Seeding operation agricultural machinery scheduling method and device based on joint optimization

Abstract

The invention provides a sowing operation agricultural machinery scheduling method and device based on joint optimization, and the method comprises the steps: constructing an agricultural machinery configuration model N according to the total area of a farm and the width of an agricultural implement in harvesting operation; according to the yield of the standard sowing period and the early sowing area, constructing an actual yield model Yac under the influence of the sowing period; according to the fuel price and the number of days of sowing operation, a fuel consumption cost model Cs of sowing agricultural machinery operation is constructed; constructing a labor cost model Cc according to the labor cost per unit time; according to the agricultural machinery configuration model N, the actual yield model Yac, the oil consumption cost model Cs and the labor cost model Cc, constructing a target model representing the relationship among various agricultural machinery resources, operation time and profits required by sowing; according to the agricultural machinery operation time upper limit, the agricultural machinery total number upper limit and the farm total area upper limit, constructing a restriction constraint of the target model, and under the restriction constraint, solving an agricultural machinery scheduling strategy when the output profit of the target model is maximum; and executing the seeding task of the crops according to the agricultural machinery scheduling strategy.

Description

Technical Field

[0001] The present invention relates to the cross-technical field of agriculture and information technology, and in particular to a method and device for dispatching agricultural machinery for sowing operations based on joint optimization. Background Art

[0002] Agricultural production is a complex process. Global climate change has led to the high frequency of extreme weather, breaking the traditional agricultural production rules. Therefore, operating in the traditional way will inevitably reduce farm output and reduce corporate profits. In addition, affected by the global market economy, the cost of labor, production materials, agricultural machinery, etc. has increased year by year, further compressing corporate profits. Therefore, how to propose the optimal agricultural production plan and dynamic scheduling plan of agricultural machinery resources based on actual data is the key to increasing output and reducing costs.

[0003] Improving agricultural production efficiency and promoting agricultural economic development by optimizing the allocation of agricultural machinery resources has always been widely concerned by the industry. Relevant literature has developed a DDS decision system based on regression analysis to improve harvesting income. However, the system only schedules the harvesting process and cannot support agricultural machinery scheduling processes such as sowing and plant protection. Based on the above problems, relevant personnel have improved it. By applying linear and multivariate linear regression analysis, the power, weight, working width, number of rows and price of the seeder were studied, and the parameter equations related to the seeder were established to evaluate and predict the performance and cost of the seeder, providing decision support for agricultural machinery selection and guidance. However, the above model does not consider the impact of factors such as labor cost, maintenance cost, operating area, and sowing cycle on agricultural machinery selection, so it has limitations. Although existing results have established a corn crop management model with the goal of maximizing farmers' profits by considering factors such as planting area, labor cost, agricultural machinery cost, and crop moisture content. However, most of the existing agricultural machinery scheduling problems are modeled as MIP problems, linear programming problems, dynamic programming problems, etc. through operations research and solved by branch and bound methods or heuristic algorithms.

[0004] Although domestic and foreign scholars have achieved some research milestones in the allocation of agricultural machinery resources, there are still some limitations to be addressed. Existing studies usually consider factors such as farm area, type of agricultural machinery, and labor costs in the process of optimizing agricultural machinery resources to optimize the allocation of agricultural machinery resources in order to minimize farm costs or maximize profits. However, these studies ignore the impact of factors such as the external environment (such as temperature and humidity), operation time windows (such as early sowing, traditional sowing, and late sowing), and cost inputs (fuel consumption, labor, maintenance, and seeds) on agricultural machinery scheduling operations. As far as we know, there is currently no research on the problem of agricultural machinery resource allocation based on the sowing operation scenario. Summary of the invention

[0005] The purpose of the present invention is to solve the problem of agricultural machinery resource scheduling and production operation planning in the corn sowing process in my country. At present, there is no agricultural machinery resource scheduling model that combines crop growth characteristics to complete the sowing process, so it has limitations. The present invention proposes a resource scheduling method based on agricultural machinery scheduling and operation time planning for the specific sowing scenario.

[0006] In view of the shortcomings of existing technologies, such as Figure 4 As shown, the present invention proposes a method for scheduling agricultural machinery for seeding operations based on joint optimization, which includes:

[0007] Step 1: Construct an agricultural machinery configuration model N according to the total farm area, agricultural machinery width, number of agricultural machinery, operation rate, operation time, and operation efficiency in the harvesting operation;

[0008] Step 2: Based on the yield of standard sowing date, early sowing area, standard sowing area, and the yield influencing factors of early sowing and standard sowing, construct the actual yield model Y under the influence of sowing date ac ;

[0009] Step 3: Based on the fuel price, the number of days for sowing operations, the number of agricultural machinery used in sowing operations, and the fuel consumption of agricultural machinery, a fuel consumption cost model C for sowing agricultural machinery operations is constructed. s ;

[0010] Step 4: Construct a labor cost model C based on the operation area, sowing amount per mu, number of sowing machines, labor cost per unit time, and early sowing operation time. c ;

[0011] Step 5: Based on the agricultural machinery configuration model N and the actual yield model Y ac 、Fuel consumption cost model C s , Labor cost model C c , construct a target model that represents the relationship between various types of agricultural machinery resources required for sowing, operation time and profit;

[0012] Step 6: According to the upper limit of agricultural machinery operation time, the upper limit of the total number of agricultural machinery, and the upper limit of the total farm area, the restriction constraints of the target model are constructed. Under the restriction constraints, the agricultural machinery scheduling strategy when the output profit of the target model is maximized is solved; the sowing operation task of the crop is performed using the agricultural machinery scheduling strategy.

[0013] The agricultural machinery scheduling method for seeding operation based on joint optimization, wherein the agricultural machinery configuration model N is:

[0014]

[0015] Among them, N represents the number of agricultural machinery, A is the total area of ​​the farm, w represents the width of the agricultural machinery, v represents the operation rate, t represents the operation time, and e represents the operation efficiency of the agricultural machinery;

[0016] The actual yield model Y under the influence of the sowing date ac for:

[0017]

[0018] Y ac represents the total yield under different sowing periods, Y ha is the yield of the empirical sowing period, A ea is the early sowing area, A st is the standard sowing area, is the yield influencing factor under early sowing time j, is the yield influencing factor under standard sowing time j;

[0019] Fuel consumption cost model of seeding agricultural machinery operation C s for:

[0020]

[0021] L represents the number of types of agricultural machinery horsepower segments, and J represents the number of operating days; is the operation time of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage, is the number of agricultural machinery with horsepower segment i in the early sowing period operating on day j, Normal sowing operation time. fis represents the fuel consumption of the seed drill with horsepower segment i, θ d for diesel prices;

[0022] The labor cost model C c :

[0023]

[0024] Among them, θ seed is the sowing amount per mu, is the number of early sowing machines, θ p For labor costs, It is the time for early sowing. The number of agricultural machinery used in the normal sowing period, Normal sowing operation time;

[0025] The target model is:

[0026]

[0027] where θ ma is the crop price, T is N is

[0028] The restrictions are:

[0029]

[0030]

[0031] The above constraints (1) to (3) respectively represent the operating time of the seed drill with horsepower segment i on the jth day at different seeding stages; constraints (4) and (6) represent the number of agricultural machinery with horsepower segment i put into use on the jth day at different seeding stages; constraint (6) indicates that the number of agricultural machinery used in the plant protection process is less than or equal to the total number of plant protection machinery on the farm; constraints (7) to (9) respectively represent the actual seeding area at different seeding stages; constraint (10) indicates that the sum of the operating areas at different seeding stages is the total area of ​​the farm.

[0032] The agricultural machinery scheduling method for seeding operation based on joint optimization, wherein step 6 comprises:

[0033] Step 61: Discrete variables Convert to a continuous variable and add restrictions:

[0034]

[0035] The T and N to be solved in the target model are transformed into three new variables: make And g i =v i w i e i Substitute 0.36 into the target model to obtain the transformed new target model:

[0036]

[0037] Based on McCormick envelope theory variables The constraints are as follows:

[0038]

[0039]

[0040] After the transformation, the process of solving the target model becomes a linear programming problem composed of continuous variables. The Lagrange multiplier method is used to solve the linear programming problem. The specific linear programming problem is:

[0041]

[0042] The dual multipliers are expressed as:

[0043]

[0044] Define the matrix:

[0045] Transform the above problem:

[0046]

[0047] The corresponding Lagrangian function is:

[0048]

[0049] Step 62: Solve by gradient descent method The dual variables are updated as follows:

[0050]

[0051] Where [x] + = max{0,x}, σ(t) is the iteration step size; d(χ(t)), d(η(t)), d(ψ(t)) are the subgradients of the Lagrangian function with respect to χ, η, ψ; let k(t) = [d(η(t)), d(ψ(t)), d(χ(t))] T , set the step size UB is the upper bound of each iteration, v is a positive constant, and q(t) is the function value of the tth iteration;

[0052]

[0053] By executing step 62 again, the iteration step σ(t) is guided to converge or reach the specified number of iterations, the iteration is stopped, and the function value q(t) of the current iteration and its corresponding operating time and the number of agricultural machinery in each horsepower range are saved as the agricultural machinery scheduling strategy.

[0054] like Figure 5 As shown, the present invention also proposes a sowing operation agricultural machinery scheduling device based on joint optimization, which includes:

[0055] Module 1: Construct the agricultural machinery configuration model N according to the total farm area, agricultural machinery width, number of agricultural machinery, operation rate, operation time and operation efficiency in the harvesting operation;

[0056] Module 2: Based on the yield of standard sowing period, early sowing area, standard sowing area, and the yield influencing factors of early sowing and standard sowing, the actual yield model Y under the influence of sowing period is constructed. ac ;

[0057] Module 3: Construct the fuel consumption cost model C of the sowing agricultural machinery operation based on the fuel price, the number of sowing operation days, the number of agricultural machinery used in the sowing operation, and the fuel consumption of agricultural machinery s ;

[0058] Module 4: Construct the labor cost model C based on the operation area, sowing amount per mu, number of sowing machines, labor cost per unit time, and early sowing operation time. c ;

[0059] Module 5: Based on the agricultural machinery configuration model N and the actual yield model Y ac 、Fuel consumption cost model C s , Labor cost model C c , construct a target model that represents the relationship between various types of agricultural machinery resources required for sowing, operation time and profit;

[0060] Module 6: Based on the upper limit of agricultural machinery operation time, the upper limit of the total number of agricultural machinery, and the upper limit of the total farm area, the restriction constraints of the target model are constructed. Under the restriction constraints, the agricultural machinery scheduling strategy when the output profit of the target model is maximized is solved; the sowing operation task of the crop is performed using the agricultural machinery scheduling strategy.

[0061] The agricultural machinery scheduling device for sowing operation based on joint optimization, wherein the agricultural machinery configuration model N is:

[0062]

[0063] Among them, N represents the number of agricultural machinery, A is the total area of ​​the farm, w represents the width of the agricultural machinery, v represents the operation rate, t represents the operation time, and e represents the operation efficiency of the agricultural machinery;

[0064] The actual yield model Y under the influence of the sowing date ac for:

[0065]

[0066] Y ac represents the total yield under different sowing periods, Y ha is the yield of the empirical sowing period, A ea is the early sowing area, A st is the standard sowing area, is the yield influencing factor under early sowing time j, is the yield influencing factor under standard sowing time j;

[0067] Fuel consumption cost model of seeding agricultural machinery operation C s for:

[0068]

[0069] L represents the number of types of agricultural machinery horsepower segments, and J represents the number of operating days; is the operation time of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage, is the number of agricultural machinery with horsepower segment i in the early sowing period operating on day j, is the normal sowing operation time, f i s represents the fuel consumption of the seed drill with horsepower segment i, θ d for diesel prices;

[0070] The labor cost model C c :

[0071]

[0072] Among them, θ seed is the sowing amount per mu, is the number of early sowing machines, θ p For labor costs, It is the time for early sowing. The number of agricultural machinery used in the normal sowing period, Normal sowing operation time;

[0073] The target model is:

[0074]

[0075] where θ ma is the crop price, T is N is

[0076] The restrictions are:

[0077]

[0078] The above constraints (1) to (3) respectively represent the operating time of the seed drill with horsepower segment i on the jth day at different seeding stages; constraints (4) and (6) represent the number of agricultural machinery with horsepower segment i put into use on the jth day at different seeding stages; constraint (6) indicates that the number of agricultural machinery used in the plant protection process is less than or equal to the total number of plant protection machinery on the farm; constraints (7) to (9) respectively represent the actual seeding area at different seeding stages; constraint (10) indicates that the sum of the operating areas at different seeding stages is the total area of ​​the farm.

[0079] The agricultural machinery scheduling device for sowing operation based on joint optimization, wherein the module 6 comprises:

[0080] Module 61: Discrete variables Convert to a continuous variable and add restrictions:

[0081]

[0082] The T and N to be solved in the target model are transformed into three new variables: make And g i =vi w i e i Substitute 0.36 into the target model to obtain the transformed new target model:

[0083]

[0084] Based on McCormick envelope theory variables The constraints are as follows:

[0085]

[0086] After the transformation, the process of solving the target model becomes a linear programming problem composed of continuous variables. The Lagrange multiplier method is used to solve the linear programming problem. The specific linear programming problem is:

[0087]

[0088] The dual multipliers are expressed as:

[0089]

[0090] Define the matrix:

[0091] Transform the above problem:

[0092]

[0093] The corresponding Lagrangian function is:

[0094]

[0095] Module 62: Solving by gradient descent The dual variable is updated

[0096] as follows:

[0097]

[0098] Where [x] + = max{0,x}, σ(t) is the iteration step size; d(χ(t)), d(η(t)), d(ψ(t)) are the subgradients of the Lagrangian function with respect to χ, η, ψ; let k(t) = [d(η(t)), d(ψ(t)), d(χ(t))] T , set the step size UB is the upper bound of each iteration, v is a positive constant, and q(t) is the function value of the tth iteration;

[0099]

[0100] By executing module 62 again, the iteration step σ(t) is guided to converge or reach the specified number of iterations, the iteration is stopped, and the function value q(t) of the current iteration and its corresponding operating time and the number of agricultural machinery in each horsepower range are saved as the agricultural machinery scheduling strategy.

[0101] The present invention also proposes an electronic device, which includes a sowing operation agricultural machinery scheduling device based on joint optimization as described in any one of claims 4-6.

[0102] The electronic device is connected to an information display device, and the information display device is used to display the agricultural machinery scheduling strategy.

[0103] The present invention also proposes a storage medium for storing a computer program for executing the method for scheduling agricultural machinery for seeding operations based on joint optimization as described in any one of claims 1-3.

[0104] The present invention also proposes a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method for scheduling agricultural machinery for seeding operations based on joint optimization described in any one of claims 1 to 4 are implemented.

[0105] It can be seen from the above scheme that the advantages of the present invention are:

[0106] The invention can obtain the optimal solution with fewer iterations. In addition, compared with the TAMS algorithm, the proposed algorithm can provide a profit of about 11%, and can provide scientific agricultural machinery configuration and production operation planning for agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 Performance comparison chart of the proposed algorithm and the branch and bound method in terms of the number of iterations;

[0108] Figure 2 Relationship between total number of agricultural machinery and profit under different algorithms;

[0109] Figure 3 Relationship between total operation time and profit under different algorithms;

[0110] Figure 4 is a flow chart of the method of the present invention;

[0111] Figure 5 It is a module diagram of the device of the present invention;

[0112] Figure 6 This is a schematic diagram of the structure of a first electronic device of the present invention;

[0113] Figure 7 This is a schematic diagram of the application environment structure of the first electronic device of the present invention;

[0114] Figure 8 It is a schematic diagram of the structure of a second electronic device of the present invention.

[0115] Reference numerals:

[0116] A-First electronic device;

[0117] B-Agricultural machinery scheduling device for seeding operation based on joint optimization;

[0118] C-data acquisition equipment;

[0119] D-information display device;

[0120] 1000 - second electronic device;

[0121] Ⅰ-computational unit;

[0122] II-ROM;

[0123] III-RAM;

[0124] IV-bus;

[0125] V-interface;

[0126] VI - input unit;

[0127] VII-output unit;

[0128] VIII- Storage medium;

[0129] Ⅸ-Communication unit. DETAILED DESCRIPTION

[0130] It should be noted that the processor described in the present invention is the control center of the electronic device, which can be a processor or a general term for multiple processing elements. For example, it can be one or more central processing units (CPUs), or application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0131] Optionally, the processor can perform various functions of the electronic device by running or executing a software program stored in the memory, and calling data stored in the memory.

[0132] In a specific implementation, as an embodiment, the processor may include one or more CPUs. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions). Electronic devices may include: servers, desktop computers, laptops, smart phones, tablet computers, embedded computers, etc., wherein the embedded computers include vehicles and robots, etc.

[0133] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0134] It should be noted that the structure of the electronic device shown in the drawings of the present invention does not constitute a limitation thereto, and the actual knowledge structure recognition device may include more or fewer components than shown in the drawings, or a combination of certain components, or a different arrangement of components.

[0135] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0136] It should also be understood that the term "and / or" in the present invention is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship, which can be understood by referring to the context.

[0137] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0138] It should also be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0139] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0142] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0143] The present invention aims to maximize profits. It considers various factors such as weather, number of agricultural machinery, labor costs, fuel consumption, etc. for the corn sowing process, and constructs an agricultural machinery scheduling problem for corn crop sowing operations. Since the proposed agricultural machinery scheduling problem is a large-scale mixed integer nonlinear optimization problem (MINLP) with the increasing number of agricultural machinery types, it is very challenging to solve it within low time complexity. To address the above challenges, the present invention further proposes a resource allocation algorithm that integrates agricultural machinery scheduling and operation planning. The algorithm can obtain an approximate optimal solution at a lower algorithm complexity while increasing production and improving corporate profits.

[0144] The core technical points of the present invention include: 1. Considering the influence of external environment (such as temperature and humidity), operation time window (such as early sowing, traditional sowing, late sowing) and cost input (fuel consumption, labor, maintenance, seeds) on agricultural machinery scheduling, the present invention constructs a sowing production plan and agricultural machinery scheduling problem with the goal of maximizing farm profits. 2. Proposes a resource scheduling method based on agricultural machinery scheduling and operation time planning.

[0145] In order to make the above features and effects of the present invention more clearly and understandably described, embodiments are given below and described in detail with reference to the accompanying drawings. This specification discloses one or more embodiments that include the features of the present invention. The disclosed embodiments are only for illustration. The scope of protection of the present invention is not limited to the disclosed embodiments, and the present invention is defined by the attached claims.

[0146] The present invention obtains the agricultural machinery scheduling strategy including the type of sowing agricultural machinery horsepower, the number of agricultural machinery, and the overall operation time when the profit is maximized based on the farm area, agricultural machinery speed, width, fuel consumption, hourly labor cost, factors affecting yield, and the unit price of each kilogram of crops. Description of the agricultural machinery scheduling and operation production management system model based on sowing operation:

[0147] Agricultural machinery scale model:

[0148] For operation scenarios such as sowing, plant protection, and harvesting, it is necessary to configure the corresponding number of agricultural machinery based on factors such as the size of the farmland, the speed of agricultural machinery, the width, and the speed. In addition, in order to make the configuration more reasonable and accurate, agricultural machinery is divided into different horsepower segments, and the horsepower segment is represented by i. The specific agricultural machinery configuration model is as follows:

[0149]

[0150] Among them, N represents the number of agricultural machinery, A is the operating area, w represents the operating width, v represents the operating rate, t represents the operating time, e represents the operating efficiency of agricultural machinery, and 0.36 is the conversion factor from square meters per second to acres per hour.

[0151] Model of correlation between sowing operation cost input and yield:

[0152] Sowing area and yield relationship model. Different sowing cycles will have a greater impact on yield. Appropriate early or late sowing can increase yield. Therefore, the relationship between sowing area and yield is as follows:

[0153]

[0154] Here Y ac represents the total yield under different sowing periods, Y ha is the yield of the empirical sowing period, For early sowing area, For the standard planting area, To delay the planting area, is the yield influencing factor under early sowing time j, is the yield influencing factor under standard sowing time j, is the yield influencing factor under delayed sowing time j. The relationship between sowing time and yield influencing factor is as follows:

[0155]

[0156] in, It represents the number of sowing operation hours of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage. It represents the number of sowing operation hours of the agricultural machinery with horsepower segment i on the jth day during the normal sowing stage. To delay the sowing time;

[0157] The input cost of agricultural machinery during the sowing operation. Here, no-till seeders are mainly used, and the input cost of agricultural machinery operation is mainly fuel consumption cost. The specific fuel consumption cost model is as follows:

[0158]

[0159] L represents the total horsepower range of agricultural machinery, J represents the number of operating days, where i∈L; is the operation time of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage, is the number of agricultural machinery for early sowing, is the number of agricultural machinery with horsepower range i in the normal sowing period and the number of agricultural machinery put into operation on the Jth day, is the normal sowing operation time, f i s The fuel consumption of the seed drill with horsepower segment i is, θ d is the unit diesel price.

[0160] Seed and labor costs. In addition to fuel costs, there are also seed and labor costs during the sowing process, of which the seed and labor costs are expressed as follows:

[0161]

[0162] Where A represents the operating area, θ seed For the sowing amount per mu, is the number of agricultural machinery for early sowing, θ p is the daily labor cost, It is the time for early sowing. The number of agricultural machinery used in the normal sowing period, This is the normal sowing operation time.

[0163] The problem is formalized. The goal of this invention is to optimize the type of agricultural machinery resources and operation time required for the sowing stage to maximize farm profits. The specific mathematical model is as follows:

[0164]

[0165]

[0166] Among them, θ ma It means the price of the crop, such as the selling price per kilogram; constraints (1) to (3) represent the operating time of the seed drill with horsepower segment i on the jth day at different seeding stages. Constraints (4) and (6) represent the number of agricultural machinery with horsepower segment i put into use on the jth day at different seeding stages; constraint (6) indicates that the number of agricultural machinery used in the plant protection process does not exceed the total number of plant protection machinery on the farm; constraints (7) to (9) represent the actual sowing area at different seeding stages. Constraint (10) indicates that the sum of the operating areas at different seeding stages is the total area of ​​the farm. Since the above model is a mixed integer nonlinear programming problem consisting of continuous variables and discrete variables, it has a very high complexity and is therefore difficult to solve. Therefore, it is necessary to design a low-complexity optimization algorithm to obtain an approximate optimal solution.

[0167] Solution:

[0168] Agricultural machinery scheduling and operation planning resource joint optimization algorithm based on seeding operation (AMOPS)

[0169] To solve the above problems, we propose a joint optimization algorithm for agricultural machinery resource scheduling and operation time planning for seeding operations. Since the problem contains integer variables and discrete variables and the discrete variables are tightly coupled with the product form of continuous variables, the problem is a mixed integer nonlinear problem and is difficult to solve. Therefore, it is necessary to transform the problem into a linear convex optimization problem and then obtain an approximate optimal solution. The specific solution is as follows: First, the integer variable N is approximated as a continuous variable. At this time, the problem consists entirely of continuous variables. Then the product of the two continuous variables is reconstructed so that the original problem becomes a linear problem, and finally it is solved by a convex optimization method.

[0170] First, the discrete variables Converted to a continuous variable. At this point, the model becomes a nonlinear programming problem, and the transformed constraints are as follows:

[0171]

[0172] Problem transformation based on linear reconstruction technique:

[0173] Since the above problem is a nonlinear programming problem, the linear reconstruction technology is used to transform the above problem model. The variables that need to be solved in the present invention are and This is transformed to solve for three new variables: make The transformed problem model introduced below can be solved Then we can solve the corresponding and The transformed problem model is as follows:

[0174]

[0175] in To make it more intuitive, let g i =v i w i e i 0.36, and after substituting it into the above problem model, we get the following formula:

[0176]

[0177] The corresponding McCormick envelope theory variables The constraints are as follows:

[0178]

[0179] After transformation, the problem becomes a traditional linear programming problem consisting of continuous variables, and the problem is convex and can be solved by the Lagrange multiplier method. In order to use this method to transform the original problem:

[0180]

[0181] The dual multipliers are expressed as:

[0182]

[0183] Where L refers to the horsepower range of the agricultural machinery, for example, L = 1, 2, 3;

[0184] Define the matrix:

[0185] Here is the transformation of the above problem:

[0186]

[0187] The corresponding Lagrangian function can be written as:

[0188]

[0189] Therefore, the teammate problem is formed as follows:

[0190] This problem can be solved by gradient descent method, where the dual variable is updated as follows:

[0191]

[0192] Where [x] + = max{0,x}, σ(t) is the iteration step size. d(χ(t)), d(η(t)), d(ψ(t)) are the subgradients of the Lagrangian function with respect to χ, η, ψ. Let k(t) = [d(η(t)), d(ψ(t)), d(χ(t))] T , set the step size UB is the upper bound of each iteration, v is a positive constant, and q(t) is the function value of the tth iteration.

[0193]

[0194] The above algorithm can eventually converge to a suboptimal solution that is infinitely close to the optimal solution through iteration. The iteration process includes: Then through the for loop instruction, the length of the loop can be designed by yourself, for example, 100 rounds. There will be initialization after the loop, which includes T and N. The above q = tC.

[0195] Each iteration starts at:

[0196]

[0197] When the loop ends, the iteration stops when the step length σ(t) reaches a constant fluctuation or the loop ends. At this time, q, t, C, etc. are all obtained, where Q = TN. At this time, T and N are also obtained.

[0198] Simulation experiment and verification:

[0199] Data input:

[0200]

[0201] UB=100,t max =2000, ε = 0.01, t = 1, T es =16, A = 168,000 mu,

[0202]

[0203] T es =16T ns =16T ls =16v=8km / h

[0204] w=1.8m (4 rows, 45cm, 70kw), w=3.15m 7 rows 99kw, w=5.4m, 12 rows 117kw Maschio 12, w=8.1m, 18 rows, Maschio 18 rows, 180kw.

[0205] Results and experimental verification (data output)

[0206] Agricultural Machinery and Operation Planning Based on Sowing (AMOPS)

[0207] Traditional agricultural machinery scheduling algorithm Traditional agricultural machinery scheduling, TAMS: The sowing process ignores the impact of weather on yield and jointly optimizes agricultural machinery resources and time.

[0208] Figure 1 The performance results of the two algorithms at different iteration numbers are shown. The proposed algorithm can converge after 200 iterations, while the branch and bound rule needs nearly 800 iterations to converge. In addition, it can be seen from the above figure that the proposed algorithm can obtain a near-optimal solution with a smaller number of iterations.

[0209] Depend on Figure 2 It can be seen that the proposed algorithm is better than the TAMS algorithm and that as the number of agricultural machinery increases, the two algorithms first increase and then tend to remain unchanged. Under standard seeding operations, although the profit gradually increases with the increase in the number of agricultural machinery, the profit reaches the critical value when the number of agricultural machinery is 8. When considering early seeding operations, the profit of the enterprise gradually increases with the increase in the number of agricultural machinery. When the number of agricultural machinery is 27, the agricultural machinery configuration reaches the optimal level, and the profit no longer increases with the increase of agricultural machinery.

[0210] Depend on Figure 3 It can be seen that the profit of the proposed algorithm gradually increases as the operation time is delayed. This is because when the operation time is short, the existing agricultural machinery cannot complete the sowing of all farmland areas. As the operation time is extended, the operation area gradually increases and the yield gradually increases. When the time is 12 days, the existing agricultural machinery resources can complete the sowing operation of all areas. Compared with the TAMS algorithm, the profit of the proposed algorithm can be increased by about 11%.

[0211] The following is a system embodiment corresponding to the above method embodiment. This embodiment can be implemented in conjunction with the above embodiment. The relevant technical details mentioned in the above embodiment are still valid in this embodiment. In order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above embodiment.

[0212] like Figure 5 As shown, the present invention also proposes a sowing operation agricultural machinery scheduling device based on joint optimization, which includes:

[0213] Module 1: Construct the agricultural machinery configuration model N according to the total farm area, agricultural machinery width, number of agricultural machinery, operation rate, operation time and operation efficiency in the harvesting operation;

[0214] Module 2: Based on the yield of standard sowing period, early sowing area, standard sowing area, and the yield influencing factors of early sowing and standard sowing, the actual yield model Y under the influence of sowing period is constructed. ac ;

[0215] Module 3: Construct the fuel consumption cost model C of the sowing agricultural machinery operation based on the fuel price, the number of sowing operation days, the number of agricultural machinery used in the sowing operation, and the fuel consumption of agricultural machinery s ;

[0216] Module 4: Construct the labor cost model C based on the operation area, sowing amount per mu, number of sowing machines, labor cost per unit time, and early sowing operation time. c ;

[0217] Module 5: Based on the agricultural machinery configuration model N and the actual yield model Y ac 、Fuel consumption cost model Cs , Labor cost model C c , construct a target model that represents the relationship between various types of agricultural machinery resources required for sowing, operation time and profit;

[0218] Module 6: Based on the upper limit of agricultural machinery operation time, the upper limit of the total number of agricultural machinery, and the upper limit of the total farm area, the restriction constraints of the target model are constructed. Under the restriction constraints, the agricultural machinery scheduling strategy when the output profit of the target model is maximized is solved; the sowing operation task of the crop is performed using the agricultural machinery scheduling strategy.

[0219] The agricultural machinery scheduling device for sowing operation based on joint optimization, wherein the agricultural machinery configuration model N is:

[0220]

[0221] Among them, N represents the number of agricultural machinery, A is the total area of ​​the farm, w represents the width of the agricultural machinery, v represents the operation rate, t represents the operation time, and e represents the operation efficiency of the agricultural machinery;

[0222] The actual yield model Y under the influence of the sowing date ac for:

[0223]

[0224] Y ac represents the total yield under different sowing periods, Y ha is the yield of the empirical sowing period, A ea is the early sowing area, A st is the standard sowing area, is the yield influencing factor under early sowing time j, is the yield influencing factor under standard sowing time j;

[0225] Fuel consumption cost model of seeding agricultural machinery operation C s for:

[0226]

[0227] L represents the number of types of agricultural machinery horsepower segments, and J represents the number of operating days; is the operation time of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage, is the number of agricultural machinery with horsepower segment i in the early sowing period operating on day j, is the normal sowing operation time, f i s represents the fuel consumption of the seed drill with horsepower segment i, θ d for diesel prices;

[0228] The labor cost model C c :

[0229]

[0230] Among them, θ seed is the sowing amount per mu, is the number of early sowing machines, θ p For labor costs, It is the time for early sowing. The number of agricultural machinery used in the normal sowing period, Normal sowing operation time;

[0231] The target model is:

[0232]

[0233] where θ ma is the crop price, T is N is

[0234] The restrictions are:

[0235]

[0236] The above constraints (1) to (3) respectively represent the operating time of the seed drill with horsepower segment i on the jth day at different seeding stages; constraints (4) and (6) represent the number of agricultural machinery with horsepower segment i put into use on the jth day at different seeding stages; constraint (6) indicates that the number of agricultural machinery used in the plant protection process is less than or equal to the total number of plant protection machinery on the farm; constraints (7) to (9) respectively represent the actual seeding area at different seeding stages; constraint (10) indicates that the sum of the operating areas at different seeding stages is the total area of ​​the farm.

[0237] The agricultural machinery scheduling device for sowing operation based on joint optimization, wherein the module 6 comprises:

[0238] Module 61: Discrete variables Convert to a continuous variable and add restrictions:

[0239]

[0240] The T and N to be solved in the target model are transformed into three new variables: make And g i =v i w i e i Substitute 0.36 into the target model to obtain the transformed new target model:

[0241]

[0242] Based on McCormick envelope theory variables The constraints are as follows:

[0243]

[0244] After the transformation, the process of solving the target model becomes a linear programming problem composed of continuous variables. The Lagrange multiplier method is used to solve the linear programming problem. The specific linear programming problem is:

[0245]

[0246] The dual multipliers are expressed as:

[0247]

[0248] Define the matrix:

[0249] Transform the above problem:

[0250]

[0251] The corresponding Lagrangian function is:

[0252]

[0253] Module 62: Solving by Gradient Descent The dual variable update

[0254] as follows:

[0255]

[0256] Where [x] + = max{0,x}, σ(t) is the iteration step size; d(χ(t)), d(η(t)), d(ψ(t)) are the subgradients of the Lagrangian function with respect to χ, η, ψ; let k(t) = [d(η(t)), d(ψ(t)), d(χ(t))] T , set the step size UB is the upper bound of each iteration, v is a positive constant, and q(t) is the function value of the tth iteration;

[0257]

[0258] By executing module 62 again, the iteration step σ(t) is guided to converge or reach the specified number of iterations, the iteration is stopped, and the function value q(t) of the current iteration and its corresponding operating time and the number of agricultural machinery in each horsepower range are saved as the agricultural machinery scheduling strategy.

[0259] like Figure 6 As shown, the present invention further proposes a first electronic device A in another embodiment, which includes the above-mentioned agricultural machinery scheduling device for sowing operations based on joint optimization.

[0260] like Figure 7 As shown, the first electronic device A can also be connected to the data acquisition device C and the information display device D through a wired or wireless information transmission scheme. The data acquisition device C is used to collect farm area, agricultural machinery speed, width, fuel consumption, hourly labor cost, factors affecting yield and unit price per kilogram of crops. The information display device D is used to display the agricultural machinery scheduling strategy analyzed by the present invention.

[0261] The information display device D can sort and process the data output by the first electronic device A based on the information display mechanism to improve the readability of the data output by the first electronic device A. The information display mechanism can be manually preset, for example, the data output by the first electronic device A is visually displayed, which can be based on the display parameters and / or attributes set by the user. The display parameters can be, for example, the display data range, and the display attributes can be, for example, the display font, color, whether to scroll and play, etc. The user is presented with the key information specified by the user, and the user can understand the information more promptly without having to access the secondary page or scroll the page, saving the user's operation. Or the information display mechanism can be an artificial intelligence AI display model, which can learn the user's key information based on the user's previous usage habits, such as viewing time, number of clicks, number of edits, etc., and then automatically present rich and necessary key information to the user.

[0262] The present invention also proposes a storage medium VIII in another embodiment for storing a computer program for executing the agricultural machinery scheduling method for sowing operations based on joint optimization. It should be understood that the storage medium in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0263] Figure 8 A schematic block diagram of a second electronic device 1000 that can be used to implement an embodiment of the present invention is shown. The second electronic device 1000 electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The second electronic device 1000 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present invention described and / or required in the present invention. The second electronic device 1000 may be the same as or different from the first electronic device A.

[0264] The second electronic device 1000 includes a computing unit I, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory II (ROM) or a computer program loaded from a storage medium VIII into a random access memory (RAM) III. In RAM III, various programs and data required for the operation of the device 1000 can also be stored. The computing unit I, ROM II, and RAM III are connected to each other via a bus IV. An input / output (I / O) interface V is also connected to the bus IV.

[0265] A plurality of components in the second electronic device 1000 are connected to the I / O interface V, including: an input unit VI, such as a keyboard, a mouse, etc.; an output unit VII, such as various types of displays, speakers, etc.; a storage medium VIII, such as a disk, an optical disk, etc.; and a communication unit IX, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit IX allows the second electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0266] The computing unit I may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of computing unit I include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit I performs the various methods and processes described above, such as method steps S1-S6. For example, in some embodiments, the method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage medium VIII. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1000 via ROM II and / or a communication unit IX. When the computer program is loaded into RAM III and executed by the computing unit I, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit I may be configured to execute the method in any other appropriate manner (e.g., by means of firmware).

[0267] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a readable storage medium. When the computer program is executed by a processor, the computer can execute the agricultural machinery scheduling method for seeding operations based on joint optimization provided by the above methods.

[0268] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for scheduling agricultural machinery for seeding operations based on joint optimization. It is characterized in that include: Step 1: Construct an agricultural machinery configuration model N according to the total farm area, agricultural machinery width, number of agricultural machinery, operation rate, operation time, and operation efficiency in the harvesting operation; Step 2: Based on the yield of standard sowing date, early sowing area, standard sowing area, and the yield influencing factors of early sowing and standard sowing, construct the actual yield model Y under the influence of sowing date ac ; Step 3: Based on the fuel price, the number of days for sowing operations, the number of agricultural machinery used in sowing operations, and the fuel consumption of agricultural machinery, a fuel consumption cost model C for sowing agricultural machinery operations is constructed. s ; Step 4: Construct a labor cost model C based on the operation area, sowing amount per mu, number of sowing machines, labor cost per unit time, and early sowing operation time. c ; Step 5: Based on the agricultural machinery configuration model N and the actual yield model Y ac 、Fuel consumption cost model C s , Labor cost model C c , construct a target model that represents the relationship between various types of agricultural machinery resources required for sowing, operation time and profit; Step 6: According to the upper limit of agricultural machinery operation time, the upper limit of the total number of agricultural machinery, and the upper limit of the total farm area, the restriction constraints of the target model are constructed. Under the restriction constraints, the agricultural machinery scheduling strategy when the output profit of the target model is maximized is solved; the sowing operation task of the crop is performed using the agricultural machinery scheduling strategy.

2. The method for dispatching agricultural machinery for seeding operation based on joint optimization according to claim 1, It is characterized in that The agricultural machinery configuration model N is: Among them, N represents the number of agricultural machinery, A is the total area of ​​the farm, w represents the width of the agricultural machinery, v represents the operation rate, t represents the operation time, and e represents the operation efficiency of the agricultural machinery; The actual yield model Y under the influence of the sowing date ac for: Y ac represents the total yield under different sowing periods, Y ha is the yield of the empirical sowing period, A ea is the early sowing area, A st is the standard sowing area, is the yield influencing factor under early sowing time j, is the yield influencing factor under standard sowing time j; Fuel consumption cost model of seeding agricultural machinery operation C s for: L represents the number of types of agricultural machinery horsepower segments, and J represents the number of operating days; is the operation time of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage, is the number of agricultural machinery with horsepower segment i in the early sowing period operating on day j, is the normal sowing operation time, f i s represents the fuel consumption of the seed drill with horsepower segment i, θ d for diesel prices; The labor cost model C c : Among them, θ seed is the sowing amount per mu, is the number of early sowing machines, θ p For labor costs, It is the time for early sowing. The number of agricultural machinery used in the normal sowing period, Normal sowing operation time; The target model is: where θ ma is the crop price, T is N is The restrictions are: The above constraints (1) to (3) respectively represent the operating time of the seed drill with horsepower segment i on the jth day at different seeding stages; constraints (4) and (6) represent the number of agricultural machinery with horsepower segment i put into use on the jth day at different seeding stages; constraint (6) indicates that the number of agricultural machinery used in the plant protection process is less than or equal to the total number of plant protection machinery on the farm; constraints (7) to (9) respectively represent the actual seeding area at different seeding stages; constraint (10) indicates that the sum of the operating areas at different seeding stages is the total area of ​​the farm.

3. The method for dispatching agricultural machinery for seeding operation based on joint optimization according to claim 1, It is characterized in that This step 6 includes: Step 61: Discrete variables Convert to a continuous variable and add restrictions: The T and N to be solved in the target model are transformed into three new variables: make And g i =v i w i e i Substitute 0.36 into the target model to obtain the transformed new target model: Based on McCormick envelope theory variables The constraints are as follows: After the transformation, the process of solving the target model becomes a linear programming problem composed of continuous variables. The Lagrange multiplier method is used to solve the linear programming problem. The specific linear programming problem is: The dual multipliers are expressed as: Define the matrix: Transform the above problem: The corresponding Lagrangian function is: Step 62: Solve by gradient descent method The dual variables are updated as follows: Where [x] + = max{0,x}, σ(t) is the iteration step size; d(χ(t)), d(η(t)), d(ψ(t)) are the subgradients of the Lagrangian function with respect to χ, η, ψ; let k(t) = [d(η(t)), d(ψ(t)), d(χ(t))] T , set the step size UB is the upper bound of each iteration, v is a positive constant, and q(t) is the function value of the tth iteration; By executing step 62 again, the iteration step σ(t) is guided to converge or reach the specified number of iterations, the iteration is stopped, and the function value q(t) of the current iteration and its corresponding operating time and the number of agricultural machinery in each horsepower range are saved as the agricultural machinery scheduling strategy.

4. A sowing operation agricultural machinery scheduling device based on joint optimization, It is characterized in that include: Module 1: Construct the agricultural machinery configuration model N according to the total farm area, agricultural machinery width, number of agricultural machinery, operation rate, operation time and operation efficiency in the harvesting operation; Module 2: Based on the yield of standard sowing period, early sowing area, standard sowing area, and the yield influencing factors of early sowing and standard sowing, the actual yield model Y under the influence of sowing period is constructed. ac ; Module 3: Construct the fuel consumption cost model C of the sowing agricultural machinery operation based on the fuel price, the number of sowing operation days, the number of agricultural machinery used in the sowing operation, and the fuel consumption of agricultural machinery s ; Module 4: Construct the labor cost model C based on the operation area, sowing amount per mu, number of sowing machines, labor cost per unit time, and early sowing operation time. c ; Module 5: Based on the agricultural machinery configuration model N and the actual yield model Y ac 、Fuel consumption cost model C s , Labor cost model C c , construct a target model that represents the relationship between various types of agricultural machinery resources required for sowing, operation time and profit; Module 6: Based on the upper limit of agricultural machinery operation time, the upper limit of the total number of agricultural machinery, and the upper limit of the total farm area, the restriction constraints of the target model are constructed. Under the restriction constraints, the agricultural machinery scheduling strategy when the output profit of the target model is maximized is solved; the sowing operation task of the crop is performed using the agricultural machinery scheduling strategy.

5. The agricultural machinery scheduling device for seeding operation based on joint optimization as claimed in claim 4, It is characterized in that The agricultural machinery configuration model N is: Among them, N represents the number of agricultural machinery, A is the total area of ​​the farm, w represents the width of the agricultural machinery, v represents the operation rate, t represents the operation time, and e represents the operation efficiency of the agricultural machinery; The actual yield model Y under the influence of the sowing date ac for: Y ac represents the total yield under different sowing periods, Y ha is the yield of the empirical sowing period, A ea is the early sowing area, A st is the standard sowing area, is the yield influencing factor under early sowing time j, is the yield influencing factor under standard sowing time j; Fuel consumption cost model of seeding agricultural machinery operation C s for: L represents the number of types of agricultural machinery horsepower segments, and J represents the number of operating days; is the operation time of the agricultural machinery with horsepower segment i on the jth day in the early sowing stage, is the number of agricultural machinery with horsepower segment i in the early sowing period operating on day j, is the normal sowing operation time, f i s represents the fuel consumption of the seed drill with horsepower segment i, θ d for diesel prices; The labor cost model C c : Among them, θ seed is the sowing amount per mu, is the number of early sowing machines, θ p For labor costs, It is the time for early sowing. The number of agricultural machinery used in the normal sowing period, Normal sowing operation time; The target model is: where θ ma is the crop price, T is N is The restrictions are: The above constraints (1) to (3) respectively represent the operating time of the seed drill with horsepower segment i on the jth day at different seeding stages; constraints (4) and (6) represent the number of agricultural machinery with horsepower segment i put into use on the jth day at different seeding stages; constraint (6) indicates that the number of agricultural machinery used in the plant protection process is less than or equal to the total number of plant protection machinery on the farm; constraints (7) to (9) respectively represent the actual seeding area at different seeding stages; constraint (10) indicates that the sum of the operating areas at different seeding stages is the total area of ​​the farm.

6. The agricultural machinery scheduling device for seeding operation based on joint optimization according to claim 1, It is characterized in that This module 6 includes: Module 61: Discrete variables Convert to a continuous variable and add restrictions: The T and N to be solved in the target model are transformed into three new variables: make And g i =v i w i e i Substitute 0.36 into the target model to obtain the transformed new target model: Based on McCormick envelope theory variables The constraints are as follows: After the transformation, the process of solving the target model becomes a linear programming problem composed of continuous variables. The Lagrange multiplier method is used to solve the linear programming problem. The specific linear programming problem is: The dual multipliers are expressed as: Define the matrix: Transform the above problem: The corresponding Lagrangian function is: Module 62: Solving by Gradient Descent The dual variables are updated as follows: Where [x] + = max{0,x}, σ(t) is the iteration step size; d(χ(t)), d(η(t)), d(ψ(t)) are the subgradients of the Lagrangian function with respect to χ, η, ψ; let k(t) = [d(η(t)), d(ψ(t)), d(χ(t))] T , set the step size UB is the upper bound of each iteration, v is a positive constant, and q(t) is the function value of the tth iteration; By executing module 62 again, the iteration step σ(t) is guided to converge or reach the specified number of iterations, the iteration is stopped, and the function value q(t) of the current iteration and its corresponding operating time and the number of agricultural machinery in each horsepower range are saved as the agricultural machinery scheduling strategy.

7. An electronic device, It is characterized in that It includes a sowing operation agricultural machinery scheduling device based on joint optimization as described in any one of claims 4-6.

8. The electronic device according to claim 7, It is characterized in that The electronic device is connected to an information display device, and the information display device is used to display the agricultural machinery scheduling strategy.

9. A storage medium for storing a computer program for executing the method for scheduling agricultural machinery for seeding operations based on joint optimization as recited in any one of claims 1 to 3.

10. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method for scheduling agricultural machinery for seeding operations based on joint optimization described in any one of claims 1 to 4 are implemented.

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