Cultivation optimization method and device based on multi-objective analysis, equipment and medium

Abstract

The invention discloses a cultivation optimization method, device and equipment based on multi-target analysis and a medium, and the method comprises the steps: carrying out the comparative analysis processing of cultivation measures of a target region, obtaining the proportionality coefficients of different cultivation modes, carrying out the water supply amount change simulation processing of the target region according to different change scenes, and obtaining a simulation result, and constructing a multi-objective optimization model according to the multi-objective function and the constraint condition, inputting the proportionality coefficient and the simulation result into the multi-objective optimization model for optimization solution processing to obtain an optimization mode, and performing cultivation measure adjustment and water and soil resource allocation optimization on the target region according to the optimization mode. According to the embodiment of the invention, the optimization and sustainable allocation efficiency of agricultural resources of the farming system in a changing environment scene can be improved. The method can be widely applied to the technical field of agricultural cultivation optimization.

Description

Technical Field

[0001] This application relates to the field of agricultural tillage optimization technology, and in particular to a tillage optimization method, apparatus, equipment and medium based on multi-objective analysis. Background Technology

[0002] Agricultural water, soil, and fertilizer resources are fundamental natural resources for ensuring human production and life. They are related to agricultural production and food security, and are also indispensable natural support factors for the sustainable development of the ecological environment. While some technologies study conservation tillage models, practical applications have revealed inconsistent conclusions regarding their impact on crop yield and water use efficiency. This leads to insufficient allocation of agricultural water and soil resources within agricultural systems, affecting the efficiency of resource optimization in agricultural farming.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a tillage optimization method, apparatus, equipment, and medium based on multi-objective analysis, which can improve the optimization allocation efficiency of agricultural tillage systems.

[0005] To achieve the above objectives, one aspect of this application proposes a tillage optimization method based on multi-objective analysis, the method comprising:

[0006] A comparative analysis of farming practices in the target area was conducted to obtain the proportion coefficients of different farming methods.

[0007] The available water supply changes in the target area are simulated according to different changing scenarios to obtain simulation results;

[0008] A multi-objective optimization model is constructed based on the multi-objective function and constraints.

[0009] The proportionality coefficient and the simulation results are input into the multi-objective optimization model for optimization and solution processing to obtain the optimization mode;

[0010] Based on the optimization model, the farming practices and water and soil resource allocation in the target area are adjusted.

[0011] In some embodiments, the comparative analysis of farming practices in the target area to obtain the proportion coefficients of different farming methods includes:

[0012] Keyword search processing was performed on the database to obtain literature related to farming;

[0013] The aforementioned farming-related literature was subjected to quality testing and screening to obtain the screened literature;

[0014] Data extraction and processing were performed on the selected literature to obtain response variables for different farming methods; the farming methods included deep tillage, no-tillage, shallow tillage and conventional tillage, and the response variables included yield, carbon sequestration and emission reduction and irrigation water use efficiency data;

[0015] Meta-analysis was performed on the response variables to obtain the proportion coefficients of the different farming methods.

[0016] In some embodiments, the meta-analysis of the response variables to obtain the proportion coefficients of different farming methods includes:

[0017] The response variables are grouped to obtain a treatment group and an experimental group;

[0018] The effect value was obtained by calculating the average value and natural logarithm of the treatment group and the experimental group.

[0019] Based on the standard deviation and number of repetitions of the response variable, the effect value is subjected to inverse and weight calculations to obtain the effect variance and effect weight.

[0020] The effect value is calculated by weighted summation based on the effect variance and effect weight to obtain the weighted comprehensive effect value.

[0021] The weighted composite effect value is converted into a percentage to obtain the proportion coefficient of the different farming methods.

[0022] In some embodiments, the step of simulating the available water quantity changes in the target area according to different change scenarios to obtain simulation results includes:

[0023] Obtain historical hydrological data for the target area, including population, temperature, rainfall, and available water supply.

[0024] The historical hydrological data were subjected to a quadratic polynomial stepwise regression analysis to obtain the regression analysis results.

[0025] The regression analysis results are processed by correlation calculation to determine the correlation between the available water supply and the population, rainfall, and temperature.

[0026] The correlation is simulated based on the scenario of available water quantity change according to the scenario pattern comparison plan, and the simulation results are obtained.

[0027] In some embodiments, the construction of a multi-objective optimization model based on a multi-objective function and constraints includes:

[0028] The multi-objective function is constructed based on the formulas for maximizing economic benefits, minimizing environmental pollution, and maximizing irrigation water use efficiency.

[0029] The constraint conditions are constructed based on the area constraint formula, food security constraint formula, environmental carbon emission constraint formula, water constraint formula, and non-negativity constraint formula.

[0030] The multi-objective function is modeled according to the constraints to obtain the multi-objective optimization model.

[0031] In some embodiments, the construction of the multi-objective function based on the economic benefit maximization formula, the environmental pollution minimization formula, and the irrigation water use efficiency maximization formula includes:

[0032] The economic benefit maximization formula is obtained by calculating and processing planting income, planting cost and water cost.

[0033] The formula for minimizing environmental pollution is obtained by calculating and processing greenhouse gas emissions and organic carbon sequestration.

[0034] The ratio of crop yield to irrigation water volume is calculated to obtain the formula for maximizing irrigation water use efficiency.

[0035] The objective functions of the economic benefit maximization formula, the environmental pollution minimization formula, and the irrigation water use efficiency maximization formula are processed to obtain the multi-objective function.

[0036] In some embodiments, the step of inputting the scaling factor and the simulation results into the multi-objective optimization model for optimization solution processing to obtain the optimization mode includes:

[0037] The scaling factor is used as the decision vector, and the simulation results are used as constraint variables;

[0038] The multi-objective optimization model is subjected to fuzzy mathematical programming calculations based on the decision vector and the constraint variables to obtain the optimization mode.

[0039] To achieve the above objectives, another aspect of this application proposes a tillage optimization device based on multi-objective analysis, the device comprising:

[0040] The comparative analysis module is used to compare and analyze farming practices in the target area to obtain the proportion coefficients of different farming methods.

[0041] The scenario simulation module is used to simulate the changes in available water in the target area according to different changing scenarios, and obtain simulation results.

[0042] The model building module is used to construct a multi-objective optimization model based on the multi-objective function and constraints.

[0043] The optimization solution module is used to input the scaling factor and the simulation results into the multi-objective optimization model for optimization solution processing to obtain the optimization mode;

[0044] The tillage optimization module is used to adjust tillage measures and optimize water and soil resource allocation in the target area according to the optimization mode.

[0045] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0046] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0047] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0048] The embodiments of this application include at least the following beneficial effects: This application provides a tillage optimization method, apparatus, equipment, and medium based on multi-objective analysis. This scheme obtains the proportion coefficients of different tillage methods by comparing and analyzing tillage measures in a target area, simulates changes in available water in the target area under different changing scenarios to obtain simulation results, constructs a multi-objective optimization model based on multi-objective functions and constraints, inputs the proportion coefficients and simulation results into the multi-objective optimization model for optimization and solution processing to obtain an optimization mode, and adjusts tillage measures and optimizes water and soil resource allocation in the target area based on the optimization mode. By comparing and analyzing different tillage measures and constructing a multi-objective optimization model, the embodiments of this application can optimize water and fertilizer resources and adjust planting areas, providing decision-makers with a more reliable agricultural water, soil, and fertilizer resource allocation scheme, and improving the optimization and sustainable allocation efficiency of agricultural resources in tillage systems under changing environmental scenarios. Attached Figure Description

[0049] Figure 1 This is a flowchart of a tillage optimization method based on multi-objective analysis provided in an embodiment of this application;

[0050] Figure 2 This is a schematic diagram of a tillage optimization device based on multi-objective analysis provided in an embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0053] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0054] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] In related technologies, research on conservation tillage regarding its effects on soil carbon sequestration and emission reduction, crop yield, and water use efficiency is controversial, with significant differences in results across different regions and sites. Some studies argue that the climate change mitigation effects of conservation tillage are exaggerated, suggesting that its carbon sequestration effect is largely limited to the topsoil, effectively improving soil texture and providing some degree of climate change adaptation. Other studies indicate that conservation tillage and similar measures promote direct greenhouse gas emissions from farmland. Conclusions regarding the impact of conservation tillage on crop yield and water use efficiency are also inconsistent, with different techniques exhibiting varying effects on crop yield and water use efficiency across different climate types, soil textures, crop types, and years of application. These contradictions have, to some extent, affected the accurate understanding of the effects of conservation tillage and limited its widespread adoption.

[0057] For example, most studies focus on the single optimal allocation of water or land resources, while studies on the combined optimal allocation of both are relatively few. Furthermore, the established water and land resource optimization models do not adequately consider factors such as ecological benefits and water use efficiency, and are mostly single-objective models that prioritize maximizing economic benefits. In addition, climate change directly affects the hydrological cycle, causing not only changes in the total amount of water resources but also altering their spatial and temporal distribution, severely impacting water resource development, utilization, and planning.

[0058] In view of this, this application provides a method, apparatus, equipment, and medium for optimizing tillage based on multi-objective analysis. This scheme obtains the proportion coefficients of different tillage methods by comparing and analyzing tillage measures in a target area. It then simulates changes in available water in the target area under different changing scenarios to obtain simulation results. A multi-objective optimization model is constructed based on multi-objective functions and constraints. The proportion coefficients and simulation results are input into the multi-objective optimization model for optimization and solution processing to obtain an optimization mode. Based on the optimization mode, tillage measures are adjusted and water and soil resource allocation is optimized in the target area. This application, by comparing and analyzing different tillage measures and constructing a multi-objective optimization model, can optimize water and fertilizer resources and adjust planting area, providing decision-makers with a more reliable agricultural water, soil, and fertilizer resource allocation scheme and improving the optimization and sustainable allocation efficiency of agricultural resources in tillage systems under changing environmental scenarios.

[0059] This application provides a tillage optimization method based on multi-objective analysis, relating to the field of agricultural tillage optimization technology. The tillage optimization method based on multi-objective analysis provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application implementing the tillage optimization method based on multi-objective analysis, etc., but is not limited to the above forms.

[0060] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0061] Figure 1 This is an optional flowchart of a tillage optimization method based on multi-objective analysis provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0062] Step S101: Compare and analyze the farming practices in the target area to obtain the proportion coefficients of different farming methods;

[0063] Step S102: Simulate the changes in available water volume in the target area according to different change scenarios to obtain simulation results;

[0064] Step S103: Construct a multi-objective optimization model based on the multi-objective function and constraints;

[0065] Step S104: Input the proportional coefficient and the simulation results into the multi-objective optimization model for optimization and solution processing to obtain the optimization mode;

[0066] Step S105: Adjust farming practices and optimize water and soil resource allocation in the target area according to the optimization mode.

[0067] Steps S101 to S105 of this embodiment involve comparative analysis of farming practices in the target area to obtain proportional coefficients for different farming methods. The farming system includes traditional farming and conservation tillage, with conservation tillage encompassing deep tillage, no-tillage, and shallow tillage. This embodiment obtains the rate of change of response variables under deep tillage, no-tillage, and shallow tillage relative to traditional farming methods through comparative analysis. This rate of change is directly used as the proportional coefficients for deep tillage, no-tillage, and shallow tillage relative to the response variables. The response variables are yield, carbon sequestration and emission reduction, and irrigation water use efficiency data. Then, simulations of available water changes in the target area under different change scenarios are conducted to obtain simulation results. These simulation results can be used as constraint variables in a multi-objective optimization model, enabling dynamic analysis of the farming system and water and soil resource optimization in the target area. This multi-objective optimization model is constructed based on multi-objective functions and constraints. It aims to balance the conflict between resource utilization and economic and environmental effects, employing three objective functions: maximizing economic benefits, minimizing greenhouse gas emissions, and maximizing irrigation water use efficiency. These functions are constrained by water supply demand, land supply, food security, and energy supply. By optimizing the multi-objective optimization model, an optimized pattern is obtained. This pattern, through effective decision-making regarding water resource utilization, planting structure, and farming methods, facilitates the synergistic achievement of multiple objectives, including improving agricultural economic benefits, increasing water use efficiency, and reducing carbon emissions. It can help decision-makers manage water and soil resources and farming methods in an efficient and environmentally friendly manner.

[0068] In some embodiments, the comparative analysis of farming practices in the target area to obtain the proportion coefficients of different farming methods includes:

[0069] Keyword search processing was performed on the database to obtain literature related to farming;

[0070] The aforementioned farming-related literature was subjected to quality testing and screening to obtain the screened literature;

[0071] Data extraction and processing were performed on the selected literature to obtain response variables for different farming methods; the farming methods included deep tillage, no-tillage, shallow tillage and conventional tillage, and the response variables included yield, carbon sequestration and emission reduction and irrigation water use efficiency data;

[0072] Meta-analysis was performed on the response variables to obtain the proportion coefficients of the different farming methods.

[0073] In this embodiment, literature related to farming systems is collected through keyword retrieval, and further screened through a series of standard tests. First, the independence of the collected literature data sources is tested to ensure the accuracy and objectivity of the final results. Quality testing and evaluation of the literature and data in the database are conducted, specifically including literature quality assessment, publication bias testing, and heterogeneity testing. Literature quality assessment utilizes a bias risk assessment tool to evaluate six aspects: selection bias, implementation bias, measurement bias, follow-up bias, reporting bias, and other biases. Literature can be categorized into three types: "low-risk bias," "high-risk bias," and "unclear." Publication bias testing of each data point is primarily reflected through weighting, but can also be analyzed using quantitative charts, thereby improving data reliability and result accuracy. This application typically uses funnel plots to test for bias in presenting results. A scatter plot of sample size and effect size is used. If the scatter plot is distributed on the more symmetrical front side of the funnel plot, the data selected has less or no publication bias. However, drawing a funnel plot requires collecting a large amount of data; if the data volume is small, the scatter plot may be mostly distributed on the bottom left and right sides, affecting the test for publication bias. Alternatively, normal percentile plots can be verified using software. The effect size and normal percentiles are used as the ordinate and abscissa of the normal percentile plot, respectively. If the effect size of the response variable is evenly distributed within the highest and lowest quantiles of the confidence interval, it satisfies a normal distribution, indicating no or minimal publication bias. Different data may exhibit heterogeneity, making it impossible to merge them during research. Heterogeneity mainly stems from two aspects: intra-study differences, primarily due to sampling error, and inter-study differences, mainly due to differences in data from different independent studies, leading to differences in effect size calculations. Heterogeneity testing can be performed using I... 2 The test and the Q test, where I 2 A value exceeding 50% indicates high heterogeneity in the selected data; the Q test assesses heterogeneity based on the p-value, with p < 0.05 indicating high heterogeneity. This application's embodiment addresses high heterogeneity by verifying the mean and standard deviation of the selected literature against the data. Literature that does not meet the requirements is removed to reduce heterogeneity. If the effect size indicator is problematic, it is replaced, or the unit or scale of the effect size is standardized. Subgroup analysis can be used to determine the causes of heterogeneity and address the high heterogeneity issue accordingly.

[0074] In this embodiment, yield, carbon sequestration and emission reduction, and irrigation water use efficiency data for deep tillage (DT), no-tillage (NT), shallow tillage (ST), and conventional tillage (CT) treatments are extracted from the literature screened by the above-described operations and used as response variables. Based on meta-analysis, the rate of change of the response variables under the three conservation tillage methods (DT, NT, and ST) relative to conventional tillage is obtained, and this rate of change is directly used as the proportionality coefficient of each method relative to the response variable. By obtaining the proportionality coefficients of different tillage methods, this embodiment determines the impact of deep tillage, no-tillage, and shallow tillage on yield, irrigation water use efficiency, and greenhouse gas emissions, enabling the inclusion of these parameters in the model to determine the optimal tillage method for crop adaptation.

[0075] In some embodiments, the meta-analysis of the response variables to obtain the proportion coefficients of different farming methods includes:

[0076] The response variables are grouped to obtain a treatment group and an experimental group;

[0077] The effect value was obtained by calculating the average value and natural logarithm of the treatment group and the experimental group.

[0078] Based on the standard deviation and number of repetitions of the response variable, the effect value is subjected to inverse and weight calculations to obtain the effect variance and effect weight.

[0079] The effect value is calculated by weighted summation based on the effect variance and effect weight to obtain the weighted comprehensive effect value.

[0080] The weighted composite effect value is converted into a percentage to obtain the proportion coefficient of the different farming methods.

[0081] In this embodiment, the tillage method (CT) with the greatest soil disturbance was selected as the treatment group, and the experimental groups consisted of different conservation tillage practices (deep tillage (DT), no-tillage (NT), and shallow tillage (ST)). This embodiment quantifies the impact of conservation tillage (deep tillage, no-tillage, and shallow tillage) on crop yield, carbon sequestration and emission reduction, and irrigation water use efficiency by defining the natural logarithm of the response ratio (ln R) as a measure of effect size. The formula for calculating the natural logarithm of the response ratio for a specific indicator in different studies is as follows:

[0082]

[0083] In the formula, χ c It is the average of the response variables (yield, irrigation water use efficiency, greenhouse gas emissions, and organic carbon sequestration) for conventional farming (CT); χ² tThese are the average values ​​of these response variables in conservation tillage (DP, NT, SP). When the ln R value is higher than 0, conservation tillage has a positive effect on the variables; when it is lower than 0, it has a negative effect.

[0084] To obtain accurate estimates of the effect size, embodiments of this application use the standard deviation and the number of repetitions to calculate the variance (v) and corresponding weights of the corresponding response effect size, as shown in the following formula:

[0085]

[0086] In the formula, SD t and n t These are the standard deviation and number of repetitions of the CT response variable, respectively; SD c and n c These are the standard deviations and number of replicates for the DT, NT, and ST response variables, respectively. To determine the final accurate effect size, the overall or combined effect size (or average effect size) of the treatment group is ln R. ++ It is obtained by weighting and summing different research data pairs, and is calculated according to the following formula:

[0087]

[0088] In the formula, ln R ++ This is the weighted combined effect value; ln R i and W i These are the effect size and weight of the i-th data pair, respectively; the weight of each data pair is the reciprocal of the variance of the corresponding effect size.

[0089] To reflect the variability of effect sizes, the embodiments of this application calculate the weighted combined effect value ln R. ++ The 95% confidence interval (95% CI) is used to determine the impact of a cropping action on a variable. If the 95% CI value of the effect of the crop does not overlap with 0, the cropping action is considered to have a significant effect on the variable; otherwise, the effect is not significant. The calculation formula is as follows:

[0090]

[0091] In the formula, The weighted combined effect value is ln R. ++ The standard deviation and weighted combined effect value are calculated using the following formula:

[0092]

[0093] To better represent the impact of conservation tillage relative to total climatology (CT) on yield, water use efficiency, greenhouse gas emissions, and organic carbon sequestration, the embodiments of this application use a weighted comprehensive effect value ln R. ++ Converting to a percentage E, the formula for calculating the proportionality coefficient is as follows:

[0094] E=(exp(ln R ++ )-1)×100%;

[0095] In the formula, E represents the rate of change in crop yield, carbon sequestration and emission reduction, and irrigation water use efficiency under conservation tillage relative to traditional tillage methods, i.e., the proportionality coefficient.

[0096] In some embodiments, the step of simulating the available water quantity changes in the target area according to different change scenarios to obtain simulation results includes:

[0097] Obtain historical hydrological data for the target area, including population, temperature, rainfall, and available water supply.

[0098] The historical hydrological data were subjected to a quadratic polynomial stepwise regression analysis to obtain the regression analysis results.

[0099] The regression analysis results are processed by correlation calculation to determine the correlation between the available water supply and the population, rainfall, and temperature.

[0100] The correlation is simulated based on the scenario of available water quantity change according to the scenario pattern comparison plan, and the simulation results are obtained.

[0101] In this embodiment, historical hydrological data is selected based on the actual historical values ​​of population, temperature, rainfall, and available water volume in the target area. These historical hydrological data are then subjected to stepwise quadratic polynomial regression analysis. The correlation coefficients are used to determine the correlation between available water volume and the population, rainfall, and temperature, thereby establishing the correlation among these three factors. Then, the Sixth Coupled Model Intercomparison Project (CMIP6) is used to obtain rainfall, temperature, and population data under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The collected data is processed using stepwise quadratic polynomial regression in the data processing system. Based on the set required by the Scenario Model Intercomparison Project (CMIP) modeling center, simulations of four different changing environmental scenarios are conducted to obtain the potential changes in available water volume in the target area under future changing environments. Through the above simulations, this embodiment can use the simulation results of available water volume changes under the four changing scenarios as constraint variables in the model, achieving dynamic analysis of the optimal allocation of agricultural systems and water and soil resources in the target area. This approach not only reveals the potential impact of future climate change on water supply, but also provides data support and decision-making reference for developing adaptive management measures, thereby effectively reducing the risks of water and soil resource management in arable areas.

[0102] In some embodiments, the construction of a multi-objective optimization model based on a multi-objective function and constraints includes:

[0103] The multi-objective function is constructed based on the formulas for maximizing economic benefits, minimizing environmental pollution, and maximizing irrigation water use efficiency.

[0104] The constraint conditions are constructed based on the area constraint formula, food security constraint formula, environmental carbon emission constraint formula, water constraint formula, and non-negativity constraint formula.

[0105] The multi-objective function is modeled according to the constraints to obtain the multi-objective optimization model.

[0106] In this embodiment, a multi-objective nonlinear tillage system optimization model is established to optimize tillage measures, irrigation surface water, groundwater volume, and planting structure for different crops in different regions within a decision-making tillage system. This embodiment utilizes meta-analysis to quantify the impact of conservation tillage on crop yield, carbon sequestration and emission reduction, and irrigation water use efficiency, incorporating these proportionality coefficients into the function. Model construction and optimization are performed based on the multi-objective function, constraints, and decision variables. These constraints include area constraint formulas, food security constraint formulas, environmental carbon emission constraint formulas, water volume constraint formulas, and non-negativity constraint formulas.

[0107] Area constraints indicate that the actual planted area of ​​each crop zone in a farming system cannot exceed the maximum and minimum planted area required for that zone. The expression for the area constraint formula is shown below:

[0108] A min,rkc ≤A rkc ≤A max,rkc ;

[0109] In the formula, A min,rkc and A max,rkc These are the minimum and maximum planting areas (hm²). 2 ).

[0110] The food security constraint means that the total amount of food production and imports / exports should exceed the minimum food supply for the region's population. The formula for the food security constraint is shown below:

[0111]

[0112] In the formula, IM c Grain import volume (kg); EX c For grain exports (kg); M r Population (people); Ma rkc Food supply per person (kg / person).

[0113] Environmental carbon emission constraints indicate that carbon emissions in the environment should not exceed the maximum greenhouse gas emissions required by national regulations. The formula for environmental carbon emission constraints is shown below:

[0114]

[0115] In the formula, CE rkc This represents the maximum greenhouse gas emissions (kgCO2) that are limited in the environment.

[0116] Water constraints include supply constraints and demand constraints. Supply constraints mean that the amount of surface water and groundwater used for irrigation in each zone of a planting area cannot exceed the total water supply of that zone. Demand constraints mean that the amount of surface water and groundwater used for irrigation in each zone of a planting area should be between the maximum and minimum values ​​of the water storage capacity. The expression for the water constraint formula is as follows:

[0117]

[0118] In the formula, Is r The supply of surface water (m 3 );ηs r It is the surface water irrigation utilization coefficient; r The supply of groundwater (m³) 3 );ηt r This is the groundwater irrigation utilization coefficient.

[0119] The nonnegativity constraint means that the decision variables in the model (planted area, surface water, and groundwater distribution) should all satisfy the real-world condition of nonnegativity. The expression for the nonnegativity constraint formula is shown below:

[0120]

[0121] This application embodiment constructs a multi-objective optimization model that can balance the conflict between resource utilization and economic and environmental effects. It adopts three objective functions: maximizing economic benefits, minimizing greenhouse gas emissions, and maximizing irrigation water use efficiency, and is subject to constraints related to water supply, land supply, food security, and energy supply.

[0122] In some embodiments, the construction of the multi-objective function based on the economic benefit maximization formula, the environmental pollution minimization formula, and the irrigation water use efficiency maximization formula includes:

[0123] The economic benefit maximization formula is obtained by calculating and processing planting income, planting cost and water cost.

[0124] The formula for minimizing environmental pollution is obtained by calculating and processing greenhouse gas emissions and organic carbon sequestration.

[0125] The ratio of crop yield to irrigation water volume is calculated to obtain the formula for maximizing irrigation water use efficiency.

[0126] The objective functions of the economic benefit maximization formula, the environmental pollution minimization formula, and the irrigation water use efficiency maximization formula are processed to obtain the multi-objective function.

[0127] In this embodiment, the multi-objective function includes a formula for maximizing economic benefits, a formula for minimizing environmental pollution, and a formula for maximizing irrigation water use efficiency. The economic benefits maximization formula aims to maximize the economic benefits per unit area of ​​crop cultivation, which is the difference between planting income (crop yield) and planting and water costs. Planting costs include material costs (seeds, pesticides, fertilizers, agricultural film, and manual labor) and machinery costs (ownership and operation of agricultural machinery). Water costs refer to irrigation using surface water and groundwater. The expression for this objective function is shown below:

[0128]

[0129] In the formula, max feco refers to the objective function (in yuan) of the total net economic benefit; r refers to the regional index, R refers to the total number of cultivated areas; k refers to the farming method index, K refers to the total number of farming methods studied; c refers to the crop index, C refers to the total number of crops studied; P rkc Market unit price (yuan / hm) 2 );Y rkc Yield per unit area (kg / hm) 2 A rkc Refers to the planting area (hm) 2 ); CE rkc Planting cost (yuan / hm) 2 IQS rkc Surface water irrigation quota (m 3 / hm 2 IQG rkc Groundwater irrigation quota (m 3 / hm 2 ); qs rkc The unit price of surface water for irrigation (yuan / m³) 3 );qg rkc The unit price of groundwater irrigation water (yuan / m³) 3 ).

[0130] Since relevant literature shows that 14% of global grain production is lost during the process from production to sale, and that harvesting, storage, and transportation all contribute to this loss, it is crucial to consider yield losses in yield calculations. The expression for yield loss is as follows:

[0131] YOL rkc =Y rkc ×(D i );

[0132] The expression for the production loss rate is as follows:

[0133]

[0134] In the formula, YOL rkc The amount of yield loss per unit area (kg / hm) 2 );D i The yield loss rate (%) is indicated by β, which is the loss coefficient. A(ε) represents the Atkinson index, which is used to represent the uneven proportion of the grain loss process.

[0135] Planting costs include material costs (seeds, pesticides, fertilizers, agricultural film), machinery costs (ownership and operation of agricultural machinery), and labor costs. The formulas for planting costs, material costs, and labor costs are shown below:

[0136] CE rkc =CP rkc +CG rkc +CH rkc ;

[0137] CP rkc =Ca rkc +Cb rkc +Cc rkc +Cd rkc ;

[0138] CH rkc =CF rkc +CE rkc ;

[0139] In the formula, CP rkc Material cost (yuan / hm) 2 ); CG rkc Machinery cost (yuan / hm) 2 );CH rkc Labor cost (yuan / hm) 2 );Ca rkc Seed cost (yuan / hm) 2 );Cb rkc Pesticide cost (yuan / hm) 2 );Cc rkc Fertilizer cost (yuan / hm) 2 ); Cd rkc Agricultural film cost (yuan / hm) 2 );CF rkc This refers to the discounted fee for domestic labor based on the local daily wage (yuan / hm). 2 ); CErkc Labor costs (yuan / hm) 2 ).

[0140] Machinery costs include ownership (also known as capital costs) and operating costs; the total cost is calculated per hectare (hm²). 2 The formula is used. The formula for calculating the annualized cost of each machine is as follows:

[0141] CG rkc = (CG1 + CG2) × N rkc ×tillageK k ;

[0142] In the formula, CG1 refers to the total annual cost of ownership (yuan / hm). 2 CG2 refers to the total annual operating cost (RMB / hm²). 2 ), N rkc The required number of machines (units / hm) 2 tillagek k This refers to the ratio of mechanical costs among the three conservation tillage methods compared to conventional tillage.

[0143] The total cost of ownership includes depreciation, interest, and insurance costs. The formula for calculating the total cost of ownership each year is shown below:

[0144] CG1 = PG × Co;

[0145] Multiply the purchase price of the machine by the annualized percentage of cost of ownership. The formula for calculating the percentage of cost of ownership is as follows:

[0146]

[0147] In the formula, PG is the purchase cost of the tillage machinery (yuan / unit); Co is the annual percentage of ownership cost; Gv is the residual value at the end of the machine's life; T is the machine's life (10 years); I is the interest rate; and M is the insurance factor.

[0148] Total operating costs, calculated annually, include repair and maintenance costs as well as fuel costs. The formula for calculating total operating costs is as follows:

[0149] CG2 = CGR + Gf;

[0150] The formula for calculating repair and maintenance costs is as follows:

[0151] CGR = PG × RF1 × (h / 1000) RF2 / T;

[0152] In the formula, CGR is the average annual maintenance and repair cost of the tillage machine (yuan); Gf represents the annual fuel cost of the tillage machine (yuan); RF1 and RF2 are maintenance and repair factors; and h is the cumulative number of hours the machine is used (h).

[0153] The formula for calculating the annualized fuel cost of a given machine in this application embodiment is as follows:

[0154] Gf = Mf × NA × Pf;

[0155] In the formula, Mf represents the average fuel consumption per hour (L / h); NA represents the actual annual usage time of the tillage machinery (h); and Pf represents the current fuel price in China (CNY / L).

[0156] The function of the environmental pollution minimization formula is to minimize the environmental impact of greenhouse gas emissions. This objective function is calculated from the difference between greenhouse gas emissions and the amount of organic carbon fixed. Greenhouse gases in farmland include CO2, CH4, and N2O. The expression for this objective function is shown below:

[0157]

[0158] In the formula, min fenv represents the objective function of environmental effects (kgCO2); CEG rkc This refers to the emissions (kgCO2) of greenhouse gases (CO2, CH4, N2O); CFG rkc Refers to the amount of organic carbon fixed (kgCO2); tillagel k The greenhouse gas emissions ratio of conservation tillage (DT, NT, ST) relative to conventional tillage; tillagef k This refers to the ratio of organic carbon absorbed by conservation tillage (DT, NT, ST) relative to conventional tillage; These refer to the emissions of CO2, CH4, and N2O (kgCO2), respectively; δ f δ p δ i δ l δ e These refer to the carbon emission coefficients (kgCO2 / kg) for fertilizers, pesticides, fuels, agricultural films, and electricity, respectively; D rkc,f D rkc,p D rkc,i D rkc,l These refer to the amount of fertilizer, pesticide, fuel, and agricultural film used per unit area (kg / hm²). 2 );F rkc,e Electricity consumption per unit area (kWh / hm) 2 );κγ rkcThe emission factor for CH4 (kg / hm) 2 ). λf rkc Refers to the fertilizer N2O emission factor (kgN2O / kg); μ rkc The crop background N2O emission factor (kgN2O / hm) 2 ).

[0159] Soil carbon sequestration efficiency is considered as the carbon sequestration efficiency of biochar during the planning period. The formula for calculating soil carbon sequestration is as follows:

[0160]

[0161] In the formula, coal c This refers to the actual amount of biochar used (kg) in the crop planting area; ω c Carbon absorption coefficient (kg / hm) 2 ).

[0162] Irrigation water use efficiency (IWUE) is the ratio of crop yield to irrigation water consumption. This objective function aims to maximize water use efficiency and conserve water in crop production. The formula for maximizing irrigation water use efficiency is shown below:

[0163]

[0164] In the formula, max I WUE represents the objective function of crop irrigation water use efficiency (kg / m³). 3 );tillageS k The ratio of irrigation water use efficiency to that of different conservation tillage methods (deep tillage, no tillage, shallow tillage) under conventional tillage; e refers to the ratio of irrigated area to planted area.

[0165] This application's embodiments construct a multi-objective nonlinear model framework based on meta-analysis for optimizing cropping systems in target regions under varying environments. Through effective decision-making regarding water resource utilization, planting structure, and farming methods, it facilitates the synergistic achievement of multiple objectives, including improving agricultural economic benefits, increasing water use efficiency, and reducing carbon emissions. This application's embodiments establish a multi-objective optimization model considering the synergy of economic benefits, environmental pollution, and water resource utilization efficiency, enabling the adjustment and optimization of farming methods, planting structure, and irrigation water volume. This application's embodiments also consider uncertainties such as rainfall, temperature, and population changes, deeply analyzing the impact of different environmental changes on farming methods, planting structure, and irrigation schemes. Based on the constructed model, this application's embodiments explore optimization patterns for planting structure and irrigation water resources under different conservation tillage methods in the target region, effectively and sustainably assisting in agricultural farming, water, and land resource management.

[0166] In some embodiments, the step of inputting the scaling factor and the simulation results into the multi-objective optimization model for optimization solution processing to obtain the optimization mode includes:

[0167] The scaling factor is used as the decision vector, and the simulation results are used as constraint variables;

[0168] The multi-objective optimization model is subjected to fuzzy mathematical programming calculations based on the decision vector and the constraint variables to obtain the optimization mode.

[0169] In this embodiment, unreasonable farming practices lead to land waste, soil degradation, environmental pollution, and ecological damage, resulting in reduced land productivity and consequently lower farmer incomes. Reasonable farming systems, such as conservation tillage, planting structure adjustments, and water and soil resource adaptation, can reduce adverse effects like land degradation and carbon mineralization, improve land productivity, reduce greenhouse gas emissions, and potentially increase carbon sequestration. Therefore, this embodiment optimizes farming methods by using a proportionality coefficient as a decision vector, effectively utilizing land resources, maintaining a healthy agricultural ecological environment, achieving higher economic benefits, and improving climate resilience. The multi-objective optimization model in this embodiment involves multiple conflicting objective functions, requiring the search for a balanced solution, i.e., the optimal solution set, across these objectives. The above multi-objective nonlinear programming model employs a fuzzy mathematical programming algorithm, defining each objective as a membership function, transforming the multi-objective problem into a problem of maximizing overall satisfaction. This can be solved using a general mathematical programming solver to obtain an optimized model. This embodiment optimizes farming practices, surface water irrigation, groundwater volume, and planting structure for different crops in different regions within the decision-making farming system through this optimization model.

[0170] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples:

[0171] This application's embodiments can be applied to the optimization of agricultural tillage systems. By conducting meta-analysis on the impact of different tillage regimes (conservation tillage, irrigation, and fertilization) on crop yield, water use efficiency, and carbon sequestration and emission reduction, the proportion coefficients of different tillage methods are obtained. Furthermore, to gain a deeper understanding of the impact of future environmental changes on the optimization and adjustment of tillage methods and water and soil resources within the tillage system, the uncertainty of available irrigation water needs to be considered. This application's embodiments simulate changes in available water in the target area under different changing scenarios, and obtain an optimization model through a multi-objective optimization model. This model enables adjustments to tillage measures and optimization of water and soil resource allocation in the target area. Experimental results from this application's embodiments show that the planted area decreased by 1.510 × 10⁻⁶ after readjustment. 5 hm 2Irrigation water consumption decreased by 12%, achieving water conservation. The harmony between economic, environmental, and resource utilization in the model increased by 37.5%, making the optimized model more stable. The economic benefits in the optimized model increased by 1.5 × 10⁻⁶. 7 The amount of water used increased by 22%, resulting in a reduction of 4.98 × 10⁸ tons of irrigation water efficiency and a decrease in environmental pollution. 6 kgCO2 effectively mitigates global warming.

[0172] Please see Figure 2 This application also provides a tillage optimization device based on multi-objective analysis, which can implement the above-described method. The device includes:

[0173] The comparative analysis module 201 is used to conduct comparative analysis of farming practices in the target area and obtain the proportion coefficients of different farming methods.

[0174] The scenario simulation module 202 is used to simulate the changes in available water in the target area according to different changing scenarios, and obtain simulation results.

[0175] The model building module 203 is used to construct a multi-objective optimization model based on the multi-objective function and constraints.

[0176] The optimization solution module 204 is used to input the scaling factor and the simulation results into the multi-objective optimization model for optimization solution processing to obtain the optimization mode;

[0177] The tillage optimization module 205 is used to adjust tillage measures and optimize water and soil resource allocation in the target area according to the optimization mode.

[0178] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0179] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0180] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0181] Please see Figure 3 , Figure 3The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0182] The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0183] The memory 302 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 302 and is called and executed by the processor 301 using the methods described in the embodiments of this application.

[0184] Input / output interface 303 is used to implement information input and output;

[0185] The communication interface 304 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0186] Bus 305 transmits information between various components of the device (e.g., processor 301, memory 302, input / output interface 303, and communication interface 304);

[0187] The processor 301, memory 302, input / output interface 303, and communication interface 304 are connected to each other within the device via bus 305.

[0188] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0189] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0190] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0191] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0192] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0193] This application provides a method, apparatus, equipment, and medium for optimizing tillage based on multi-objective analysis. The scheme obtains proportional coefficients for different tillage methods by comparing and analyzing tillage measures in a target area. It then simulates changes in available water in the target area under different changing scenarios to obtain simulation results. A multi-objective optimization model is constructed based on multi-objective functions and constraints. The proportional coefficients and simulation results are input into the multi-objective optimization model for optimization and solution processing to obtain an optimization mode. Based on the optimization mode, tillage measures are adjusted and water and soil resource allocation is optimized in the target area. This application, by comparing and analyzing different tillage measures and constructing a multi-objective optimization model, can optimize water and fertilizer resources and adjust planting area, providing decision-makers with a more reliable agricultural water, soil, and fertilizer resource allocation scheme and improving the optimization and sustainable allocation efficiency of agricultural resources in tillage systems under changing environmental scenarios.

[0194] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0195] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0198] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0199] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0200] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0201] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0203] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0204] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A tillage optimization method based on multi-objective analysis, characterized in that, The method includes the following steps: A comparative analysis of farming practices in the target area was conducted to obtain the proportion coefficients of different farming methods. The available water supply changes in the target area are simulated according to different changing scenarios to obtain simulation results; A multi-objective optimization model is constructed based on the multi-objective function and constraints. The proportionality coefficient and the simulation results are input into the multi-objective optimization model for optimization and solution processing to obtain the optimization mode; Based on the optimization model, the farming practices and water and soil resource allocation in the target area are adjusted.

2. The method according to claim 1, characterized in that, The comparative analysis of farming practices in the target area yields the proportion coefficients of different farming methods, including: Keyword search processing was performed on the database to obtain literature related to farming; The aforementioned farming-related literature was subjected to quality testing and screening to obtain the screened literature; Data extraction and processing were performed on the selected literature to obtain response variables for different farming methods; the farming methods included deep tillage, no-tillage, shallow tillage and conventional tillage, and the response variables included yield, carbon sequestration and emission reduction and irrigation water use efficiency data; Meta-analysis was performed on the response variables to obtain the proportion coefficients of the different farming methods.

3. The method according to claim 2, characterized in that, The meta-analysis of the response variables yields the proportion coefficients for different farming methods, including: The response variables are grouped to obtain a treatment group and an experimental group; The effect value was obtained by calculating the average value and natural logarithm of the treatment group and the experimental group. Based on the standard deviation and number of repetitions of the response variable, the effect value is subjected to inverse and weight calculations to obtain the effect variance and effect weight. The effect value is calculated by weighted summation based on the effect variance and effect weight to obtain the weighted comprehensive effect value. The weighted composite effect value is converted into a percentage to obtain the proportion coefficient of the different farming methods.

4. The method according to claim 1, characterized in that, The simulation of available water supply changes in the target area based on different changing scenarios yields simulation results, including: Obtain historical hydrological data for the target area, including population, temperature, rainfall, and available water supply. The historical hydrological data were subjected to a quadratic polynomial stepwise regression analysis to obtain the regression analysis results. The regression analysis results are processed by correlation calculation to determine the correlation between the available water supply and the population, rainfall, and temperature. The correlation is simulated based on the scenario of available water quantity change according to the scenario pattern comparison plan, and the simulation results are obtained.

5. The method according to claim 1, characterized in that, The multi-objective optimization model constructed based on the multi-objective function and constraints includes: The multi-objective function is constructed based on the formulas for maximizing economic benefits, minimizing environmental pollution, and maximizing irrigation water use efficiency. The constraint conditions are constructed based on the area constraint formula, food security constraint formula, environmental carbon emission constraint formula, water constraint formula, and non-negativity constraint formula. The multi-objective function is modeled according to the constraints to obtain the multi-objective optimization model.

6. The method according to claim 5, characterized in that, The multi-objective function, constructed based on the formulas for maximizing economic benefits, minimizing environmental pollution, and maximizing irrigation water use efficiency, includes: The economic benefit maximization formula is obtained by calculating and processing planting income, planting cost and water cost. The formula for minimizing environmental pollution is obtained by calculating and processing greenhouse gas emissions and organic carbon sequestration. The ratio of crop yield to irrigation water volume is calculated to obtain the formula for maximizing irrigation water use efficiency. The objective functions of the economic benefit maximization formula, the environmental pollution minimization formula, and the irrigation water use efficiency maximization formula are processed to obtain the multi-objective function.

7. The method according to any one of claims 1 to 6, characterized in that, The step of inputting the scaling factor and the simulation results into the multi-objective optimization model for optimization and solving to obtain the optimization mode includes: The scaling factor is used as the decision vector, and the simulation results are used as constraint variables; The multi-objective optimization model is subjected to fuzzy mathematical programming calculations based on the decision vector and the constraint variables to obtain the optimization mode.

8. A tillage optimization device based on multi-objective analysis, characterized in that, The device includes: The comparative analysis module is used to compare and analyze farming practices in the target area to obtain the proportion coefficients of different farming methods. The scenario simulation module is used to simulate the changes in available water in the target area according to different changing scenarios, and obtain simulation results. The model building module is used to construct a multi-objective optimization model based on the multi-objective function and constraints. The optimization solution module is used to input the scaling factor and the simulation results into the multi-objective optimization model for optimization solution processing to obtain the optimization mode; The tillage optimization module is used to adjust tillage measures and optimize water and soil resource allocation in the target area according to the optimization mode.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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