layer, and superimpose the slope direction weight coefficients with the pore connectivity parameters of the corresponding soil layer to generate a water flow direction priority parameter corresponding to each grid cell, including: Extract the final slope direction of each grid cell from the set of slope parameters, and calculate the slope direction weight coefficient according to the angular value of the angle between the final slope direction and the due north direction of the geographic coordinate system; Assign pore connectivity weight coefficients to each soil layer according to the pore connectivity parameters; Superimpose the slope direction weight coefficient with the pore connectivity weight coefficient of the corresponding soil layer to generate an initial water flow direction priority parameter; Vertically weighted fusion is performed on the initial moisture flow direction priority parameters corresponding to each soil layer in the same grid cell to generate the moisture flow direction priority parameters to be verified. The moisture flow direction priority parameters to be verified for the target grid cell are corrected so that the difference in priority parameters between the target grid cell and adjacent grid cells does not exceed a preset smoothing threshold, and finally the moisture flow direction priority parameters corresponding to each grid cell are generated.

[0010] In a second aspect, the present invention provides an intelligent irrigation control system for high-standard farmland, including: An acquisition module for acquiring crop canopy image data and farmland terrain elevation data in different crop planting areas of high-standard farmland; A construction module for determining the crop growth stage based on the morphological feature data in the crop canopy image data, and constructing a soil hydrodynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage; A first generation module for generating soil moisture migration parameters by using the soil hydrodynamics simulation model based on the farmland terrain elevation data and soil porosity parameters; A second generation module for generating a pipe network pressure regulation instruction based on the soil moisture migration parameters and a preset irrigation strategy; An adjustment module for adjusting the outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation instruction to obtain the adjusted outlet pressure distribution, and generating an irrigation control instruction at the boundary of the crop rotation area according to the adjusted outlet pressure distribution.

[0011] In a third aspect, the present invention provides a computing device including a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute an intelligent irrigation control method for high-standard farmland according to any one of the first aspect.

[0012] In a fourth aspect, the present invention provides a computer storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, an intelligent irrigation control method for high-standard farmland according to any one of the first aspect is implemented.

[0013] In the present invention, crop canopy image data and farmland terrain elevation data of different crop planting areas in high-standard farmland are acquired; based on the morphological feature data in the crop canopy image data, the crop growth stage is determined, and according to the root distribution depth interval and the theoretical water absorption rate coefficient corresponding to the crop growth stage, a soil hydrodynamics simulation model is constructed; based on the farmland terrain elevation data and the soil porosity parameter, soil water movement parameters are generated by using the soil hydrodynamics simulation model; based on the soil water movement parameters and a preset irrigation strategy, a pipe network pressure regulation instruction is generated; according to the pipe network pressure regulation instruction, the outlet pressure distribution of the intelligent irrigation pipe network is adjusted to obtain the adjusted outlet pressure distribution, and according to the adjusted outlet pressure distribution, an irrigation control instruction at the boundary of the crop rotation area is generated. The technical solution provided by the present invention breaks through the limitation of traditional single data source, realizes the multi-dimensional data fusion of crop physiological characteristics and topographic spatial characteristics, and solves the problem of one-sidedness of irrigation strategies caused by data fragmentation in the prior art; by dynamically associating the canopy morphological characteristics with the root system parameters, a simulation model adapted to the crop growth stage is constructed, overcoming the accuracy defect of traditional models relying on static root system parameters; by coupling the synergistic effect of the terrain slope and the soil pore structure, the dynamic simulation of the water movement path is realized, breaking through the problem of path prediction distortion caused by the separation of terrain-soil parameters in the existing methods; based on the deviation analysis of the water movement state and the irrigation target, a reverse pipe network pressure regulation instruction is generated, solving the industry problem that traditional open-loop control cannot dynamically suppress water loss; by dynamically adapting the boundary pressure difference and the crop characteristic difference, the water competition in the multi-crop planting area is suppressed, breaking through the boundary irrigation conflict caused by fixed threshold control. Further, by fusing the crop canopy morphological characteristics and the root growth stage parameters, the defect of traditional models relying on single-dimensional data is solved; the geographical grid modeling and the slope correction strategy significantly improve the simulation accuracy of water movement in farmland with complex terrain; the water absorption rate coefficient is dynamically adjusted to adapt to the differences in the root system characteristics of different crops, suppressing the water competition and leakage risks in the rotation area.

[0014] These aspects or other aspects of the present invention will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of an intelligent irrigation control method for a high-standard farmland provided by an embodiment of the present invention; Figure 2A schematic structural diagram of an intelligent irrigation control system for high-standard farmland provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a computing device provided by an embodiment of the present invention. Detailed implementation manners

[0017] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0018] In some processes described in the specification, claims and above-mentioned drawings of the present invention, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present invention.

[0020] Figure 1 A flowchart of an intelligent irrigation control method for high-standard farmland provided by an embodiment of the present invention, as Figure 1 shown, the method includes: In the multi-crop rotation planting mode of high-standard farmland, the existing intelligent irrigation technology cannot dynamically match the accurate irrigation requirements of the root water absorption characteristics and topographic conditions during the growth periods of different crops. Specifically, although the existing solutions achieve automatic control through soil moisture sensors and meteorological data, they have the following core defects: 1. The dynamic changes in the root distribution depth and water absorption characteristics of rotation crops are not fully considered. Especially during the rotation switching stage, the water demand of the deep or shallow roots of the new crop cannot be accurately matched; 2. Only the irrigation duration is corrected based on the topographic elevation, and the water movement path is not simulated by combining the soil porosity and the depth of the active root layer, resulting in uneven distribution of deep soil moisture in the area with undulating terrain, and ineffective infiltration or local waterlogging is likely to occur at the boundary of the rotation area; 3. There is a lack of real-time identification of the crop growth stage, and the irrigation strategy cannot be dynamically adjusted to adapt to the changes in the water absorption patterns of the same crop during different growth periods (for example, shallow-rooted crop seedlings rely on surface water, and deep-rooted crop mature periods require deep water supply). To address the above problems, the present invention proposes: by obtaining crop canopy image data and combining morphological characteristics (such as leaf density, height, etc.) to intelligently identify the crop growth stage, and then accurately obtaining the root distribution depth and water absorption characteristics (such as water absorption rate, water potential gradient) at the corresponding stage; secondly, based on the farmland topographic elevation data and soil porosity parameters, a soil hydrodynamics simulation model is constructed to simulate the water movement path in the active root layer, and quantify the interaction between the topographic slope, soil permeability and irrigation water flow; furthermore, by comparing the deviation between the simulated water movement path and the preset irrigation strategy, a pipeline network pressure control instruction is dynamically generated to accurately adjust the outlet pressure distribution of the irrigation pipeline network. Especially at the boundary of the rotation area, the irrigation water flow rate and duration are optimized through the pressure gradient to avoid water competition or resource waste caused by differences in root depth and topographic transition. Based on this, the present invention provides an intelligent irrigation control method for high-standard farmland, such as Figure 1 , including: Step 101: Obtain crop canopy image data and farmland topographic elevation data of different crop planting areas in the high-standard farmland; In this step, the high-standard farmland refers to the high-standard farmland constructed through engineering measures such as land leveling, improvement of water conservancy facilities, and soil improvement, which has the capabilities of efficient irrigation, disaster prevention and reduction. The crop planting area refers to a specific area divided in the high-standard farmland for planting different crops (such as the wheat-corn rotation area), and the irrigation strategy needs to be matched with its growth requirements. The crop canopy image data refers to the three-dimensional structure data of the crop canopy obtained through optical sensors or laser scanners, including parameters such as leaf area index, canopy height, and leaf color distribution matrix. The farmland topographic elevation data is the three-dimensional terrain data of the farmland obtained through a geographic information system mapping system or an unmanned aerial vehicle mapping, which reflects the characteristics of the surface undulation, slope, and aspect.

[0021] In the embodiments of the present invention, first, crop canopy image data is obtained through a canopy image acquisition device (such as a laser scanner equipped with a fisheye lens) deployed in high-standard farmland, and at the same time, farmland terrain elevation data is obtained in combination with a geographic information system mapping system or unmanned aerial vehicle aerial photography technology. The canopy image data needs to include morphological characteristics such as texture density parameters, color distribution matrices, and heights, and the terrain elevation data needs to cover the three-dimensional terrain undulation information of the planting area; after data collection, it is transmitted to the cloud platform through the Internet of Things gateway for storage to ensure the real-time and integrity of subsequent analysis.

[0022] Step 102: Based on the morphological characteristic data in the crop canopy image data, determine the crop growth stage, and construct a soil hydrodynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage; In this step, the morphological characteristic data refers to the crop growth state parameters extracted from the crop canopy image data, such as leaf density, color, height, coverage, etc., which are used to judge the growth stage. The crop growth stage refers to the division stage of the crop life cycle (such as the seedling stage, heading stage, maturity stage), and the root distribution and water absorption requirements are significantly different in different stages. The root distribution depth refers to the vertical distribution range of the crop roots in the soil, such as shallow-rooted crops (0 - 30 cm) and deep-rooted crops (30 - 60 cm). The root water absorption characteristics refer to the rate of root water absorption, water potential gradient, and the response ability to different soil moisture conditions. The soil hydrodynamics simulation model refers to a mathematical model constructed based on the Richards equation and the Van Genuchten model, which is used to simulate the movement law of water in the soil.

[0023] In the embodiments of the present invention, first, morphological characteristic data (including the color distribution matrix, texture density parameters, and structural contour parameters of the crop canopy) in the crop canopy image data is extracted through an image processing algorithm (such as an improved Otsu threshold segmentation method); the crop growth stage is determined according to the expansion rate of the structural contour parameters and the main wavelength offset of the color distribution matrix in the morphological characteristic data; the root distribution depth interval (such as 0 - 30 cm for shallow-rooted crops and 30 - 60 cm for deep-rooted crops) and the theoretical water absorption rate coefficient per unit root length corresponding to the crop growth stage are selected from the pre-stored crop root parameter database; the theoretical water absorption rate coefficient is adjusted according to the root distribution depth interval, farmland terrain elevation data, and the preset slope water absorption rate correction coefficient to generate the actual water absorption rate coefficient, and based on this, a soil hydrodynamics simulation model is constructed in a three-dimensional space coordinate system to simulate the movement law of water in the root active layer.

[0024] Step 103: Based on the farmland terrain elevation data and soil porosity parameters, use the soil hydrodynamics simulation model to generate soil water movement parameters; In this step, the soil porosity parameter refers to a parameter reflecting the soil pore structure, such as total porosity and effective porosity, which is obtained by a soil three-phase instrument deployed on the farmland monitoring node. The crop root layer refers to the organ of the crop that absorbs water and nutrients, and its distribution depth and activity directly affect the formulation of irrigation strategies. The water migration path refers to the path of water infiltration and diffusion in the soil, which is jointly affected by the terrain slope, soil porosity parameter, and root distribution. The soil water migration parameter refers to the quantitative index output by the soil hydrodynamics simulation model, such as permeability coefficient, matrix suction, water saturation, etc.

[0025] In the embodiment of the present invention, first, the farmland terrain elevation data is divided into multiple grid cells, the elevation difference between each grid cell and its adjacent grid cell is calculated, and the direction of the largest elevation difference is selected as the initial slope direction; the angle difference between the initial slope direction and the actual slope direction of the adjacent grid cell is calculated. If the angle difference exceeds the preset threshold, the initial slope direction is adjusted to the arithmetic mean of the actual slope directions, and finally a slope parameter set is generated; based on this, a slope direction weight coefficient is assigned to each grid cell, and the pore connectivity parameters of each soil layer are superimposed to generate a water flow direction priority parameter; further, through the reciprocal relationship between the pore connectivity parameter and the preset resistance base value, the penetration resistance coefficient of each soil layer is calculated; finally, the water flow priority parameter and the penetration resistance coefficient are input into the soil hydrodynamics simulation model to output the soil water migration parameter.

[0026] Step 104: Generate a pipe network pressure regulation instruction based on the soil water migration parameter and the preset irrigation strategy; In this step, the preset irrigation strategy refers to an irrigation plan set based on historical data or experience, such as a fixed irrigation duration or irrigation triggered by a soil moisture threshold. The deviation amount refers to the difference value between the simulated water migration path and the preset strategy, which is used to guide the adjustment of irrigation parameters. The pipe network pressure regulation instruction refers to the control signal generated by the algorithm, which is used to adjust the outlet pressure distribution of the irrigation pipe network.

[0027] In the embodiment of the present invention, the water migration amount and the dominant direction of each grid cell are extracted from the soil water migration parameters, the absolute difference between them and the target value of the preset irrigation strategy is calculated to form a migration amount deviation parameter, and the direction deviation parameter is calculated based on the angle difference of the dominant direction. The two are weighted and fused to generate a comprehensive deviation index, and the corresponding pressure adjustment amplitude and direction are determined according to the preset pressure regulation mapping table. Based on this, a local pressure regulation instruction is generated. If the included angle of the pressure adjustment directions between adjacent cells exceeds the threshold, the adjustment amplitude is reallocated according to the water migration amount to optimize the instruction, and finally a pipe network pressure regulation instruction is generated.

[0028] Step 105: According to the pipe network pressure regulation instruction, adjust the outlet pressure distribution of the intelligent irrigation pipe network to obtain the adjusted outlet pressure distribution, and generate an irrigation control instruction at the boundary of the crop rotation area according to the adjusted outlet pressure distribution; In this step, the outlet pressure distribution refers to the pressure configuration of each outlet of the intelligent irrigation pipe network, which determines the irrigation water flow rate and penetration depth. The crop rotation area refers to the farmland area where multiple crops are rotated, and it is necessary to coordinate the irrigation conflicts between the root depths of different crops and the terrain conditions. The irrigation control instruction refers to the specific execution command containing parameters such as irrigation duration and irrigation water flow rate, which is sent to the field equipment to achieve precise irrigation.

[0029] In the embodiment of the present invention, at the boundary of the crop rotation area, extract the adjusted outlet pressure distribution of the grid units in the adjacent crop planting areas, and calculate the pressure difference on both sides of the boundary; combine the actual water absorption rate coefficients and soil porosity ratios on both sides to calculate the boundary irrigation correction amount; if there is a direction conflict, recalculate the boundary irrigation correction amount according to the preset priority weight of the crop growth stage to generate the target boundary irrigation correction amount; finally, generate an irrigation control instruction according to the target boundary irrigation correction amount to achieve precise regulation and promote the healthy growth of crops.

[0030] The embodiment of the present invention solves the problems of low irrigation efficiency and uneven water distribution caused by differences in root depths and terrain undulations in the multi-crop rotation area; specifically, it precisely matches the root water absorption characteristics to reduce the waste of deep or shallow water; combines the terrain elevation and porosity parameters to avoid ineffective infiltration or waterlogging at the rotation boundary; dynamically adjusts the irrigation parameters through the pressure regulation instruction to improve the water use efficiency.

[0031] The present invention provides a specific embodiment. Step 102, based on the morphological feature data in the crop canopy image data, determine the crop growth stage, and construct a soil hydrodynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage, which specifically includes the following steps: Step 201: Extract the color distribution matrix, texture density parameter, and structural contour parameter of the crop canopy from each image frame of the crop canopy image data to generate the morphological feature data of the crop canopy; In this step, the color distribution matrix refers to the statistical distribution of pixel values in each color channel of the crop canopy image data, including color proportion and spatial distribution characteristics, which are used to quantify the color change of the crop canopy. The texture density parameter refers to the density of texture changes in the local area of the image, which is calculated through the contrast feature of the gray-level co-occurrence matrix and reflects the surface roughness of the canopy. The structural contour parameter refers to the boundary shape of the canopy projection area, which is obtained through edge detection and curve fitting and is used to characterize the expansion form of the canopy.

[0032] In the embodiment of the present invention, pixel-level analysis is performed on each frame of image data of the crop canopy, the color channel values of each pixel point within the crop canopy coverage area are extracted, the distribution histogram of each color channel in the canopy area is calculated, and a color distribution matrix is obtained; based on the gray-level co-occurrence matrix algorithm, the contrast feature of the local area of the image is statistically analyzed, the density of texture changes per unit area is calculated, and a texture density parameter is obtained; the contour boundary point set of the canopy is identified through an edge detection algorithm, the contour curve is fitted and its curvature change is calculated, and a structural contour parameter is obtained; the difference degree of the color distribution matrix between adjacent frames (calculated by histogram cosine similarity) and the change rate of the texture density parameter (calculated by the difference in texture values between adjacent frames) are weighted and summed to generate morphological feature data reflecting the growth dynamics of the canopy.

[0033] Step 202: According to the expansion rate of the structural contour parameter and the main wavelength offset of the color distribution matrix in the morphological feature data, match the preset mapping relationship of crop growth stages to determine the crop growth stage; In this step, the expansion rate refers to the growth rate of the projected area of the structural contour parameter per unit time, which is calculated by dividing the difference in contour areas between adjacent frames by the time interval. The main wavelength offset refers to the cumulative change amount of the main color wavelength value in the color distribution matrix. The preset mapping relationship of crop growth stages refers to a predefined table or function that stores the corresponding relationships between the morphological features (expansion rate, wavelength offset) of different crop varieties and the growth stages (seedling stage, jointing stage, etc.).

[0034] In the embodiment of the present invention, time series analysis is performed on the morphological feature data. The difference in the diameter of the maximum inscribed circle of the structural contour parameter between two consecutive image frames is divided by the time interval to obtain the expansion rate of the structural contour parameter (unit: cm / day); the color distribution matrix is subjected to Fourier transform, the wavelength value corresponding to the peak of the energy spectrum is taken, and the difference in this wavelength value between adjacent frames is calculated as the main wavelength offset; the expansion rate and the main wavelength offset are input into the predefined mapping table of crop growth stages. For example, the maize growth stage table includes the seedling stage (expansion rate ≤ 0.5 cm / day, main wavelength offset ≤ 10 nm), the jointing stage (0.5 cm / day < expansion rate ≤ 2 cm / day, 10 nm < offset ≤ 30 nm), and the tasseling stage (expansion rate > 2 cm / day, offset > 30 nm). The closest expansion rate interval and wavelength offset threshold are matched through the nearest neighbor matching algorithm to determine the current crop growth stage.

[0035] Step 203: Select the root distribution depth interval and the theoretical water absorption rate coefficient per unit root length corresponding to the crop growth stage from the pre-stored crop root parameter database; In this step, the pre-stored crop root parameter database refers to a structured data set storing the vertical distribution range and water absorption capacity of roots of different crop varieties at each growth stage. The root distribution depth interval refers to the range of the active root area divided according to the vertical stratification of the soil (such as 0 - 30 cm, 30 - 60 cm). The theoretical water absorption rate coefficient refers to the water absorption amount per unit length of roots measured under standard experimental conditions (no terrain slope, homogeneous soil), which reflects the theoretical water absorption capacity of the crop and is calibrated through water absorption experiments in a laboratory-controlled environment.

[0036] In the embodiment of the present invention, according to the crop growth stage, the pre-stored crop root parameter database is accessed, and the corresponding root distribution depth interval (such as 0 - 50 cm at the jointing stage of corn, 0 - 80 cm at the tasseling stage) and the water absorption rate coefficient per unit root length (such as the water absorption rate of shallow roots is 0.8 L / (cm·d), and that of deep roots is 0.3 L / (cm·d)) are called.

[0037] Step 204: Divide the farmland terrain elevation data into multiple grid cells, and bind the root distribution depth interval and elevation value to the corresponding grid cells to generate a geographical feature association table. In this step, the geographical feature association table refers to a structured data table containing geographical coordinates, elevation, slope angle, and corresponding root parameters.

[0038] In the embodiment of the present invention, the longitude and latitude coordinates of the farmland are divided into regular geographical coordinate grid cells with a preset resolution of 10 m × 10 m through a geographic information system. The geographical range of each cell is determined by the starting and ending values of longitude and latitude, and a division data table containing all grid geographical boundary information is generated. According to the root distribution depth interval of different crop growth stages, for example, 20 - 60 cm at the jointing stage of corn, the soil is vertically divided into a shallow active area, a middle expansion area, and a deep stable area, and they are respectively marked with level identifiers L1, L2, and L3. The elevation value of the center point of each grid is obtained through real-time kinematic carrier-phase differential technology and is bound to the longitude and latitude coordinates of the corresponding grid to form a parameter table containing grid coordinates and elevation values. Finally, the grid division data table, the root distribution level identifier, and the elevation parameter table are associated, the unique number of each grid cell is bound to the root level identifier, and the elevation value is corresponding to each level depth range to generate an association table integrating geographical coordinates, elevation data, and root distribution characteristics.

[0039] Step 205: Adjust the theoretical water absorption rate coefficient according to the geographical feature association table and a preset slope water absorption rate correction coefficient to generate an actual water absorption rate coefficient. In this step, the preset slope water absorption rate correction coefficient refers to the proportional parameter for adjusting the root water absorption rate according to the elevation gradient slope angle. For example, the slope water absorption rate correction coefficient corresponding to the elevation gradient slope angle α is 1 - 0.02α. The actual water absorption rate coefficient refers to the value obtained by correcting the theoretical water absorption rate coefficient according to the actual terrain slope of the farmland, reflecting the actual root water absorption capacity under terrain constraints.

[0040] In the embodiment of the present invention, the elevation gradient slope angles of each coordinate point in the geographical feature association table are read, and the theoretical water absorption rate coefficient is adjusted gradientually according to the slope water absorption rate correction coefficient table (which stores the corresponding relationship between the elevation gradient slope angle and the adjustment ratio of the root water absorption rate. For example, for every 5° increase in slope, the water absorption rate of shallow roots decreases by 10%) to generate the actual water absorption rate coefficient.

[0041] Step 206: Construct a soil hydrodynamics simulation model in the three-dimensional space coordinate system according to the actual water absorption rate coefficient.

[0042] In the embodiment of the present invention, with the three-dimensional space coordinate system (the X - Y - Z axes correspond to geographical coordinates and the depth direction respectively) as the framework, taking the actual water absorption rate coefficient as the boundary condition, combining the soil hydrodynamics equation (such as the Richards equation) and the van Genuchten model parameters (such as the relationship between soil matrix suction and water content), a soil hydrodynamics simulation model is constructed. The model solves the spatio-temporal distribution of water movement through the finite element method, predicts the water infiltration path, saturation change and effective absorption amount in the root layer under different irrigation strategies, and provides a scientific basis for the regulation of pipe network pressure.

[0043] The embodiment of the present invention accurately determines the crop growth stage through morphological feature data and matches the root parameters; realizes the three-dimensional coupling simulation of terrain, roots and water through geographical coordinate mapping and slope angle correction; dynamically adjusts the pipe network pressure according to the deviation amount of the water movement path, and inhibits the water competition in the rotation area.

[0044] The present invention provides a specific embodiment. In step 205, according to the geographical feature association table and the preset slope water absorption rate correction coefficient, the theoretical water absorption rate coefficient is adjusted to generate the actual water absorption rate coefficient, which specifically includes the following steps: Step 211: Extract the slope angle corresponding to each grid unit from the geographical feature association table; In this step, the slope angle refers to the surface tilt angle of the high-standard farmland, which is calculated by the elevation difference between adjacent grid units and reflects the influence degree of the terrain on water migration.

[0045] In the embodiment of the present invention, the slope data of each geographic coordinate grid cell stored in the geographic feature association table is parsed to obtain the corresponding slope angle value. The slope angle is obtained by calculating the elevation difference between adjacent grid cells (the elevation of the current grid minus the elevation of the adjacent grid on the east / south side) and then taking the tangent value in the direction of the maximum difference. For example, the elevation difference on the east side of grid cell G1002 is +0.5 m, and on the south side is -0.3 m. Selecting the east side difference direction, the slope angle = arctan(0.5 / 10) = 2.86° (assuming a horizontal spacing of 10 m).

[0046] Step 212: Define correction factors corresponding to different slope angles according to a preset slope water absorption rate correction factor; In this step, the preset slope water absorption rate correction factor refers to a mapping relationship table predefined between the slope angle and the attenuation ratio of the water absorption rate, which is used to quantify the inhibitory effect of the slope on the root water absorption capacity. The correction factor refers to the attenuation ratio value (0.0 - 1.0) obtained by looking up the table according to the slope angle of the current grid cell, and is used to adjust the theoretical water absorption rate.

[0047] In the embodiment of the present invention, according to the preset slope water absorption rate correction factor, the decreasing ratio of the correction factor corresponding to each 1-degree increase in the slope angle is 2%. For example, the correction factor corresponding to a slope angle of 5° = 1 - 5×0.02 = 0.9, and the correction factor corresponding to a slope angle of 10° = 1 - 10×0.02 = 0.8. Determine the correction factor corresponding to the slope angle of the current grid cell by looking up the table.

[0048] Step 213: Generate the water absorption rate parameter after the first adjustment based on the correction factor and the theoretical water absorption rate coefficient; In this step, the water absorption rate parameter after the first adjustment refers to the preliminary correction value generated by multiplying the theoretical water absorption rate coefficient by the correction factor.

[0049] In the embodiment of the present invention, multiply the theoretical water absorption rate coefficient (such as 0.12 cm³ / cm·h for the shallow layer) by the correction factor of the corresponding grid cell to generate the water absorption rate parameter after the first adjustment. For example, the correction factor corresponding to a slope angle of 8° is 0.84, and the adjusted parameter for the shallow layer = 0.12×0.84 = 0.1008 cm³ / cm·h.

[0050] Step 214: If the parameter difference between adjacent soil layers in the same grid cell exceeds the preset layer difference threshold, reallocate the water absorption rate parameter after the first adjustment according to the root distribution depth interval to obtain the water absorption rate parameter after the second adjustment; In this step, the parameter difference refers to the difference between the water absorption rate parameters after the first adjustment of different soil layers within the same grid cell, which is used to trigger vertical optimization. The preset layer difference threshold refers to the maximum critical value of the parameter difference allowed between adjacent soil layers (such as 0.02 cm³ / cm·h), and if it is exceeded, reallocation is required.

[0051] In the embodiment of the present invention, the preset layer difference threshold is 0.02 cm³ / cm·h. If the difference 0.0158 between the shallow layer parameter 0.1008 and the middle layer parameter 0.085 exceeds the preset layer difference threshold, the water absorption rate parameters after the first adjustment are reallocated according to the length ratio of the root distribution depth intervals to obtain the water absorption rate parameters after the second adjustment. For example, the shallow layer is 0 - 30 cm (length 30 cm), the middle layer is 30 - 60 cm (30 cm), the length ratio is 1:1, and the adjusted shallow layer parameter = (0.1008 + 0.085) / 2 = 0.0929 cm³ / cm·h.

[0052] Step 215: If the slope angle difference between adjacent grid cells exceeds the preset angle mutation threshold, then recalculate the water absorption rate parameters after the second adjustment according to the average value of the correction coefficients of the adjacent grid cells to obtain the actual water absorption rate coefficient corresponding to each grid cell; In this step, the slope angle difference refers to the absolute difference between the slope angles of adjacent grid cells, which is used to judge terrain mutation. The preset angle mutation threshold refers to the maximum critical value of the slope angle difference allowed between adjacent grid cells (such as 5°), and if it is exceeded, spatial smoothing correction is triggered. The average value of the correction coefficients refers to the arithmetic average of the correction coefficients between adjacent grid cells, which is used for spatial consistency optimization.

[0053] In the embodiment of the present invention, the preset angle mutation threshold is 5°. If the slope angle of grid cell G1002 is 8° and the slope angle of the adjacent cell G1003 is 14°, and the difference 6° exceeds the preset angle mutation threshold, then take the average value of the correction coefficients of the adjacent grid cells (such as the correction coefficient of G1003 is 0.72, the correction coefficient of G1002 is 0.84, and the average value of the correction coefficients is 0.78), and recalculate the actual water absorption rate coefficient = the water absorption rate parameter after the second adjustment × 0.78.

[0054] The embodiment of the present invention accurately suppresses the water loss on the slope by the correction coefficient driven by the slope angle, improves the water-saving efficiency and terrain adaptability; the vertical verification mechanism ensures that the water absorption rates of different soil layers conform to the root distribution law; the angle mutation threshold suppresses parameter jumps and improves the stability of the pipe network pressure control.

[0055] The present invention provides a specific embodiment. Step 103, based on the farmland terrain elevation data and soil porosity parameters, use the soil water dynamics simulation model to generate soil water migration parameters, which specifically include the following steps: Step 301: Define the pore connectivity parameters of each soil layer in the high-standard farmland according to the soil porosity parameters; In this step, the soil porosity parameter refers to the proportion of the pore volume in the unit volume of soil, which is obtained through soil sampling and testing and reflects the soil water permeability. The pore connectivity parameter refers to the parameter characterizing the connection ability of the pore channels in the soil layer and is generated based on the porosity correction.

[0056] In the embodiment of the present invention, the soil is vertically stratified into a shallow layer (0 - 30 cm), a middle layer (30 - 60 cm), and a deep layer (60 - 100 cm). The pore connectivity parameters of each layer are calculated based on the soil porosity parameters. Specifically, the shallow-layer pore connectivity = porosity × 0.8 (compaction coefficient correction), the middle layer = porosity × 0.6, and the deep layer = porosity × 0.4; for example, the connectivity of the shallow layer with a soil porosity parameter of 0.35 is 0.35 × 0.8 = 0.28.

[0057] Step 302: Calculate the elevation difference between each grid cell and its adjacent grid cells to obtain an elevation difference set, and use the direction of the maximum elevation difference in the elevation difference set as the initial slope direction; In this step, the maximum elevation difference refers to the maximum value of the absolute values of the elevation differences between the current grid cell and its adjacent grid cells in the east and south directions, and is used to determine the initial slope direction. The initial slope direction refers to the preliminary water flow tendency direction determined based on the direction of the maximum elevation difference.

[0058] In the embodiment of the present invention, for each grid cell, calculate the elevation difference between the current cell and its adjacent grid cell in the east direction (the elevation of the current cell minus the elevation of the east cell) and the elevation difference between the current cell and its adjacent grid cell in the south direction (the elevation of the current cell minus the elevation of the south cell), respectively, to generate an elevation difference set containing the eastward difference and the southward difference. Compare the absolute values of the two differences and select the direction with the largest absolute value as the initial slope direction. For example, if the eastward difference is +0.5 m and the southward difference is -0.3 m, the initial slope direction is eastward.

[0059] Step 303: Calculate the angular difference between the initial slope direction and the actual slope direction of the adjacent grid cells. If the angular difference exceeds the preset threshold, adjust the initial slope direction to the arithmetic mean of the actual slope direction to generate a slope parameter set; In this step, the actual slope direction refers to the slope direction of the adjacent grid cells after calibration and adjustment. The angular difference refers to the included angle between the two slope directions and is used to verify the rationality of the direction. The preset threshold refers to the set critical value of the angular difference (such as 45°), and if it is exceeded, the direction adjustment is triggered. The slope parameter set refers to the data set storing the final slope directions of all grid cells.

[0060] In the embodiments of the present invention, the initial slope direction of the current grid cell (such as 30° north of east) is calculated for the angular difference with the actual slope directions of the adjacent grid cells on the east and south sides (such as 15° south of east for the east-side grid cell and 45° east of south for the south-side cell). If the included angle between the actual slope direction of any adjacent cell and the initial slope direction exceeds the preset threshold, the initial slope direction is adjusted to the arithmetic mean of the actual slope directions of the adjacent grid cells. For example, the average slope direction of the 15° for the east-side grid cell and 45° for the south-side grid cell is 30°, and the adjusted slope direction is generated and stored in the slope parameter set.

[0061] Step 304: Based on the slope parameter set, assign slope direction weight coefficients to each soil layer, and superimpose the slope direction weight coefficients with the pore connectivity parameters of the corresponding soil layer to generate a moisture flow direction priority parameter corresponding to each grid cell; In this step, the soil layer refers to the soil levels (shallow layer, middle layer, deep layer) divided by vertical depth. The slope direction weight coefficient refers to the weight value assigned according to the size of the slope angle and is used for priority calculation. The moisture flow direction priority parameter refers to the moisture migration tendency value that comprehensively combines the slope direction weight coefficient and the pore connectivity parameter.

[0062] In the embodiments of the present invention, according to the slope directions of each grid cell in the slope parameter set, weight coefficients are assigned according to the size of the slope angle (such as a slope angle of 10° corresponding to a weight of 0.7 and 5° corresponding to 0.5). The weight coefficient is superimposed with the pore connectivity parameter of the soil layer (such as 0.6 for the shallow layer and 0.4 for the middle layer) according to a preset ratio (slope weight 70% + pore weight 30%) to generate a moisture flow direction priority parameter. For example, slope weight 0.7×70% + pore weight 0.6×30% = 0.67.

[0063] Step 305: According to the reciprocal relationship between the pore connectivity parameter and the preset resistance base value, calculate the permeability resistance coefficient corresponding to each soil layer in each grid cell; In this step, the preset resistance base value refers to the reference resistance value set according to the soil type (such as a base value of 1.2 for sandy loam and 2.5 for clay) and is used to calculate the permeability resistance coefficient. The permeability resistance coefficient refers to the quantified value of the resistance force when water penetrates the soil layer and is inversely proportional to the pore connectivity.

[0064] In the embodiments of the present invention, the permeability resistance coefficient = preset resistance base value / pore connectivity parameter. For example, the resistance coefficient corresponding to the pore connectivity of 0.28 in the shallow layer = 1.2 / 0.28 ≈ 4.29. This formula shows that the higher the pore connectivity, the smaller the permeability resistance and the smoother the water flow.

[0065] Step 306: Input the water flow direction priority parameter and the osmotic resistance coefficient into the soil hydrodynamics simulation model to calculate the water migration amount and migration direction data between adjacent grid cells. Combine the water migration amount and migration direction data to generate soil water transport parameters.

[0066] In this step, the water migration amount refers to the volume of water transferred between adjacent grid cells per unit time. The migration direction data refers to the dominant direction information of water migration (such as 30° north of east).

[0067] In the embodiment of the present invention, the water flow direction priority parameter and the osmotic resistance coefficient are input into the soil hydrodynamics simulation model. By solving the Richards equation, the water migration amount (such as calculating the flow rate through the difference equation) and migration direction (such as selecting the dominant direction based on the water flow direction priority parameter, such as 70% eastward and 30% southward) between adjacent grid cells are calculated. The migration directions and water migration amounts of all grid cells are summarized, and finally soil water transport parameters are generated to guide irrigation optimization.

[0068] The embodiment of the present invention solves the problem of parameter fragmentation in traditional models through grid-based terrain-soil parameter coupling (asymmetric superposition of slope direction and pore connectivity); the migration allocation ratio driven by the priority parameter improves the accuracy of path simulation.

[0069] The present invention provides a specific embodiment. In step 305, based on the slope parameter set, a slope direction weight coefficient is assigned to each soil layer, and the slope direction weight coefficient is superimposed on the pore connectivity parameter of the corresponding soil layer to generate a water flow direction priority parameter corresponding to each grid cell, which specifically includes the following steps: Step 311: Extract the final slope direction of each grid cell from the slope parameter set, and calculate the slope direction weight coefficient according to the angle value between the final slope direction and the true north direction of the geographic coordinate system. In this step, the final slope direction refers to the slope direction after being adjusted by angle difference verification, which reflects the dominant influence direction of the terrain on water flow. The true north direction of the geographic coordinate system refers to the true north direction defined based on the WGS84 geographic coordinate system, which has nothing to do with the magnetic north direction and is used to unify the angle calculation benchmark. The angle value refers to the clockwise rotation angle value between the final slope direction and the true north direction of the geographic coordinate system, which is used to quantify the geographical orientation of the slope direction. The slope direction weight coefficient refers to the weight value generated by the ratio of the angle value to the maximum slope angle, which is used to characterize the influence intensity of the slope on water flow.

[0070] In the embodiment of the present invention, the final slope direction of each grid cell is obtained through the slope parameter set (such as 25° east-south), with the due north direction in the geographic coordinate system (the due north benchmark of the WGS84 coordinate system) as the 0° benchmark, and the included angle value between the final slope direction and the due north direction is measured clockwise (such as 155° corresponding to 25° east-south); the slope direction weight coefficient is generated by dividing the included angle value by the preset maximum slope angle (such as 90°), and if the calculation result exceeds 1.0, it is truncated to 1.0; for example, when the included angle value is 120°, the slope direction weight coefficient = 120 / 90 = 1.33, which is truncated to 1.0.

[0071] Step 312: Assign a pore connectivity weight coefficient to each soil layer according to the pore connectivity parameter; In this step, the pore connectivity weight coefficient refers to the weight value generated according to the pore connectivity parameter and the layer correction coefficient of the soil layer, reflecting the water penetration ability of different soil layers.

[0072] In the embodiment of the present invention, the pore connectivity parameters of the shallow, middle, and deep soil layers (such as 0.35 for the shallow layer, 0.25 for the middle layer, and 0.15 for the deep layer) are respectively multiplied by the preset layer correction coefficients (0.8 for the shallow layer, 0.6 for the middle layer, and 0.4 for the deep layer) to generate the pore connectivity weight coefficients. For example, the pore connectivity weight coefficient of the shallow layer = 0.35×0.8 = 0.28, the middle layer = 0.25×0.6 = 0.15, and the deep layer = 0.15×0.4 = 0.06.

[0073] Step 313: Superimpose the slope direction weight coefficient and the pore connectivity weight coefficient of the corresponding soil layer to generate an initial water flow direction priority parameter; In this step, the initial water flow direction priority parameter refers to the preliminary priority parameter generated after superimposing the slope direction weight coefficient and the pore connectivity weight coefficient, and has not undergone vertical fusion and smoothing verification.

[0074] In the embodiment of the present invention, the slope direction weight coefficient (accounting for 70%) and the pore connectivity weight coefficient of the corresponding soil layer (accounting for 30%) are superimposed according to a preset ratio. For example, if the slope direction weight coefficient is 1.0 and the shallow layer pore weight is 0.28, then the shallow layer priority parameter = 1.0×0.7 + 0.28×0.3 = 0.7 + 0.084 = 0.784.

[0075] Step 314: Perform vertical weighted fusion on the initial water flow direction priority parameters corresponding to each soil layer within the same grid cell to generate a water flow direction priority parameter to be verified; In this step, the water flow direction priority parameter to be verified refers to the intermediate priority parameter that has undergone vertical weighted fusion but has not been verified for adjacent cells.

[0076] In the embodiment of the present invention, the initial moisture flow direction priority parameters of each soil layer are weighted and summed according to the vertical stratification weights. For example, if the shallow layer weight is 0.784, the middle layer is 0.15, and the deep layer is 0.06, then the comprehensive priority = 0.784×0.6 + 0.15×0.3 + 0.06×0.1 = 0.470 + 0.045 + 0.006 = 0.521.

[0077] Step 315: Modify the moisture flow direction priority parameter to be verified for the target grid cell so that the difference in priority parameters between the target grid cell and adjacent grid cells does not exceed a preset smoothing threshold, and finally generate the moisture flow direction priority parameter corresponding to each grid cell; In this step, the priority parameter difference refers to the absolute difference in priority parameters between adjacent grid cells, which is used to determine whether smoothing correction is required. The preset smoothing threshold refers to the maximum critical value allowing the difference in priority parameters between adjacent grid cells (such as 0.1), and if it is exceeded, correction is triggered.

[0078] In the embodiment of the present invention, calculate the priority parameter difference between the target grid cell and its adjacent cells to the east and south (for example, for the target grid cell 0.521 and the grid cell to the east 0.48, the priority parameter difference is 0.041). If the priority parameter difference exceeds the preset smoothing threshold (such as 0.1), then adjust the moisture flow direction priority parameter to be verified for the target grid cell to the moving average of the moisture flow direction priority parameters of the adjacent grid cells (such as for the target grid cell 0.521, it corresponds to (0.521 + 0.48) / 2 = 0.5005); finally generate the moisture flow direction priority parameter corresponding to each grid cell after smoothing verification.

[0079] The embodiment of the present invention improves the accuracy of the moisture migration path through the dynamic coupling of the slope direction and pore connectivity, achieving terrain-soil collaborative optimization; the shallow layer dominant weight distribution conforms to the law of crop root water absorption; the preset smoothing threshold suppresses parameter mutations and reduces the instability of the simulation results.

[0080] The present invention provides a specific embodiment. Step 104, based on the soil moisture migration parameters and a preset irrigation strategy, generate a pipeline network pressure regulation instruction, which specifically includes the following steps: Step 401: Extract the moisture migration amount and the dominant migration direction of each grid cell from the soil moisture migration parameters; In this step, the moisture migration amount refers to the volume of moisture transferred between adjacent grid cells per unit time (cm³ / day), which reflects the intensity of moisture migration. The dominant migration direction refers to the flow direction with the largest moisture migration amount, which is determined by analyzing the distribution of moisture migration amounts in adjacent grid cells (such as 30° east of south).

[0081] In the embodiment of the present invention, by analyzing the water migration parameters, the water migration amount (such as the daily average migration amount of 15 cm³) and the dominant migration direction (such as 30° south by east) of each grid unit are extracted.

[0082] Step 402: Calculate the absolute difference between the water migration amount and the target migration amount corresponding in the preset irrigation strategy to generate a migration amount deviation parameter; In this step, the preset irrigation strategy refers to a set of target irrigation parameters preset according to the water requirement of the crop and the soil characteristics. The target migration amount refers to the target value of the water migration amount required in the preset irrigation strategy (such as 20 cm³ / day). The migration amount deviation parameter refers to the deviation percentage between the water migration amount and the target migration amount (5 / 20×100% = 25%).

[0083] In the embodiment of the present invention, the difference between the water migration amount (15 cm³) and the target migration amount (20 cm³) in the preset irrigation strategy is calculated, and the migration amount deviation parameter = |15 - 20| / 20×100% = 25%. The target migration amount is calculated based on the water requirement of the crop and the soil water holding capacity.

[0084] Step 403: Calculate a direction deviation degree parameter according to the angle difference between the dominant migration direction and the target migration direction in the preset irrigation strategy; In this step, the target migration direction refers to the water migration direction required in the preset irrigation strategy (such as due east). The angle difference refers to the minimum included angle between the dominant migration direction and the target migration direction (30°). The direction deviation degree parameter refers to the ratio of the angle difference to 180° (30 / 180≈0.167), which quantifies the degree of direction deviation.

[0085] In the embodiment of the present invention, the dominant migration direction is 30° south by east, the preset target migration direction is due east (0°), and the angle difference = 30°. The direction deviation degree parameter = 30° / 180° = 0.167, which reflects the degree of deviation of the water migration direction from the target migration direction.

[0086] Step 404: Perform weighted fusion on the migration amount deviation parameter and the direction deviation degree parameter to generate a comprehensive deviation index; In this step, the comprehensive deviation index refers to the weighted fusion value (0.225) of the migration amount deviation parameter and the direction deviation degree parameter, which is used for pressure regulation decision-making.

[0087] In the embodiment of the present invention, the weight of the migration amount deviation parameter accounts for 70% (0.25×0.7 = 0.175), the weight of the direction deviation degree parameter accounts for 30% (0.167×0.3≈0.05), and the calculated comprehensive deviation index = 0.175 + 0.05 = 0.225.

[0088] Step 405: Determine the pressure adjustment amplitude and pressure adjustment direction corresponding to the comprehensive deviation index according to a preset pressure regulation mapping table; In this step, the preset pressure regulation mapping table defines the corresponding relationship between the comprehensive deviation index and the pressure adjustment parameter (for example, 0.2 corresponds to +5 kPa). The pressure adjustment amplitude indicates the pressure value that the outlet pressure needs to be adjusted (such as +5 kPa). The pressure adjustment direction indicates the spatial direction of the pressure adjustment (opposite to the water migration direction).

[0089] In the embodiment of the present invention, by querying the preset pressure regulation mapping table, it is obtained that the comprehensive deviation index of 0.225 corresponds to a pressure adjustment amplitude of +5 kPa, and the pressure adjustment direction is opposite to the dominant migration direction (for example, the pressure adjustment direction of east by south 30° corresponds to the dominant migration direction of west by north 30°).

[0090] Step 406: Based on the pressure adjustment amplitude and pressure adjustment direction, generate local pressure regulation instructions corresponding to each network unit. If the included angle between the pressure adjustment directions of adjacent grid units exceeds a preset conflict threshold, then re - distribute the pressure adjustment amplitudes of the adjacent grid units according to the water migration amount to optimize the local pressure regulation instructions and generate pipeline network pressure regulation instructions.

[0091] In this step, the local pressure regulation instruction refers to the pressure adjustment parameter including a single grid unit (such as +5 kPa in the direction of west by north 30°). The preset conflict threshold refers to the maximum included angle allowed between the pressure adjustment directions of adjacent grid units (such as 15°), and if it is exceeded, re - distribution is required.

[0092] In the embodiment of the present invention, the pressure adjustment direction of adjacent grid unit A is west by north 30°, and the pressure adjustment direction of unit B is west by north 10°. The included angle between the directions is 20° > the preset conflict threshold of 15°. Re - distribute the pressure increase amplitude according to the proportion of the water migration amount (unit A accounts for 60% and unit B accounts for 40%): the pressure adjustment of unit A is +5 kPa × 60% = +3 kPa, and the adjustment of unit B is +5 kPa × 40% = +2 kPa.

[0093] The embodiment of the present invention improves the irrigation efficiency through the fusion of the two - dimensional deviation of the water migration amount and the dominant migration direction; designs the pressure adjustment direction opposite to the water migration to inhibit the water loss on the slope; and ensures the smooth transition of the pipeline network pressure through the preset conflict threshold check, reducing equipment loss.

[0094] The present invention provides a specific embodiment. In step 105: Generate irrigation control instructions at the boundary of the crop rotation area according to the adjusted outlet pressure distribution, which specifically includes the following steps: Step 501: At the boundary of the crop rotation area, extract the adjusted outlet pressure distribution of the grid cells corresponding to adjacent crop planting areas, where the adjusted outlet pressure distribution includes the pressure values of the grid cells on both sides of the boundary of the crop rotation area. In this step, the pressure value refers to the pressure value (unit: kPa) of the adjusted outlet pressure distribution, which reflects the driving force of the pipe network for water migration.

[0095] In the embodiment of the present invention, the geographical coordinate grid cells at the boundary of the crop rotation area are located through a geographic information system (such as grid G2001 is a corn field and G2002 is a soybean field), and the pressure values (such as the pressure of G2001 is 120 kPa and the pressure of G2002 is 105 kPa) and the water migration direction (such as the water in the corn field migrates east-southeast and the water in the soybean field migrates east-northeast) of the grid cells on both sides of the boundary are extracted from the adjusted outlet pressure distribution to generate the adjusted outlet pressure distribution.

[0096] Step 502: According to the adjusted outlet pressure distribution, calculate the pressure difference between the grid cells on both sides of the boundary, and combine the actual water absorption rate coefficients and the soil porosity ratios of the crop planting areas on both sides of the boundary to calculate the boundary irrigation correction amount, where the boundary irrigation correction amount includes an irrigation duration correction coefficient and a water flow rate correction coefficient. In this step, the pressure difference refers to the absolute difference between the pressure values of the grid cells on both sides of the boundary (such as 15 kPa), which is used to quantify the adjustment intensity of irrigation parameters. The soil porosity ratio refers to the ratio of the soil porosity parameters of the crop planting areas on both sides of the boundary (corn field / soybean field = 1.4), which reflects the difference in soil permeability. The boundary irrigation correction amount refers to the adjustment value calculated based on the pressure difference, the actual water absorption rate coefficient, and the soil porosity parameters, including the duration and water flow rate correction coefficients. The irrigation duration correction coefficient refers to the percentage of the irrigation time that needs to be adjusted, which is calculated based on the ratio of the pressure difference to the actual water absorption rate coefficient. The water flow rate correction coefficient refers to the percentage of the water flow rate that needs to be adjusted, which is calculated based on the ratio of the pressure difference to the soil porosity ratio.

[0097] In the embodiment of the present invention, calculate the pressure difference on both sides of the boundary (120 - 105 = 15 kPa), combine the actual water absorption rate coefficient of the corn field (0.18 cm³ / cm·h) with the ratio of the soybean field (0.12 cm³ / cm·h) (0.18 / 0.12 = 1.5), and the ratio of soil porosities (corn field 0.35 / soybean field 0.25 = 1.4) to generate the boundary irrigation correction amount. For example, the irrigation duration correction coefficient = (pressure difference 15 kPa / preset reference pressure 100 kPa) × ratio of actual water absorption rate coefficient 1.5 = 0.15 × 1.5 = 0.225; the water flow rate correction coefficient = (15 / 100) × ratio of soil porosities 1.4 = 0.15 × 1.4 = 0.21. Output result: The irrigation duration of the corn field needs to be reduced by 22.5% and the water flow needs to be reduced by 21%; the soybean field needs to increase by the corresponding proportion.

[0098] Step 503: If there is a direction conflict in the boundary irrigation correction amounts on both sides of the boundary, recalculate the boundary irrigation correction amounts on both sides of the boundary according to the preset priority weights of the crop growth stages on both sides of the boundary to generate the target boundary irrigation correction amount, so as to generate an irrigation control instruction at the boundary of the crop rotation area according to the target boundary irrigation correction amount. The preset priority weights are set according to the importance levels of the critical crop growth periods; In this step, the direction conflict means that the adjustment directions of the boundary irrigation correction amounts on both sides of the boundary are opposite (such as one side needs to increase the time and the other side needs to decrease the time). The preset priority weights refer to the weight ratios set according to the critical crop growth periods (such as 70% for the tasseling stage and 30% for the flowering stage) and are used for conflict resolution. The target boundary irrigation correction amount refers to the final correction value after being allocated by the preset priority weights and directly drives the irrigation equipment. The critical crop growth period refers to the growth stage when the crop is most sensitive to water (such as the tasseling stage and the flowering stage), which determines the regulation priority. The importance level refers to the critical period level set based on agricultural expert knowledge (such as level 1 is the highest) and is used for weight allocation.

[0099] In the embodiment of the present invention, if the corn field needs to reduce the irrigation duration while the soybean field needs to increase it, allocate according to the preset priority weights (70% for the corn tasseling stage and 30% for the soybean flowering stage): For example, the target boundary irrigation correction amount of the corn field: the target irrigation duration correction coefficient = -22.5% × 70% ≈ -15.75%, the target water flow rate correction coefficient = -21% × 70% ≈ -14.7%; the target boundary irrigation correction amount of the soybean field: the target irrigation duration correction coefficient = +22.5% × 30% ≈ +6.75%, the target water flow rate correction coefficient = +21% × 30% ≈ +6.3%.

[0100] The embodiment of the present invention dynamically corrects the irrigation amount through the pressure difference and crop and soil parameters, reduces the water competition in the rotation area; the preset priority weights ensure the irrigation priority of the main food crops and realize intelligent conflict resolution.

[0101] Figure 2 The following is a schematic structural diagram of an intelligent irrigation control system for high-standard farmland provided by an embodiment of the present invention. As Figure 2 shown, the system includes: An acquisition module 21, configured to acquire crop canopy image data and farmland terrain elevation data of different crop planting areas in high-standard farmland; A construction module 22, configured to determine the crop growth stage based on the morphological feature data in the crop canopy image data, and construct a soil water dynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage; A first generation module 23, configured to generate soil water migration parameters by using the soil water dynamics simulation model based on the farmland terrain elevation data and soil porosity parameters; A second generation module 24, configured to generate a pipeline network pressure regulation instruction based on the soil water migration parameters and a preset irrigation strategy; An adjustment module 25, configured to adjust the outlet pressure distribution of the intelligent irrigation pipeline network according to the pipeline network pressure regulation instruction to obtain an adjusted outlet pressure distribution, and generate an irrigation control instruction at the boundary of the crop rotation area according to the adjusted outlet pressure distribution.

[0102] Figure 2 The described intelligent irrigation control system for high-standard farmland can execute Figure 1 the intelligent irrigation control method for high-standard farmland described in the embodiment shown. The implementation principle and technical effects will not be elaborated here. For the intelligent irrigation control system for high-standard farmland in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0103] In a possible design, Figure 2 the intelligent irrigation control system for high-standard farmland in the embodiment shown can be implemented as a computing device. As Figure 3 shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0104] The processing component 32 is used for: acquiring crop canopy image data and farmland terrain elevation data of different crop planting areas in high-standard farmland; determining the crop growth stage based on the morphological feature data in the crop canopy image data, and constructing a soil water dynamics simulation model according to the root distribution depth interval and the theoretical water absorption rate coefficient corresponding to the crop growth stage; generating soil moisture migration parameters by using the soil water dynamics simulation model based on the farmland terrain elevation data and the soil porosity parameter; generating a pipe network pressure regulation instruction based on the soil moisture migration parameters and a preset irrigation strategy; adjusting the outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation instruction to obtain an adjusted outlet pressure distribution, and generating an irrigation control instruction at the boundary of the crop rotation area according to the adjusted outlet pressure distribution.

[0105] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0106] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0107] Of course, the computing device may necessarily further include other components, such as input / output interfaces, display components, communication components, etc.

[0108] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0109] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0110] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from the cloud computing platform.

[0111] An embodiment of the present invention also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, the above Figure 1 intelligent irrigation control method for high-standard farmland shown in the embodiment can be implemented.

[0112] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0113] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0114] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. An intelligent irrigation control method for high-standard farmland, characterized in that: include: Obtain crop canopy image data and farmland terrain elevation data for different crop planting areas in high-standard farmland; Determine the crop growth stage based on the morphological feature data in the crop canopy image data, and construct a soil hydrodynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage; Based on the farmland terrain elevation data and soil porosity parameters, soil water migration parameters are generated using the soil hydrodynamics simulation model; Based on the soil moisture migration parameters and the preset irrigation strategy, generating a pipe network pressure control instruction; According to the pipe network pressure control instruction, the outlet pressure distribution of the intelligent irrigation pipe network is adjusted to obtain the adjusted outlet pressure distribution, and according to the adjusted outlet pressure distribution, the irrigation control instruction at the boundary of the crop rotation area is generated.

2. The method according to claim 1, characterized in that Based on the morphological feature data in the crop canopy image data, the crop growth stage is determined, and according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage, a soil hydrodynamics simulation model is constructed, including: Extracting a color distribution matrix, a texture density parameter, and a structural profile parameter of the crop canopy from each image frame of the crop canopy image data to generate morphological feature data of the crop canopy; According to the expansion rate of the structural profile parameters in the morphological feature data and the main wavelength offset of the color distribution matrix, a preset crop growth stage mapping relationship is matched to determine the crop growth stage; Selecting a root distribution depth interval and a theoretical water absorption rate coefficient per unit root length corresponding to the crop growth stage from a pre-stored crop root parameter database; Dividing the farmland terrain elevation data into a plurality of grid units, binding the root distribution depth intervals and elevation values ​​to corresponding grid units to generate a geographic feature association table; According to the geographical feature association table and the preset slope water absorption rate correction coefficient, the theoretical water absorption rate coefficient is adjusted to generate an actual water absorption rate coefficient; According to the actual water absorption rate coefficient, a soil hydrodynamics simulation model is constructed in a three-dimensional space coordinate system.

3. The method according to claim 2, characterized in that According to the geographical feature association table and the preset slope water absorption rate correction coefficient, the theoretical water absorption rate coefficient is adjusted to generate an actual water absorption rate coefficient, including: Extracting the slope angle corresponding to each grid cell from the geographic feature association table; According to the preset slope water absorption rate correction coefficient, the correction coefficient corresponding to different slope angles is defined; Based on the correction coefficient and the theoretical water absorption rate coefficient, generating a first adjusted water absorption rate parameter; If the parameter difference between adjacent soil layers in the same grid unit exceeds a preset layer difference threshold, the water absorption rate parameter adjusted for the first time is reallocated according to the root distribution depth interval to obtain the water absorption rate parameter adjusted for the second time; If the slope angle difference between adjacent grid cells exceeds a preset angle mutation threshold, the water absorption rate parameter adjusted for the second time is recalculated according to the average value of the correction coefficients of the adjacent grid cells to obtain the actual water absorption rate coefficient corresponding to each grid cell.

4. The method according to claim 1, characterized in that Based on the farmland terrain elevation data and soil porosity parameters, the soil water migration parameters are generated using the soil hydrodynamics simulation model, including: According to the soil porosity parameter, defining the pore connectivity parameter of each soil layer in the high-standard farmland; Calculate the elevation difference between each grid cell and an adjacent grid cell to obtain an elevation difference set, and use the direction of the maximum elevation difference in the elevation difference set as the initial slope direction; Calculating the angle difference between the initial slope direction and the actual slope direction of the adjacent grid cells, and if the angle difference exceeds a preset threshold, adjusting the initial slope direction to the arithmetic mean of the actual slope directions to generate a slope parameter set; Based on the slope parameter set, a slope direction weight coefficient is assigned to each soil layer, and the slope direction weight coefficient is superimposed with the pore connectivity parameter of the corresponding soil layer to generate a water flow direction priority parameter corresponding to each grid unit; Calculate the permeability resistance coefficient corresponding to each soil layer in each grid unit according to the inverse relationship between the pore connectivity parameter and the preset resistance base value; The water flow direction priority parameter and the penetration resistance coefficient are input into the soil hydrodynamics simulation model to calculate the water migration amount and migration direction data between adjacent grid cells, and the soil water migration parameters are generated by combining the water migration amount and migration direction data.

5. The method according to claim 4, characterized in that Based on the slope parameter set, a slope direction weight coefficient is assigned to each soil layer, and the slope direction weight coefficient is superimposed with the pore connectivity parameter of the corresponding soil layer to generate a water flow direction priority parameter corresponding to each grid unit, including: Extracting the final slope direction of each grid cell from the slope parameter set, and calculating the slope direction weight coefficient according to the angle between the final slope direction and the true north direction of the geographic coordinate system; According to the pore connectivity parameter, a pore connectivity weight coefficient is assigned to each soil layer; The slope direction weight coefficient is superimposed with the pore connectivity weight coefficient of the corresponding soil layer to generate an initial water flow direction priority parameter; The initial water flow direction priority parameters corresponding to each soil layer in the same grid unit are vertically weighted and fused to generate the water flow direction priority parameters to be verified; The water flow direction priority parameter to be verified of the target grid cell is corrected so that the priority parameter difference between the target grid cell and the adjacent grid cells does not exceed the preset smoothing threshold, and finally the water flow direction priority parameter corresponding to each grid cell is generated.

6. The method according to claim 1, characterized in that Based on the soil moisture migration parameters and the preset irrigation strategy, a pipe network pressure control instruction is generated, including: Extracting the water migration amount and dominant migration direction of each grid cell from the soil water migration parameters; Calculating the absolute difference between the water migration amount and the target migration amount corresponding to the preset irrigation strategy to generate a migration amount deviation parameter; Calculating a direction deviation parameter according to an angle difference between the dominant migration direction and a target migration direction in a preset irrigation strategy; Performing weighted fusion on the migration deviation parameter and the direction deviation parameter to generate a comprehensive deviation index; According to a preset pressure control mapping table, determining a pressure adjustment amplitude and a pressure adjustment direction corresponding to the comprehensive deviation index; Based on the pressure adjustment amplitude and pressure adjustment direction, a local pressure control instruction corresponding to each network unit is generated. If the pressure adjustment direction angle between adjacent grid units exceeds a preset conflict threshold, the pressure adjustment amplitudes of adjacent grid units are redistributed according to the moisture migration amount to optimize the local pressure control instruction and generate a pipeline network pressure control instruction.

7. The method according to claim 1, characterized in that Generating irrigation control instructions at the boundary of the crop rotation zone according to the adjusted outlet pressure distribution, including: At the boundary of the crop rotation area, extracting the adjusted outlet pressure distribution of the grid cells corresponding to the adjacent crop planting area, the adjusted outlet pressure distribution includes the pressure values ​​of the grid cells on both sides of the boundary of the crop rotation area; According to the adjusted outlet pressure distribution, the pressure difference of the grid cells on both sides of the boundary is calculated, and the boundary irrigation correction amount on both sides of the boundary is calculated in combination with the actual water absorption rate coefficient and the soil porosity ratio of the crop planting areas on both sides of the boundary, wherein the boundary irrigation correction amount includes an irrigation time correction coefficient and a water flow rate correction coefficient; If there is a directional conflict between the boundary irrigation correction amounts on both sides of the boundary, the boundary irrigation correction amounts on both sides of the boundary are recalculated according to the preset priority weights of the crop growth stages on both sides of the boundary to generate a target boundary irrigation correction amount, so as to generate irrigation control instructions at the boundary of the crop rotation area according to the target boundary irrigation correction amount, and the preset priority weights are set according to the importance level of the critical period of crop growth.

8. An intelligent irrigation control system for high-standard farmland, characterized in that: include: An acquisition module is used to acquire crop canopy image data and farmland terrain elevation data of different crop planting areas in high-standard farmland; A construction module is used to determine the crop growth stage based on the morphological feature data in the crop canopy image data, and to construct a soil hydrodynamics simulation model according to the root distribution depth interval and theoretical water absorption rate coefficient corresponding to the crop growth stage; A first generating module is used to generate soil water migration parameters using the soil hydrodynamics simulation model based on the farmland terrain elevation data and soil porosity parameters; A second generation module is used to generate a pipe network pressure control instruction based on the soil moisture migration parameter and the preset irrigation strategy; The adjustment module is used to adjust the outlet pressure distribution of the intelligent irrigation network according to the pipe network pressure control instruction, obtain the adjusted outlet pressure distribution, and generate irrigation control instructions at the boundary of the crop rotation area according to the adjusted outlet pressure distribution.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an intelligent irrigation control method for high-standard farmland as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an intelligent irrigation control method for high-standard farmland as described in any one of claims 1 to 7 is implemented.

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