Planting planning method based on crop planting uncertainty and related device

Abstract

The invention discloses a crop planting uncertainty-based planting planning method and a related device, and belongs to the field of agricultural planting, and the method comprises the following steps: calculating the probability distribution of crop uncertain factors through a Monte Carlo method to determine a decision variable; a target planning model is constructed based on the decision variables, the target planning model comprises a target function and constraint conditions, and the target function is maximized agricultural planting income; and solving the target planning model by adopting a genetic algorithm to obtain an agricultural planting strategy with maximum income, and realizing planting planning based on crop planting uncertainty. According to the invention, the problem of inaccurate planting strategy caused by low yield prediction precision of various crops due to neglect of various key uncertain factors in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural planting, and particularly to a planting planning method and related device based on the uncertainty of crop planting. Background Art

[0002] In the field of traditional agricultural production, farmers often face multiple challenges in the process of formulating planting plans, including the instability of market demand, the limitation of land resources, and the uncertainty of crop planting costs. These factors together have led to the problems of low planting efficiency and serious waste of resources for farmers. Due to the lack of a scientific decision support system, farmers often rely on personal experience or simplified mathematical models when formulating planting plans, which results in deviations in yield prediction, improper selection of crop varieties, and failure to comprehensively consider planting costs.

[0003] Traditional methods often ignore the comprehensive influence of multiple key factors such as sales volume, sales price, yield per mu, and planting cost, resulting in deviations in the yield prediction of various crops, and thus the planting strategies lack comprehensiveness and accuracy. Therefore, the existing crop planting optimization methods urgently need to be improved. Summary of the Invention

[0004] The purpose of the present invention is to provide a planting planning method and related device based on the uncertainty of crop planting, so as to solve the problem that the existing technology ignores multiple key uncertain factors, resulting in low accuracy of yield prediction for various crops, and thus inaccurate planting strategies. The present invention can consider the uncertainty of key planting factors to increase farmers' income and avoid waste of resources.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, a planting planning method based on the uncertainty of crop planting includes the following steps: Calculating the probability distribution of crop uncertain factors by the Monte Carlo method to determine decision variables; Constructing a goal programming model based on the decision variables, the goal programming model including an objective function and constraint conditions, and the objective function being to maximize agricultural planting income; Using a genetic algorithm to solve the goal programming model to obtain an agricultural planting strategy with the maximum income, and realizing planting planning based on the uncertainty of crop planting.

[0006] In some embodiments, the crop uncertain factors include: expected sales volume, yield per mu, crop planting cost, and sales price; The decision variables include the following formula:

[0007]

[0008]

[0009]

[0010] Among them, is the expected sales volume, is the yield per mu, is the change rate of yield per mu, is the planting cost, is the selling price, is the change rate of planting cost, is the change rate of selling price, t is the year, i is the land number, is the crop type.

[0011] In some embodiments, the objective function is the following formula:

[0012] Among them, is the profit, t is the year, i is the land number, is the crop type, k is the quarter, is the coefficient of unsalable products, is in the quarter of the th piece of land, the selling price of the th crop, is in the quarter of the th piece of land, the number of mu of the th crop planted, is in the quarter of the th piece of land, the planting cost of the th crop, is in the quarter of the th piece of land, the yield per mu of the th crop, is in the year, the expected sales volume of the

[0013] In some embodiments, the constraint conditions include: Each crop cannot be continuously replanted in the same plot; All lands should be planted with leguminous crops at least once within three years; Only one crop can be planted on each plot at a time; Dry upland fields, terraced fields, and hillside fields are planted with food crops once a year; Irrigated fields are planted with vegetables twice a year; Ordinary greenhouses are planted with vegetables and edible fungi once a year; Smart greenhouses are planted with vegetables twice a year.

[0014] In some embodiments, the target planning model includes:

[0015] Among them, , , , ,

[0016] Among them, is the profit, t is the year, i is the land number, is the crop type, k is the quarter, is the unsalable coefficient, is In the quarter of the year, the selling price of the th crop planted on the is In the quarter of the year, the acreage of the th crop planted on the is In the quarter of the year, the planting cost of the th crop planted on the is whether to plant the th crop on the quarter of the i year on the th plot of land, is the th plot of land area, is the th plot of land type, is the expected sales volume, is the per-acre yield, is the per-acre yield change rate, is the planting cost, is the selling price, is the planting cost change rate, is the selling price change rate, is In the quarter of the The yield per mu of the first crop planted on a plot of land, is the expected sales volume of the first crop in year

[0017] In some embodiments, the step of solving the target programming model by using a genetic algorithm specifically includes: Determine that the chromosome is encoded as an integer array, where each chromosome represents the planting arrangement of a crop on different lands, in different years, and in different seasons; Randomly generate the integer array to form an initial population; Traverse the planting arrangements of each individual in the initial population, calculate the profits of all planting arrangements and accumulate them into the total profit, and use the total profit as the fitness value of the current individual; The initial population is iterated through crossover and mutation, and each population is sorted during the iteration process according to the fitness value until the final population is obtained after reaching the maximum number of iterations; Select the individual with the highest fitness value from the final population as the agricultural planting strategy with the maximum benefit.

[0018] In a second aspect, a planting planning system based on the uncertainty of crop planting includes: An uncertainty analysis module for crop planting, which is used to calculate the probability distribution of crop uncertainty factors by the Monte Carlo method to determine decision variables; A target programming model construction module, which is used to construct a target programming model based on the decision variables. The target programming model includes an objective function and constraint conditions, and the objective function is to maximize agricultural planting benefits; An agricultural planting planning module, which is used to solve the target programming model by using a genetic algorithm to obtain an agricultural planting strategy with the maximum benefit and realize the planting planning based on the uncertainty of crop planting.

[0019] In a third aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor. When the processor executes the computer program, the steps of the planting planning method based on the uncertainty of crop planting are implemented.

[0020] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the planting planning method based on the uncertainty of crop planting are implemented.

[0021] Fifth aspect, a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the planting planning method based on the uncertainty of crop planting.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a planting planning method based on the uncertainty of crop planting, uses the Monte Carlo method to conduct a large number of random experiments, simulates the changes of various uncertainty factors of crops, and thus obtains a more comprehensive probability distribution result to determine decision variables, quantify uncertainty factors, and enhance the adaptability of the target planning model of the present invention to uncertainty; the present invention optimizes the planting plan by using the genetic algorithm on the basis of the Monte Carlo method, can fully consider the optimal solutions in different situations, and improve the efficiency and accuracy of model solving; the present invention maximizes the total annual profit by optimizing the planted crops and area allocation. Therefore, the present invention can fully consider the uncertainty of key planting factors, improve the yield prediction accuracy of various crops, thereby quantifying a more accurate planting amount for planting planning to increase farmers' income and avoid waste of resources. Description of the Drawings

[0023] Figure 1 It is a flowchart of the planting planning method based on the uncertainty of crop planting provided in this embodiment; Figure 2 It is a structural diagram of the planting planning system based on the uncertainty of crop planting provided in this embodiment. Detailed Embodiments

[0024] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the drawings. The content is an explanation of the present invention rather than a limitation.

[0025] It should be noted that the terms "including" and "having" and any variations thereof in the description and claims of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, systems, products or devices.

[0026] Only one season of crops can be planted in a certain village every year. The village currently has 1,201 mu of open cultivated land, scattered into 34 plots of different sizes, including 4 types: flat dry land, terraced fields, hillside land, and irrigated land. Flat dry land, terraced fields, and hillside land are suitable for planting one season of grain crops every year; irrigated land is suitable for planting one season of rice or two seasons of vegetables every year. The village also has 16 ordinary greenhouses and 4 intelligent greenhouses, with each greenhouse having a cultivated area of 0.6 mu. Ordinary greenhouses are suitable for planting one season of vegetables and one season of edible fungi every year, and intelligent greenhouses are suitable for planting two seasons of vegetables every year. Different crops can be interplanted in the same plot (including greenhouses) in each season. According to the growth law of crops, each crop cannot be continuously replanted in the same plot (including greenhouses), otherwise the yield will decrease; because the soil containing the root bacteria of leguminous crops is beneficial to the growth of other crops, since 2023, it has been required that all land in each plot (including greenhouses) be planted with leguminous crops at least once within three years. At the same time, the planting plan should take into account convenient farming operations and field management. For example, the planting land of each crop in each season should not be too scattered, and the planting area of each crop in a single plot (including greenhouses) should not be too small, etc. According to experience, the expected sales volume of wheat and corn in the future has a growing trend, with an average annual growth rate between 5% and 10%. The expected sales volume of other crops in the future will change by about ±5% compared with 2023. The per-mu yield of crops is often affected by factors such as climate, with a ±10% change every year. Due to market conditions, the planting cost of crops increases by about 5% on average every year. The selling price of grain crops is basically stable; the selling price of vegetable crops has a growing trend, with an average annual growth rate of about 5%. The selling price of edible fungi is stable with a slight decline, about 1% - 5% per year, especially the selling price of Morchella esculenta decreases by 5% per year.

[0027] The following examples need to follow the following assumptions when implemented: (1) The crop yield in 2023 is the expected sales volume; (2) Other costs except planting costs are not considered; (3) The planting plan aims to obtain the maximum benefit; (4) Various crops are completely irreplaceable.

[0028] Table 1 below shows the explanations of the parameters involved in the text.

[0029] Table 1 Parameter Definitions

[0030] Taking into comprehensive consideration the uncertainties of the expected sales volume, per-mu yield, planting cost, and selling price of various crops, as well as potential planting risks, such as Figure 1 shown, this embodiment provides a planting planning method based on the uncertainty of crop planting, including the following steps: S1 Determine decision variables (1) Expected sales volume The expected sales volume of wheat and corn increases by 5% - 10% annually, while the expected sales volume of other crops has a change of approximately ±5% compared to the expected sales volume in 2023. Therefore, the expected sales volume is no longer a fixed value.

[0031] (1-1) (2)Yield per mu The yield per mu of all crops will have a change of ±10% annually. Therefore, the yield per mu has the following relationship: (1-2) (3)Crop planting cost The planting cost of crops increases by 5% annually. Therefore, the planting cost of crops is no longer a fixed value and has the following relationship: (1-3) (4)Selling price The selling price of vegetable crops increases by an average of 5% annually. At the same time, the selling price of edible fungi decreases by approximately 1% - 5% annually. For the new selling price, there is the following relationship: (1-4) S2 Determine the objective function Assumption (3) stipulates that the planting plan aims to maximize the profit. Therefore, the objective function is determined to maximize the planting profit. The final profit is calculated by summing the comprehensive profits of each plot of land in each quarter of each year, and calculating the single profit based on the basic idea that profit equals sales revenue minus cost.

[0032] First, for the calculation of sales revenue, the basic idea of unit price × quantity can be used. The "quantity" is obtained by multiplying the yield per mu of the crop by the number of mu, and the selling price is used as the "unit price", and the two are multiplied to obtain the sales revenue. Since the sales revenue includes the normal sales part within the expected sales volume, that is, the market saturation, and the abnormal sales part exceeding the expected sales volume, the two are calculated separately. Take the smaller value of "quantity" and the expected sales volume, and multiply it by the selling price to obtain the sales revenue of the normal sales part; take the smaller value of the difference between "quantity" and the expected sales volume and 0, and multiply it by the selling price to obtain the sales revenue of the abnormal sales part. Year Quarter, in the th plot of land, the profit calculation formula for planting the th type of crop is as follows: (2-1) In addition, for the part exceeding the expected self-sales volume, it is completely unsold and wasted and sold at a 50% discount. After calculating the product of the excess quantity and the selling price, that is, the original selling price of the excess part, and the unsold coefficient Multiply. For the case where the excess part is completely unsalable, let the unsalable coefficient be 0. The resulting value can be considered that the excess part is unsalable, causing waste and generating no income at all. For the case where the excess part is sold at a 50% discount, let the unsalable coefficient be 0.5, and the resulting value conforms to the rule of a 50% price cut.

[0033] Finally, the cost, which is the planting cost on this piece of land, can be solved using the following formula: (2-2) Determine the objective function of the linear goal programming model for maximizing revenue as follows: (2-3) Among them, the part that does not exceed the expected sales volume is , and the part that exceeds the expected sales volume is .

[0034] (2-4) (2-5) Substitute the decision variables in S1 into the above formula (2-3). At the same time, select the case where the part exceeding the expected sales is considered as unsalable waste, and set the unsalable coefficient to 0, and the following objective function is obtained: (2-6) S3 Determine the constraint conditions (1)Each type of crop cannot be continuously replanted in the same plot (including greenhouses), otherwise the yield will decrease. That is, for the land for single-season crops, the crop type in the first year cannot be the same as that in the second year; for the land for double-season crops, the crop type in the first season of the first year cannot be the same as that in the second season of the first year, and the crop type in the second season of the first year cannot be the same as that in the first season of the second year. Translate the above conditions into symbolic language as follows: (3-1) , , (3-2) (2)All land must be planted with leguminous crops at least once within three years. That is, sum up the cases where leguminous crops are determined to be planted from the nth year to the n+2th year, and the result is greater than 1. Translate the above conditions into symbolic language as follows: (3-3) (3-4) (3) The planting areas of each crop per season should not be too scattered, and the planting area of each crop in a single plot (including greenhouses) should not be too small. Therefore, it is restricted that only one crop is planted in a plot at a time. Translating the above conditions into symbolic language is as follows: (3-5) (4) Dry flatlands, terraced fields, and hillside fields are suitable for planting food crops once a year. It can be stipulated that in all years, planting is only carried out in the first season and not in the second season. Translating the above conditions into symbolic language is as follows: , (3-6) , (3-7) (5) Irrigated land is suitable for planting one season of rice or two seasons of vegetables. Combining the data preprocessing results, only two kinds of vegetables are planted in irrigated land. That is, it is determined that planting is carried out in both seasons, and the types of planted crops are all vegetables. Translating the above conditions into symbolic language is as follows: (3-8) (6) Ordinary greenhouses are suitable for planting one season of vegetables and one season of edible fungi every year. That is, planting is determined for each season of each year, and the planting types are and types respectively. Translating the above conditions into symbolic language is as follows: , (3-9) , (3-10) (7) Smart greenhouses are suitable for planting two seasons of vegetables every year. That is, for each season of each year, smart greenhouses are planted. Translating the above conditions into symbolic language is as follows: , (3-11) , (3-12) S4 Establish a goal programming model Based on the above objective function and constraints, the goal programming model is established as follows: (4-1) Where , , ,

[0035]

[0036] S5 Solving the goal programming model, based on the Monte Carlo method, simulates the uncertainties of expected sales volume, yield per mu, planting cost, and selling price through random sampling, and simulates the essence of the problem through a large number of random experiments. The specific algorithm steps are as follows: (1) Based on the above analysis, implement probability distribution sampling of the uncertainties of expected sales volume, yield per mu, planting cost, and selling price.

[0037] (2) Establish a statistic, and use a genetic algorithm to solve the goal programming model in S4. This genetic algorithm is a computational model that simulates the biological evolution process of Darwinian genetic selection and natural elimination for solution.

[0038] (2.1) Determine the chromosome coding method In this embodiment, the chromosome uses integer coding. The chromosome is an integer array, and each integer represents the area of a crop. The length of the array is the number of lands multiplied by the number of planting years multiplied by the yield per season. Such a coding method makes each chromosome represent the planting arrangement of a crop on different lands, in different years, and seasons.

[0039] (2.2) Initialize the population Randomly generate an integer array with the gene length, specifying the size of the array, that is, determined according to the number of lands, the number of planting years, and the number of each season. By looping a sufficient number of times, generate a specified number of integer arrays to form the initial population.

[0040] (2.3) Determine the fitness function In this embodiment, only obtaining the maximum profit is the optimization goal, and the fitness function is confirmed by calculating the maximum profit part of each solution. The specific process is as follows: Traverse each planting arrangement in the optimal planting plan, that is, for each plot, each year, and each season. Obtain the crop number of the current planting arrangement, and according to the crop number, obtain the yield per mu, planting cost, and selling price of the crop from the crop information, calculate the profit of the current planting arrangement, and accumulate it into the total profit.

[0041] (2.4) Screen excellent individuals The method for screening excellent individuals is based on the evaluation and selection of the fitness function, and the specific process is as follows: First, for each individual in the population, that is, the solution, it is judged whether it meets the constraints of the problem. For those that do not meet, their fitness is set to negative infinity. Then, calculate the individuals that meet the constraints and calculate their fitness, that is, calculate the profit according to the relevant information. The higher the profit, the higher the fitness. Sort the population according to the fitness and perform crossover and mutation operations to pass on better genes to the next generation and guide the population to evolve towards a better state.

[0042] (2.5) Determination and Design of Genetic Operators Through crossover and mutation operations, the genetic algorithm can explore different solutions in the search space, increase the diversity of the population, and thus improve the possibility of finding the optimal solution.

[0043] Crossover operation: Randomly select a cutting point, cut the two parent individuals at this point, and combine the first half of the first parent individual with the second half of the second parent individual to form a new individual.

[0044] Mutation operation: Randomly select a mutation point, randomly generate a new value, and replace the value at this mutation point with the new value to achieve the mutation operation.

[0045] (2.6) Set the relevant parameters of the genetic algorithm Set the population size to 100 to ensure a sufficient population size. The larger the population size, the easier it is to find the optimal solution; set the mutation rate to 0.1. Setting a larger mutation rate can increase the diversity of the population and avoid premature convergence to local optimal solutions; set the maximum number of genetic generations to 100 to ensure that the optimization results converge sufficiently. The above steps are implemented using Python.

[0046] (3) Examine and verify the simulation results.

[0047] (4) According to the initial simulation conditions, set the relevant parameters, return to the first step, and perform iteration.

[0048] (5) Obtain the final maximum profit and planting plan.

[0049] The total annual profit is 30,639,203 yuan. The optimal planting plans from 2024 to 2030 are shown in Tables 2 to 8 below.

[0050] Table 2 Crop Planting Plan for 2024

[0051] Table 3 Crop Planting Plan for 2025

[0052] Table 4 Crop Planting Plan for 2026

[0053] Table 5 Crop Planting Plan for 2027

[0054] Table 6 Crop Planting Plan for 2028

[0055] Table 7 Crop Planting Plan for 2029

[0056] Table 8 Crop Planting Plan for 2030

[0057] The planting planning method based on crop uncertainty provided by this embodiment has the following advantages: (1) Introduction of uncertainty factors, annual change rates of sales volume and yield per mu: In this embodiment, the uncertainties of expected sales volume, yield per mu, planting cost, and sales price are quantified, and the annual growth rates and change rates of different crops are set (for example, the annual growth rate of sales volume is set between 5% and 10%, and the annual change rates of yield per mu and planting cost are ±10% and 5% respectively). This processing method enables the model to better adapt to the uncertain market environment and improves the practical applicability and flexibility of the model.

[0058] (2) Combining the genetic algorithm and the Monte Carlo method, this model combines the Monte Carlo method and the genetic algorithm to optimize the planting plan. The Monte Carlo method is used to simulate the uncertainties of variables such as sales volume and price, and the model is solved through a large number of random experiments. The genetic algorithm is used to optimize the planting plan and select the optimal planted crops and area allocation. The combination of the two can fully consider the optimal solutions in different scenarios and improve the efficiency and accuracy of the solution.

[0059] (3) Detailed model solution and simulation process: In this embodiment, not only the goal programming model is constructed, but also the model solution process is described in detail. This includes the definition of decision variables, the setting of the objective function, the simulation and simulation process, etc. Through simulation calculation, this embodiment obtains the optimal planting plan with an annual total profit of 30,639,203 yuan, and verifies the effectiveness and stability of the model through different market environments.

[0060] After data analysis and simulation verification, the goal programming model of this embodiment can significantly reduce crop sales waste and improve the income of agricultural planting. By comparing with the actual data in 2023, the effectiveness and feasibility of this model in practical applications are verified.

[0061] As Figure 2 shown, this embodiment provides a planting planning system based on the uncertainty of crop planting, including: A crop planting uncertainty analysis module for calculating the probability distribution of crop uncertainty factors through the Monte Carlo method to determine decision variables; A goal programming model construction module for constructing a goal programming model based on the decision variables. The goal programming model includes an objective function and constraint conditions, and the objective function is to maximize agricultural planting benefits; An agricultural planting planning module for solving the goal programming model by using a genetic algorithm to obtain an agricultural planting strategy with the maximum benefit, and realizing planting planning based on the uncertainty of crop planting.

[0062] The division of modules in the embodiments of the present invention is illustrative. It is only a logical function division. In actual implementation, there may be other division methods. In addition, in each embodiment of the present invention, each functional module may be integrated in a processor, or may exist separately physically, or two or more modules may be integrated in one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0063] This embodiment also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program (in this embodiment, the computer program includes a calculation component and an iteration component, which can perform model calculation and model update). The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method process or corresponding function; the processor described in the embodiments of the present invention can be used for the operation of a planting planning method based on the uncertainty of crop planting.

[0064] This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory). The computer-readable storage medium is a memory device in a computer device, used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, the extended storage medium supported by the computer device. The computer-readable storage medium provides storage space, and this storage space stores the operating system of the terminal. Moreover, in this storage space, there is also stored one or more instructions suitable for being loaded and executed by the processor. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of a planting planning method based on the uncertainty of crop planting in the above embodiment.

[0065] This embodiment also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by the processor, it implements the corresponding steps of a planting planning method based on the uncertainty of crop planting in the above embodiment.

[0066] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0067] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0068] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the procedures Figure 1 one or more procedures and / or blocks Figure 1 specified in the block or blocks.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the procedures Figure 1 one or more procedures and / or blocks Figure 1 specified in the block or blocks.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A planting planning method based on uncertainty of crop planting, characterized in that: The following steps are involved: The probability distribution of crop uncertainties is calculated by Monte Carlo method to determine the decision variables; Constructing a target programming model based on the decision variables, wherein the target programming model includes an objective function and constraints, and the objective function is to maximize agricultural planting benefits; A genetic algorithm is used to solve the target programming model to obtain the agricultural planting strategy with the maximum benefit, thereby realizing the planting planning based on the uncertainty of crop planting.

2. A method for crop planting planning based on uncertainty according to claim 1, characterized in that: The uncertainties of crops include: expected sales volume, per-acre yield, crop planting costs and sales prices; The decision variables include the following: in, is the expected sales volume, is the yield per mu, is the per-acre yield change rate, For planting costs, is the selling price, is the planting cost change rate, is the sales price change rate, t is the year, i is the land number, It is the crop type.

3. A method for crop planting planning based on uncertainty according to claim 1, characterized in that: The objective function is as follows: in, is the profit, t is the year, i is the land number, is the crop type, k is the season, is the unsalable coefficient, for Year Season Planting the first The sales price of crops, for Year Season Planting the first The number of acres planted with crops, for Year Season Planting the first The cost of growing crops, for Year Season Planting the first The yield of crops per mu, for Year Expected sales of the crop.

4. A method for crop planting planning based on uncertainty according to claim 1, characterized in that: The constraints include: Each crop cannot be planted continuously in the same plot of land; All land is planted with pulse crops at least once in three years; Each plot of land is planted with only one crop at a time; Flat dry land, terraced fields and hillside land are used to grow food crops once a year; Two crops of vegetables are grown each year on irrigated land; Ordinary greenhouses grow one season of vegetables and one season of edible fungi each year; The smart greenhouse grows two crops of vegetables every year.

5. The method for crop planting planning based on uncertainty in crop planting according to claim 1, characterized in that: The target programming model includes: in, , , , , in, is the profit, t is the year, i is the land number, is the crop type, k is the season, is the unsalable coefficient, for Year Season Planting the first The sales price of crops, for Year Season Planting the first The number of acres planted with crops, for Year Season Planting the first The cost of growing crops, Whether in the Year Season i Plot planting Crops, For the The area of ​​the plot, For the The type of plot, For the expected sales volume, is the yield per mu, is the per-acre yield change rate, For planting costs, is the selling price, is the planting cost change rate, is the sales price change rate, for Year Season Planting the first The yield of crops per mu, for Year Expected sales of the crop.

6. A method for crop planting planning based on uncertainty according to claim 1, characterized in that: The step of using a genetic algorithm to solve the target programming model specifically includes: Determine the chromosome to be encoded as an integer array, where each chromosome represents the planting arrangement of a crop in different lands, different years and different seasons; Randomly generate the integer array to form an initial population; Traverse the planting arrangement of each individual in the initial population, calculate the profits of all planting arrangements and add them to the total profit, and use the total profit as the fitness value of the current individual; The initial population is iterated through crossover and mutation, and each population is sorted according to the fitness value during the iteration process until a final population is obtained after a maximum number of iterations is reached; The individual with the highest fitness value is selected from the final population as the agricultural planting strategy with the maximum benefit.

7. A planting planning system based on uncertainty in crop planting, characterized in that: include: The crop planting uncertainty analysis module is used to calculate the probability distribution of crop uncertainty factors through the Monte Carlo method to determine the decision variables; A target programming model building module, used to build a target programming model based on the decision variables, the target programming model includes an objective function and constraints, and the objective function is to maximize the agricultural planting income; The agricultural planting planning module is used to solve the target planning model using a genetic algorithm to obtain an agricultural planting strategy with maximum benefits and realize planting planning based on uncertainty in crop planting.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable in the processor, wherein the processor implements the steps of a planting planning method based on uncertainty in crop planting as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a planting planning method based on crop planting uncertainty as described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, the steps of a planting planning method based on uncertainty in crop planting as described in any one of claims 1 to 6 are implemented.

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