A dryland wheat green high yield cultivation optimization method and system

Abstract

This invention discloses an optimized method and system for green and high-yield cultivation of dryland wheat, relating to the field of agricultural cultivation technology. The method includes: firstly, acquiring typical precipitation and soil characteristic data of the target planting area, constructing a micro-ridge furrow simulation model, and optimizing the solution with the goal of maximizing predicted water catchment to obtain optimal micro-ridge furrow parameters. Then, calculating the confidence wetting depth based on the precipitation characteristic data and optimization results, and determining the furrow sowing parameters according to the optimization results and wheat water requirements. Finally, performing cultivation operations based on the micro-ridge furrow parameters, furrow sowing parameters, and confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth. This invention achieves precise matching of micro-ridge furrow structure, crop layout, and water and fertilizer management, solving the problems of low water use efficiency and poor spatial matching of water and fertilizer in traditional cultivation, and improving the precipitation use efficiency and yield level of dryland wheat.

Description

Technical Field

[0001] This invention relates to the field of agricultural cultivation technology, specifically to an optimized method and system for green and high-yield cultivation of dryland wheat. Background Technology

[0002] Dryland wheat production holds an important position globally; however, its yield stability is often severely constrained by insufficient natural rainfall and poor soil moisture retention. Traditional cultivation methods rely mainly on farmers' experience for field management, lacking systematic analysis of regional rainfall characteristics and soil physical properties, resulting in low water use efficiency and untapped yield potential.

[0003] While existing technologies offer improved furrow cultivation methods, most focus on adjusting only a single factor, failing to comprehensively consider the synergistic relationship between rainfall characteristics, soil parameters, and crop water requirements. Furthermore, when determining sowing parameters and fertilization depth, current technologies often neglect the dynamic changes in soil wetting depth caused by rainfall infiltration, leading to insufficient matching of water and nutrients within the root zone and hindering the crop's efficient use of water and fertilizer. Summary of the Invention

[0004] This invention addresses the technical problem of low water use efficiency and poor spatial matching of water and fertilizer in existing dryland wheat cultivation, which lacks comprehensive quantitative analysis and synergistic optimization of regional precipitation characteristics, soil physical properties and crop water requirements. It provides a green and high-yield cultivation optimization method and system for dryland wheat.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0006] In a first aspect, the present invention provides an optimized method for green and high-yield cultivation of wheat in dryland areas, comprising:

[0007] Obtain typical precipitation and soil property data for the target planting area;

[0008] Based on the typical precipitation and soil characteristics data, a micro-ridge furrow simulation model of the target planting area was constructed, and the micro-ridge furrow parameters were obtained by optimizing the solution with the maximization of predicted water catchment as the optimization objective.

[0009] Based on the typical precipitation characteristic data and the optimization solution results of the micro-ridge furrow simulation model, the confidence wetting depth corresponding to the micro-ridge furrow parameters is obtained.

[0010] Based on the optimization results and the water requirement characteristics of the target wheat variety, the hole spacing and row spacing corresponding to the micro-ridge furrow parameters are determined, and the output is the furrow sowing parameters;

[0011] Wheat cultivation operations are performed in the target planting area based on the micro-ridge parameters, the furrow sowing parameters, and the confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth.

[0012] Secondly, the present invention provides an optimized system for green and high-yield cultivation of dryland wheat, comprising:

[0013] The data acquisition module is used to acquire typical precipitation and soil characteristic data of the target planting area;

[0014] The micro-ridge furrow optimization module is used to construct a micro-ridge furrow simulation model of the target planting area based on the typical precipitation characteristic data and soil characteristic data, and to perform optimization and solution with the optimization objective of maximizing the predicted water catchment to obtain the micro-ridge furrow parameters.

[0015] The wetting depth analysis module is used to obtain the confidence wetting depth corresponding to the micro-ridge furrow parameters based on the typical precipitation characteristic data and the optimization solution results of the micro-ridge furrow simulation model.

[0016] The furrow sowing parameter determination module is used to determine the hole spacing and row spacing corresponding to the micro-ridge furrow parameters based on the optimization solution results and the water requirement characteristics of the target wheat variety, and outputs the furrow sowing parameters.

[0017] The cultivation execution module is used to perform wheat cultivation operations in the target planting area based on the micro-ridge parameters, the furrow sowing parameters and the confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth.

[0018] The beneficial effects of this invention are:

[0019] Compared to existing technologies, this invention firstly constructs and optimizes a micro-ridge and furrow simulation model, enabling precise design of the micro-ridge and furrow structure and significantly improving rainfall collection efficiency. Secondly, it calculates the confidence wetting depth based on the optimization results, providing a scientific basis for water management. Furthermore, it determines furrow sowing parameters based on the optimization results and wheat water requirements, achieving an optimal match between crop layout and water supply. Finally, it comprehensively applies various parameters to guide cultivation operations and determines fertilization depth based on the confidence wetting depth, effectively promoting water and fertilizer synergy and comprehensively improving water use efficiency and yield levels of dryland wheat. Attached Figure Description

[0020] Figure 1 A flowchart illustrating an optimized method for green and high-yield cultivation of dryland wheat provided by this invention;

[0021] Figure 2 This is a schematic diagram of the structure of an optimized system for green and high-yield cultivation of dryland wheat provided by the present invention.

[0022] In the attached diagram, the components represented by each number are as follows:

[0023] Data acquisition module 11, micro-ridge and furrow optimization module 12, wetting depth analysis module 13, furrow sowing parameter determination module 14, cultivation execution module 15. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0027] Example 1, as Figure 1 As shown, this embodiment of the invention provides an optimized method for green and high-yield cultivation of dryland wheat, including:

[0028] S10: Obtain typical precipitation and soil characteristic data for the target planting area;

[0029] Specifically, the target planting area is a dryland wheat planting area where dryland agriculture is the main production method. The typical precipitation and soil characteristics data of the target planting area are obtained by integrating precipitation observation data from local meteorological stations and soil survey databases, combined with on-site sampling and testing. The purpose is to accurately grasp the precipitation patterns and soil physical characteristics of the target planting area, provide reliable input parameters for the subsequent establishment of micro-ridge and furrow simulation models, and ensure that the optimized cultivation plan can be best adapted to local natural conditions.

[0030] The typical precipitation characteristic data include at least annual precipitation, precipitation time distribution and precipitation intensity distribution, and the soil characteristic data include at least bulk density, porosity, field water holding capacity, saturated hydraulic conductivity and angle of repose.

[0031] Specifically, regarding typical precipitation characteristic data, three key indicators need to be collected. Annual precipitation refers to the total amount of precipitation resources received by the target planting area within a complete hydrological year, reflecting the basic situation of regional water supply. Precipitation temporal distribution records in detail the occurrence patterns of precipitation events throughout the year, including seasonal variations and the distribution of precipitation at different growth stages. Precipitation intensity distribution describes the variation characteristics of precipitation per unit time, reflecting the concentration and intensity level of precipitation, and is of significant value in assessing precipitation infiltration efficiency and runoff formation potential.

[0032] Regarding soil property data, five core parameters need to be obtained. Bulk density characterizes the mass of a unit volume of soil under natural conditions, directly affecting soil compaction and the root growth environment. Porosity reflects the proportion of pore volume in the soil, determining its water storage capacity and aeration. Field holding capacity refers to the maximum water content that soil can stably maintain after natural drainage, a key indicator for assessing effective soil water capacity. Saturated hydraulic conductivity describes the soil's ability to conduct water under saturated conditions, directly affecting precipitation infiltration rate and soil moisture redistribution. Angle of repose characterizes the maximum slope at which soil particles remain stable under natural deposition, providing important reference value for the stability design of micro-ridge and furrow structures.

[0033] S20: Based on the typical precipitation characteristic data and soil characteristic data, construct a micro-ridge furrow simulation model of the target planting area, and optimize the solution with the maximization of predicted water catchment as the optimization objective to obtain the micro-ridge furrow parameters;

[0034] Specifically, based on the typical precipitation characteristic data and soil characteristic data, a micro-ridge furrow simulation model of the target planting area is constructed, and the optimization objective is to maximize the predicted water catchment volume. The micro-ridge furrow parameters are then obtained, including:

[0035] Based on the soil characteristic data, the boundary conditions of the simulation model are configured to construct a micro-ridge and furrow simulation model for simulating the furrow water runoff process.

[0036] Define the ridge width, ridge height, and ridge edge angle of the micro-ridge furrow as independent variables, and determine the multidimensional variable space of the independent variables based on the equipment control domain of the ridging equipment in the target planting area;

[0037] By combining the typical precipitation characteristic data with the micro-ridge and furrow simulation model, using the multidimensional variable space as the optimization space and maximizing the predicted water catchment as the optimization objective, the micro-ridge and furrow parameters are obtained through iterative optimization.

[0038] First, a micro-ridged furrow simulation model needs to be constructed to model the furrow water collection process. The furrow water collection process refers to the hydrological process in which natural precipitation, guided by the surface morphology of micro-ridges, generates runoff and collects at the bottom of the furrow. This process directly determines the spatial redistribution efficiency of precipitation resources and has a crucial impact on crop root zone water supply. By accurately simulating the furrow water collection process, the influence of different micro-ridged furrow structural parameters on precipitation collection can be quantitatively evaluated, providing a theoretical basis for optimal design.

[0039] Specifically, the micro-ridge furrow simulation model is used to predict the water catchment performance of micro-ridge furrows under specific precipitation scenarios and analyze the mechanisms by which different furrow configurations affect precipitation interception, runoff formation, and water collection. This micro-ridge furrow simulation model can simulate the complete hydrological process from precipitation occurrence to water collection within the furrow, providing a reliable quantitative analysis tool for subsequent parameter optimization. Specifically, the establishment of this micro-ridge furrow simulation model is based on acquired soil characteristic data. Key parameters such as soil bulk density, porosity, and saturated hydraulic conductivity are extracted to configure the basic physical parameters of the micro-ridge furrow simulation model. Simultaneously, the boundary conditions of the micro-ridge furrow simulation model need to be determined based on the soil angle of repose and field capacity to ensure that the simulation environment can realistically reflect the hydraulic characteristics and structural stability of actual soil.

[0040] Specifically, based on the soil characteristic data, the boundary conditions of the simulation model are configured to construct a micro-ridge-furrow simulation model for simulating the furrow water runoff process, including:

[0041] Based on the soil property data, the field water holding capacity and the angle of repose under the most unfavorable conditions are extracted, and the boundary conditions of the simulation model are defined accordingly.

[0042] Based on the soil property data, model parameters are configured and a corresponding simulation physical model is constructed. The model parameters include at least the bulk density, the porosity, and the saturated hydraulic conductivity. The simulation physical model is a two-dimensional planar model.

[0043] The micro-ridge simulation model is constructed by combining the boundary conditions and the simulation physical model.

[0044] In constructing the micro-ridge furrow simulation model, the first step is to determine the key boundary conditions based on soil property data. By extracting field capacity (FHC) data under the most unfavorable conditions, a critical value for soil moisture saturation is defined. This critical FHC value determines the threshold conditions for simulating water infiltration and runoff formation in the micro-ridge furrow simulation model. Only when the soil moisture content reaches FHC will water undergo gravity-driven infiltration. Simultaneously, soil angle of repose data under the most unfavorable conditions is used to determine the maximum slope limit for maintaining the stability of the micro-ridge furrow structure, ensuring that the simulation results conform to actual soil mechanical properties. Specifically, the soil angle of repose under the most unfavorable conditions is used to constrain the maximum value of the ridge edge angle, while field capacity is used to define the upper limit of the soil's water storage capacity. It should be noted that, considering the dynamic correlation between the angle of repose value and soil moisture content, and the variability of actual soil moisture conditions, the angle of repose value under the most unfavorable conditions is specifically selected as the boundary condition. This most unfavorable condition is independent of the most unfavorable condition for field capacity, ensuring that the micro-ridge furrow simulation model maintains structural stability under different moisture conditions.

[0045] Secondly, configuring model parameters based on soil characteristic data and constructing a simulation physical model is the core step in building a micro-ridge furrow simulation model. Specifically, soil bulk density determines soil compaction and infiltration characteristics, porosity affects soil water storage capacity and water movement paths, and saturated hydraulic conductivity controls the rate of water migration in the soil. These key parameters collectively constitute the physical basis of the simulation physical model. The constructed simulation physical model employs a two-dimensional planar modeling approach, which effectively characterizes the hydrological processes in the cross-section of the micro-ridge furrow, ensuring computational accuracy while improving simulation efficiency.

[0046] Finally, the determined boundary conditions are integrated with the configured simulation physical model to construct a complete micro-ridge and furrow simulation model. This micro-ridge and furrow simulation model considers both the intrinsic laws of soil hydraulic parameters and the external constraints of soil structural stability, and can realistically simulate the water collection process of different micro-ridge and furrow morphologies under precipitation conditions, thus providing a reliable numerical experimental platform for subsequent optimization analysis.

[0047] After obtaining the micro-ridge and furrow simulation model, further optimization algorithms are used to search for parameters within the multi-dimensional variable space defined by the equipment control domain, based on the established model, to find the optimal parameter configuration that maximizes the predicted water catchment. This optimization process is essentially about finding the optimal combination of micro-ridge and furrow parameters, and the fundamental purpose of finding these optimal parameters is to achieve efficient utilization of precipitation resources under dryland farming conditions. In dryland wheat-growing areas with limited natural precipitation, the micro-ridge and furrow structure can directly influence the spatial distribution and utilization efficiency of precipitation resources by altering the landform. Therefore, determining the optimal micro-ridge and furrow parameters enables the overall micro-ridge and furrow system to achieve maximum hydrological benefits under specific environmental conditions.

[0048] First, it is necessary to determine the independent variables and their value ranges for the optimization problem. The geometric characteristics of the micro-ridges are defined as the independent variables, specifically including the ridge width, ridge height, and ridge edge angle. From a hydrological perspective, the geometric parameters of the micro-ridges determine the collection path and efficiency of surface runoff. The ridge width affects the rainwater collection area, the ridge height affects the catchment volume, and the ridge edge angle controls the runoff rate and slope stability. By optimizing the combination of these parameters, it is possible to ensure that, when effective rainfall occurs, dispersed rainfall is collected to the crop root zone to the maximum extent, improving the utilization efficiency of a single rainfall event. Specifically, the value ranges of the above independent variables are determined based on the technical parameters of the existing ridging equipment in the target planting area, forming a multi-dimensional variable space with engineering feasibility. The technical parameters of the ridging equipment include the maximum shaping width adjustment range, the hydraulic lifting height limit, and the scraper angle adjustment range, obtained through technical manuals provided by the equipment manufacturer and field operation verification. Examples include the common 1.2-1.8 meter ridge width adjustment range, 15-25 cm height control range, and 30-45 degree tilt angle operation capability. By constraining the theoretical optimization space within the actual operating capacity of the equipment, the engineering feasibility of the optimization results is ensured, as well as the mechano operability of subsequent field implementation.

[0049] Furthermore, typical precipitation characteristic data are input into the micro-ridge furrow simulation model to construct complete external environmental input conditions. An optimization algorithm is used for iterative search within a multidimensional variable space. By continuously adjusting the micro-ridge furrow geometric parameters and running the micro-ridge furrow simulation model to calculate the predicted water catchment, the optimal combination of micro-ridge furrow parameters that maximizes the predicted water catchment is finally obtained. This optimization process ensures that the obtained micro-ridge furrow structure can achieve the best rainwater collection effect under specific precipitation characteristics and soil conditions.

[0050] Specifically, combining the typical precipitation characteristic data with the micro-ridge and furrow simulation model, using the multidimensional variable space as the optimization space and maximizing the predicted catchment volume as the optimization objective, iterative optimization is performed to obtain the micro-ridge and furrow parameters, including:

[0051] Random selection is performed in the multidimensional variable space to obtain candidate micro-furrow parameters, wherein the candidate micro-furrow parameters include candidate ridge width, candidate ridge height and candidate ridge edge angle;

[0052] Verify the candidate ridge edge angles based on the boundary conditions;

[0053] If the candidate ridge edge angle satisfies the boundary conditions, the candidate micro-ridge furrow parameters are input into the micro-ridge furrow simulation model. The planting period of the target wheat variety is used as the analysis window. The water catchment prediction is performed by combining the precipitation intensity distribution and the preset simulation granularity to obtain a set of micro-ridge furrow water catchment coefficients under multiple precipitation intensities.

[0054] Based on the micro-ridge furrow drainage coefficient set, the drainage coefficient distribution curve corresponding to the candidate micro-ridge furrow parameters is fitted and determined, and the predicted drainage volume is calculated by combining the annual precipitation and the precipitation time distribution.

[0055] The candidate micro-furrow parameters are iteratively updated based on the multidimensional variable space, and multiple predicted water catchment volumes are calculated accordingly.

[0056] Select the candidate micro-ridge and furrow parameters corresponding to the one with the largest predicted water catchment volume, and output them as the micro-ridge and furrow parameters.

[0057] First, within a multidimensional variable space consisting of ridge width, ridge height, and ridge edge angle, an initial combination of candidate micro-furrow parameters is selected using a random sampling method as the starting point for optimization. These candidate micro-furrow parameters include specific values ​​for candidate ridge width, candidate ridge height, and candidate ridge edge angle.

[0058] Secondly, the selected candidate ridge edge angles are verified for compliance with boundary conditions. Specifically, the verification process is based on the maximum value of the ridge edge angle determined by the soil angle of repose, ensuring that the candidate ridge edge angles meet the safety requirements of the micro-ridge furrow structure.

[0059] Once the candidate ridge edge angles are verified, the complete combination of candidate micro-ridge and furrow parameters is input into the micro-ridge and furrow simulation model. Using the planting period of the target wheat variety as the analysis window, and combining the rainfall intensity distribution characteristics and the preset simulation granularity, the micro-ridge and furrow drainage coefficient under different rainfall intensity levels is simulated and calculated. The target wheat variety refers to the wheat variety selected for planting in the target planting area, and its planting period refers to the complete growth stage from the suitable sowing period to physiological maturity. The rainfall intensity distribution is the rainfall characteristic data collected in step S10, specifically the frequency and duration of different rainfall intensity intervals in historical data. The preset time simulation granularity refers to the smallest time calculation unit used in the model calculation, which is set comprehensively based on the rate of change of the rainfall process and the computer's computing efficiency; for example, ten minutes or one hour is used as the basic simulation step size.

[0060] Specifically, this simulation process, through refined time-step calculations, accurately simulates the water collection process of micro-ridge furrow structures under precipitation events of varying intensities. This provides a reliable data foundation for evaluating the performance of micro-ridge furrow parameters, ultimately yielding a set of micro-ridge furrow catchment coefficients covering multiple precipitation intensity levels. This set of micro-ridge furrow catchment coefficients describes the performance of micro-ridge furrows under different precipitation conditions, specifically defined as the ratio of actual catchment volume to theoretical precipitation. Actual catchment volume refers to the effective amount of water collected on the slope of the micro-ridge furrow and reaching the bottom of the furrow, while theoretical precipitation represents the total precipitation corresponding to the same precipitation event on flat ground without micro-ridge furrow structures. The numerical range of the micro-ridge furrow catchment coefficient is between 0 and 1, with values ​​closer to 1 indicating higher precipitation collection efficiency of the micro-ridge furrow.

[0061] Furthermore, based on the obtained micro-ridge drainage coefficient set, a continuous distribution curve of the drainage coefficient as a function of precipitation intensity is established using mathematical fitting methods, i.e., the drainage coefficient distribution curve. This drainage coefficient distribution curve uses precipitation intensity as the independent variable and the corresponding micro-ridge drainage coefficient as the dependent variable, and forms a continuous functional relationship through regression analysis, thereby achieving the ability to predict the drainage coefficient under any precipitation intensity.

[0062] Specifically, the established catchment coefficient distribution curve is coupled with annual precipitation data and the temporal distribution characteristics of precipitation for analysis. The coupling process involves using the precipitation intensity distribution function as the basic input, and performing point-by-point product calculations on the occurrence probability corresponding to each precipitation intensity and the catchment coefficient value for the same intensity in the catchment coefficient distribution curve. This product calculation reflects the actual catchment efficiency of micro-ridge furrows under different precipitation intensities, accurately reflecting the influence of precipitation intensity on the catchment effect. Under low precipitation intensity conditions, the catchment coefficient is relatively small, indicating that water is mainly absorbed by the soil and lost through evaporation during weak precipitation events, resulting in a low proportion of runoff. As precipitation intensity increases, the catchment coefficient increases accordingly, reflecting the ability of the micro-ridge furrow structure to effectively collect runoff during stronger precipitation events.

[0063] Furthermore, by integrating the coupling results over the time domain, the predicted water catchment of the candidate micro-ridge furrow parameters during the entire growth period is obtained. The integration interval covers the entire planting cycle of the target wheat variety, and the integration variables include two dimensions: time and precipitation intensity. In summary, the obtained predicted water catchment fully considers the dynamic changes in precipitation intensity and the nonlinear effects of water catchment efficiency, ensuring the accuracy and reliability of the calculation results and providing a precise quantitative basis for the optimal selection of micro-ridge furrow parameters.

[0064] Furthermore, an optimization algorithm is employed to continuously update the candidate micro-furrow parameter combinations in a multi-dimensional variable space. For each newly generated micro-furrow parameter, the boundary condition verification, simulation calculation, and predicted catchment volume assessment processes are repeatedly executed. Through multiple iterative calculations, multiple predicted catchment volume values ​​corresponding to different parameter combinations are obtained.

[0065] Finally, the predicted water catchment values ​​obtained in all iterations are compared, and the candidate micro-furrow parameter combination corresponding to the largest predicted water catchment value is selected as the final optimization result. This candidate micro-furrow parameter combination is output as the determined micro-furrow parameters, including the optimal ridge width, optimal ridge height, and optimal ridge edge angle.

[0066] S30: Based on the typical precipitation characteristic data and the optimization solution results of the micro-ridge furrow simulation model, obtain the confidence wetting depth corresponding to the micro-ridge furrow parameters;

[0067] Specifically, based on the typical precipitation characteristic data and the optimization results of the micro-ridge furrow simulation model, the confidence wetting depth corresponding to the micro-ridge furrow parameters is obtained, including:

[0068] Based on the optimization solution results, the water catchment coefficient distribution curve is extracted;

[0069] Based on the annual precipitation and the precipitation time distribution, the precipitation time distribution is calculated using the planting period of the target wheat variety as the analysis window.

[0070] Based on the drainage coefficient distribution curve and the precipitation time distribution, calculate the drainage time distribution corresponding to the micro-ridge furrow parameters;

[0071] Based on the water catchment time distribution, combined with the bulk density, porosity and field water holding capacity under the most unfavorable conditions, the wetting depth time distribution of the target planting area is calculated and obtained.

[0072] Based on the time distribution of wetting depth, the wetting depth that meets the preset statistical significance level is selected as the confidence wetting depth.

[0073] After obtaining the optimal micro-ridge furrow parameters determined through iterative optimization, it is necessary to calculate the confidence wetting depth corresponding to the micro-ridge furrow parameters. Since these parameters mainly ensure the maximization of precipitation collection efficiency, they fail to directly characterize the spatiotemporal distribution of collected water in the soil profile. The confidence wetting depth, on the other hand, is a wetting layer depth with a specific guarantee rate determined through statistical analysis, which can accurately reflect the actual distribution of water in the root zone. At the same time, this confidence wetting depth can also provide key spatial positioning basis for precise deep application of fertilizer, realizing the synergistic coupling of water and fertilizer in the crop root zone.

[0074] First, based on the optimization results, the drainage coefficient distribution curve is extracted. This drainage coefficient distribution curve fully describes the quantitative relationship between different rainfall intensities and drainage efficiency under optimal micro-ridge and furrow parameters, and is an important basis for calculating the actual drainage volume.

[0075] Secondly, based on annual precipitation data and precipitation temporal distribution, the hourly or daily precipitation temporal distribution is calculated using the planting period of the target wheat variety as the analysis window. This transformation process considers the actual distribution patterns of precipitation events during the crop growth period, and can establish a precipitation time series that matches the physiological demand cycle of wheat, thus providing a time-domain data foundation for subsequent refined water transport simulation.

[0076] For example, if the average annual precipitation in a dryland area is 480 mm, the annual precipitation during the wheat overwintering period (November to February of the following year) accounts for 15%, the greening and jointing period (March-April) accounts for 20%, and the heading and grain-filling period (May-June) accounts for 35%. By decomposing the annual precipitation according to the distribution ratio of growth stages and combining it with typical daily precipitation distribution patterns recorded by meteorological stations, a daily precipitation sequence can be generated. For example, for the heading and grain-filling period, the cumulative precipitation of 84 mm in May can be simulated to be distributed over 6 precipitation days, including 3 effective precipitation events with an intensity of 10-15 mm / day and 3 ineffective precipitation events with an intensity of less than 5 mm / day. This refined time-series data can accurately reflect the matching degree between precipitation and the critical period of wheat water demand, providing a quantitative basis for evaluating the water collection effect of micro-ridge furrows at different growth stages.

[0077] Furthermore, by combining the catchment coefficient distribution curve with the obtained precipitation time distribution, the catchment time distribution corresponding to the micro-ridge furrow parameters is calculated. This process of calculating the catchment time distribution by combining the catchment coefficient distribution curve with the precipitation time distribution essentially involves transforming the precipitation input through the micro-ridge furrow's catchment efficiency function. The specific calculation method is as follows:

[0078] First, the continuous precipitation time series is discretized into several calculation periods. For each calculation period, the average precipitation intensity value of that period is extracted and input into the drainage coefficient distribution curve. The corresponding micro-ridge drainage coefficient value is obtained through function mapping. For example, when the precipitation intensity is 10 mm / h in a certain period, the drainage coefficient at that intensity is found to be 0.75 by querying the drainage coefficient distribution curve; while when the precipitation intensity is 5 mm / h in another period, the corresponding drainage coefficient may only be 0.45.

[0079] Secondly, the effective catchment volume of the micro-ridge furrows during that period is obtained by multiplying the actual precipitation for that period by the corresponding catchment coefficient. The calculation formula can be expressed as: the catchment volume for that period equals the precipitation for that period multiplied by the catchment coefficient corresponding to that precipitation intensity.

[0080] Furthermore, by repeating the above calculations for all calculation periods within the planting period and arranging the runoff volume of each period in chronological order, a complete temporal distribution of runoff volume is ultimately formed. This calculation process fully reflects the nonlinear influence of precipitation intensity on runoff efficiency. Although high-intensity precipitation events are short in duration, they may generate significant runoff volume due to their high runoff coefficient; while low-intensity precipitation events, although longer in duration, have limited actual runoff volume due to their lower runoff coefficient. This dynamic calculation method ensures that the predicted runoff volume accurately reflects the water collection performance of micro-ridge furrows under actual precipitation conditions.

[0081] Furthermore, based on the temporal distribution data of water catchment, combined with soil bulk density, porosity, and field water holding capacity under the most unfavorable conditions, the temporal distribution of wetting depth in the target planting area was calculated.

[0082] Specifically, a two-dimensional water distribution model is first established with time as the horizontal axis and soil depth as the vertical axis. For each calculation period, the catchment volume is input into the model as the upper boundary condition. The maximum water storage capacity per unit depth of soil is calculated based on soil bulk density and porosity, and the effective water storage space is determined by combining this with the field capacity under the most unfavorable condition. During the calculation, the movement of water in the soil follows the infiltration-redistribution law. When the catchment volume for a certain period is input, water preferentially replenishes the water deficit of the upper soil layer. Once the upper soil layer reaches the field capacity, the wetting front advances to the lower layer. The wetting depth for each period can be obtained by balancing the cumulative catchment volume and the soil profile water storage capacity for that period. The specific calculation formula is: Wetting depth = Cumulative catchment volume / (Porosity × Field capacity).

[0083] By calculating the advance depth of the wetting front at different time intervals, a complete temporal distribution curve of wetting depth is ultimately formed. Each point on this curve represents the critical depth at which soil moisture content reaches field capacity at a specific time point. This calculation process comprehensively considers multiple factors such as water infiltration, redistribution, and soil water storage capacity, accurately describing the dynamic changes of the soil wetting front over time.

[0084] Then, based on the obtained wetting depth time distribution, the final confidence wetting depth is calculated.

[0085] Specifically, based on the time distribution of wetting depth, the wetting depth that meets a preset statistical significance level is selected as the confidence wetting depth, including:

[0086] Statistical depth is selected based on the dichotomy method;

[0087] Based on the wettability depth time distribution, statistical analysis is performed to obtain the cumulative duration of time intervals where the wettability depth is greater than or equal to the statistical depth;

[0088] Calculate the ratio of the cumulative duration to the analysis window, obtain statistical significance, and determine whether the statistical significance is greater than or equal to the preset statistical significance level;

[0089] If the statistical significance is greater than or equal to the preset statistical significance level, then the corresponding statistical depth is output as the confidence wetting depth;

[0090] If the statistical significance is less than the preset statistical significance level, the statistical depth is iteratively updated based on the bisection method until the statistical significance is greater than or equal to the preset statistical significance level.

[0091] Specifically, the process of selecting the confidence wetting depth based on the temporal distribution of wetting depth employs an iterative calculation using the bisection method. First, an initial statistical depth value is set based on the bisection method, serving as a temporary threshold for judging the effectiveness of the wetting layer. The bisection method is a numerical calculation method that approximates the optimal solution through interval binary search, and its selection process has clear mathematical specifications. Specifically, the minimum wetting depth in the temporal distribution of wetting depth is used as the lower bound of the search interval, and the maximum wetting depth is used as the upper bound. The initial statistical depth value is taken as the median of this interval, which is half the sum of the minimum and maximum wetting depths. For example, when the monitored wetting depth fluctuates between 10 cm and 50 cm, the initial statistical depth value is set to 30 cm.

[0092] Secondly, based on the complete temporal distribution of wetting depth, statistical analysis was performed on all continuous time intervals where the wetting depth value was greater than or equal to the current statistical depth, and the cumulative duration was calculated. This cumulative duration represents the total duration during the planting period of the target wheat variety when the soil wetting layer reaches or exceeds the current statistical depth level.

[0093] Then, the ratio of the cumulative duration to the total planting period is calculated to obtain the statistical significance level corresponding to the current statistical depth. This statistical significance level is compared with a preset statistical significance threshold. When the actual statistical significance level is greater than or equal to the preset threshold, it indicates that the current statistical depth meets the reliability requirements, and this statistical depth is output as the final confidence wetting depth. The preset statistical significance level is a reliability threshold set based on crop water requirements and cultivation management requirements, for example, 80%. This value indicates that during 80% of the target wheat's entire growth period, the actual soil wetting depth can reach or exceed this confidence wetting depth value. The setting of this level needs to comprehensively consider multiple factors such as crop drought resistance, yield targets, and water resource utilization efficiency to ensure that a reliable and economical depth reference value for deep fertilizer application is provided based on probabilistic statistics.

[0094] If the actual statistical significance level is lower than the preset statistical significance level, the statistical depth value is adjusted using a dichotomy method. Specifically, the adjustment involves reducing the statistical depth value to broaden the time interval that meets the criteria. The updated statistical depth value is then used to repeat the above statistical analysis process, recalculating the statistical significance level and making a judgment. This iterative process continues until the maximum depth value that meets the statistical significance requirement is found, which is the confidence wetting depth with sufficient statistical guarantee. This iterative calculation method based on the dichotomy method can scientifically balance the relationship between depth requirements and reliability, ensuring that the determined confidence wetting depth has both sufficient agronomical significance and a reliable statistical basis.

[0095] S40: Based on the optimization solution results and the water requirement characteristics of the target wheat variety, determine the hole spacing and row spacing corresponding to the micro-ridge furrow parameters, and output them as furrow sowing parameters;

[0096] First, based on the optimal micro-ridge and furrow parameters obtained from the optimization solution, the basic spatial pattern of crop planting is determined. The ridge width directly affects the distribution of planting rows, while the ridge height relates to the soil moisture distribution in the crop root zone.

[0097] Secondly, considering the water requirements of the target wheat variety, including daily water consumption intensity at different growth stages, total water requirement throughout the entire growth period, and root development characteristics, the appropriate basic seedling number per unit area is calculated. Based on the basic seedling number requirements and the effective planting space provided by the micro-ridge and furrow structure, a reasonable row spacing configuration that meets both the light requirements of the entire population and the water supply to individual plants is calculated using mathematical modeling methods. Determining the row spacing requires comprehensive consideration of the plant canopy development scale and field ventilation and light penetration requirements.

[0098] Based on the determined row spacing, the optimal hill spacing is calculated according to the suitable number of seedlings per hill and the target population density. Determining the hill spacing requires balancing individual competition with overall population yield, avoiding water stress in individual plants due to overcrowding, and preventing land waste due to oversparse planting. The final output furrowing parameters include optimized row spacing and hill spacing values, ensuring that wheat plants can fully access water resources and growing space in the micro-ridges and furrows, achieving a synergistic improvement in yield and water use efficiency.

[0099] S50: Based on the micro-ridge parameters, the furrow sowing parameters, and the confidence wetting depth, perform wheat cultivation operations in the target planting area, wherein the fertilizer application depth is determined based on the confidence wetting depth.

[0100] Specifically, based on the optimized micro-ridge parameters, precise ridging is implemented in the target planting area. Standardized micro-ridges are constructed according to the determined ridge width, ridge height, and ridge edge angles to ensure that the field micro-topography meets the design requirements for rainfall collection. Then, the sowing operation is guided by the output furrow sowing parameters. Planting row positions are planned within the furrows according to the determined row spacing, and precise sowing is performed based on the calculated plant spacing to ensure optimal matching between plant spatial distribution and micro-ridge water collection characteristics, achieving spatiotemporal consistency between water supply and crop needs.

[0101] In fertilization management, the confidence wetting depth is used as the basis for determining the fertilizer application depth. The confidence wetting depth characterizes the depth of the stable wetting layer formed by precipitation infiltration. Applying fertilizer to this soil depth ensures that nutrients are distributed in the main root absorption zone of the crop, promoting the effective coupling of water and nutrients in the soil. This fertilization method avoids water and nutrient separation caused by surface fertilization, while preventing nutrient leaching loss caused by deep fertilization, significantly improving the efficiency of water and fertilizer synergistic utilization.

[0102] In summary, through the overall implementation of the above cultivation operations, the micro-ridge furrow structure, crop layout, and water and fertilizer management are organically unified, ultimately achieving the production goal of green and high-yield dryland wheat.

[0103] In summary, the embodiments of this application have at least the following technical effects:

[0104] Compared to existing technologies, this invention first constructs a micro-ridge furrow simulation model and optimizes it with the goal of maximizing predicted water catchment. This allows the micro-ridge furrow parameters to accurately match the rainfall characteristics and soil conditions of the target area, significantly improving the capture and collection efficiency of natural rainfall. Secondly, by combining rainfall characteristics and optimization results to calculate the confidence wetting depth, it provides a quantitative basis for the dynamic distribution of water in the soil, ensuring the scientific nature of water management. Thirdly, based on the optimization results and wheat water requirements, it determines the furrow sowing parameters, achieving the optimal spatial configuration of crop layout and water supply. Finally, by comprehensively applying the micro-ridge furrow parameters, furrow sowing parameters, and confidence wetting depth to cultivation operations, and determining the fertilizer application depth based on the confidence wetting depth, it effectively promotes the synergistic coupling of water and fertilizer in the crop root zone, comprehensively improving the water use efficiency and yield of dryland wheat, and achieving the goal of green and high-yield production.

[0105] Example 2, as Figure 2 As shown, based on the same inventive concept as the method for optimizing green and high-yield cultivation of dryland wheat provided in Embodiment 1, this embodiment of the invention also provides a system for optimizing green and high-yield cultivation of dryland wheat, comprising:

[0106] Data acquisition module 11 is used to acquire typical precipitation characteristic data and soil characteristic data of the target planting area;

[0107] The micro-ridge furrow optimization module 12 is used to construct a micro-ridge furrow simulation model of the target planting area based on the typical precipitation characteristic data and soil characteristic data, and to perform optimization and solution with the maximization of predicted water catchment as the optimization objective to obtain micro-ridge furrow parameters.

[0108] The wetting depth analysis module 13 is used to obtain the confidence wetting depth corresponding to the micro-ridge furrow parameters based on the typical precipitation characteristic data and the optimization solution results of the micro-ridge furrow simulation model.

[0109] The furrow sowing parameter determination module 14 is used to determine the hole spacing and row spacing corresponding to the micro-ridge furrow parameters based on the optimization solution results and the water requirement characteristics of the target wheat variety, and output the furrow sowing parameters.

[0110] The cultivation execution module 15 is used to perform wheat cultivation operations in the target planting area according to the micro-ridge parameters, the furrow sowing parameters and the confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth.

[0111] Specifically, the data acquisition module 11 is used for:

[0112] Obtain typical precipitation characteristic data and soil characteristic data of the target planting area. The typical precipitation characteristic data includes at least annual precipitation, precipitation time distribution and precipitation intensity distribution. The soil characteristic data includes at least bulk density, porosity, field water holding capacity, saturated hydraulic conductivity and angle of repose.

[0113] The micro-furrow optimization module 12 is specifically used for:

[0114] Based on the typical precipitation and soil characteristic data, a micro-ridge furrow simulation model of the target planting area was constructed. The optimization objective was to maximize the predicted water catchment volume, and the micro-ridge furrow parameters were obtained, including:

[0115] Based on the soil characteristic data, the boundary conditions of the simulation model are configured to construct a micro-ridge and furrow simulation model for simulating the furrow water runoff process.

[0116] Define the ridge width, ridge height, and ridge edge angle of the micro-ridge furrow as independent variables, and determine the multidimensional variable space of the independent variables based on the equipment control domain of the ridging equipment in the target planting area;

[0117] By combining the typical precipitation characteristic data with the micro-ridge and furrow simulation model, using the multidimensional variable space as the optimization space and maximizing the predicted water catchment as the optimization objective, the micro-ridge and furrow parameters are obtained through iterative optimization.

[0118] Specifically, based on the soil characteristic data, the boundary conditions of the simulation model are configured to construct a micro-ridge-furrow simulation model for simulating the furrow water runoff process, including:

[0119] Based on the soil property data, the field water holding capacity and the angle of repose under the most unfavorable conditions are extracted, and the boundary conditions of the simulation model are defined accordingly.

[0120] Based on the soil property data, model parameters are configured and a corresponding simulation physical model is constructed. The model parameters include at least the bulk density, the porosity, and the saturated hydraulic conductivity. The simulation physical model is a two-dimensional planar model.

[0121] The micro-ridge simulation model is constructed by combining the boundary conditions and the simulation physical model.

[0122] Specifically, by combining the typical precipitation characteristic data with the micro-ridge and furrow simulation model, using the multidimensional variable space as the optimization space, and taking the maximization of predicted catchment volume as the optimization objective, iterative optimization is performed to obtain the micro-ridge and furrow parameters, including:

[0123] Random selection is performed in the multidimensional variable space to obtain candidate micro-furrow parameters, wherein the candidate micro-furrow parameters include candidate ridge width, candidate ridge height and candidate ridge edge angle;

[0124] Verify the candidate ridge edge angles based on the boundary conditions;

[0125] If the candidate ridge edge angle satisfies the boundary conditions, the candidate micro-ridge furrow parameters are input into the micro-ridge furrow simulation model. The planting period of the target wheat variety is used as the analysis window. The water catchment prediction is performed by combining the precipitation intensity distribution and the preset simulation granularity to obtain a set of micro-ridge furrow water catchment coefficients under multiple precipitation intensities.

[0126] Based on the micro-ridge furrow drainage coefficient set, the drainage coefficient distribution curve corresponding to the candidate micro-ridge furrow parameters is fitted and determined, and the predicted drainage volume is calculated by combining the annual precipitation and the precipitation time distribution.

[0127] The candidate micro-furrow parameters are iteratively updated based on the multidimensional variable space, and multiple predicted water catchment volumes are calculated accordingly.

[0128] Select the candidate micro-ridge and furrow parameters corresponding to the one with the largest predicted water catchment volume, and output them as the micro-ridge and furrow parameters.

[0129] The wetting depth analysis module 13 is specifically used for:

[0130] Based on the typical precipitation characteristic data and the optimization results of the micro-ridge furrow simulation model, the confidence wetting depth corresponding to the micro-ridge furrow parameters is obtained, including:

[0131] Based on the optimization solution results, the water catchment coefficient distribution curve is extracted;

[0132] Based on the annual precipitation and the precipitation time distribution, the precipitation time distribution is calculated using the planting period of the target wheat variety as the analysis window.

[0133] Based on the drainage coefficient distribution curve and the precipitation time distribution, calculate the drainage time distribution corresponding to the micro-ridge furrow parameters;

[0134] Based on the water catchment time distribution, combined with the bulk density, porosity and field water holding capacity under the most unfavorable conditions, the wetting depth time distribution of the target planting area is calculated and obtained.

[0135] Based on the time distribution of wetting depth, the wetting depth that meets the preset statistical significance level is selected as the confidence wetting depth.

[0136] Among them, based on the time distribution of wetting depth, the wetting depth that meets the preset statistical significance level is selected as the confidence wetting depth, including:

[0137] Statistical depth is selected based on the dichotomy method;

[0138] Based on the wettability depth time distribution, statistical analysis is performed to obtain the cumulative duration of time intervals where the wettability depth is greater than or equal to the statistical depth;

[0139] Calculate the ratio of the cumulative duration to the analysis window, obtain statistical significance, and determine whether the statistical significance is greater than or equal to the preset statistical significance level;

[0140] If the statistical significance is greater than or equal to the preset statistical significance level, then the corresponding statistical depth is output as the confidence wetting depth;

[0141] If the statistical significance is less than the preset statistical significance level, the statistical depth is iteratively updated based on the bisection method until the statistical significance is greater than or equal to the preset statistical significance level.

[0142] The trenching parameter determination module 14 is specifically used for:

[0143] Based on the optimization results and the water requirement characteristics of the target wheat variety, the hole spacing and row spacing corresponding to the micro-ridge furrow parameters are determined, and the output is the furrow sowing parameters.

[0144] The cultivation execution module 15 is specifically used for:

[0145] Wheat cultivation operations are performed in the target planting area based on the micro-ridge parameters, the furrow sowing parameters, and the confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth.

[0146] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0147] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0148] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An optimized method for green and high-yield cultivation of dryland wheat, characterized in that, include: Obtain typical precipitation and soil property data for the target planting area; Based on the typical precipitation and soil characteristics data, a micro-ridge furrow simulation model of the target planting area was constructed, and the micro-ridge furrow parameters were obtained by optimizing the solution with the maximization of predicted water catchment as the optimization objective. Based on the typical precipitation characteristic data and the optimization solution results of the micro-ridge furrow simulation model, the confidence wetting depth corresponding to the micro-ridge furrow parameters is obtained. Based on the optimization results and the water requirement characteristics of the target wheat variety, the hole spacing and row spacing corresponding to the micro-ridge furrow parameters are determined, and the output is the furrow sowing parameters; Wheat cultivation operations are performed in the target planting area based on the micro-ridge and furrow parameters, the furrow sowing parameters, and the confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth; Based on the typical precipitation and soil characteristic data, a micro-ridge furrow simulation model of the target planting area is constructed. The model is then optimized with the goal of maximizing predicted water catchment to obtain micro-ridge furrow parameters, including: Based on the soil characteristic data, the boundary conditions of the simulation model are configured to construct a micro-ridge and furrow simulation model for simulating the furrow water runoff process. Define the ridge width, ridge height, and ridge edge angle of the micro-ridge furrow as independent variables, and determine the multidimensional variable space of the independent variables based on the equipment control domain of the ridging equipment in the target planting area; By combining the typical precipitation characteristic data with the micro-ridge furrow simulation model, using the multidimensional variable space as the optimization space and maximizing the predicted water catchment as the optimization objective, the micro-ridge furrow parameters are obtained through iterative optimization. Based on the soil characteristic data, the boundary conditions of the simulation model are configured to construct a micro-ridge-furrow simulation model for simulating the furrow water runoff process, including: Based on the soil property data, the field water holding capacity and angle of repose under the most unfavorable conditions are extracted, and the boundary conditions of the simulation model are defined accordingly. Based on the soil property data, model parameters are configured and a corresponding simulation physical model is constructed. The model parameters include at least bulk density, porosity, and saturated hydraulic conductivity. The simulation physical model is a two-dimensional planar model. The micro-ridge simulation model is constructed by combining the boundary conditions and the simulation physical model.

2. The optimized method for green and high-yield cultivation of dryland wheat as described in claim 1, characterized in that, The typical precipitation characteristic data include at least annual precipitation, precipitation time distribution and precipitation intensity distribution, and the soil characteristic data include at least bulk density, porosity, field water holding capacity, saturated hydraulic conductivity and angle of repose.

3. The optimized method for green and high-yield cultivation of dryland wheat as described in claim 1, characterized in that, Combining the typical precipitation characteristic data with the micro-ridge and furrow simulation model, using the multidimensional variable space as the optimization space and maximizing the predicted catchment volume as the optimization objective, an iterative optimization solution is performed to obtain the micro-ridge and furrow parameters, including: Random selection is performed in the multidimensional variable space to obtain candidate micro-furrow parameters, wherein the candidate micro-furrow parameters include candidate ridge width, candidate ridge height and candidate ridge edge angle; Verify the candidate ridge edge angles based on the boundary conditions; If the candidate ridge edge angle satisfies the boundary condition, the candidate micro-ridge furrow parameters are input into the micro-ridge furrow simulation model. The planting period of the target wheat variety is used as the analysis window. Water catchment prediction is performed by combining the precipitation intensity distribution and the preset simulation granularity to obtain a set of micro-ridge furrow water catchment coefficients under multiple precipitation intensities. Based on the micro-ridge furrow drainage coefficient set, the drainage coefficient distribution curve corresponding to the candidate micro-ridge furrow parameters is fitted and determined, and the predicted drainage volume is calculated by combining the annual precipitation and precipitation time distribution. The candidate micro-furrow parameters are iteratively updated based on the multidimensional variable space, and multiple predicted water catchment volumes are calculated accordingly. Select the candidate micro-ridge and furrow parameters corresponding to the one with the largest predicted water catchment volume, and output them as the micro-ridge and furrow parameters.

4. The optimized method for green and high-yield cultivation of dryland wheat as described in claim 3, characterized in that, Based on the typical precipitation characteristic data and the optimization results of the micro-ridge furrow simulation model, the confidence wetting depth corresponding to the micro-ridge furrow parameters is obtained, including: Based on the optimization solution results, the water catchment coefficient distribution curve is extracted; Based on the annual precipitation and the precipitation time distribution, the precipitation time distribution is calculated using the planting period of the target wheat variety as the analysis window. Based on the drainage coefficient distribution curve and the precipitation time distribution, calculate the drainage time distribution corresponding to the micro-ridge furrow parameters; Based on the water catchment time distribution, combined with the bulk density, porosity and field water holding capacity under the most unfavorable conditions, the wetting depth time distribution of the target planting area is calculated and obtained. Based on the time distribution of the wetting depth, the wetting depth that meets the preset statistical significance level is selected as the confidence wetting depth.

5. The optimized method for green and high-yield cultivation of dryland wheat as described in claim 4, characterized in that, Based on the time distribution of wetting depth, the wetting depth that meets the preset statistical significance level is selected as the confidence wetting depth, including: Statistical depth is selected based on the dichotomy method; Based on the wettability depth time distribution, statistical analysis is performed to obtain the cumulative duration of time intervals where the wettability depth is greater than or equal to the statistical depth; Calculate the ratio of the cumulative duration to the analysis window, obtain statistical significance, and determine whether the statistical significance is greater than or equal to the preset statistical significance level; If the statistical significance is greater than or equal to the preset statistical significance level, then the corresponding statistical depth is output as the confidence wetting depth; If the statistical significance is less than the preset statistical significance level, the statistical depth is iteratively updated based on the bisection method until the statistical significance is greater than or equal to the preset statistical significance level.

6. A green and high-yield cultivation optimization system for dryland wheat, characterized in that, A method for optimizing the green and high-yield cultivation of dryland wheat according to any one of claims 1-5 includes: The data acquisition module is used to acquire typical precipitation and soil characteristic data of the target planting area; The micro-ridge furrow optimization module is used to construct a micro-ridge furrow simulation model of the target planting area based on the typical precipitation characteristic data and soil characteristic data, and to perform optimization and solution with the optimization objective of maximizing the predicted water catchment to obtain the micro-ridge furrow parameters. The wetting depth analysis module is used to obtain the confidence wetting depth corresponding to the micro-ridge furrow parameters based on the typical precipitation characteristic data and the optimization solution results of the micro-ridge furrow simulation model. The furrow sowing parameter determination module is used to determine the hole spacing and row spacing corresponding to the micro-ridge furrow parameters based on the optimization solution results and the water requirement characteristics of the target wheat variety, and outputs the furrow sowing parameters. The cultivation execution module is used to perform wheat cultivation operations in the target planting area based on the micro-ridge parameters, the furrow sowing parameters and the confidence wetting depth, wherein the fertilizer application depth is determined based on the confidence wetting depth.

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