A method and system for intelligent regulation and control of integrated water and fertilizer in high-standard farmland

Abstract

The present invention provides a method and system for intelligent regulation of integrated water and fertilizer in high-standard farmland. By acquiring the topographic point cloud data and crop canopy water stress data of high-standard farmland, and generating a set of elevation gradient distribution features to generate an elevation model based on this set; analyzing the crop canopy water stress data to calculate the water stress index; performing spatial position matching with the micro-topography difference parameters to generate a set of terrain compensation parameters; establishing a temporal optimization correlation model in the 5G edge computing node, and according to the set of terrain compensation parameters and historical irrigation records, using the temporal optimization correlation model to optimize the pulse irrigation timing factor and flow distribution weight to generate a multi-objective irrigation control instruction; according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameters corresponding to the micro-topography difference parameters, perform intelligent regulation of integrated water and fertilizer in high-standard farmland; the present invention realizes accurate on-demand distribution of water and fertilizer, and improves irrigation uniformity, resource utilization efficiency and crop yield.

Description

Technical Field

[0001] The present invention relates to the technical field of water and fertilizer regulation, and particularly to an intelligent regulation method and system for integrated water and fertilizer in high-standard farmland. Background Art

[0002] In the construction of high-standard farmland, although the land flatness has reached the specification requirements, micro-topographic differences (such as standardized plots with a slope drop ≤ 0.5%) will still cause uneven movement of water and fertilizer in the field. Due to the local elevation differences caused by terrain undulations, problems such as water accumulation in low-lying areas, insufficient water supply in high-lying areas, and fertilizer loss in runoff areas are likely to occur during the irrigation process, resulting in uneven absorption of water and nutrients by crop roots, and affecting yield and resource utilization efficiency.

[0003] Currently, for the problem of uneven movement of water and fertilizer caused by micro-topographic differences, the existing mainstream technical solutions generate a three-dimensional elevation model through topographic surveying, combine the data of soil moisture sensors, preset irrigation thresholds for different topographic regions, and control the drip irrigation system to supply water differentially in different regions through a fixed program, and regularly calibrate the parameters manually. Such existing solutions have some limitations: relying on static parameters, resulting in the lack of dynamic response of irrigation decisions to the actual water demand of crops, and phenomena such as drought in high-lying areas and over-wetness in low-lying areas may occur; parameter updates rely on manual calibration or sensor data at fixed intervals, and cannot quickly respond to the subtle changes in local micro-topography caused by factors such as tillage and settlement, resulting in the lag of water and fertilizer distribution strategies behind actual demands. Summary of the Invention

[0004] The present invention provides an intelligent regulation method and system for integrated water and fertilizer in high-standard farmland to solve the problems in the prior art that rely on static parameters, resulting in the lack of dynamic response of irrigation decisions to the actual water demand of crops; parameter updates rely on manual calibration or sensor data at fixed intervals, and cannot quickly respond to the subtle changes in local micro-topography caused by factors such as tillage and settlement, resulting in the lag of water and fertilizer regulation strategies behind actual demands.

[0005] In a first aspect, the present invention provides an intelligent regulation method for integrated water and fertilizer in high-standard farmland, including:

[0006] Obtain the topographic point cloud data and crop canopy water stress data of the high-standard farmland;

[0007] According to the topographic point cloud data, generate a set of elevation gradient distribution features, and generate an elevation model based on the set of elevation gradient distribution features, where the elevation model includes irrigation low-lying area identifiers, runoff area boundary parameters, and micro-topography difference parameters;

[0008] Analyze the crop canopy water stress data to calculate the water stress index;

[0009] Match the water stress index with the microtopography difference parameters in terms of spatial location to generate a terrain compensation parameter set that fuses crop physiological status and topographic features;

[0010] Establish a timing optimization correlation model in the 5G edge computing node. According to the terrain compensation parameter set and historical irrigation records, use the timing optimization correlation model to optimize the pulse irrigation timing factor and flow allocation weight to generate a multi-objective irrigation control instruction;

[0011] Perform intelligent regulation of water and fertilizer integration in high-standard farmland according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the microtopography difference parameter.

[0012] Optionally, generate an elevation gradient distribution feature set based on the terrain point cloud data, and generate an elevation model according to the elevation gradient distribution feature set. The elevation model includes irrigation low-lying area identification, runoff area boundary parameters, and microtopography difference parameters, including:

[0013] Based on a preset adjacent point quantity threshold, calculate the elevation difference and horizontal distance between each coordinate point in the terrain point cloud data and its adjacent coordinate points to generate an elevation gradient distribution feature set. The gradient distribution feature set includes slope value, aspect angle, and curvature value;

[0014] Perform spatial interpolation processing on the slope value to generate a slope distribution surface. Mark the closed area where the slope value in the slope distribution surface is less than or equal to the first preset threshold and the coverage area is greater than the second preset threshold as the irrigation low-lying area identification;

[0015] Mark the boundary line segment where the slope difference between each coordinate point and its adjacent coordinate points in the slope distribution surface exceeds the third preset threshold as the runoff area boundary;

[0016] Divide the terrain point cloud data to obtain multiple data grid units, calculate the standard deviation of the slope values of all coordinate points within each data grid unit, and mark the data grid unit where the standard deviation of the slope values is greater than or equal to the fourth preset threshold as the microtopography difference parameter;

[0017] Perform associated mapping on the spatial coordinate range of the irrigation low-lying area identification, the topological connection relationship of the runoff area boundary, and the coding information of the data grid unit corresponding to the microtopography difference parameter to generate an elevation model.

[0018] Optionally, analyze the crop canopy water stress data to calculate the water stress index, including:

[0019] Divide the crop canopy water stress data into multiple detection areas, and extract the first reflectivity value and the second reflectivity value of the detection area;

[0020] Generate a normalized reflectance eigenvalue corresponding to each detection area based on the first reflectance value and the second reflectance value;

[0021] Obtain the growth stage code of the crops in the high-standard farmland, and determine the corresponding determination threshold range according to the preset stress determination threshold mapping table;

[0022] Compare the normalized reflectance eigenvalue with the determination threshold range to generate a water status label;

[0023] Calculate the water stress index corresponding to each detection area based on the preset weight coefficient and linear conversion rule corresponding to the water status label.

[0024] In a second aspect, the present invention provides a high-standard farmland water and fertilizer integrated intelligent control system, including:

[0025] An acquisition module for acquiring the topographic point cloud data and crop canopy water stress data of the high-standard farmland;

[0026] A generation module for generating an elevation gradient distribution feature set according to the topographic point cloud data, and generating an elevation model according to the elevation gradient distribution feature set, where the elevation model includes an irrigation low-lying area identifier, a runoff area boundary parameter, and a micro-topography difference parameter;

[0027] [[ID=2,2]]An analysis module for analyzing the crop canopy water stress data to calculate the water stress index;

[0028] A matching module for spatially matching the water stress index with the micro-topography difference parameter to generate a topographic compensation parameter set that fuses the crop physiological state and topographic features;

[0029] An optimization module for establishing a timing optimization association model in a 5G edge computing node, and optimizing the pulse irrigation timing factor and flow distribution weight according to the topographic compensation parameter set and historical irrigation records, and generating a multi-objective irrigation control instruction by using the timing optimization association model;

[0030] A control module for performing high-standard farmland water and fertilizer integrated intelligent control according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the micro-topography difference parameter.

[0031] In a third aspect, the present invention provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the methods of the first aspect for high-standard farmland water and fertilizer integrated intelligent control.

[0032] Fourthly, the present invention provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the intelligent regulation method for integrated water and fertilizer management in high-standard farmland described in any one of the first aspects is implemented.

[0033] In the present invention, topographic point cloud data and crop canopy water stress data of high-standard farmland are acquired; according to the topographic point cloud data, an elevation gradient distribution feature set is generated, and an elevation model is generated based on the elevation gradient distribution feature set, where the elevation model includes irrigation low-lying area identifiers, runoff area boundary parameters, and micro-topography difference parameters; the crop canopy water stress data is analyzed to calculate a water stress index; the water stress index is spatially matched with the micro-topography difference parameters to generate a topographic compensation parameter set that fuses crop physiological states and topographic features; a timing optimization association model is established in a 5G edge computing node, and according to the topographic compensation parameter set and historical irrigation records, the timing optimization association model is used to optimize the pulse irrigation timing factor and flow distribution weight to generate a multi-objective irrigation control instruction; according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameters corresponding to the micro-topography difference parameters, intelligent regulation of water and fertilizer integration in high-standard farmland is carried out. The technical solution provided by the present invention synchronously acquires high-precision topographic features and crop physiological state data, providing a multi-dimensional input basis for water and fertilizer regulation, and solving the problem of spatio-temporal inconsistency caused by the separate acquisition of topographic and crop data in traditional methods; quantifying irrigation low-lying areas, runoff paths, and micro-topography difference parameters based on topographic point cloud data, and constructing an elevation model to solve the problem that traditional topographic surveys cannot capture the micro-topography fluctuations of high-standard farmland; solving the subjectivity and insufficient adaptability of traditional visual interpretation or single-band threshold methods through a standardized water stress index; constructing a coupling decision basis for crop water demand and topographic diversion ability by spatially matching and fusing the water stress index and micro-topography difference parameters, and solving the problem of local water and fertilizer imbalance caused by the regulation of a single topographic or physiological index; using the edge computing node to fuse the real-time topographic compensation parameter set and historical irrigation rules, and dynamically optimizing the pulse irrigation timing factor and flow distribution weight to solve the problem that the traditional fixed-time and fixed-quantity irrigation mode cannot adapt to the dynamic changes of micro-topography; combining the water and fertilizer migration rate parameters to implement spatio-temporal differential execution of irrigation actions, and solving the compound defects of waste in high-leakage areas and insufficient supply in low-permeability areas caused by traditional uniform irrigation. Further, the crop canopy water stress data is divided into detection areas, and the first reflectance value and the second reflectance value are extracted therefrom to generate a normalized reflectance feature value; by matching the growth stage coding to determine the threshold range, based on the preset weight coefficient and linear conversion rule corresponding to the water state label generated by the normalized reflectance feature value and the determination threshold range, a water stress index is generated. Among them, through the cooperation of the dynamic threshold mechanism and growth stage coding, the defect that the traditional fixed threshold method cannot adapt to the changes in the crop growth period is solved; based on the linear mapping of the normalized reflectance feature value and the water state label, a refined grading determination of water stress is realized, significantly improving the spatial adaptability and physiological response accuracy of the water and fertilizer regulation decision in high-standard farmland.

[0034] 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

[0035] 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 use in 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, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 It is a flowchart of an intelligent regulation method for integrated water and fertilizer in high-standard farmland provided by an embodiment of the present invention;

[0037] Figure 2 It is a schematic structural diagram of an intelligent regulation system for integrated water and fertilizer in high-standard farmland provided by an embodiment of the present invention;

[0038] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention.

[0040] In some processes described in the specification and claims of the present invention and the above-mentioned drawings, a plurality of operations 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.

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0042] Figure 1The present invention provides a flowchart of an intelligent regulation method for integrated water and fertilizer in high-standard farmland, as Figure 1 shown. The method includes:

[0043] Regarding the problem of uneven water and fertilizer migration caused by micro-topographic differences in high-standard farmland, the existing technology relies on static preset parameters and fixed program control, and cannot dynamically respond to the actual water demand of crops and subtle topographic changes, resulting in lagging irrigation decisions and unbalanced water and fertilizer distribution. The present invention is based on the following core breakthroughs: by integrating high-precision topographic point cloud data and crop canopy water stress data for real-time monitoring, constructing a topographic compensation parameter set, and combining the time-series optimization correlation model of 5G edge computing, the dynamic adaptive adjustment of irrigation strategies is realized. Specifically, first, the elevation model is used to accurately identify micro-topographic differences (such as low-lying areas and runoff areas). At the same time, the physiological state of crops is quantified through the water stress index, and the topographic characteristics are spatially matched with the water demand of crops to generate a topographic compensation parameter set. Secondly, relying on the low-latency characteristics of 5G edge computing nodes, historical irrigation records and the topographic compensation parameter set are integrated in real time to establish a time-series optimization model, and the pulse irrigation time sequence and flow distribution weight are optimized with multi-objective constraints, thus breaking through the limitations of traditional fixed thresholds. This method solves key problems such as the lag of static parameters, the failure to capture local topographic changes in time, and the disconnection between irrigation strategies and the actual needs of crops through multi-dimensional coupling of topographic-physiological-historical data, realizes the accurate on-demand distribution of water and fertilizer in the micro-topographic difference scenario, and improves resource utilization efficiency and crop yield. Based on this, the present invention provides an intelligent regulation method for integrated water and fertilizer in high-standard farmland, as Figure 1 follows:

[0044] Step 101: Obtain the topographic point cloud data and crop canopy water stress data of high-standard farmland.

[0045] In this step, high-standard farmland refers to farmland that has reached the standards of flat fields, concentrated and contiguous areas, and complete facilities through land improvement, meeting the land flatness requirements (slope drop ≤ 0.5%). The topographic point cloud data refers to a set of dense three-dimensional coordinate points obtained by lidar scanning, used to construct an elevation model and reflect the micro-topographic undulation characteristics. The crop canopy water stress data refers to the crop canopy reflectance data obtained by a multispectral sensor, including the reflectance values of preset water-sensitive bands and preset reference bands.

[0046] In the embodiments of the present invention, the high-standard farmland is scanned three-dimensionally by airborne lidar to generate topographic point cloud data containing three-dimensional coordinates (X / Y / Z); at the same time, the reflectance spectrum data of the crop canopy is collected by a drone carrying a multispectral sensor to obtain the crop canopy water stress data, which is used to characterize the transpiration intensity and water deficit state of the crops.

[0047] Step 102: Generate a set of elevation gradient distribution features based on the terrain point cloud data, and generate an elevation model according to the set of elevation gradient distribution features. The elevation model includes irrigation low-lying area identifiers, runoff area boundary parameters, and micro-topography difference parameters.

[0048] In this step, the set of elevation gradient distribution features refers to the set of slope, aspect, and curvature values extracted from the terrain point cloud data, which quantifies the characteristics of surface morphology changes. The elevation model refers to a three-dimensional surface model expressed in the form of a digital matrix, including irrigation low-lying area identifiers, runoff area boundaries, and micro-topography difference parameters. The irrigation low-lying area identifier refers to a closed area in the elevation model where the slope value ≤ the first preset threshold and the coverage area > the second preset threshold, which is prone to waterlogging or salinization. The runoff area boundary parameter refers to the boundary line segment of the surface runoff path delimited according to the slope difference, which is used to predict high-risk areas of water and fertilizer loss. The micro-topography difference parameter refers to the standard deviation of the slope calculated in units of data grids, which quantifies the intensity of local terrain fluctuations (the standard deviation ≥ 0.15 indicates a significant difference).

[0049] In the embodiment of the present invention, calculate the elevation difference and horizontal distance between each coordinate point in the terrain point cloud data and its adjacent points to generate a set of elevation gradient distribution features including slope values, aspect angles, and curvature values; then use spatial interpolation to process the slope values to generate a slope distribution surface, and mark the closed area where the slope value ≤ the first preset threshold and the coverage area > the second preset threshold as the irrigation low-lying area; at the same time, identify the boundary line segment where the slope difference > the third preset threshold as the runoff area boundary. Then divide the terrain point cloud data into data grid units, calculate the standard deviation of the slope values in each unit, and mark the units where the standard deviation of the slope values ≥ the fourth preset threshold as the micro-topography difference parameters. Finally, integrate all the above results to generate an elevation model including terrain features.

[0050] Step 103: Analyze the crop canopy water stress data to calculate the water stress index.

[0051] In this step, the water stress index refers to a continuous value of 0-1 generated by reflectance characteristic values and dynamic thresholds, which reflects the degree of crop water deficit.

[0052] In the embodiment of the present invention, first divide the crop canopy water stress data into multiple detection regions, extract the reflectance values of each region and generate normalized reflectance characteristic values; then combine the current growth stage coding of the crop to match the corresponding judgment threshold range from the preset stress judgment threshold table; generate a water status label by comparing the normalized reflectance with the judgment threshold range; finally, calculate the water stress index of each region based on the weight coefficients and linear rules corresponding to the water status label to achieve a quantitative evaluation of the water stress degree.

[0053] Step 104: Perform spatial position matching on the water stress index and the microtopography difference parameter to generate a terrain compensation parameter set that fuses crop physiological status and terrain features.

[0054] In this step, the crop physiological status refers to the physiological response characteristics exhibited by the crop due to water stress, which is quantified by the spectral reflectance difference. The terrain feature refers to the difference in surface water diversion capacity described by the microtopography difference parameter, which affects the water and fertilizer migration rate. The terrain compensation parameter set refers to the set of terrain compensation parameters (referring to the quantified values generated by fusing the water stress index and the microtopography difference parameter within a single data grid unit, reflecting the intensity of crop water demand compensation caused by microtopography differences in the data grid unit) for all data grid units covering the high-standard farmland, stored in the form of a spatial distribution matrix, including the coordinates, compensation parameter values, and priority tags of each data grid unit, used to identify the areas that need to be preferentially compensated for water and fertilizer and their regulation intensities.

[0055] In the embodiment of the present invention, perform spatial position matching on the first gridded data of the water stress index (constituted by the water stress index) and the second gridded data of the microtopography difference parameter (constituted by the standard deviation of the slope value) to obtain the matched data grid units; according to the water demand sensitivity parameter and soil water holding capacity parameter corresponding to the crop type, assign crop physiological weight coefficients and terrain feature weight coefficients to each matched data grid unit; combine the crop physiological weight coefficient, terrain feature weight coefficient, water stress index, and microtopography difference parameter corresponding to each data grid unit to generate a fusion weight value corresponding to each data grid unit; perform proportional scaling on it with a preset compensation reference value to generate a terrain compensation parameter set.

[0056] Step 105: Establish a timing optimization association model in the 5G edge computing node. According to the terrain compensation parameter set and historical irrigation records, use the timing optimization association model to optimize the pulse irrigation timing factor and flow distribution weight to generate a multi-objective irrigation control instruction.

[0057] In this step, the historical irrigation records refer to the historical irrigation duration, fertilization amount, and corresponding crop yield data stored in the farmland management system. The timing optimization association model refers to a mathematical model that generates optimization decisions by analyzing the dynamic association between historical irrigation records and the real-time terrain compensation parameter set. The pulse irrigation timing factor refers to the parameter that controls the opening and closing duration of the irrigation device, which is dynamically adjusted according to the crop water demand peak and microtopography differences. The flow distribution weight refers to the proportion of the water and fertilizer flow per unit time in each irrigation area, which is optimized and distributed based on the terrain compensation parameter set. The multi-objective irrigation control instruction refers to a triple instruction set including pulse timing, flow weight, and execution priority, used to coordinate multi-region irrigation operations.

[0058] In an embodiment of the present invention, historical irrigation records are aligned with a terrain compensation parameter set to determine historical growth stage codes; combining the irrigation duration change trend corresponding to the codes and the terrain compensation parameter set, calculating a pulsed irrigation timing factor, and determining the flow distribution weights between adjacent data grid cells; through a timing optimization association model constructed by a 5G edge computing node, using multi-objective constraints to optimize the pulsed irrigation timing factor and the flow distribution weights, obtaining optimized values of the pulsed irrigation timing factor and the optimized values of the flow distribution weights, and encapsulating and processing the two to generate a multi-objective irrigation control instruction.

[0059] Step 106: Perform intelligent regulation of water and fertilizer integration in high-standard farmland according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the micro-topography difference parameter.

[0060] [[ID=`6]]In this step, the water and fertilizer migration rate parameter refers to the horizontal diffusion rate of water and fertilizer in the soil, which is calculated based on an empirical formula of micro-topography slope and soil porosity.

[0061] In an embodiment of the present invention, the specific implementation process of performing intelligent regulation of water and fertilizer integration is as follows: According to the optimized values of the pulsed irrigation timing factor and the flow distribution weights in the multi-objective irrigation control instruction, combined with the water and fertilizer migration rate parameter corresponding to the micro-topography difference parameter, control the pressure-compensated drip irrigation device to perform gradient pulsed irrigation. Among them, the acquisition process of the water and fertilizer migration rate parameter is as follows: Based on the micro-topography difference parameter of each data grid cell in the elevation model, query the preset soil type database, match the water and fertilizer horizontal diffusion empirical parameters of the corresponding soil type (such as sandy soil, clay soil), and then calculate the water and fertilizer migration rate parameters of each grid cell according to the product relationship between the standard deviation of the slope and the empirical parameters of the soil type; finally, adjust the irrigation duration and flow according to the water and fertilizer migration rate parameters. Among them, for low-permeability areas (rate parameter ≤ 0.03 m / s), extend the single-pulse irrigation duration, and for high-permeability areas (rate parameter ≥ 0.05 m / s), shorten the duration and reduce the flow distribution weight.

[0062] In an embodiment of the present invention, through the elevation model of micro-topography difference and crop water stress, combined with the real-time optimization ability of 5G edge computing, precise matching regulation of the water and fertilizer migration rate in high-standard farmland is realized; compared with the traditional static threshold method, the present invention can dynamically respond to the water demand changes of crops and micro-topography fluctuations, improve problems such as water accumulation in low-lying areas and nutrient loss in runoff areas, and enhance the uniformity of water and fertilizer distribution and resource utilization efficiency.

[0063] The present invention provides a specific embodiment. Step 102: Generate an elevation gradient distribution feature set according to the terrain point cloud data, and generate an elevation model according to the elevation gradient distribution feature set. The elevation model includes irrigation low-lying area identifiers, runoff area boundary parameters, and micro-topography difference parameters, and specifically includes the following steps:

[0064] Step 201: Based on a preset threshold of the number of adjacent points, calculate the elevation difference and horizontal distance between each coordinate point in the terrain point cloud data and its adjacent coordinate points, and generate an elevation gradient distribution feature set, where the gradient distribution feature set includes slope value, slope direction angle, and curvature value;

[0065] In this step, the preset threshold of the number of adjacent points refers to the lower limit of the number of adjacent coordinate points to be selected when calculating the elevation gradient feature of a single point. For example, 8 points are used to ensure the local consistency of slope value calculation. The elevation difference refers to the vertical height difference between two adjacent coordinate points, which is obtained by subtracting the elevation of the adjacent coordinate point from the elevation of the target coordinate point and reflects the undulation degree of the local terrain. The horizontal distance refers to the straight-line distance between two adjacent coordinate points in the plane coordinate system, which is calculated by the Euclidean distance formula of the plane coordinates. The elevation gradient distribution feature set refers to a feature data set including slope value, slope direction angle, and curvature value, which is used to describe the geometric shape change of the terrain surface. The slope value refers to a quantitative index of the surface inclination degree. The slope direction angle refers to the azimuth angle of the surface inclination direction, with a range of 0° - 360°. The curvature value refers to a quantitative index of the surface bending degree, which reflects the concave and convex characteristics of the terrain.

[0066] In the embodiment of the present invention, taking the current coordinate point as the center, select the nearest adjacent coordinate points (such as 8 points), calculate the elevation difference (the elevation of the adjacent coordinate point minus the elevation of the current coordinate point) and horizontal distance (the Euclidean distance of the plane coordinates) between the current point and each adjacent coordinate point. Subsequently, take the average value of the elevation differences of all adjacent coordinate points as the slope value of the current coordinate point (the average elevation difference divided by the average horizontal distance), calculate the weighted average value of the connection direction angles of the adjacent coordinate points as the slope direction angle (the weight is the reciprocal of the horizontal distance), and determine the curvature value based on the absolute value of the second derivative of the elevation difference of three consecutive points. Integrate the slope value, slope direction angle, and curvature value of each coordinate point into a gradient distribution feature set.

[0067] Step 202: Perform spatial interpolation processing on the slope value to generate a slope distribution surface, and mark the closed areas in the slope distribution surface where the slope value is less than or equal to the first preset threshold and the coverage area is greater than the second preset threshold as irrigation low-lying area identifiers;

[0068] In this step, the slope distribution surface refers to a continuous two-dimensional slope distribution map generated by an interpolation algorithm, and each pixel point corresponds to a slope value. The first preset threshold refers to the upper limit value of the slope for determining the irrigation low-lying area (for example, 1%), which is set based on the land flatness standard of high-standard farmland. The second preset threshold refers to the minimum area requirement for the irrigation low-lying area (for example, 10 square meters), which is used to exclude scattered small-range low-lying points. The irrigation low-lying area identifier refers to the closed area marked in the slope distribution surface, where the slope value meets the standard and the area meets the requirements, and is prone to water accumulation.

[0069] In the embodiment of the present invention, first, a linear interpolation method is used to convert the slope values of all points into a continuous slope distribution surface covering the entire high-standard farmland plot. In this surface, continuous regions with slope values less than or equal to the first preset threshold (such as 1%) are screened, and the areas of these regions are further calculated. Only closed regions with a coverage area greater than the second preset threshold (such as 10 square meters) are retained. Finally, the boundary coordinates and the elevation of the center point of these closed regions are recorded as the irrigation low-lying area identifiers.

[0070] Step 203: Mark the boundary line segments where the slope difference between each coordinate point and its adjacent coordinate points in the slope distribution surface exceeds the third preset threshold as the runoff area boundaries;

[0071] In this step, the mutation amplitude of the slope value refers to the absolute difference in slope values between adjacent coordinate points, which is used to detect potential paths of surface runoff. The third preset threshold refers to the lower limit of slope mutation for determining the runoff area boundary (for example, 5%), which is set based on the critical slope of hydraulics. The boundary line segment refers to a broken line formed by connecting continuous mutation points, which reflects the natural path dividing line of surface runoff. The runoff area boundary refers to the set of boundary line segments marked, which is used to predict high-risk areas of water and fertilizer loss.

[0072] In the embodiment of the present invention, each point on the slope distribution surface is traversed, and the absolute value of the slope difference between it and its adjacent coordinate points is calculated. If the slope difference of a certain coordinate point exceeds the third preset threshold (such as 5%), it is marked as a mutation point. Subsequently, the continuous mutation points are connected into broken line segments according to the spatial adjacency relationship, and discrete line segments with a length less than 1 meter are removed. Finally, the starting and ending coordinates and the slope mutation values of the remaining broken line segments are stored as the runoff area boundaries.

[0073] Step 204: Divide the terrain point cloud data to obtain multiple data grid units, calculate the standard deviation of the slope values of all coordinate points within each data grid unit, and mark the data grid units with the standard deviation of the slope values greater than or equal to the fourth preset threshold as micro-topography difference parameters;

[0074] In this step, the data grid unit refers to a rectangular area obtained by dividing the terrain point cloud data according to a regular size (such as 0.1 m × 0.1 m), which serves as the basic unit for micro-topography analysis. The standard deviation of the slope value refers to an index of the dispersion degree of the slope values of all points within a single data grid unit, which reflects the intensity of local terrain fluctuations. The fourth preset threshold refers to the lower limit of the standard deviation for determining significant micro-topography differences (for example, 0.15), which is set based on the micro-topography control requirements of high-standard farmland. The micro-topography difference parameter refers to the encoding and quantization value of the data grid unit with a qualified standard deviation of the slope value, which is used to identify micro-topography regions that require special regulation.

[0075] In the embodiment of the present invention, first, the topographic point cloud data is divided into regular data grid units with a preset size (such as 0.1 m × 0.1 m). Subsequently, the standard deviation of the slope values of all coordinate points within each data grid unit is calculated, and the data grid units with a slope value standard deviation greater than or equal to the fourth preset threshold (such as 0.15) are screened out. Finally, the codes (row and column numbers) of these data grid units and the standard deviation values are recorded as micro-topographic difference parameters.

[0076] Step 205: Correlate and map the spatial coordinate range of the identified irrigation low-lying area, the topological connection relationship of the runoff area boundary, and the coding information of the data grid units corresponding to the micro-topographic difference parameters to generate an elevation model.

[0077] In this step, the spatial coordinate range refers to the set of geometric boundary coordinates of the identified irrigation low-lying area, represented by a sequence of polygon vertices. The topological connection relationship refers to the rule of endpoint sharing between the boundary line segments of the runoff area, ensuring the connectivity and consistency of the boundary network. The coding information refers to the unique identifier (such as row and column numbers) of the micro-topographic difference parameter grid, used for rapid matching with the spatial position.

[0078] In the embodiment of the present invention, first, the spatial coordinate range of the identified irrigation low-lying area is aligned with the coordinate system of the preset initial model. Subsequently, the topological connection relationship of the runoff area boundary is established to ensure the continuity of the boundary line segments. At the same time, the data grid unit codes (such as G001, G0) corresponding to the micro-topographic difference parameters are associated with the coordinates of the grid center points. Finally, all the spatial coordinate ranges, topological connection relationships, and coding information are integrated into unified elevation model data, which is input into the preset initial model to generate an elevation model.

[0079] The embodiment of the present invention uses high-precision topographic gradient feature extraction and spatial interpolation technology to accurately identify the irrigation low-lying area and the runoff boundary. Combining with the grid-based micro-topographic difference quantification model, it realizes the dynamic matching and regulation of the water and fertilizer transport rates in high-standard farmland, improves the terrain feature resolution and the recognition accuracy of micro-topographic difference regions, and shortens the irrigation response time.

[0080] The present invention provides a specific embodiment. In step 103, the crop canopy water stress data is analyzed to calculate the water stress index, which specifically includes the following steps:

[0081] Step 301: Divide the crop canopy water stress data into multiple detection regions, and extract the first reflectance value and the second reflectance value of the detection regions.

[0082] In this step, the detection area refers to an independent analysis unit divided according to a fixed grid size, which is the same as the data grid unit in the elevation model and is used to achieve the spatial alignment of moisture data and terrain data. The first reflectance value refers to the measured reflectance of a preset moisture-sensitive band (such as the near-infrared band) extracted from the crop canopy moisture stress data, which reflects the change in the water content of crop tissues. The second reflectance value refers to the measured reflectance of a preset reference band (such as the red-edge band) extracted from the crop canopy moisture stress data and is used as a reference value to eliminate environmental interference.

[0083] In an embodiment of the present invention, first, the crop canopy moisture stress data is spatially divided according to a fixed grid size (such as 0.1 m × 0.1 m) that is the same as the data grid unit size in the elevation model to form a plurality of independent detection areas, and each detection area corresponds to a physical space unit in the high-standard farmland; then, the first reflectance value and the second reflectance value are extracted from the crop canopy moisture stress data of each detection area to ensure that the reflectance data of each detection area strictly corresponds to its spatial position.

[0084] Step 302: Generate a normalized reflectance eigenvalue corresponding to each detection area based on the first reflectance value and the second reflectance value.

[0085] In this step, the normalized reflectance eigenvalue refers to a standardized index generated through difference summation operation, and the calculation formula is (first reflectance value - second reflectance value) / (first reflectance value + second reflectance value), which is used to quantify the crop moisture status.

[0086] In an embodiment of the present invention, a difference summation operation is performed on the first reflectance value and the second reflectance value of each detection area, that is, the difference is obtained by subtracting the second reflectance value from the first reflectance value, and then the difference is divided by the sum of the first reflectance value and the second reflectance value to generate a normalized reflectance eigenvalue; this operation converts the reflectance difference between the two bands into a standardized eigenvalue, eliminating the influence of the light intensity difference, so that the eigenvalue only reflects the relative change in the crop canopy moisture status.

[0087] Step 303: Obtain the growth stage code of the crops in the high-standard farmland, and determine the corresponding determination threshold range according to the preset stress determination threshold mapping table.

[0088] In this step, the growth stage code refers to the stage identifier generated by matching the current date with the preset crop growth cycle, including the sowing date, the number of days in each stage, and the stage code mapping relationship, which is used to dynamically adjust the moisture determination logic. The preset stress determination threshold mapping table refers to the lookup table storing the threshold ranges corresponding to different growth stage codes, including fields such as stage code, minimum threshold value, and maximum threshold value. The determination threshold range refers to the closed interval numerical range of the normalized reflectance eigenvalue corresponding to the current growth stage code, which is used to divide the classification boundary of the crop moisture stress state.

[0089] In the embodiment of the present invention, the growth stage code of the crop is generated by matching the current number of crop growth days with the preset growth cycle stage division rule; subsequently, the preset stress determination threshold mapping table is queried according to this code to obtain the corresponding determination threshold range (such as the seedling stage threshold range [0.1, 0.3], the heading stage threshold range [0.2, 0.5]), ensuring that the threshold adapts to the moisture demand characteristics of different growth periods of the crop.

[0090] Step 304: Compare the normalized reflectance eigenvalue with the determination threshold range to generate a moisture status label;

[0091] In this step, the moisture status label refers to the classification identifier generated according to the comparison result between the normalized reflectance eigenvalue and the determination threshold range, including three discrete labels: severe stress, moderate stress, and normal status.

[0092] In the embodiment of the present invention, the normalized reflectance eigenvalue of each detection area is compared with the determination threshold range. If the normalized reflectance eigenvalue is less than the minimum value of the determination threshold range, it is marked as a severe moisture stress label; if it is within the determination threshold range, it is marked as a moderate moisture stress label; if it is greater than the maximum value of the determination threshold range, it is marked as a normal moisture status label, and the moisture status label is bound to the spatial position of the detection area.

[0093] Step 305: Calculate the moisture stress index corresponding to each detection area based on the preset weight coefficient and linear conversion rule corresponding to the moisture status label.

[0094] In this step, the preset weight coefficient refers to the reference numerical value assigned to each moisture status label, which is used to convert the discrete label into a continuous index value. The linear conversion rule refers to the calculation method of mapping the eigenvalue to the index value proportionally within the threshold interval to achieve a smooth transition from the label to the numerical value.

[0095] In this step, according to the preset weight coefficients corresponding to the moisture status labels (e.g., severe = 0.9, moderate = 0.6, normal = 0.1), the linear conversion rule is used to map the label to a continuous value between 0 and 1. Specifically, the exponential value is calculated by proportional interpolation within the threshold interval corresponding to the moisture status label (for example, the eigenvalue 0.35 within the moderate label interval [0.2, 0.5] is mapped to the exponential value 0.6+(0.35 - 0.2) / (0.5 - 0.2)×(0.9 - 0.6)=0.75), and the moisture stress index of each detection area is generated.

[0096] For example, for a high-standard farmland (winter wheat planting area, grid size 0.1 m × 0.1 m), first, the crop canopy moisture stress data is divided into 1500 detection areas, the reflectance values of near-infrared (850 nm) and red edge (720 nm) in each area are extracted, and the normalized reflectance eigenvalue is calculated as (850 - 720) / (850 + 720)=0.15; the current date is the 55th day after sowing, so the growth stage code 02 (jointing stage) is matched, and the corresponding determination threshold range [0.2, 0.5] is queried according to the preset stress determination threshold mapping table; since the normalized reflectance eigenvalue 0.15 < 0.2, it is marked as a severe stress label; according to the preset weight coefficient 0.9 and the linear conversion rule, the corresponding moisture stress index is calculated as 0.85.

[0097] The embodiment of the present invention solves the misjudgment problem caused by the change of crop growth period in the traditional method; through the coordinated conversion of the normalized reflectance eigenvalue and the moisture status label, taking into account the discrete state recognition and continuous regulation requirements, the spatial distribution of the moisture stress index is accurately coupled with the micro-topography difference parameters of the high-standard farmland, providing a reliable basis for the integrated water and fertilizer regulation.

[0098] The present invention provides a specific embodiment, step 303, obtaining the growth stage code of the crop in the high-standard farmland, and determining the corresponding determination threshold range according to the preset stress determination threshold mapping table, specifically including the following steps:

[0099] Step 311: Retrieve the pre-stored sowing date data from the high-standard farmland management system, and calculate the crop growth days according to the sowing date data and the actual collection date, where the actual collection date is the system record date when obtaining the crop canopy moisture stress data.

[0100] In this step, the high-standard farmland management system refers to a computer system used to store and manage the basic data of high-standard farmland, including data such as sowing dates, soil properties, and historical irrigation records, and provides data query services through a standardized interface. The pre-stored sowing date data refers to the data of the crop sowing operation dates pre-entered in the high-standard farmland management system, in the format of year-month-day, and is used to calculate the crop growth cycle. The actual acquisition date refers to the specific date when the crop canopy water stress data is obtained by a drone equipped with a multispectral sensor, automatically recorded and stored by the data acquisition device. The number of crop growth days refers to the cumulative number of days from the sowing date to the actual acquisition date, calculated by subtracting the sowing date from the actual acquisition date. The system record date refers to the date stamp automatically generated by the data acquisition device when performing the operation of obtaining the crop canopy water stress data, and is bound and stored with the metadata of the data file.

[0101] In the embodiment of the present invention, by accessing the database interface of the high-standard farmland management system, the pre-stored sowing date data is read; the system record date automatically recorded when collecting the crop canopy water stress data is obtained as the actual acquisition date; the date difference between the actual acquisition date and the sowing date data is calculated to obtain the number of crop growth days.

[0102] Step 312: Match the number of crop growth days with the preset growth cycle stage division rule to extract the target associated stage code corresponding to the number of crop growth days as the growth stage code of the crop. The preset growth cycle stage division rule includes multiple time intervals, and each time interval includes a start day, an end day, and an associated stage code.

[0103] In this step, the preset growth cycle stage division rule refers to the standard rule for dividing the entire growth period of the crop into multiple consecutive time intervals by days. The target associated stage code refers to the unique identifier determined by matching the number of crop growth days with the preset growth cycle stage division rule, reflecting the current physiological stage of the crop. The time interval refers to the description of a single growth stage in the preset growth cycle stage division rule, including the start day (the start day of the interval), the end day (the end day of the interval), and the associated stage code. The start day refers to the start day of the time interval of a certain growth stage, starting from the sowing day as the first day for accumulation. The end day refers to the end day of the time interval of a certain growth stage, starting from the sowing day as the first day for accumulation. The associated stage code refers to the unique identifier bound to the time interval preset, such as seedling stage = 01, jointing stage = 02, and is used to associate the stress determination threshold.

[0104] In an embodiment of the present invention, the number of days of crop growth is compared with the start day and end day of each time interval in sequence. When the number of days of crop growth is greater than or equal to the start day of a certain time interval and less than or equal to its end day, the target associated stage code corresponding to this time interval is extracted as the growth stage code of the crop, and the target associated stage code uniquely identifies the growth stage where the current crop is located.

[0105] Step 313: Extract the target threshold lower limit number and the target threshold upper limit number corresponding to the growth stage code from a preset stress determination threshold mapping table. The stress determination threshold mapping table includes a stage code field, a threshold lower limit number field, and a threshold upper limit number field. The threshold lower limit number field and the threshold upper limit number field respectively represent the determination boundaries of the normalized reflectance eigenvalue corresponding to different growth stages.

[0106] In this step, the target threshold lower limit number refers to the lower limit of the determination boundary of the normalized reflectance eigenvalue extracted from the preset stress determination threshold mapping table and corresponding to the target associated stage code. The target threshold upper limit number refers to the upper limit of the determination boundary of the normalized reflectance eigenvalue extracted from the preset stress determination threshold mapping table and corresponding to the target associated stage code. The stage code field refers to the data column in the preset stress determination threshold mapping table that stores the associated stage code exactly the same as in the growth cycle stage division rule. The threshold lower limit number field refers to the data column in the preset stress determination threshold mapping table that stores the determination lower limit of the normalized reflectance eigenvalue corresponding to each growth stage. The threshold upper limit number field refers to the data column in the preset stress determination threshold mapping table that stores the determination upper limit of the normalized reflectance eigenvalue corresponding to each growth stage. The determination boundary refers to the numerical interval range of the normalized reflectance eigenvalue, which is used to divide the crop water stress level and is jointly defined by the threshold lower limit number and the threshold upper limit number.

[0107] In an embodiment of the present invention, the preset stress determination threshold mapping table is a structured data table, including a stage code field, a threshold lower limit number field, and a threshold upper limit number field, where the stage code field is exactly the same as the associated stage code; using the target associated stage code as the query key value, retrieve the matching record in this mapping table, and extract the target threshold lower limit number and the target threshold upper limit number from the threshold lower limit number field and the threshold upper limit number field of this record respectively.

[0108] Step 314: Combine the target threshold lower limit number and the target threshold upper limit number to generate a determination threshold range corresponding to the growth stage code.

[0109] In the embodiments of the present invention, the extracted lower limit number of the target threshold and the upper limit number of the target threshold are sorted according to the numerical size. If the lower limit number of the target threshold is less than the upper limit number of the target threshold, they are directly combined into a determination threshold range in the form of a closed interval; if the lower limit number of the target threshold is greater than the upper limit number of the target threshold, the two values are exchanged and then combined into a closed interval to ensure that the lower limit value of the determination threshold range is always not greater than the upper limit value.

[0110] In the embodiments of the present invention, through the dynamic matching of the crop growth days and the preset growth cycle stage division rules, the adaptive adjustment of the water stress determination threshold is realized, solving the defect that the traditional fixed threshold method cannot adapt to the changes in the crop growth period; the growth stage coding is accurately associated with the determination boundary to ensure the scientificity and reliability of the water status classification; the closed interval combination mechanism of the determination threshold range avoids the logical abnormality caused by the wrong threshold order, providing an accurate decision-making basis for the integrated water and fertilizer regulation of high-standard farmland.

[0111] The present invention provides a specific embodiment. Step 104: Perform spatial position matching on the water stress index and the microtopography difference parameter to generate a topographic compensation parameter set that fuses the crop physiological state and topographic features, which specifically includes the following steps:

[0112] Step 401: Perform spatial position matching on the first spatial distribution data of the water stress index and the second spatial distribution data of the microtopography difference parameter to obtain the matched data grid cells.

[0113] In this step, the first spatial distribution data refers to the spatial set of the water stress index calculated by dividing the detection area through step 301. Each detection area corresponds to an index value, and the two-dimensional matrix data arranged according to the spatial coordinates is used to reflect the field-level distribution characteristics of the crop water status. The second spatial distribution data refers to the spatial set of the slope standard deviation values calculated by dividing the data grid cells through step 204. Each data grid cell corresponds to a standard deviation value, and the two-dimensional matrix data arranged according to the spatial coordinates is used to quantify the spatial distribution characteristics of the small topographic undulations in high-standard farmland. The matched data grid cells refer to the data grid cells unified through the spatial position alignment operation, including both the water stress index and the microtopography difference parameter values, and the size remains unchanged.

[0114] In an embodiment of the present invention, first, the first spatial distribution data formed by arranging the water stress index according to spatial coordinates and the second spatial distribution data formed by arranging the slope standard deviation value according to spatial coordinates are unified in the geographic coordinate system (such as the WGS84 coordinate system or the local farmland plane coordinate system) of the two spatial distribution data to ensure that the physical positions corresponding to the same coordinate points are consistent. If the size of the detection area is the same as that of the data grid unit, they are directly corresponding one by one according to the coordinates. If the sizes are different, the water stress index is redistributed to the center point of the data grid unit by interpolation method. Specifically, taking the center point of the data grid unit as the reference, search for the water stress indexes of all detection areas within a preset radius (such as 0.05 meters) around it; take the arithmetic mean of the searched water stress indexes as the matching value of this data grid unit, and associate each matched data grid unit with its corresponding water stress index and micro-topography difference parameter to form a matched data grid unit.

[0115] Step 402: Based on the water demand sensitivity parameter and soil water holding capacity parameter corresponding to the crop type of the high-standard farmland, assign a crop physiological weight coefficient and a topographic feature weight coefficient to each matched data grid unit.

[0116] In this step, the water demand sensitivity parameter refers to a preset value (0-1) according to the crop type, which reflects the tolerance of the crop to water shortage. For example, the water demand sensitivity parameter of rice is 0.9 and that of corn is 0.7. The soil water holding capacity parameter refers to a preset value (0-1) according to the soil type, which reflects the water retention performance of the soil. For example, the water holding capacity parameter of clay is 0.8 and that of sandy soil is 0.3. The crop physiological weight coefficient refers to the weight value (0-1) assigned to the water stress index, which is positively correlated with the water demand sensitivity parameter and is used to amplify the crop water demand signal. The topographic feature weight coefficient refers to the weight value (0-1) assigned to the micro-topography difference parameter, which is negatively correlated with the soil water holding capacity parameter and is used to strengthen the influence of topographic diversion.

[0117] In an embodiment of the present invention, the preset water demand sensitivity parameter and soil water holding capacity parameter are called according to the crop type planted in the high-standard farmland. The higher the water demand sensitivity parameter, the greater the crop physiological weight coefficient, and the lower the soil water holding capacity parameter, the greater the topographic feature weight coefficient. And through normalization processing, it is ensured that the sum of the two weight coefficients is always 1. For example, the water demand sensitivity parameter of wheat is 0.8, corresponding to the crop physiological weight coefficient of 0.7, and the water holding capacity parameter of sandy soil is 0.3, corresponding to the topographic feature weight coefficient of 0.6.

[0118] Step 403: Combine the crop physiological weight coefficient, topographic feature weight coefficient, water stress index, and micro-topography difference parameter corresponding to each matched data grid unit to generate a fusion weight value corresponding to each data grid unit.

[0119] In this step, the fusion weight value refers to the comprehensive parameter value generated by weighted superposition, which reflects both the water requirement urgency of the crop and the efficiency of water and fertilizer transport in the terrain.

[0120] In the embodiment of the present invention, the water stress index of each data grid unit is multiplied by the crop physiological weight coefficient, and the micro-topography difference parameter is multiplied by the terrain feature weight coefficient, and the product results of the two are added to generate the fusion weight value. The calculation formula is the product of the water stress index and the crop physiological weight coefficient plus the product of the micro-topography difference parameter and the terrain feature weight coefficient. If the calculation result exceeds the range of 0 to 2, it is truncated according to the boundary value.

[0121] Step 404: Perform proportional scaling processing on the fusion weight value based on a preset compensation reference value to generate a terrain compensation parameter set.

[0122] In this step, the preset compensation reference value refers to the reference numerical value set based on historical irrigation effects, which is used to scale the fusion weight value to the actual regulation dimension (such as the multiple of irrigation duration).

[0123] In the embodiment of the present invention, the preset compensation reference value is extracted from the historical irrigation records as the regulation dimension reference, and the fusion weight value is divided by this reference value to complete the proportional scaling. For example, when the preset compensation reference value is 0.5, the terrain compensation parameter corresponding to the fusion weight value of 0.8 is 1.6. Finally, the scaled terrain compensation parameters corresponding to each data grid unit are associated with the spatial coordinates to generate a terrain compensation parameter set covering the entire field block.

[0124] The embodiment of the present invention solves the problem of regulation deviation caused by the isolated analysis of (water stress index) and terrain features (micro-topography difference parameters) in the traditional method, and realizes the precise matching of the water and fertilizer requirements of high-standard farmland and the terrain diversion ability.

[0125] The present invention provides a specific embodiment. In step 105, a time series optimization association model is established in the 5G edge computing node. According to the terrain compensation parameter set and historical irrigation records, the time series optimization association model is used to optimize the pulse irrigation time series factor and flow distribution weight to generate a multi-objective irrigation control instruction, which specifically includes the following steps:

[0126] Step 501: Align the terrain compensation parameter set with the time series data of the historical irrigation records to obtain the aligned historical irrigation records.

[0127] In this step, the historical irrigation records refer to the past stored irrigation operation data, including fields such as historical irrigation dates, single irrigation durations, and regional flow distribution ratios, which are used to analyze historical laws. The time series data refers to the set of historical irrigation records arranged in chronological order, and its time stamps are strictly associated with the crop growth stages.

[0128] In the embodiment of the present invention, according to the terrain compensation parameter set, the terrain compensation parameters of each data grid unit and the corresponding spatial coordinates of the unit are read in the order of data grid unit coding. From the irrigation log database of the high-standard farmland management system, complete historical irrigation records including historical irrigation dates, single irrigation durations, regional division codes, and flow distribution ratio fields in the most recent three years are screened in reverse chronological order to generate time series data. By matching on the time axis, the historical irrigation dates are aligned with the crop growth days corresponding to the terrain compensation parameters. The spatial coordinates of each data grid unit in the terrain compensation parameter dataset are converted into a coordinate system (such as WGS84) consistent with the regional division code, and the regional division code is mapped to the current data grid unit coding system to ensure that each historical irrigation record strictly corresponds to the current data grid unit in the spatio-temporal dimension, and finally the aligned historical irrigation records are generated.

[0129] Step 502: Based on the aligned historical irrigation records and the preset growth cycle stages, generate historical growth stage codes corresponding to the historical irrigation dates.

[0130] In this step, the aligned historical irrigation records refer to the subset of historical data associated with the data grid unit corresponding to the current terrain compensation parameter through spatio-temporal alignment operations to ensure spatio-temporal dimension consistency. The preset growth cycle stages refer to the predefined crop growth period division rules, including sowing dates, the number of days in each stage, and the mapping relationship with stage codes (such as 01 - seedling stage, 02 - jointing stage). The historical irrigation dates refer to the dates when irrigation operations were actually carried out in the past, used to match the historical growth stages of the crops. The historical growth stage codes refer to the stage identifiers calculated from the historical irrigation dates and sowing dates, reflecting the growth period in which the historical irrigation operations occurred.

[0131] In the embodiment of the present invention, based on the historical irrigation dates and the preset growth cycle stages in the aligned historical irrigation records, calculate the number of days between the historical irrigation dates and the historical sowing dates, match the number of days with the time intervals of the preset growth cycle stages (for example, an interval of 70 days corresponds to the heading stage code 03), and generate historical growth stage codes corresponding to the historical irrigation dates to identify the crop growth stages in which the historical irrigation operations occurred.

[0132] Step 503: According to the change trend of the irrigation duration corresponding to the historical growth stage code, combined with the terrain compensation parameter set, calculate the pulsed irrigation timing factor.

[0133] In this step, the change trend of the irrigation duration refers to the historical statistical law of the single irrigation duration within a specific growth stage (such as the duration of the heading stage increasing by 5% week by week). The pulsed irrigation timing factor refers to the parameter that controls the opening duration of a single irrigation, and the initial value is calculated based on the historical duration and the current terrain compensation parameters.

[0134] In an embodiment of the present invention, the irrigation duration change trend of the corresponding growth stage is queried based on the historical growth stage code (for example, the historical average single irrigation duration during the heading period is 120 seconds), and combined with the terrain compensation parameter mean value of the current terrain compensation parameter set (for example, the terrain compensation parameter mean value is 1.3), the historical average irrigation duration is multiplied by the terrain compensation parameter mean value to generate an initial pulse irrigation timing factor (for example, 120 seconds × 1.3 = 156 seconds), which serves as the benchmark control parameter for pulse irrigation.

[0135] Step 504: Generate a spatial variation gradient based on the parameter difference rate between adjacent data grid cells in the terrain compensation parameter set to assign a flow distribution weight to each matched data grid cell.

[0136] In this step, the parameter difference rate (parameter difference rate) represents the rate of change of the terrain compensation parameters between adjacent data grid cells in the terrain compensation parameter set. It is used to quantify the degree of spatial variation in terrain drainage capacity, reflecting the significant topographic undulation in the region. The spatial variation gradient (spatial variation gradient) refers to the rate of change of the terrain compensation parameters between adjacent data grid cells, reflecting local terrain drainage differences. The flow allocation weight (flow allocation weight) refers to the proportion of water and fertilizer flow allocated to each data grid cell. It is positively correlated with the spatial variation gradient and is used to compensate for terrain drainage differences.

[0137] In an embodiment of the present invention, each data grid cell in the terrain compensation parameter set is traversed, and the parameter difference rate between it and the adjacent data grid cells is calculated (the terrain compensation parameter difference between adjacent data grid cells is divided by the horizontal spacing of the cells). All parameter difference rates are normalized to a range of 0-1 to obtain a normalized gradient value. A larger gradient value indicates a more significant change in the terrain drainage capacity. A flow distribution weight that is positively correlated with the normalized gradient value is assigned to each data grid cell according to a positive correlation relationship (for example, a normalized gradient value of 0.8 corresponds to a flow distribution weight of 0.75). High-weight areas are preferentially allocated more water and fertilizer flows to compensate for runoff losses caused by the terrain.

[0138] Step 505: Construct a timing optimization association model in the 5G edge computing node, and use the timing optimization association model to perform multi-objective constraint optimization on the pulse irrigation timing factor and the flow distribution weight according to the terrain compensation parameter set and historical irrigation records, and generate the pulse irrigation timing factor optimization value and the flow distribution weight optimization value.

[0139] In this step, the optimized value of the pulse irrigation timing factor refers to the final single irrigation duration parameter generated through multi-objective optimization, which satisfies terrain adaptation and resource constraints. The optimized value of the flow distribution weight refers to the final regional flow ratio parameter generated through multi-objective optimization, which balances terrain diversion and global uniformity.

[0140] In an embodiment of the present invention, a terrain compensation parameter set (reflecting the global water demand situation) and historical irrigation records (such as the average single irrigation duration corresponding to the growth stage code) are loaded into the 5G edge computing node, and multi-objective constraint conditions such as setting the upper limit of the daily irrigation total are set. By iteratively optimizing the pulse irrigation timing factor and the flow allocation weight, an optimized value of the pulse irrigation timing factor and an optimized value of the flow allocation weight are generated to simultaneously meet the terrain diversion requirements, historical law adaptability, and resource total limit.

[0141] Step 506: Perform instruction encapsulation processing on the optimized value of the pulse irrigation timing factor and the optimized value of the flow allocation weight to generate a multi-objective irrigation control instruction.

[0142] In an embodiment of the present invention, the optimized value of the pulse irrigation timing factor (such as 138 seconds) and the optimized value of the flow allocation weight (such as 0.48) are encapsulated according to the data grid unit code (such as G12, B07) to generate a machine-parsable multi-objective irrigation control instruction, which includes the code, timing parameter, and flow parameter of each data grid unit, and drives the irrigation system to perform differential regulation. This instruction is sent to the irrigation control system for execution through the 5G network.

[0143] The embodiment of the present invention realizes the spatio-temporal collaborative decision-making of high-standard farmland water and fertilizer regulation by aligning and fusing historical irrigation laws and terrain compensation parameters in space and time, and combining the real-time optimization ability of 5G edge computing. Compared with traditional methods, the irrigation duration and flow allocation can simultaneously adapt to the water demand changes during the crop growth period and the micro-topography diversion differences, improving the water and fertilizer utilization rate and the irrigation uniformity in terrain complex areas.

[0144] The present invention provides a specific embodiment. In step 505, a timing optimization association model is constructed in the 5G edge computing node. According to the terrain compensation parameter set and historical irrigation records, using the timing optimization association model, multi-objective constraint optimization is performed on the pulse irrigation timing factor and the flow allocation weight to generate an optimized value of the pulse irrigation timing factor and an optimized value of the flow allocation weight, which specifically includes the following steps:

[0145] Step 511: Based on historical irrigation records and growth stage codes, statistically calculate the average single irrigation duration and the fluctuation range of the flow allocation ratio corresponding to each growth stage code.

[0146] In this step, it refers to the arithmetic mean of the single irrigation durations in all historical irrigation records under the same growth stage code, reflecting the standard irrigation duration of this stage. The fluctuation range of the flow allocation ratio refers to the interval formed by the historical maximum and minimum values of the flow allocation ratio under the same growth stage code, which is used to limit the reasonable range of weight adjustment.

[0147] In the embodiment of the present invention, first, historical irrigation records are grouped according to growth stage codes (such as the seedling stage code 01 and the heading stage code 03), and the arithmetic mean of all single irrigation durations within each group is calculated as the mean single irrigation duration for this stage. At the same time, the maximum and minimum values of the flow distribution ratio within each group are statistically analyzed to generate a range of fluctuations in the flow distribution ratio (such as the range of flow ratio fluctuations at the heading stage is 0.5 - 0.7).

[0148] Step 512: Set multi-objective constraint conditions, where the multi-objective constraint conditions include the upper limit of the daily irrigation total, the flow difference threshold between adjacent crop planting areas, and the equipment operation energy consumption threshold.

[0149] In this step, the multi-objective constraint conditions refer to the composite limiting conditions that need to be satisfied simultaneously during the optimization process, including the total amount of resources, regional balance, and equipment capabilities. The upper limit of the daily irrigation total refers to the maximum allowable amount of water and fertilizer irrigation per day, which is calculated based on the rated water supply of the water pump and the farmland area. The flow difference threshold refers to the maximum allowable difference in the flow distribution ratio per unit time between adjacent crop planting areas, which is used to prevent local over-irrigation or under-irrigation. The equipment operation energy consumption threshold refers to the maximum allowable daily operation duration or power consumption of the irrigation equipment, which is set based on the equipment performance parameters.

[0150] In the embodiment of the present invention, the upper limit of the daily irrigation total (such as 500 tons) is set according to the maximum daily water supply of the water pump in high-standard farmland, the flow difference threshold (such as the flow ratio difference between adjacent areas does not exceed 10%) is set based on the difference in crop water demand characteristics in adjacent areas of the farmland plot, and the equipment operation energy consumption threshold (such as the maximum daily operation duration of 8 hours) is set based on the rated power of the irrigation equipment and the operation time limit.

[0151] Step 513: Based on the mean single irrigation duration and the range of fluctuations in the flow distribution ratio, calculate the global adaptation deviation value of the pulse irrigation timing factor and the flow distribution weight.

[0152] In this step, the global adaptation deviation value refers to an index of the degree of deviation of the current parameter from the historical law, which is used to quantify the degree of deviation of the current parameter from the historical law.

[0153] In the embodiment of the present invention, the absolute value of the difference between the current pulse irrigation timing factor (such as 160 seconds) and the mean single irrigation duration for the corresponding growth stage (such as 130 seconds) is added to the absolute value of the difference between the current flow distribution weight (such as 0.7) and the upper limit of the historical flow distribution ratio range (such as 0.6) to generate a comprehensive deviation value (i.e., |160 - 130| + |0.7 - 0.6| = 30.1).

[0154] Step 514: Based on the global adaptation deviation value and the parameter difference rate, establish a parameter adjustment rule in the timing optimization correlation model. The parameter adjustment rule includes that the adjustment amplitude of the pulsed irrigation timing factor is positively correlated with the global adaptation deviation value, and the adjustment direction of the flow distribution weight is positively correlated with the parameter difference rate.

[0155] In this step, the parameter adjustment rule refers to the logical basis for parameter modification during the optimization process.

[0156] In the embodiment of the present invention, define the adjustment amplitude of the pulsed irrigation timing factor in the timing optimization correlation model as a preset ratio of the global adaptation deviation value (such as deviation value × 0.1). At the same time, it is stipulated that the adjustment direction of the flow distribution weight needs to be positively correlated with the parameter difference rate of adjacent grid cells in the terrain compensation parameter set (such as a difference rate of 0.8 corresponding to a weight adjustment amplitude of +0.15), and the adjusted weight shall not exceed the historical flow distribution ratio fluctuation range.

[0157] Step 515: According to the parameter adjustment rule, use the timing optimization correlation model to iteratively optimize the pulsed irrigation timing factor and the flow distribution weight until the multi-objective constraint conditions are met, and obtain the optimized value of the pulsed irrigation timing factor and the optimized value of the flow distribution weight.

[0158] In the embodiment of the present invention, gradually adjust the pulsed irrigation timing factor and the flow distribution weight according to the parameter adjustment rule, and detect whether the daily total amount, regional flow difference, and energy consumption constraints are met after each adjustment. If any constraint is exceeded, reduce the adjustment amplitude proportionally (such as the original adjustment amplitude × 0.5) and recalculate until all constraint conditions are met, and obtain the optimized value of the pulsed irrigation timing factor and the optimized value of the flow distribution weight.

[0159] The embodiment of the present invention realizes multi-objective dynamic optimization in the 5G edge computing node by integrating terrain compensation parameters and historical irrigation rules, solves the problems of uneven resource allocation and disconnection from historical experience in traditional methods; the optimized values of the pulsed irrigation timing factor and the flow distribution weight simultaneously meet terrain diversion compensation, historical rule adaptation, and resource constraints, improving the water and fertilizer utilization rate and the irrigation uniformity of the entire field.

[0160] Figure 2 The following is a schematic structural diagram of a high-standard farmland water and fertilizer integration intelligent control system provided by the embodiment of the present invention, as Figure 2 shown. The system includes:

[0161] An acquisition module 21, configured to acquire the terrain point cloud data and crop canopy water stress data of the high-standard farmland;

[0162] A generation module 22, configured to generate a set of elevation gradient distribution features based on the terrain point cloud data, so as to generate an elevation model according to the set of elevation gradient distribution features, where the elevation model includes an irrigation low-lying area identifier, a runoff area boundary parameter, and a micro-topography difference parameter;

[0163] An analysis module 23, configured to analyze the crop canopy water stress data to calculate a water stress index;

[0164] A matching module 24, configured to perform spatial position matching between the water stress index and the micro-topography difference parameter to generate a terrain compensation parameter set that fuses crop physiological states and terrain features;

[0165] An optimization module 25, configured to establish a timing optimization association model in a 5G edge computing node, and optimize the pulse irrigation timing factor and the flow distribution weight according to the terrain compensation parameter set and historical irrigation records by using the timing optimization association model to generate a multi-objective irrigation control instruction;

[0166] A regulation module 26, configured to perform intelligent regulation of integrated water and fertilizer in high-standard farmland according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the micro-topography difference parameter.

[0167] Figure 2 The described intelligent regulation system for integrated water and fertilizer in high-standard farmland can execute Figure 1 The described intelligent regulation method for integrated water and fertilizer in high-standard farmland in the illustrated embodiment, and its implementation principle and technical effects will not be elaborated. For the intelligent regulation system for integrated water and fertilizer in 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 embodiments related to the method, and will not be elaborated here.

[0168] In a possible design, Figure 2 The intelligent regulation system for integrated water and fertilizer in high-standard farmland in the illustrated embodiment can be implemented as a computing device, as Figure 3 shown, and the computing device may include a storage component 31 and a processing component 32;

[0169] 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.

[0170] The processing component 32 is configured to: obtain the topographic point cloud data and crop canopy water stress data of high-standard farmland; generate an elevation gradient distribution feature set according to the topographic point cloud data, and generate an elevation model according to the elevation gradient distribution feature set, where the elevation model includes irrigation low-lying area identifiers, runoff area boundary parameters, and micro-topographic difference parameters; analyze the crop canopy water stress data to calculate a water stress index; perform spatial position matching between the water stress index and the micro-topographic difference parameters to generate a topographic compensation parameter set that fuses crop physiological states and topographic features; establish a timing optimization association model in the 5G edge computing node, and use the timing optimization association model to optimize the pulse irrigation timing factor and flow distribution weight according to the topographic compensation parameter set and historical irrigation records to generate a multi-objective irrigation control instruction; perform intelligent regulation of water and fertilizer integration in high-standard farmland according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameters corresponding to the micro-topographic difference parameters.

[0171] 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.

[0172] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can 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, a magnetic disk, or an optical disc.

[0173] Of course, the computing device may also necessarily include other components, such as an input / output interface, a display component, a communication component, etc.

[0174] 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.

[0175] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0176] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0177] 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, it can implement the above Figure 1 shown embodiment of a high-standard farmland water and fertilizer integration intelligent regulation method.

[0178] 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.

[0179] 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 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 labor.

[0180] 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 this 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., including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0181] 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 the embodiments of the present invention.

Claims

1. An intelligent regulation method for integrated water and fertilizer in high-standard farmland, characterized in that, Including: Obtain the topographic point cloud data and crop canopy water stress data of high-standard farmland; Generate an elevation gradient distribution feature set based on the topographic point cloud data, and generate an elevation model according to the elevation gradient distribution feature set. The elevation model includes irrigation low-lying area identification, runoff area boundary parameters, and micro-topographic difference parameters; Analyze the crop canopy water stress data to calculate the water stress index; Perform spatial position matching between the water stress index and the micro-topographic difference parameters to generate a topographic compensation parameter set that fuses crop physiological state and topographic features; Align the topographic compensation parameter set with the time series data of historical irrigation records to obtain the aligned historical irrigation records; Generate historical growth stage codes corresponding to historical irrigation dates based on the aligned historical irrigation records and preset growth cycle stages; Calculate the pulsed irrigation timing factor based on the irrigation duration change trend corresponding to the historical growth stage code and in combination with the topographic compensation parameter set; Generate a spatial change gradient according to the parameter difference rate between adjacent data grid cells in the topographic compensation parameter set, and assign a flow distribution weight to each matched data grid cell; Based on historical irrigation records and growth stage codes, statistically calculate the average value of single irrigation duration and the fluctuation range of flow distribution ratio corresponding to each growth stage code; Set multi-objective constraint conditions, where the multi-objective constraint conditions include the upper limit of daily irrigation volume, the flow difference threshold between adjacent crop planting areas, and the equipment operation energy consumption threshold; Calculate the global adaptation deviation value of the pulsed irrigation timing factor and the flow distribution weight based on the average value of single irrigation duration and the fluctuation range of the flow distribution ratio; Based on the global adaptation deviation value and the parameter difference rate, establish a parameter adjustment rule in the timing optimization association model. The parameter adjustment rule includes that the adjustment amplitude of the pulsed irrigation timing factor is positively correlated with the global adaptation deviation value, and the adjustment direction of the flow distribution weight is positively correlated with the parameter difference rate; According to the parameter adjustment rule, use the timing optimization association model to iteratively optimize the pulsed irrigation timing factor and the flow distribution weight until the multi-objective constraint conditions are met, and obtain the optimized value of the pulsed irrigation timing factor and the optimized value of the flow distribution weight; Perform instruction encapsulation processing on the optimized value of the pulsed irrigation timing factor and the optimized value of the flow distribution weight to generate a multi-objective irrigation control instruction; Carry out intelligent regulation of water and fertilizer integration in high-standard farmland according to the multi-objective irrigation control instruction and the water and fertilizer migration rate parameters corresponding to the micro-topographic difference parameters.

2. The method according to claim 1, characterized in that, Generate an elevation gradient distribution feature set based on the topographic point cloud data, and generate an elevation model according to the elevation gradient distribution feature set. The elevation model includes irrigation low-lying area identification, runoff area boundary parameters, and micro-topographic difference parameters, including: Based on a preset adjacent point quantity threshold, calculate the elevation difference and horizontal distance between each coordinate point in the topographic point cloud data and adjacent coordinate points, and generate an elevation gradient distribution feature set. The gradient distribution feature set includes slope values, slope direction angles, and curvature values; Perform spatial interpolation on the slope values to generate a slope distribution surface, and mark the closed areas in the slope distribution surface where the slope value is less than or equal to the first preset threshold and the coverage area is greater than the second preset threshold as irrigation low-lying area identifiers; Mark the boundary line segments in the slope distribution surface where the slope difference between each coordinate point and its adjacent coordinate points exceeds the third preset threshold as runoff area boundaries; Divide the terrain point cloud data to obtain multiple data grid units, calculate the standard deviation of the slope values of all coordinate points within each data grid unit, and mark the data grid units with the standard deviation of the slope values greater than or equal to the fourth preset threshold as micro-topography difference parameters; Perform associated mapping on the spatial coordinate range of the irrigation low-lying area identifier, the topological connection relationship of the runoff area boundary, and the coding information of the data grid units corresponding to the micro-topography difference parameters to generate an elevation model.

3. The method according to claim 1, wherein Parse the crop canopy water stress data to calculate the water stress index, including: Divide the crop canopy water stress data into multiple detection areas, and extract the first reflectance value and the second reflectance value of the detection areas; Generate the normalized reflectance characteristic value corresponding to each detection area based on the first reflectance value and the second reflectance value; Obtain the growth stage code of the crops in the high-standard farmland, and determine the corresponding determination threshold range according to the preset stress determination threshold mapping table; Compare the normalized reflectance characteristic value with the determination threshold range to generate a water status label; Calculate the water stress index corresponding to each detection area based on the preset weight coefficient and linear conversion rule corresponding to the water status label.

4. The method according to claim 3, wherein Obtain the growth stage code of the crops in the high-standard farmland, and determine the corresponding determination threshold range according to the preset stress determination threshold mapping table, including: Retrieve the pre-stored sowing date data from the high-standard farmland management system, and calculate the crop growth days based on the sowing date data and the actual collection date, where the actual collection date is the system record date when obtaining the crop canopy water stress data; Match the crop growth days with the preset growth cycle stage division rules to extract the target associated stage code corresponding to the crop growth days as the crop growth stage code, and the preset growth cycle stage division rules include multiple time intervals, and each time interval includes a start day, an end day, and an associated stage code; Extract the target threshold lower limit number and the target threshold upper limit number corresponding to the growth stage code from the preset stress determination threshold mapping table, where the stress determination threshold mapping table includes a stage code field, a threshold lower limit number field, and a threshold upper limit number field, and the threshold lower limit number field and the threshold upper limit number field respectively represent the determination boundaries of the normalized reflectance characteristic values corresponding to different growth stages; Combine the target threshold lower limit number and the target threshold upper limit number to generate the determination threshold range corresponding to the growth stage code.

5. The method according to claim 1, wherein Spatially match the water stress index with the micro-topography difference parameter to generate a terrain compensation parameter set that fuses crop physiological status and topographic features, including: Spatially match the first spatial distribution data of the water stress index with the second spatial distribution data of the micro-topography difference parameter to obtain a matched data grid cell; Based on the water demand sensitivity parameter and soil water holding capacity parameter corresponding to the crop type in the high-standard farmland, assign a crop physiological weight coefficient and a topographic feature weight coefficient to each matched data grid cell; Combine the crop physiological weight coefficient, topographic feature weight coefficient, water stress index, and micro-topography difference parameter corresponding to each matched data grid cell to generate a fusion weight value for each data grid cell; Perform proportional scaling on the fusion weight value based on a preset compensation reference value to generate a terrain compensation parameter set.

6. An intelligent regulation system for integrated water and fertilizer management in high-standard farmland, characterized in that, Including: An acquisition module for acquiring the topographic point cloud data and crop canopy water stress data of the high-standard farmland; A generation module for generating a set of elevation gradient distribution features based on the topographic point cloud data, and generating an elevation model according to the set of elevation gradient distribution features, where the elevation model includes irrigation low-lying area identifiers, runoff area boundary parameters, and micro-topography difference parameters; An analysis module for analyzing the crop canopy water stress data to calculate the water stress index; A matching module for spatially matching the water stress index with the micro-topography difference parameter to generate a terrain compensation parameter set that fuses crop physiological status and topographic features; An optimization module, configured to align the terrain compensation parameter set with the time series data of historical irrigation records to obtain aligned historical irrigation records; generate historical growth stage codes corresponding to historical irrigation dates based on the aligned historical irrigation records and preset growth cycle stages; calculate pulse irrigation timing factors according to the irrigation duration change trend corresponding to the historical growth stage codes and in combination with the terrain compensation parameter set; generate a spatial change gradient according to the parameter difference rate between adjacent data grid units in the terrain compensation parameter set to assign flow distribution weights to each matched data grid unit; based on historical irrigation records and growth stage codes, statistically calculate the average single irrigation duration and the fluctuation range of flow distribution ratios corresponding to each growth stage code; set multi-objective constraint conditions, where the multi-objective constraint conditions include the upper limit of daily irrigation total, the flow difference threshold between adjacent crop planting intervals, and the equipment operation energy consumption threshold; calculate the global adaptation deviation values of the pulse irrigation timing factors and flow distribution weights based on the average single irrigation duration and the fluctuation range of flow distribution ratios; establish parameter adjustment rules in the timing optimization association model based on the global adaptation deviation values and the parameter difference rate, where the adjustment amplitude of the pulse irrigation timing factor is positively correlated with the global adaptation deviation value, and the adjustment direction of the flow distribution weight is positively correlated with the parameter difference rate; according to the parameter adjustment rules, use the timing optimization association model to iteratively optimize the pulse irrigation timing factors and flow distribution weights until the multi-objective constraint conditions are met, to obtain optimized values of the pulse irrigation timing factors and optimized values of the flow distribution weights; Perform instruction encapsulation processing on the optimized values of the pulse irrigation timing factors and the optimized values of the flow distribution weights to generate multi-objective irrigation control instructions; A regulation module, configured to perform intelligent regulation of water and fertilizer integration in high-standard farmland according to the multi-objective irrigation control instructions and the water and fertilizer migration rate parameters corresponding to the micro-topography difference parameters.

7. A computing device, characterized in that, Includes 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 a method for intelligent regulation of water and fertilizer integration in high-standard farmland according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that, Stores a computer program, and when the computer program is executed by a computer, it implements a method for intelligent regulation of water and fertilizer integration in high-standard farmland according to any one of claims 1 to 5.

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