Regional agricultural planting structure optimization method, device, equipment and medium based on constrained multi-objective evolutionary algorithm

Abstract

The present application relates to a method, apparatus, equipment, and medium for optimizing regional agricultural planting structures. The method comprises: using a first agricultural planting structure optimization algorithm to calculate and determine, based on an objective function and constraints, a first fitness value for each individual in a first mixed population and a second fitness value for each individual in a second mixed population; when a preset iteration condition is reached, outputting the individual solution set of the first population and the individual solution set of the second population; using the individual solution set of the second population as the individual solution set of the third population, and using a second agricultural planting structure optimization algorithm to iterate the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population; when the number of iterations reaches a preset number of iterations, outputting the non-dominated solution in the first population as the optimal agricultural planting structure solution set. The present application enables agricultural planting structures to achieve a balance between ensuring water conservation and improving economic benefits.

Description

Technical Field

[0001] The present application relates to the field of agricultural production, and in particular to a method for optimizing regional agricultural planting structure based on a constrained multi-objective evolutionary algorithm, corresponding devices, electronic equipment, and computer-readable storage media. Background Art

[0002] With the rapid development of agricultural mechanization and modernization, while total grain output continues to grow, it also brings significant pressure on water resource protection and agricultural carbon emissions.

[0003] When optimizing agricultural planting structure, multiple objectives and constraints need to be considered. Multiple objectives are often conflicting with each other. Pursuing one objective will often lead to the deterioration of other objectives. The existence of constraints will make the constrained multi-objective evolutionary algorithm easily fall into the dilemma of local optimality or poor convergence during the optimization process, affecting the quality of the final optimization solution set.

[0004] To sum up, in the existing technology, when optimizing agricultural planting structure, it is necessary to consider multiple objectives and constraints. Multiple objectives are often conflicting with each other. Pursuing one of the objectives will often lead to the deterioration of other objectives. The existence of constraints will make the constrained multi-objective evolutionary algorithm easy to fall into the dilemma of local optimality or poor convergence during the optimization process, affecting the quality of the final optimization solution set. In order to solve this problem, the applicant has made corresponding explorations. Summary of the Invention

[0005] The purpose of this application is to solve the above problems and provide a regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm, a corresponding device, an electronic device and a computer-readable storage medium.

[0006] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0007] A regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm is proposed to meet one of the purposes of this application, including:

[0008] In response to an instruction to optimize the agricultural planting structure in a target area, decision variables, objective functions, and constraints are obtained, wherein the decision variables are the planting areas corresponding to various crops, the objective functions include the total water requirements of the crops corresponding to the various crops, the economic benefits, and the difference between carbon absorption and carbon emissions, and the constraints include a total cultivated land area constraint, a regional water supply constraint, a food security constraint, and a non-negative difference between carbon absorption and carbon emissions constraint;

[0009] Taking minimizing the total water requirement of the crops, maximizing the economic benefits, and minimizing the difference between the carbon absorption and carbon emissions as the optimization direction, a preset first agricultural planting structure optimization algorithm is used to randomly generate a first population and a second population, and the first population and the second population are iterated to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population, wherein the first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population coevolutionary strategy;

[0010] Determine a first mixed population and a second mixed population according to the first population, the second population, the first offspring population, and the second offspring population, respectively; and calculate and determine a first fitness value corresponding to each population individual in the first mixed population and a second fitness value corresponding to each population individual in the second mixed population according to the objective function and the constraint conditions, wherein the population includes a plurality of population individuals, and each population individual represents a planting area corresponding to various crops;

[0011] The above steps are executed repeatedly until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and all individual solutions in the second population are non-dominated solutions, then the individual solution set of the first population and the individual solution set of the second population are output;

[0012] The individual solution set of the second population is used as the individual solution set of the third population, and the preset second agricultural planting structure optimization algorithm is used to iterate the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population. When the number of iterations reaches the preset number of iterations, the non-dominated solutions in the first population are output as the optimal agricultural planting structure solution set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

[0013] Optionally, the objective function corresponding to the total water requirement of crops is expressed as:

[0014]

[0015] Among them, f1(x) represents the total water demand of crops, w i represents the water requirement per unit area of ​​the i-th crop, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0016] The expression of the objective function corresponding to the economic benefit is:

[0017]

[0018] Among them, f2(x) represents economic benefits, p i represents the unit output price of the i-th crop, y i represents the yield per unit area of ​​the i-th crop, c i represents the fixed cost per unit area of ​​the i-th crop, which includes any number of labor cost, seed cost, diesel cost, fertilizer cost, pesticide cost, and agricultural film cost. i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0019] The expression of the objective function corresponding to the difference between carbon absorption and carbon emission is:

[0020]

[0021] Where f3(x) represents the difference between carbon absorption and carbon emission, a i represents the carbon absorption coefficient per unit yield of the i-th crop, y i represents the yield per unit area of ​​the i-th crop, e i represents the carbon emission coefficient per unit area of ​​the i-th crop, g j represents the emission coefficient per unit usage of the jth production factor, b ij represents the usage per unit area of ​​the jth production factor on the ith crop, where the production factors include any number of diesel, fertilizers, pesticides, and agricultural films. i represents the unit planting area of ​​the i-th crop, I represents the total number of crop types, and J represents the total number of production factors;

[0022] The expression of the total cultivated land area constraint is:

[0023]

[0024] Among them, S represents the total area of ​​agricultural land in the target area, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0025] The expression of the regional water supply total amount constraint is:

[0026]

[0027] Where W represents the total water supply for agricultural cultivation in the target area, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0028] The food security constraint is expressed as:

[0029]

[0030] Where N represents the annual per capita food demand in the target area, K represents the population of the target area, and y i represents the yield per unit area of ​​the i-th crop, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0031] The expression for the non-negative constraint on the difference between carbon absorption and carbon emission is:

[0032]

[0033] Among them, a i represents the carbon absorption coefficient of the yield per unit area of ​​the i-th crop, y i represents the yield per unit area of ​​the i-th crop, e i represents the carbon emission coefficient per unit area of ​​the i-th crop, g j represents the emission coefficient per unit usage of the i-th production factor, b ij represents the usage per unit area of ​​the jth production factor in the ith crop, x i represents the unit planting area of ​​the i-th crop, I represents the total number of crop types, and J represents the total number of production factors.

[0034] Optionally, the steps of randomly generating a first population and a second population using a preset first agricultural planting structure optimization algorithm, and iterating the first population and the second population to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population include:

[0035] Taking the minimum total water requirement of the crops, the maximum economic benefit, and the minimum difference between the carbon absorption and carbon emission as the optimization direction, the first agricultural planting structure optimization algorithm is used to initialize the first population, the second population, the population size, the maximum number of iterations, the current number of iterations variable, and the preset threshold, wherein the current number of iterations variable is t1, the population size is N, and the maximum number of iterations is T 1max， The preset threshold is α, which is used to determine whether the population has fallen into a stopped state;

[0036] generating a first mating pool based on the first population using a random selection strategy, and randomly selecting parent individuals from the first mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine the first offspring population;

[0037] generating a second mating pool based on the second population using a random selection strategy, and randomly selecting parent individuals from the second mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine the second offspring population;

[0038] Merging the first population, the first offspring population, and the second offspring population to determine the first mixed population, calculating and determining a fitness value corresponding to the first mixed population by combining constraint dominance rule sorting and crowding distance sorting, and selecting the best multiple population individuals as the first population of the next generation;

[0039] Merging the second population, the first offspring population, and the second offspring population to determine the second mixed population, calculating the fitness value corresponding to the second mixed population by combining non-dominated sorting and crowding distance sorting, and selecting the best multiple individuals from the population as the second population of the next generation;

[0040] The above steps are performed repeatedly until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold α, and all individual solutions in the second population are non-dominated solutions, then the individual solution set of the first population and the individual solution set of the second population are output;

[0041] Or until the current number of iterations t1 reaches the maximum number of iterations T 1max , then stop the iteration and output the individual solution set of the first population and the individual solution set of the second population.

[0042] Optionally, the step of using the individual solution set of the second population as the individual solution set of the third population, iterating the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population using a preset second agricultural planting structure optimization algorithm, and when the number of iterations reaches a preset number of iterations, outputting the non-dominated solutions in the first population as the optimal agricultural planting structure solution set includes:

[0043] Taking minimizing the total water requirement of the crops, maximizing the economic benefits, and minimizing the difference between the carbon absorption and carbon emission as the optimization direction, the individual solution set of the second population is used as the individual solution set of the third population;

[0044] The preset second agricultural planting structure optimization algorithm is used to initialize the ideal point, the maximum constraint violation value of the second population, the population size, the maximum number of iterations, the current number of iterations variable, and the minimum range of mating allowed between individuals, and a set of uniform reference vectors are generated in the target space, where the ideal point is Z * , which represents the minimum value of each target value in the second population, the maximum constraint violation value of the second population is ε0, the population size is N, and the maximum number of iterations is T2max , the current iteration number variable is t2, the minimum range of mating allowed between individuals is β, and the reference vector set is Λ={λ1,λ2,λ3,...,λ N};

[0045] Invoking a preset double traction mechanism, adopting a binary tournament selection strategy to generate a third mating pool based on the first population, and adopting simulated binary crossover and polynomial mutation to randomly select parent individuals from the third mating pool to generate offspring individuals, so as to determine a third offspring population;

[0046] generating a fourth mating pool based on the second population using a random selection strategy, and randomly selecting parent individuals from the fourth mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine a fourth offspring population;

[0047] Using a dynamic search attention strategy to select individuals entering the fifth mating pool and parent individuals participating in mating based on the third population, using simulated binary crossover and polynomial mutation to generate offspring individuals based on the parent individuals to determine the fifth offspring population;

[0048] Merging the first population, the third offspring population, the fourth offspring population, and the fifth offspring population to determine a third mixed population, combining constraint dominance rule sorting with crowding distance sorting to calculate and determine a fitness value corresponding to the third mixed population, and selecting the best multiple population individuals as the first population of the next generation;

[0049] The second population, the third offspring population, the fourth offspring population, and the fifth offspring population are merged to determine the fourth mixed population. The fitness value corresponding to the fourth mixed population is determined by combining the ε-constraint method with the crowding distance sorting calculation, and the optimal multiple population individuals are selected as the second population of the next generation. The calculation formula of the ε value used in this iteration in the ε-constraint method is expressed as:

[0050]

[0051] Among them, ε(t2) represents the ε value of this iteration, t2 represents the current number of iterations, T 2max represents the maximum number of iterations, F2 represents the feasible rate of the second population, and ε0 represents the maximum constraint violation value of the second population at the beginning of the second round of optimization;

[0052] Merging the third population, the third offspring population, the fourth offspring population, and the fifth offspring population to obtain a fifth mixed population, and selecting, from the fifth mixed population, a plurality of population individuals with low constraint violation values ​​and good diversity as the third population of the next generation according to an environmental selection strategy based on a reference vector and a constraint violation value;

[0053] If the current number of iterations t2 is equal to the maximum number of iterations T 2max , stop the iteration, and output the non-dominated solutions in the first population as the optimal agricultural planting structure solution set.

[0054] Optionally, the steps of adopting a dynamic search attention strategy to select individuals entering the fifth mating pool and parent individuals participating in mating based on the third population, and adopting simulated binary crossover and polynomial mutation to generate offspring individuals based on the parent individuals to determine the fifth offspring population include:

[0055] Use the binary tournament selection strategy to select N in the third population fit individuals enter the fifth mating pool, where N fit The calculation formula of the value is expressed as:

[0056]

[0057] Among them, N fit represents the number of individuals selected in the third population using the binary tournament selection strategy, t2 represents the current number of iterations, and T 2max Indicates the maximum number of iterations;

[0058] Use random selection strategy to select NN in the third population fit Individuals enter the fifth mating pool, and the range of mating allowed between two individuals at the current iteration number is calculated and determined, where the range of mating allowed between two individuals at the current iteration number is the Dis value;

[0059] When the Dis value is less than the minimum range β allowed for mating between two individuals, let the Dis value be equal to β. The calculation formula of the Dis value is expressed as:

[0060]

[0061] Among them, the Dis value represents the range of mating allowed between two individuals at the current iteration number, t2 represents the current iteration number, and T 2max Indicates the maximum number of iterations;

[0062] Each individual in the fifth mating pool randomly selects a parent individual to mate with from the Dis individuals in the fifth mating pool with the closest Euclidean distance to it, and uses simulated binary crossover and polynomial mutation to generate offspring individuals to form a fifth offspring population.

[0063] Optionally, the steps of merging the third population, the third offspring population, the fourth offspring population, and the fifth offspring population to obtain a fifth mixed population, and selecting, according to an environmental selection strategy based on a reference vector and a constraint violation value, a plurality of population individuals with low constraint violation values ​​and good diversity from the fifth mixed population as the third population of the next generation include:

[0064] Update the ideal point Z according to the target information of the first population and the second population * , assigning individuals in the first population and the fifth mixed population to N sub-regions through the reference vector set;

[0065] The individuals associated with the ith subregion in the first population are denoted as M i , the individuals associated with the ith subregion in the fifth mixed population are denoted as P i ;

[0066] If M i is an empty set and P i is an empty set, select the individual closest to the ith subregion from the first population to enter the third population of the next generation; otherwise, if M i is an empty set and P i It is not an empty set, from P i Select the individual with the smallest constraint violation value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation; otherwise, i Select the individual with the smallest constraint violation value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation, where g te The value is the function value calculated by the modified Chebyshev method, and its calculation formula is expressed as:

[0067]

[0068] Among them, g te (x|λ,Z * ) represents the g of an individual te value, λ h represents the hth target of the reference vector, f h (x) represents the h-th fitness value of the individual, Represents the h-th fitness value of the ideal point.

[0069] Optionally, the agricultural planting structure scheme set represents an individual solution set constructed by the planting areas corresponding to various crops; the crops include rice crops, wheat crops and cotton crops.

[0070] A regional agricultural planting structure optimization device based on a constrained multi-objective evolutionary algorithm is provided to meet another purpose of the present application, comprising:

[0071] a parameter acquisition module configured to acquire decision variables, an objective function, and constraints in response to an instruction to optimize the agricultural planting structure in a target area, wherein the decision variables are the planting areas corresponding to various crops, the objective function includes the total water requirements of the crops corresponding to various crops, the economic benefits, and the difference between carbon absorption and carbon emissions, and the constraints include a total cultivated land area constraint, a regional water supply constraint, a food security constraint, and a non-negative difference between carbon absorption and carbon emissions constraint;

[0072] A first planting structure optimization module is configured to optimize the crop by minimizing the total water requirement, maximizing the economic benefit, and minimizing the difference between carbon absorption and carbon emission. A first population and a second population are randomly generated using a preset first agricultural planting structure optimization algorithm, and the first population and the second population are iterated to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population. The first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population coevolutionary strategy.

[0073] a fitness value determination module, configured to respectively determine a first mixed population and a second mixed population based on the first population, the second population, the first offspring population, and the second offspring population, and respectively calculate and determine a first fitness value corresponding to each population individual in the first mixed population and a second fitness value corresponding to each population individual in the second mixed population based on the objective function and the constraint conditions, wherein the population includes a plurality of population individuals, and each population individual represents a planting area corresponding to various crops;

[0074] an individual solution set determination module, configured to cyclically execute the above steps until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and all individual solutions in the second population are non-dominated solutions, and then output the individual solution set of the first population and the individual solution set of the second population;

[0075] The second planting structure optimization module is configured to use the individual solution set of the second population as the individual solution set of the third population, and use a preset second agricultural planting structure optimization algorithm to iterate the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population. When the number of iterations reaches a preset number of iterations, the non-dominated solution in the first population is output as the optimal agricultural planting structure solution set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

[0076] An electronic device provided to meet another purpose of the present application includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm described in the present application.

[0077] A computer-readable storage medium is provided to meet another purpose of the present application, which stores a computer program implemented according to the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0078] Compared with the existing technology, this application addresses the problems in the existing technology that need to consider multiple objectives and constraints when optimizing agricultural planting structures. Multiple objectives are often conflicting with each other. Pursuing one objective often leads to the deterioration of other objectives. The existence of constraints makes the constrained multi-objective evolutionary algorithm easily fall into the dilemma of local optimality or poor convergence during the optimization process, affecting the quality of the final optimized solution set. This application includes but is not limited to the following beneficial effects:

[0079] First, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm of this application adopts a dual-population co-evolution strategy in the first round of optimization. This strategy introduces two groups of populations, which evolve separately and influence each other, and can avoid the problem of poor convergence caused by difficulty in crossing the infeasible area during the solution process. Dual-population co-evolution can enhance the diversity of the population and avoid the optimization process from converging to an undesirable solution too early. Therefore, the first round of optimization can effectively cross a large range of infeasible areas, provide a wider search space, and ensure that a more reasonable planting plan is found.

[0080] Secondly, the proposed regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm employs a dual-traction mechanism and a dynamic search attention strategy in the second round of optimization. The dual-traction mechanism includes a binary tournament selection strategy and a random selection strategy, helping the algorithm avoid the dilemmas of premature convergence and local optimal solutions. Specifically, the binary tournament selection strategy ensures competitiveness between individual solutions, ensuring that only high-quality solutions are selected, while the random selection strategy prevents the population from being trapped in the limitations of a single choice.

[0081] Third, this application's regional agricultural planting structure optimization method, based on a constrained multi-objective evolutionary algorithm, further enhances the population's adaptability within the search space by introducing a dynamic search attention strategy. In the initial optimization phase, the algorithm conducts extensive exploration to discover possible optimal solutions. As the number of iterations increases, attention gradually shifts to local development, concentrating more closely on areas near potential optimal solutions, thereby accelerating convergence and avoiding local optima.

[0082] Fourthly, the proposed regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm, through two rounds of optimization, yields a set of agricultural planting structure solutions that not only exhibits improved convergence—that is, the ability to quickly approach the optimal solution—but also exhibits greater diversity, providing multiple effective solutions. This diversity means that different agricultural planting plans can be flexibly selected based on different regional needs, climate conditions, and market conditions, resulting in greater adaptability in practical applications.

[0083] Fifth, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm of this application, by comprehensively considering multiple factors such as water resource conservation and economic benefits, the obtained agricultural planting structure optimization plan has better sustainable development potential. Through this optimization method, not only can the agricultural production efficiency and income be improved, but also it can ensure that agricultural activities can continue in the context of limited resources and increasing environmental pressure.

[0084] Furthermore, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm proposed in this application provides a new solution for optimizing regional agricultural planting structures, with significant beneficial effects. Through reasonable algorithm design and multi-objective optimization, the agricultural planting structure can achieve a balance between ensuring water conservation and improving economic benefits. Combined with the dual-population co-evolutionary strategy and dual traction mechanism, the algorithm can improve convergence and avoid local optimal solutions, thereby improving the quality and practicality of the optimization solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0086] Figure 1 A flow chart of a method for optimizing regional agricultural planting structure based on a constrained multi-objective evolutionary algorithm in an embodiment of the present application;

[0087] Figure 2 Schematic diagram of a constrained multi-objective evolutionary algorithm based on a dual-population co-evolutionary strategy in an embodiment of the present application;

[0088] Figure 3Schematic diagram of a multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy constraint in an embodiment of the present application;

[0089] Figure 4 This is a schematic diagram of the effect of the mating range between individuals on the search range of offspring in the embodiments of the present application;

[0090] Figure 5 This is a principle block diagram of a regional agricultural planting structure optimization device based on a constrained multi-objective evolutionary algorithm in an embodiment of the present application;

[0091] Figure 6 Schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0092] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0093] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0094] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0095] It will be understood by those skilled in the art that the terms "client," "terminal," and "terminal device" as used herein include both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers and tablet computers, which have single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service), which may combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices, which have and / or include a radio frequency receiver. As used herein, the terms "client," "terminal," or "terminal device" may be portable, transportable, or installed in a vehicle (air, sea, and / or land), or may be adapted and / or configured to operate locally and / or in a distributed manner at any other location on Earth and / or in space. As used herein, the terms "client," "terminal," or "terminal device" may also refer to a communication terminal, an Internet terminal, or a music / video playback terminal, such as a PDA, an MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or may include a smart TV, a set-top box, or other device.

[0096] The hardware referred to by names such as "server", "client", and "service node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. Computer programs are stored in its memory, and the central processing unit loads the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0097] It should be noted that the concept of "server" referred to in this application can also be extended to server clusters. Based on the network deployment principles understood by those skilled in the art, the servers described should be logically divided. In physical space, these servers can be independent of each other but callable through interfaces, or integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method of this application.

[0098] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for implementation.

[0099] Unless expressly specified otherwise, the neural network models referenced or may be referenced in this application may be deployed on a remote server and remotely called on the client, or may be deployed and directly called on a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence may be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid excessive occupation of the client's hardware operating resources.

[0100] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as they are suitable for being called by the technical solution of this application.

[0101] Those skilled in the art should be aware that although the various methods of this application are described based on the same concept and thus exhibit commonality, unless otherwise specified, these methods can be independently executed. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept. Therefore, concepts with the same expression, as well as concepts that are appropriately transformed for convenience despite different expression, should be understood as equivalent.

[0102] Unless expressly stated to be mutually exclusive, the various embodiments disclosed in this application may be cross-combined with the relevant technical features of the various embodiments to flexibly construct new embodiments, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or resolve certain deficiencies in the prior art. Those skilled in the art should be aware of such flexibility.

[0103] See also Figure 1 In one embodiment, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm of the present application includes:

[0104] Step S10: Responding to an instruction to optimize the agricultural planting structure in a target area, obtaining decision variables, objective functions, and constraints, wherein the decision variables are the planting areas corresponding to various crops, the objective functions include the total water requirements of various crops, economic benefits, and the difference between carbon absorption and carbon emissions, and the constraints include a constraint on the total cultivated land area, a constraint on the total regional water supply, a constraint on food security, and a constraint that the difference between carbon absorption and carbon emissions is non-negative.

[0105] The regional agricultural planting structure optimization system in the terminal device can respond to instructions for optimizing the agricultural planting structure in a target region and obtain decision variables, objective functions, and constraints. The decision variables are the corresponding planting areas for various crops. The objective functions include the total water requirements, economic benefits, and the difference between carbon absorption and carbon emissions for each crop. The constraints include total cultivated land area, regional water supply, food security, and a non-negative difference between carbon absorption and carbon emissions. The crops include rice, wheat, and cotton. The agricultural planting structure refers to the configuration and distribution of crop types, area, yield, and layout within a target region over a specific period of time. Specifically, it reflects the proportion and planting area of ​​different types of crops in agricultural production. The rationality of the agricultural planting structure has a significant impact on agricultural production efficiency, resource utilization, environmental protection, and farmers' income.

[0106] First, determine the primary crops grown in the target area to be optimized. Based on the regional annual plan, determine the total cultivated land area, total water supply, and population. Based on the regional historical yearbook, determine the regional per capita grain demand, water requirements per unit area for each crop, and yield per unit area for each crop. A search engine is used to determine the carbon absorption coefficient per unit yield of each crop, the carbon emission coefficient per unit area for each crop, the usage of each production factor per unit area for each crop, and the carbon emission coefficient per unit usage of each production factor. Based on local commodity trading data, determine the unit yield price of each crop and the fixed costs per unit area for each crop. Fixed costs include labor, seed costs, diesel, fertilizer, pesticides, and plastic film costs. Production factors include diesel, fertilizer, pesticides, and plastic film. Based on this data, construct an agricultural production database for the target area to be optimized.

[0107] After constructing the agricultural production database of the area to be optimized, a regional agricultural planting structure optimization model is constructed with reference to the data information in the database. The constructed objective function takes into account both water-saving benefits and economic benefits. The water-saving benefits are expressed as the total water demand of crops and as the difference between carbon absorption and carbon emissions to construct an agricultural planting structure optimization model.

[0108] In a specific embodiment, the objective function corresponding to the total water requirement of the crops is expressed as follows:

[0109]

[0110] Among them, f1(x) represents the total water demand of crops, w i represents the water requirement per unit area of ​​the i-th crop, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0111] The expression of the objective function corresponding to the economic benefit is:

[0112]

[0113] Among them, f2(x) represents economic benefits, p i represents the unit output price of the i-th crop, y i represents the yield per unit area of ​​the i-th crop, c i represents the fixed cost per unit area of ​​the i-th crop, which includes any number of labor cost, seed cost, diesel cost, fertilizer cost, pesticide cost, and agricultural film cost. i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0114] The expression of the objective function corresponding to the difference between carbon absorption and carbon emission is:

[0115]

[0116] Where f3(x) represents the difference between carbon absorption and carbon emission, a i represents the carbon absorption coefficient per unit yield of the i-th crop, y i represents the yield per unit area of ​​the i-th crop, e i represents the carbon emission coefficient per unit area of ​​the i-th crop, g j represents the emission coefficient per unit usage of the jth production factor, b ij represents the usage per unit area of ​​the jth production factor on the ith crop, where the production factors include any number of diesel, fertilizers, pesticides, and agricultural films. i represents the unit planting area of ​​the i-th crop, I represents the total number of crop types, and J represents the total number of production factors;

[0117] The expression of the total cultivated land area constraint is:

[0118]

[0119] Among them, S represents the total area of ​​agricultural land in the target area, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0120] The expression of the regional water supply total amount constraint is:

[0121]

[0122] Where W represents the total water supply for agricultural cultivation in the target area, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0123] The food security constraint is expressed as:

[0124]

[0125] Where N represents the annual per capita food demand in the target area, K represents the population of the target area, and y i represents the yield per unit area of ​​the i-th crop, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types;

[0126] The expression for the non-negative constraint on the difference between carbon absorption and carbon emission is:

[0127]

[0128] Among them, a i represents the carbon absorption coefficient of the yield per unit area of ​​the i-th crop, y i represents the yield per unit area of ​​the i-th crop, e i represents the carbon emission coefficient per unit area of ​​the i-th crop, g j represents the emission coefficient per unit usage of the i-th production factor, b ij represents the usage per unit area of ​​the jth production factor in the ith crop, x i represents the unit planting area of ​​the i-th crop, I represents the total number of crop types, and J represents the total number of production factors.

[0129] As can be seen from the above examples, by incorporating the total water requirement of crops into the objective function as an optimization factor, the optimization algorithm can effectively reduce water resource consumption for agricultural irrigation within a region. A reasonable crop planting structure can improve water resource utilization efficiency and reduce overirrigation, thereby achieving the goal of water conservation. Conserving water resources not only helps improve resource utilization efficiency in agricultural production, but also reduces over-reliance on groundwater and other water sources, avoiding overexploitation of water resources. By optimizing the crop planting structure, the algorithm can rationally allocate the planting area for different crops, thereby increasing output value and farmers' income. The optimization process takes into account factors such as crop market price, yield, and cost, ensuring the optimal economic return within regional resource constraints. This allows for dynamic adjustment of the planting structure based on market demand and supply-demand fluctuations in agricultural production, improving the overall efficiency of the regional agricultural economy. The optimization process also considers the balance between carbon absorption and emissions, particularly by crops. By optimizing the planting area for different crops, carbon absorption capacity can be improved while reducing greenhouse gas emissions. Crop photosynthesis and carbon fixation effectively absorb carbon dioxide, and by optimizing the planting structure, carbon emissions from agricultural activities (such as the use of fertilizers and pesticides) can also be reduced.

[0130] Step S20: Taking minimizing the total water requirement of the crops, maximizing the economic benefits, and minimizing the difference between the carbon absorption and carbon emissions as optimization directions, a preset first agricultural planting structure optimization algorithm is used to randomly generate a first population and a second population, and the first population and the second population are iterated to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population, wherein the first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population coevolutionary strategy;

[0131] Step S30: determining a first mixed population and a second mixed population respectively according to the first population, the second population, the first offspring population, and the second offspring population; and calculating and determining a first fitness value corresponding to each population individual in the first mixed population and a second fitness value corresponding to each population individual in the second mixed population according to the objective function and the constraint conditions, wherein the population includes a plurality of population individuals, and each population individual represents a planting area corresponding to various crops;

[0132] Step S40, looping through the above steps until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and all individual solutions in the second population are non-dominated solutions, outputting the individual solution set of the first population and the individual solution set of the second population;

[0133] After obtaining the decision variables, objective function and constraints, the optimization direction is to minimize the total water requirement of the crops, maximize the economic benefits and minimize the difference between the carbon absorption and carbon emissions. A preset first agricultural planting structure optimization algorithm is used to randomly generate a first population and a second population. The first population and the second population are iterated to determine the first offspring population corresponding to the first population and the second offspring population corresponding to the second population. A first mixed population and a second mixed population are determined according to the first population, the second population, the first offspring population and the second offspring population respectively. The first mixed population is calculated and determined according to the objective function and the constraints. The first fitness value corresponding to each population individual in the mixed population and the second fitness value corresponding to each population individual in the second mixed population are looped to execute the above steps until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and the individual solutions in the second population are all non-dominated solutions, then the individual solution set of the first population and the individual solution set of the second population are output, wherein the first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population co-evolution strategy, the population includes multiple population individuals, and each population individual represents the planting area corresponding to various crops.

[0134] In some embodiments, see Figure 2 The steps of randomly generating a first population and a second population using a preset first agricultural planting structure optimization algorithm, and iterating the first population and the second population to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population include:

[0135] Step S201: Taking the minimum total water requirement of the crops, the maximum economic benefit, and the minimum difference between the carbon absorption and carbon emission as the optimization direction, the first agricultural planting structure optimization algorithm is used to initialize the first population, the second population, the population size, the maximum number of iterations, the current number of iterations variable, and the preset threshold, wherein the current number of iterations variable is t1, the population size is N, and the maximum number of iterations is T. 1max， The preset threshold is α, which is used to determine whether the population has fallen into a stopped state;

[0136] Step S202: generating a first mating pool based on the first population using a random selection strategy, and randomly selecting parent individuals from the first mating pool using simulated binary crossover and polynomial mutation to generate offspring individuals, thereby determining the first offspring population;

[0137] Step S203: generating a second mating pool based on the second population using a random selection strategy, and randomly selecting parent individuals from the second mating pool using simulated binary crossover and polynomial mutation to generate offspring individuals, thereby determining the second offspring population;

[0138] Step S204: Merge the first population, the first offspring population, and the second offspring population to determine the first mixed population. Combine constraint dominance rule sorting and crowding distance sorting to calculate and determine the fitness value corresponding to the first mixed population, and select the best multiple population individuals as the first population of the next generation.

[0139] Step S205: Merge the second population, the first offspring population, and the second offspring population to determine the second mixed population, calculate the fitness value corresponding to the second mixed population by combining non-dominated sorting and crowding distance sorting, and select the best multiple population individuals as the next generation second population;

[0140] Step S206, loop through the above steps until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold α, and all individual solutions in the second population are non-dominated solutions, outputting the individual solution set of the first population and the individual solution set of the second population; or until the current number of iterations t1 reaches the maximum number of iterations T 1max , then stop the iteration and output the individual solution set of the first population and the individual solution set of the second population; otherwise, set t1=t1+1 and return to the above step S202 to continue the iterative evolution.

[0141] Specifically, the mating pool is a set of individuals used to reproduce the next generation in the genetic algorithm. The optimization direction is to minimize the total water requirement of the crops, maximize the economic benefits, and minimize the difference between carbon absorption and carbon emissions. The planting area of ​​each crop is used as a decision variable for real number encoding. The first round of optimization is initialized and the population size N and the maximum number of iterations T for the first round of optimization are set. 1max Randomly generate the first population and the second population, and record the sum of all target values ​​of all individuals in the initial second population as the sum of the initial overall target value, set the current iteration number variable t1 to 1, and set the threshold α used to determine whether the population falls into a stopped state;

[0142] Since the set constraints will hinder the evolution of the population, if the constraints create a large infeasible area in the decision space, it will cause the first population to be difficult to converge. Therefore, the second population is used to explore the target space without considering the constraints, and the offspring information is used to help the main population converge. The specific steps are as follows:

[0143] Randomly select an individual from the first population and enter the first mating pool, repeat this step until the number of individuals in the first mating pool is N; randomly select two individuals from the first mating pool, and then perform simulated binary crossover and polynomial mutation operations on them to generate an offspring individual to enter the first offspring population, repeat this step until the number of individuals in the first offspring population is N; randomly select an individual from the second population and enter the second mating pool, repeat this step until the number of individuals in the second mating pool is N; randomly select two individuals from the second mating pool, and then perform simulated binary crossover and polynomial mutation operations on them to generate an offspring individual to enter the second offspring population, repeat This step is continued until the number of the second offspring population is N; the first population, the first offspring population, and the second offspring population are merged to obtain a first mixed population, and the fitness values ​​of the individuals in the first mixed population are calculated by combining the constraint dominance rule sorting that strictly follows the constraint conditions with the crowding distance sorting, and the best N individuals are selected as the first population of the next generation according to the fitness sorting; the second population, the first offspring population, and the second offspring population are merged to obtain a second mixed population, and the fitness values ​​of the individuals in the second mixed population are calculated by combining the non-dominated sorting that completely disregards the constraint conditions with the crowding distance sorting, and the best N individuals are selected as the second population of the next generation;

[0144] Record the sum of the overall target values ​​of the second population at this time; then calculate the difference between the sum of the overall target values ​​of the two generations of the second population. If the difference is less than the set threshold α and all the solutions in the second population are non-dominated solutions, it is determined that the evolution of the second population has stagnated. If the current number of iterations t1 is equal to T 1max Or if the population evolution falls into a stagnant state, the first round of iteration is stopped, and the individual solution set of the first population and the individual solution set of the second population are output as the first round optimization results; otherwise, set t1=t1+1 and return to the above step S202 to continue iterative evolution.

[0145] Step S50: Use the individual solution set of the second population as the individual solution set of the third population, and use the preset second agricultural planting structure optimization algorithm to iterate the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population. When the number of iterations reaches the preset number of iterations, output the non-dominated solution in the first population as the optimal agricultural planting structure solution set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

[0146] The agricultural planting structure scheme set represents the individual solution set constructed by the planting area corresponding to various crops; after outputting the individual solution set of the first population and the individual solution set of the second population, the individual solution set of the second population is used as the individual solution set of the third population, and the preset second agricultural planting structure optimization algorithm is used to iterate the individual solution set of the first population, the individual solution set of the second population and the individual solution set of the third population. When the number of iterations reaches the preset number of iterations, the non-dominated solution in the first population is output as the optimal agricultural planting structure scheme set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

[0147] In some embodiments, see Figure 3 The step of using the individual solution set of the second population as the individual solution set of the third population, iterating the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population using a preset second agricultural planting structure optimization algorithm, and when the number of iterations reaches a preset number of iterations, outputting the non-dominated solutions in the first population as the optimal agricultural planting structure solution set includes:

[0148] Step S501: Taking minimizing the total water requirement of the crops, maximizing the economic benefits, and minimizing the difference between the carbon absorption and carbon emission as the optimization direction, the individual solution set of the second population is used as the individual solution set of the third population;

[0149] Step S502: Use the preset second agricultural planting structure optimization algorithm to initialize the ideal point, the maximum constraint violation value of the second population, the population size, the maximum number of iterations, the current iteration number variable, and the minimum range of mating allowed between individuals, and generate a set of uniform reference vectors in the target space, where the ideal point is Z * , which represents the minimum value of each target value in the second population, the maximum constraint violation value of the second population is ε0, the population size is N, and the maximum number of iterations is T 2max , the current iteration number variable is t2, the minimum range of mating allowed between individuals is β, and the reference vector set is Λ={λ1,λ2,λ3,...,λ N};

[0150] Step S503: Invoke a preset dual-pull mechanism, adopt a binary tournament selection strategy to generate a third mating pool based on the first population, and randomly select parent individuals from the third mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine a third offspring population;

[0151] Step S504: generating a fourth mating pool based on the second population using a random selection strategy, and randomly selecting parent individuals from the fourth mating pool using simulated binary crossover and polynomial mutation to generate offspring individuals, thereby determining a fourth offspring population;

[0152] Step S505: Using a dynamic search attention strategy to select individuals entering the fifth mating pool and parent individuals participating in mating based on the third population, and using simulated binary crossover and polynomial mutation to generate offspring individuals based on the parent individuals to determine a fifth offspring population;

[0153] Step S506: Merge the first population, the third offspring population, the fourth offspring population, and the fifth offspring population to determine a third mixed population. Combine the constraint dominance rule sorting and the crowding distance sorting to calculate and determine the fitness value corresponding to the third mixed population, and select the best multiple population individuals as the first population of the next generation.

[0154] Step S507: Merge the second population, the third offspring population, the fourth offspring population, and the fifth offspring population to determine a fourth mixed population. Combine the ε-constraint method with the crowding distance sorting calculation to determine the fitness value corresponding to the fourth mixed population, and select the best multiple population individuals as the second population of the next generation. The calculation formula of the ε value used in this iteration in the ε-constraint method is expressed as:

[0155]

[0156] Among them, ε(t2) represents the ε value of this iteration, t2 represents the current number of iterations, T 2max represents the maximum number of iterations, F2 represents the feasible rate of the second population, and ε0 represents the maximum constraint violation value of the second population at the beginning of the second round of optimization;

[0157] Step S508: Merge the third population, the third offspring population, the fourth offspring population, and the fifth offspring population to obtain a fifth mixed population, and select, from the fifth mixed population, a plurality of individuals with low constraint violation values ​​and good diversity as the third population of the next generation according to an environment selection strategy based on a reference vector and a constraint violation value;

[0158] Step S509: If the current number of iterations t2 is equal to the maximum number of iterations T 2max , stop iteration, output the non-dominated solution in the first population as the optimal agricultural planting structure solution set, otherwise, let t2=t2+1, repeat the above steps until the optimal agricultural planting structure solution set is output.

[0159] Specifically, the optimization direction is to minimize the total water requirement of the crops, maximize the economic benefits, and minimize the difference between the carbon absorption and carbon emissions. The planting area of ​​each crop is used as the decision variable for real number encoding. The second round of optimization is initialized and the second population obtained in the first round of optimization is copied to the third population. The minimum value of each target value in the second population is calculated and the ideal point Z is generated. * , calculate the maximum constraint violation value of the second population as ε0, set the population size N and the maximum number of iterations T 2max , set the current iteration number variable t2 to 1, set the minimum range β allowed for mating between individuals; generate a set of uniform reference vectors Λ={λ1,λ2,λ3,...,λ N The reference vector set is obtained by using the normal boundary intersection method to generate a set of uniformly distributed reference points in the target space, and then connecting the coordinate origin with these reference points to obtain the reference vector set Λ={λ1,λ2,λ3,...,λ N};

[0160] Although the first population after the first round of optimization can obtain a set of individual solutions with good convergence, when the unconstrained Pareto front is too far away from the constrained Pareto front, it is difficult for the second population to continue to provide useful information for the first population in terms of diversity, which may cause the first population to fall into the dilemma of local optimality and lack the motivation to jump out of the local optimality. Therefore, the second population uses the ε-constraint method to approach the constrained Pareto front, continuing to provide convergence motivation for the main population. The third population uses an environmental selection strategy based on the reference vector and the constraint violation value to obtain solutions with good diversity and low constraint violation values, providing motivation for the main population to improve diversity. The specific steps are as follows:

[0161] The fitness of individuals in the first population is calculated by combining the constraint dominance rule with the crowding distance sorting; two individuals are randomly selected from the first population, and the individual with high fitness is selected to enter the third mating pool, and this step is repeated until the number of individuals in the third mating pool is N; two individuals are randomly selected from the third mating pool, and then simulated binary crossover and polynomial mutation operations are performed on them to generate an offspring individual to enter the third offspring population, and this step is repeated until the third offspring population is N; an individual is randomly selected from the second population to enter the fourth mating pool, and this step is repeated until the number of individuals in the fourth mating pool is N; two individuals are randomly selected from the fourth mating pool, and then simulated binary crossover and polynomial mutation operations are performed on them to generate an offspring individual to enter the fourth offspring population, and this step is repeated until the number of the fourth offspring population is N; a dynamic search attention strategy is used in the third population to select individuals to enter the fifth mating pool and parent individuals participating in mating, and then simulated binary crossover and polynomial mutation operations are performed on them to generate an offspring individual to enter the fifth offspring population, and this step is repeated. The steps are repeated until the number of the fifth offspring population is N; the first population, the third offspring population, the fourth offspring population and the fifth offspring population are merged to obtain a third mixed population, and the fitness of the individuals in the third mixed population is calculated by combining the constraint dominance rule sorting that strictly follows the constraint conditions with the crowding distance sorting, and the best N individuals are selected as the first population of the next generation according to the fitness sorting; the second population, the third offspring population, the fourth offspring population and the fifth offspring population are merged to obtain a fourth mixed population, and the ε value under the current number of iterations is calculated according to the calculation formula of the ε value in step S507, and the fitness is calculated by combining the ε-constraint method that relaxes the constraint conditions with the crowding distance sorting, and the best N individuals are selected as the second population of the next generation according to the fitness sorting; the third population, the third offspring population, the fourth offspring population and the fifth offspring population are merged to obtain a fifth mixed population, and N individuals with low constraint violation values ​​and good diversity are selected from the third mixed population by using the environment selection strategy based on the reference vector and the constraint violation value as the third population of the next generation;

[0162] Determine whether to stop iteration. If the current number of iterations t2 is equal to the maximum number of iterations T 2max , stop the second round of optimization, output the non-dominated solutions in the first population as the second round optimization results to determine the optimal agricultural planting structure plan set, and output the second round optimization results as the final agricultural planting structure plan set; otherwise, set t2=t2+1, and return to the above step S503 to continue iterative evolution.

[0163] For further examples, please refer to Figure 4The steps of selecting individuals entering the fifth mating pool and parent individuals participating in mating according to the third population using a dynamic search attention strategy, and generating offspring individuals according to the parent individuals using simulated binary crossover and polynomial mutation to determine the fifth offspring population include:

[0164] Step S5051: Use the binary tournament selection strategy to select N in the third group. fit individuals enter the fifth mating pool, where N fit The calculation formula of the value is expressed as:

[0165]

[0166] Among them, N fit represents the number of individuals selected in the third population using the binary tournament selection strategy, t2 represents the current number of iterations, and T 2max Indicates the maximum number of iterations;

[0167] Step S5052: Select NN in the third group using random selection strategy fit Individuals enter the fifth mating pool, and the range of mating allowed between two individuals at the current iteration number is calculated and determined, where the range of mating allowed between two individuals at the current iteration number is the Dis value;

[0168] Step S5053: When the Dis value is less than the minimum range β allowed for mating between two individuals, the Dis value is set to be equal to β. The calculation formula of the Dis value is expressed as:

[0169]

[0170] Among them, the Dis value represents the range of mating allowed between two individuals at the current iteration number, t2 represents the current iteration number, and T 2max Indicates the maximum number of iterations;

[0171] Step S5054: Each individual in the fifth mating pool randomly selects a parent individual to mate with from the Dis individuals in the fifth mating pool that are closest to it in Euclidean distance, and generates offspring individuals using simulated binary crossover and polynomial mutation to form a fifth offspring population.

[0172] Specifically, in order to improve the optimization ability of the method, the third mating pool is mainly composed of randomly selected individuals in the early stage, because the influence of fitness is ignored, it has better diversity, is more suitable for global exploration, and improves the diversity of the population. In the later stage, individuals selected from the binary tournament are mainly used, because fitness is taken into account, which is suitable for local optimization and improves the quality of individuals. The mating range between individuals will affect the range that the offspring can search. A larger mating range between individuals in the early stage can be beneficial to global exploration, while a smaller mating range between individuals in the later stage is beneficial to local optimization. The above-mentioned selection of the mating pool is combined with the selection of the mating parent individual to improve the ability of the method to find an excellent solution set. The specific steps are as follows:

[0173] The fitness of the third population is calculated by combining non-dominated sorting and crowding distance sorting, and N is calculated according to step S5051. fit Randomly select two individuals from the third population, select the one with higher fitness to enter the fifth mating pool, and repeat this step until the number of individuals in the fifth mating pool is N fit ; Randomly select an individual from the third population and enter the fifth mating pool, repeat this step until the number of individuals in the fifth mating pool is N; calculate the Euclidean distance between each individual in the fifth mating pool; number the individuals in the fifth mating pool, taking individual No. 1 as an example, individual No. 1 randomly selects an individual from the Dis individuals with the closest Euclidean distance to it in the fifth mating pool, and the two individuals use simulated binary crossover and polynomial mutation to generate an offspring individual to enter the fifth offspring population, traverse the fifth mating pool until each individual completes this step; finally generate a fifth offspring population of size N.

[0174] In a further embodiment, the steps of merging the third population, the third offspring population, the fourth offspring population, and the fifth offspring population to obtain a fifth mixed population, and selecting a plurality of population individuals with low constraint violation values ​​and good diversity from the fifth mixed population as the third population of the next generation according to an environment selection strategy based on a reference vector and a constraint violation value, include:

[0175] Step S5081: Update the ideal point Z according to the target information of the first population and the second population. * , assigning individuals in the first population and the fifth mixed population to N sub-regions through the reference vector set;

[0176] Step S5082: The individuals in the first population associated with the i-th sub-region are recorded as M i , the individuals associated with the ith subregion in the fifth mixed population are denoted as P i ;

[0177] Step S5083: If M i is an empty set and P iis an empty set, select the individual closest to the ith subregion from the first population to enter the third population of the next generation; otherwise, if M i is an empty set and P i It is not an empty set, from P i Select the individual with the smallest constraint violation value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation; otherwise, i Select the individual with the smallest constraint violation value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation, where g te The value is the function value calculated by the modified Chebyshev method, and its calculation formula is expressed as:

[0178]

[0179] Among them, g te (x|λ,Z * ) represents the g of an individual te value, λ h represents the hth target of the reference vector, f h (x) represents the h-th fitness value of the individual, Represents the h-th fitness value of the ideal point.

[0180] Specifically, the ideal point Z is updated by using the characteristic that the second population continuously converges to the constraint boundary * , the third population can be gradually shrunk to the vicinity of the constraint boundary when distributed according to the reference vector. At the same time, individuals with good diversity and constraint violation values ​​are selected from the first population and the third mixed population to provide motivation for improving the diversity of the first population. The specific steps are as follows:

[0181] Calculate the minimum value of each target of the first and second populations and update the ideal point Z * ; Subtract the ideal point Z from the individual target values ​​of the first population and the fifth mixed population * Then calculate the vector angle with all reference vectors; divide the sub-regions according to the number of reference vectors, and each individual is associated with the sub-region corresponding to the reference vector with the smallest vector angle; the individual associated with the i-th sub-region in the first population is recorded as M i , the individuals associated with the ith subregion in the fifth mixed population are denoted as P i ; If M i is an empty set and P i is an empty set, select the individual with the smallest angle with the i-th reference vector from the first population to enter the third population of the next generation; otherwise, if M i is an empty set and P i It is not an empty set, from P iSelect the individuals with the smallest constraint violation value and calculate g for these individuals according to step S40463. te value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation; otherwise, i Select the individuals with the smallest constraint violation value, and calculate g for these individuals according to step S5083 te value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation.

[0182] As can be seen from the above embodiments, compared with the prior art, the present application addresses the problems in the prior art where multiple objectives and constraints need to be considered when optimizing agricultural planting structures. Multiple objectives often conflict with each other, and pursuing one objective often leads to the deterioration of other objectives. The existence of constraints can easily cause the constrained multi-objective evolutionary algorithm to fall into a local optimum or poor convergence during the optimization process, affecting the quality of the final optimized solution set. The present application includes but is not limited to the following beneficial effects:

[0183] First, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm of this application adopts a dual-population co-evolution strategy in the first round of optimization. This strategy introduces two groups of populations, which evolve separately and influence each other, and can avoid the problem of poor convergence caused by difficulty in crossing the infeasible area during the solution process. Dual-population co-evolution can enhance the diversity of the population and avoid the optimization process from converging to an undesirable solution too early. Therefore, the first round of optimization can effectively cross a large range of infeasible areas, provide a wider search space, and ensure that a more reasonable planting plan is found.

[0184] Secondly, the proposed regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm employs a dual-traction mechanism and a dynamic search attention strategy in the second round of optimization. The dual-traction mechanism includes a binary tournament selection strategy and a random selection strategy, helping the algorithm avoid the dilemmas of premature convergence and local optimal solutions. Specifically, the binary tournament selection strategy ensures competitiveness between individual solutions, ensuring that only high-quality solutions are selected, while the random selection strategy prevents the population from being trapped in the limitations of a single choice.

[0185] Third, this application's regional agricultural planting structure optimization method, based on a constrained multi-objective evolutionary algorithm, further enhances the population's adaptability within the search space by introducing a dynamic search attention strategy. In the initial optimization phase, the algorithm conducts extensive exploration to discover possible optimal solutions. As the number of iterations increases, attention gradually shifts to local development, concentrating more closely on areas near potential optimal solutions, thereby accelerating convergence and avoiding local optima.

[0186] Fourthly, the proposed regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm, through two rounds of optimization, yields a set of agricultural planting structure solutions that not only exhibits improved convergence—that is, the ability to quickly approach the optimal solution—but also exhibits greater diversity, providing multiple effective solutions. This diversity means that different agricultural planting plans can be flexibly selected based on different regional needs, climate conditions, and market conditions, resulting in greater adaptability in practical applications.

[0187] Fifth, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm of this application, by comprehensively considering multiple factors such as water resource conservation and economic benefits, the obtained agricultural planting structure optimization plan has better sustainable development potential. Through this optimization method, not only can the agricultural production efficiency and income be improved, but also it can ensure that agricultural activities can continue in the context of limited resources and increasing environmental pressure.

[0188] Furthermore, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm proposed in this application provides a new solution for optimizing regional agricultural planting structures, with significant beneficial effects. Through reasonable algorithm design and multi-objective optimization, the agricultural planting structure can achieve a balance between ensuring water conservation and improving economic benefits. Combined with the dual-population co-evolutionary strategy and dual traction mechanism, the algorithm can improve convergence and avoid local optimal solutions, thereby improving the quality and practicality of the optimization solution.

[0189] See also Figure 5, a regional agricultural planting structure optimization device based on a constrained multi-objective evolutionary algorithm is provided to meet one of the purposes of the present application, including a parameter acquisition module 1100, a first planting structure optimization module 1200, a fitness value determination module 1300, an individual solution set determination module 1400 and a second planting structure optimization module 1500. Among them, the parameter acquisition module 1100 is configured to obtain decision variables, objective functions and constraints in response to instructions for optimizing the agricultural planting structure of the target area, wherein the decision variables are the planting areas corresponding to various crops, the objective functions include the total water demand of various crops, economic benefits and the difference between carbon absorption and carbon emissions, the constraints include the total cultivated land area constraint, the regional water supply total amount constraint, the food security constraint and the difference between carbon absorption and carbon emissions being a non-negative constraint; the first planting structure optimization module 1200 is configured to minimize the total water demand of the crops, maximize the economic benefits and the carbon The difference between carbon absorption and carbon emission is minimized as the optimization direction, and a preset first agricultural planting structure optimization algorithm is used to randomly generate a first population and a second population, and the first population and the second population are iterated to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population, wherein the first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population co-evolutionary strategy; the fitness value determination module 1300 is configured to determine a first mixed population and a second mixed population according to the first population, the second population, the first offspring population and the second offspring population respectively, and according to the objective function and the constraint conditions respectively calculate and determine the first fitness value corresponding to each population individual in the first mixed population and the second fitness value corresponding to each population individual in the second mixed population, wherein the population includes a plurality of population individuals, and each population individual represents the planting area corresponding to various crops; the individual solution set determination module 1400 is configured to execute the above steps in a loop until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and when all individual solutions in the second population are non-dominated solutions, the individual solution set of the first population and the second population are output. The second planting structure optimization module 1500 is configured to use the individual solution set of the second population as the individual solution set of the third population, and adopt a preset second agricultural planting structure optimization algorithm to iterate the individual solution set of the first population, the individual solution set of the second population and the individual solution set of the third population. When the number of iterations reaches the preset number of iterations, the non-dominated solution in the first population is output as the optimal agricultural planting structure solution set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

[0190] Based on any embodiment of this application, please refer to Figure 6 Another embodiment of the present application further provides an electronic device, which can be implemented by a computer device, such as Figure 6 As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. Among them, the computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions, and the database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor may implement a regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor may execute the regional agricultural planting structure optimization method based on a constrained multi-objective evolutionary algorithm of the present application. The network interface of the computer device is used to connect and communicate with the terminal. Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0191] In this embodiment, the processor is used to execute Figure 5 The memory stores the program code and various data required to execute the specific functions of each module in the device. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules / submodules in the regional agricultural planting structure optimization device based on the constrained multi-objective evolutionary algorithm of this application. The server can call the server's program code and data to execute the functions of all submodules.

[0192] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm described in any embodiment of the present application.

[0193] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm described in any embodiment of the present application.

[0194] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments of the present application can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the method. The aforementioned storage medium can be a computer-readable storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0195] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0196] In summary, the regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm of this application provides a new solution for optimizing regional agricultural planting structures, with significant beneficial effects. Through reasonable algorithm design and multi-objective optimization, the agricultural planting structure can achieve a balance between ensuring water conservation and improving economic benefits. Combined with the dual-population co-evolution strategy and dual traction mechanism, the algorithm can improve convergence and avoid local optimal solutions, thereby improving the quality and practicality of the optimization solution.

Claims

1. A method for optimizing regional agricultural planting structure based on a constrained multi-objective evolutionary algorithm, characterized in that: include: In response to an instruction to optimize the agricultural planting structure in a target area, decision variables, objective functions, and constraints are obtained, wherein the decision variables are the planting areas corresponding to various crops, the objective functions include the total water requirements of the crops corresponding to the various crops, the economic benefits, and the difference between carbon absorption and carbon emissions, and the constraints include a total cultivated land area constraint, a regional water supply constraint, a food security constraint, and a non-negative difference between carbon absorption and carbon emissions constraint; Taking minimizing the total water requirement of the crops, maximizing the economic benefits, and minimizing the difference between the carbon absorption and carbon emissions as the optimization direction, a preset first agricultural planting structure optimization algorithm is used to randomly generate a first population and a second population, and the first population and the second population are iterated to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population, wherein the first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population coevolutionary strategy; Determine a first mixed population and a second mixed population according to the first population, the second population, the first offspring population, and the second offspring population, respectively; and calculate and determine a first fitness value corresponding to each population individual in the first mixed population and a second fitness value corresponding to each population individual in the second mixed population according to the objective function and the constraint conditions, wherein the population includes a plurality of population individuals, and each population individual represents a planting area corresponding to various crops; The above steps are executed repeatedly until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and all individual solutions in the second population are non-dominated solutions, then the individual solution set of the first population and the individual solution set of the second population are output; The individual solution set of the second population is used as the individual solution set of the third population, and the preset second agricultural planting structure optimization algorithm is used to iterate the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population. When the number of iterations reaches the preset number of iterations, the non-dominated solutions in the first population are output as the optimal agricultural planting structure solution set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

2. The regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm according to claim 1 is characterized in that: The expression of the objective function corresponding to the total water demand of crops is: Among them, f1(x) represents the total water demand of crops, w i represents the water requirement per unit area of ​​the i-th crop, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types; The expression of the objective function corresponding to the economic benefit is: Among them, f2(x) represents economic benefits, p i represents the unit output price of the i-th crop, y i represents the yield per unit area of ​​the i-th crop, c i represents the fixed cost per unit area of ​​the i-th crop, which includes any number of labor cost, seed cost, diesel cost, fertilizer cost, pesticide cost, and agricultural film cost. i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types; The expression of the objective function corresponding to the difference between carbon absorption and carbon emission is: Where f3(x) represents the difference between carbon absorption and carbon emission, a i represents the carbon absorption coefficient per unit yield of the i-th crop, y i represents the yield per unit area of ​​the i-th crop, e i represents the carbon emission coefficient per unit area of ​​the i-th crop, g j represents the emission coefficient per unit usage of the jth production factor, b ij represents the usage per unit area of ​​the jth production factor on the ith crop, where the production factors include any number of diesel, fertilizers, pesticides, and agricultural films. i represents the unit planting area of ​​the i-th crop, I represents the total number of crop types, and J represents the total number of production factors; The expression of the total cultivated land area constraint is: Among them, S represents the total area of ​​agricultural land in the target area, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types; The expression of the regional water supply total amount constraint is: Where W represents the total water supply for agricultural cultivation in the target area, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types; The food security constraint is expressed as: Where N represents the annual per capita food demand in the target area, K represents the population of the target area, and y i represents the yield per unit area of ​​the i-th crop, x i represents the unit planting area of ​​the i-th crop, and I represents the total number of crop types; The expression for the non-negative constraint on the difference between carbon absorption and carbon emission is: Among them, a i represents the carbon absorption coefficient of the yield per unit area of ​​the i-th crop, y i represents the yield per unit area of ​​the i-th crop, e i represents the carbon emission coefficient per unit area of ​​the i-th crop, g j represents the emission coefficient per unit usage of the i-th production factor, b ij represents the usage per unit area of ​​the jth production factor in the ith crop, x i represents the unit planting area of ​​the i-th crop, I represents the total number of crop types, and J represents the total number of production factors.

3. The regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm according to claim 1 is characterized in that: The steps of randomly generating a first population and a second population using a preset first agricultural planting structure optimization algorithm, and iterating the first population and the second population to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population include: Taking the minimum total water requirement of the crops, the maximum economic benefit, and the minimum difference between the carbon absorption and carbon emission as the optimization direction, the first agricultural planting structure optimization algorithm is used to initialize the first population, the second population, the population size, the maximum number of iterations, the current number of iterations variable, and the preset threshold, wherein the current number of iterations variable is t1, the population size is N, and the maximum number of iterations is T 1max， The preset threshold is α, which is used to determine whether the population has fallen into a stopped state; generating a first mating pool based on the first population using a random selection strategy, and randomly selecting parent individuals from the first mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine the first offspring population; generating a second mating pool based on the second population using a random selection strategy, and randomly selecting parent individuals from the second mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine the second offspring population; Merging the first population, the first offspring population, and the second offspring population to determine the first mixed population, calculating and determining a fitness value corresponding to the first mixed population by combining constraint dominance rule sorting and crowding distance sorting, and selecting the best multiple population individuals as the first population of the next generation; Merging the second population, the first offspring population, and the second offspring population to determine the second mixed population, calculating the fitness value corresponding to the second mixed population by combining non-dominated sorting and crowding distance sorting, and selecting the best multiple individuals from the population as the second population of the next generation; The above steps are performed repeatedly until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold α, and all individual solutions in the second population are non-dominated solutions, then the individual solution set of the first population and the individual solution set of the second population are output; Or until the current number of iterations t1 reaches the maximum number of iterations T 1max , then stop the iteration and output the individual solution set of the first population and the individual solution set of the second population.

4. The regional agricultural planting structure optimization method based on the constrained multi-objective evolutionary algorithm according to claim 1 is characterized in that: The step of using the individual solution set of the second population as the individual solution set of the third population, iterating the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population using a preset second agricultural planting structure optimization algorithm, and when the number of iterations reaches a preset number of iterations, outputting the non-dominated solutions in the first population as the optimal agricultural planting structure solution set includes: Taking minimizing the total water requirement of the crops, maximizing the economic benefits, and minimizing the difference between the carbon absorption and carbon emission as the optimization direction, the individual solution set of the second population is used as the individual solution set of the third population; The preset second agricultural planting structure optimization algorithm is used to initialize the ideal point, the maximum constraint violation value of the second population, the population size, the maximum number of iterations, the current number of iterations variable, and the minimum range of mating allowed between individuals, and a set of uniform reference vectors are generated in the target space, where the ideal point is Z * , which represents the minimum value of each target value in the second population, the maximum constraint violation value of the second population is ε0, the population size is N, and the maximum number of iterations is T 2max , the current iteration number variable is t2, the minimum range of mating allowed between individuals is β, and the reference vector set is Λ={λ1,λ2,λ3,...,λ N }; Invoking a preset double traction mechanism, adopting a binary tournament selection strategy to generate a third mating pool based on the first population, and adopting simulated binary crossover and polynomial mutation to randomly select parent individuals from the third mating pool to generate offspring individuals, so as to determine a third offspring population; generating a fourth mating pool based on the second population using a random selection strategy, and randomly selecting parent individuals from the fourth mating pool to generate offspring individuals using simulated binary crossover and polynomial mutation to determine a fourth offspring population; Using a dynamic search attention strategy to select individuals entering the fifth mating pool and parent individuals participating in mating based on the third population, using simulated binary crossover and polynomial mutation to generate offspring individuals based on the parent individuals to determine the fifth offspring population; Merging the first population, the third offspring population, the fourth offspring population, and the fifth offspring population to determine a third mixed population, combining constraint dominance rule sorting with crowding distance sorting to calculate and determine a fitness value corresponding to the third mixed population, and selecting the best multiple population individuals as the first population of the next generation; The second population, the third offspring population, the fourth offspring population, and the fifth offspring population are merged to determine the fourth mixed population. The fitness value corresponding to the fourth mixed population is determined by combining the ε-constraint method with the crowding distance sorting calculation, and the optimal multiple population individuals are selected as the second population of the next generation. The calculation formula of the ε value used in this iteration in the ε-constraint method is expressed as: Among them, ε(t2) represents the ε value of this iteration, t2 represents the current number of iterations, T 2max represents the maximum number of iterations, F2 represents the feasible rate of the second population, and ε0 represents the maximum constraint violation value of the second population at the beginning of the second round of optimization; Merging the third population, the third offspring population, the fourth offspring population, and the fifth offspring population to obtain a fifth mixed population, and selecting, from the fifth mixed population, a plurality of population individuals with low constraint violation values ​​and good diversity as the third population of the next generation according to an environmental selection strategy based on a reference vector and a constraint violation value; If the current number of iterations t2 is equal to the maximum number of iterations T 2max , stop the iteration, and output the non-dominated solutions in the first population as the optimal agricultural planting structure solution set.

5. The method for optimizing regional agricultural planting structure based on a constrained multi-objective evolutionary algorithm according to claim 4 is characterized in that: The steps of selecting individuals entering the fifth mating pool and parent individuals participating in mating according to the third population using a dynamic search attention strategy, and generating offspring individuals according to the parent individuals using simulated binary crossover and polynomial mutation to determine the fifth offspring population include: Use the binary tournament selection strategy to select N in the third group fit individuals enter the fifth mating pool, where N fit The calculation formula of the value is expressed as: Among them, N fit represents the number of individuals selected in the third population using the binary tournament selection strategy, t2 represents the current number of iterations, and T 2max Indicates the maximum number of iterations; Use random selection strategy to select NN in the third population fit Individuals enter the fifth mating pool, and the range of mating allowed between two individuals at the current iteration number is calculated and determined, where the range of mating allowed between two individuals at the current iteration number is the Dis value; When the Dis value is less than the minimum range β allowed for mating between two individuals, let the Dis value be equal to β. The calculation formula of the Dis value is expressed as: Among them, the Dis value represents the range of mating allowed between two individuals at the current iteration number, t2 represents the current iteration number, and T 2max Indicates the maximum number of iterations; Each individual in the fifth mating pool randomly selects a parent individual to mate with from the Dis individuals in the fifth mating pool with the closest Euclidean distance to it, and uses simulated binary crossover and polynomial mutation to generate offspring individuals to form a fifth offspring population.

6. The method for optimizing regional agricultural planting structure based on a constrained multi-objective evolutionary algorithm according to claim 4, characterized in that: The steps of merging the third population, the third offspring population, the fourth offspring population, and the fifth offspring population to obtain a fifth mixed population, and selecting a plurality of population individuals with low constraint violation values ​​and good diversity from the fifth mixed population as the third population of the next generation according to an environmental selection strategy based on a reference vector and a constraint violation value, include: Update the ideal point Z according to the target information of the first population and the second population * , assigning individuals in the first population and the fifth mixed population to N sub-regions through the reference vector set; The individuals associated with the ith subregion in the first population are denoted as M i , the individuals associated with the ith subregion in the fifth mixed population are denoted as P i ; If M i is an empty set and P i is an empty set, select the individual closest to the ith subregion from the first population to enter the third population of the next generation; otherwise, if M i is an empty set and P i It is not an empty set, from P i Select the individual with the smallest constraint violation value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation; otherwise, i Select the individual with the smallest constraint violation value, and then select g from these individuals te The individual with the smallest value enters the third population of the next generation, where g te The value is the function value calculated by the modified Chebyshev method, and its calculation formula is expressed as: Among them, g te (x|λ,Z * ) represents the g of an individual te value, λ h represents the hth target of the reference vector, f h (x) represents the h-th fitness value of the individual, Represents the hth fitness value of the ideal point.

7. The method for optimizing regional agricultural planting structure based on a constrained multi-objective evolutionary algorithm according to any one of claims 1 to 6, characterized in that: The agricultural planting structure scheme set represents an individual solution set constructed by the planting areas corresponding to various crops; the crops include rice crops, wheat crops and cotton crops.

8. A regional agricultural planting structure optimization device based on a constrained multi-objective evolutionary algorithm, characterized in that: include: a parameter acquisition module configured to acquire decision variables, an objective function, and constraints in response to an instruction to optimize the agricultural planting structure in a target area, wherein the decision variables are the planting areas corresponding to various crops, the objective function includes the total water requirements of the crops corresponding to various crops, the economic benefits, and the difference between carbon absorption and carbon emissions, and the constraints include a total cultivated land area constraint, a regional water supply constraint, a food security constraint, and a non-negative difference between carbon absorption and carbon emissions constraint; A first planting structure optimization module is configured to optimize the crop by minimizing the total water requirement, maximizing the economic benefit, and minimizing the difference between carbon absorption and carbon emission. A first population and a second population are randomly generated using a preset first agricultural planting structure optimization algorithm, and the first population and the second population are iterated to determine a first offspring population corresponding to the first population and a second offspring population corresponding to the second population. The first agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual-population coevolutionary strategy. a fitness value determination module, configured to respectively determine a first mixed population and a second mixed population based on the first population, the second population, the first offspring population, and the second offspring population, and respectively calculate and determine a first fitness value corresponding to each population individual in the first mixed population and a second fitness value corresponding to each population individual in the second mixed population based on the objective function and the constraint conditions, wherein the population includes a plurality of population individuals, and each population individual represents a planting area corresponding to various crops; an individual solution set determination module, configured to cyclically execute the above steps until the difference between the sum of the second fitness values ​​in the second mixed population and the sum of the fitness values ​​of the previous generation second population is less than a preset threshold, and all individual solutions in the second population are non-dominated solutions, and then output the individual solution set of the first population and the individual solution set of the second population; The second planting structure optimization module is configured to use the individual solution set of the second population as the individual solution set of the third population, and use a preset second agricultural planting structure optimization algorithm to iterate the individual solution set of the first population, the individual solution set of the second population, and the individual solution set of the third population. When the number of iterations reaches a preset number of iterations, the non-dominated solution in the first population is output as the optimal agricultural planting structure solution set, wherein the second agricultural planting structure optimization algorithm is a constrained multi-objective evolutionary algorithm based on a dual traction mechanism and a dynamic search attention strategy, and the dual traction mechanism includes a binary tournament selection strategy and a random selection strategy.

9. An electronic device comprising a central processing unit and a memory, characterized in that: The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that It stores a computer program implemented according to the method described in any one of claims 1 to 7 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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