Planting planning method based on supply and demand cost and crop complementarity and related device

Abstract

The invention discloses a planting planning method based on supply and demand cost and crop complementarity, and belongs to the field of agricultural production.The method comprises the steps that the relation between crop yield per mu and complementarity indexes is built, and a linear regression model among the expected sales volume, sales price and planting cost of crops is built; determining a decision variable of the agricultural planting profit, and performing correction based on the decision variable in combination with the relationship between the acre yield of other crops and the complementarity index and a linear regression model to construct a target planning model; and solving the target planning model by adopting a genetic algorithm to obtain an agricultural planting strategy with maximum income, thereby realizing the planting planning method based on supply and demand cost and crop complementarity. The method can solve the problems that quantitative analysis cannot be carried out on the crop complementarity index and the acre yield during planting planning in the prior art, and dynamic analysis cannot be carried out on the correlation of the sales volume, the sales price and the planting cost, so that the prediction of the planting amount has deviation.

Description

Technical Field

[0001] The present invention relates to the field of agricultural production, and specifically to a planting planning method and related device based on supply - demand cost and crop complementarity. Background Art

[0002] In agricultural production practice, especially when formulating planting strategies, the substitutability and complementarity between crops are an important but often overlooked aspect. The ecological niche overlap or complementary relationship between different crops is complex and variable, which means that the choice of one crop may have a significant impact on the growth of another crop during the planting process. However, the existing technologies often lack in - depth understanding and quantitative analysis of such inter - crop relationships, and do not establish a quantitative relationship model between crop complementarity and per - mu yield, resulting in a lack of scientific basis for the selection of planting combinations and making it difficult for farmers to make optimal choices when formulating planting strategies. Secondly, the correlation between expected sales volume, sales price, and planting cost is also a key factor to be considered when formulating planting strategies. Changes in market demand, fluctuations in sales price, and levels of planting cost directly affect farmers' planting decisions and income levels. The existing technologies' correlation analysis of sales price, planting cost, and expected sales volume remains at a static level, and the accuracy of its associated planning model is insufficient. Therefore, the analysis in this aspect of the existing technologies is often too simplistic, failing to fully consider the complexity and dynamics of these factors, leading to a lack of scientific basis and flexibility in formulating planting strategies.

[0003] In summary, the existing technologies have obvious deficiencies in solving the substitutability and complementarity between crops, as well as the correlation between expected sales volume, sales price, and planting cost. Summary of the Invention

[0004] The purpose of the present invention is to provide a planting planning method and related device based on supply - demand cost and crop complementarity, so as to solve the problem that the existing technologies cannot quantitatively analyze the crop complementarity index and per - mu yield during planting planning, and cannot dynamically analyze the correlation between sales volume, sales price, and planting cost, resulting in deviations in predicted planting amounts.

[0005] To achieve the above - mentioned purpose, the present invention adopts the following technical solutions: In a first aspect, a planting planning method based on supply - demand cost and crop complementarity includes the following steps: Construct the relationship between the per - mu yield of other types of crops and the complementarity index based on the difference comparison method, and construct a linear regression model between the expected sales volume, sales price, and planting cost of crops; Determine the decision variables for agricultural planting profit. Based on these decision variables, combined with the relationship between the per-acre yield of other types of crops and the complementarity index, and a linear regression model for correction, to construct a goal programming model. The goal programming model includes an objective function and constraint conditions. The objective function is to maximize agricultural planting revenue; Use a genetic algorithm to solve the goal programming model to obtain an agricultural planting strategy with the maximum revenue, realizing a planting planning method based on supply-demand cost and crop complementarity.

[0006] In some embodiments, the relationship between the per-acre yield of other types of crops and the complementarity index is given by the following formula:

[0007]

[0008] where, is the complementarity index of whether the land contains hyphae of leguminous crops on the per-acre yield of the j-th type of crop, is the per-acre yield of the j-th type of crop when the land does not contain hyphae of leguminous crops, is the per-acre yield of the j-th type of crop when the land contains hyphae of leguminous crops, t is the year, i is the land number, is the crop type, is the per-acre yield of planting the -th type of crop on the -th piece of land in the

[0009] In some embodiments, the linear regression model between the expected sales volume, sales price, and planting cost of the crop is given by the following formula:

[0010] where, is the expected sales volume of the -th type of crop in the -th year, is the number of acres of planting the -th type of crop on the -th piece of land in the -th season of the -th year, is the planting cost of planting the -th type of crop on the -th piece of land in the -th season of the -th year, is the sales price of planting the

[0011] In some embodiments, the decision variables include expected sales volume, yield per acre, crop planting cost and sales price.

[0012] In some embodiments, the goal planning model includes:

[0013] in, , , , ,

[0014] Among them, among them, 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, 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, for Year Season Planting the first The yield of crops per mu, for Year Expected sales volume of a crop Is the complementary index of the yield per mu of the j-th type of crop with respect to whether the land contains hyphae of leguminous crops Is In year On plot Yield per mu of the i-th crop planted

[0015] 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 through 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.

[0016] In a second aspect, a planting planning system based on supply-demand cost and crop complementarity includes: A supply-demand cost and complementarity relationship analysis module, configured to construct a relationship between the yield per mu of other types of crops and the complementarity index based on the difference comparison method, and construct a linear regression model between the expected sales volume, sales price, and planting cost of the crops; A target programming module construction and correction module, configured to determine decision variables for agricultural planting profit, and based on the decision variables, and in combination with the relationship between the yield per mu of other types of crops and the complementarity index, and the linear regression model, perform correction to construct a target programming model, where the target programming model includes an objective function and constraint conditions, and the objective function is to maximize agricultural planting income; An agricultural planting strategy planning module, configured to solve the target programming model by using a genetic algorithm to obtain an agricultural planting strategy with the maximum benefit, and implement a planting planning method based on supply-demand cost and crop complementarity.

[0017] 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, and when the processor executes the computer program, the steps of the planting planning method based on supply-demand cost and crop complementarity are implemented.

[0018] Fourth aspect: A computer-readable storage medium stores a computer program, which when executed by a processor, implements the steps of the planting planning method based on supply-demand cost and crop complementarity.

[0019] Fifth aspect: A computer program product includes a computer program, characterized in that when the computer program is executed by a processor, it implements the steps of the planting planning method based on supply-demand cost and crop complementarity.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a planting planning method based on supply-demand cost and crop complementarity. By comprehensively considering the complementarity of crops, expected sales volume, selling price, and planting cost, a goal programming model is constructed and solved using a genetic algorithm to obtain an optimal planting strategy, significantly improving farmers' planting income. Considering the complementary relationship between crops can select the most suitable crops for planting in different lands, different years, and different seasons. This optimized planting structure helps to increase crop yields, reduce planting costs, and enhance overall economic benefits. By establishing a linear regression model, it is possible to better predict the expected sales volume and selling price of crops, helping planters better cope with market risks, flexibly adjust the planting structure, and enhance the risk resistance of agriculture. In summary, the present invention can fully consider the complementarity between crops and the correlation between expected sales volume, selling price, and planting cost, making the planted quantity of crops after goal planning more accurate. Description of the Drawings

[0021] Figure 1 It is a flowchart of the planting planning method based on supply-demand cost and crop complementarity provided in this embodiment; Figure 2 It is a structure diagram of the planting planning system based on supply-demand cost and crop complementarity provided in this embodiment. Detailed Embodiments

[0022] 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.

[0023] 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.

[0024] Only one season of crops can be planted in a certain rural area every year. The rural area currently has 1,201 mu of open cultivated land, which is 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 rural area also has 16 ordinary greenhouses and 4 intelligent greenhouses, and the cultivated land area of each greenhouse is 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 co-planted 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 the 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.

[0025] In real life, there may be certain substitutability and complementarity between various crops, and there is also a certain correlation between the expected sales volume and the sales price and planting cost. After considering the uncertainties of the expected sales volume, yield per mu, planting cost and sales price of various crops and potential planting risks in this embodiment, a planting planning method based on supply-demand cost and crop complementarity is provided.

[0026] The following embodiments 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 non-substitutable.

[0027] Table 1 below is the description of the parameters involved in the text.

[0028] Table 1 Parameter Description

[0029] The specific steps of the planting planning method are as follows: S1 Determine the substitutability between crops According to assumption (5), various crops are completely non-substitutable, that is, each crop is analyzed separately.

[0030] S2 Determine the complementarity between crops First, determine the complementary relationship between crops. According to the above rural planting background, the soil after planting leguminous crops is beneficial to the growth of other crops. Therefore, there is a complementarity between whether the soil contains leguminous crops and the per-mu yield of other crops. When other crops are planted in the soil where leguminous crops have been planted, the per-mu yield of other crops will increase.

[0031] Second, quantify the complementarity between crops. Use the complementarity index to measure the complementarity between leguminous crops and other crops. Let the per-mu yield of other crops planted on the ith plot of land in year t + 1 be when leguminous crops are planted on the ith plot of land in year t. Translate the above conditions into symbolic language as follows: (2-1) (2-2) When leguminous crops are not planted on the ith plot of land in year t, the per-mu yield of other crops planted on the ith plot of land in year t + 1 is . Translate the above conditions into symbolic language as follows: (2-3) (2-4) Define the complementarity index using the difference comparison method as: (2-5) where refers to the complementary index of whether the soil contains the mycelium of leguminous crops on the per-mu yield of the jth type of crop. When the complementarity index is closer to 1, the complementarity between the per-mu yield of leguminous crops and other crops is stronger. The relationship between the per-mu yield of other crops and the complementarity index is as follows: (2-6) S3. Determine the correlation between the expected sales volume, sales price, and planting cost S3.1 Spearman rank correlation coefficient The Spearman rank correlation coefficient is mainly calculated based on the rank order of variables rather than specific values. It determines the degree of correlation between two variables by comparing the rank differences of the two variables. Sort the expected sales volume, sales price, and planting cost in sequence, calculate the rank difference of each observation value from the data point, then calculate the Spearman correlation coefficient according to the following formula, and finally analyze the correlation coefficient to judge the correlation between the two.

[0032] (3-1) where, is the number of data points, is the rank difference of the ith pair of data points.

[0033] The Spearman correlation test was conducted on the expected sales volume, selling price, and planting cost using SPSS. The results are shown in Table 2 below.

[0034] Table 2 Results of Spearman correlation test

[0035] As can be seen from Table 2, there is a strong positive correlation between the expected sales volume and the total planting cost. There is a significant negative correlation between the expected sales volume and the selling price. Since there is a significant correlation between the expected sales volume, selling price, and planting cost, a linear regression model was further used for analysis.

[0036] S3.2 Linear regression model. As a simple statistical model, the linear regression model can establish a linear relationship between the expected sales volume and two variables, namely the total planting cost and the selling price. The form of the multiple linear regression model can be simply expressed as: (3-2) Based on the least squares method, SPSS was used to solve the correlation coefficients. The results are shown in Table 3 below.

[0037] Table 3 Correlation coefficients

[0038] The linear regression model was obtained from Table 3, as shown in the following equation: (3-3) SPSS was used to conduct an R-squared test to judge the accuracy of the model. The results are shown in Table 4 below: Table 4 R-squared test

[0039] As can be seen from Table 4, the accuracy of this linear regression model is relatively high.

[0040] S4 Model establishment and modification S4.1 Determine the decision variables (1) Expected sales volume The expected sales volume of wheat and corn increases by 5% - 10% annually. At the same time, 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.

[0041] (4-1) (2) Yield per mu The yield per mu of all crops changes by ±10% annually. Therefore, the following relationship exists for the yield per mu: (4-2) (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 there is the following relationship: (4 - 3) (4) Selling price The selling price of vegetable crops increases by 5% on average annually. At the same time, the selling price of edible fungi decreases by about 1% - 5% annually. For the new selling price, there is the following relationship: (4 - 4) S4.2 Determine the objective function Assume that as stipulated in (3), the planting plan aims to obtain the maximum profit. Therefore, the objective function is determined to be maximizing the planting profit. The final profit is calculated based on the sum of the comprehensive profits of each plot of land in each quarter of each year. The single - time profit is calculated based on the basic idea that profit equals sales revenue minus cost.

[0042] 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". 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 minimum 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 minimum value of the difference between "quantity" and the expected sales revenue and 0, and multiply it by the selling price to obtain the sales revenue of the abnormal sales part. In the th quarter of the year, the profit calculation formula for planting the th type of crop on the (4 - 5) In addition, for the part exceeding the expected self - sales volume that is completely unsold and wasted and the part 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, multiply it by the unsold coefficient Multiply. For the case where the excess part is completely unsold, let the unsold coefficient be 0, and the resulting value can be considered that the excess part is unsold and wasted, and no revenue is generated at all; for the case where the excess part is sold at a 50% price reduction, let the unsold coefficient be 0.5, and the resulting value conforms to the rule of a 50% price reduction.

[0043] Finally, the cost, that is, the planting cost of this plot of land, can be solved using the following formula: (4 - 6) The objective function of the linear goal programming model for maximizing revenue is as follows: (4-7) Among them, the part that does not exceed the expected sales volume is , and the part that exceeds the expected sales volume is .

[0044] (4-8) (4-9) Substitute the decision variables in S4.1 into the above formula (2-3). At the same time, select the part that exceeds the expected sales according to the situation of slow-moving waste, and set the slow-moving coefficient to 0, and the objective function is obtained as follows: (4-10) S4.3 Determine the constraint conditions (1)Each crop cannot be continuously replanted in the same plot (including greenhouses), otherwise the yield will decrease. That is, for the land with single-season crops, the crop type in the first year cannot be the same as that in the second year; for the land with 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: (4-11) , , (4-12) (2)All land must be planted with leguminous crops at least once within three years. That is, sum up the situation of determining to plant leguminous crops 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: (4-13) (4-14) (3)The planting areas 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. Therefore, it is restricted that only one crop is planted in a plot at a time. Translate the above conditions into symbolic language as follows: (4-15) (4)Flat dry land, terraced fields and hillside land 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. Translate the above conditions into symbolic language as follows: , (4-16) , (4-17) (5) Irrigated land is suitable for growing one season of rice or two seasons of vegetables each year. Combining the results of data preprocessing, only two kinds of vegetables are planted on irrigated land. That is, it is determined that planting is carried out at both levels, and the types of planted crops are all vegetables. The above conditions are transformed into symbolic language as follows: (4-18) (6) Ordinary greenhouses are suitable for growing one season of vegetables and one season of edible fungi each year. That is, it is determined that planting is carried out in each season of each year, and the planting types are and types respectively. The above conditions are transformed into symbolic language as follows: , (4-19) , (4-20) (7) Smart greenhouses are suitable for growing two seasons of vegetables each year. That is, in each season of each year, smart greenhouses are planted. The above conditions are transformed into symbolic language as follows: , (4-21) , (4-22) S4.4 Establish the goal programming model Combining the above objective function and constraint conditions, the goal programming model is established as follows: (4-22) Among them , , ,

[0045]

[0046] Combining the content in the above-mentioned combined data analysis, considering the complementarity and substitutability between crops, as well as the correlation between the expected sales volume and the total planting cost and selling price, the model (4-22) is corrected to obtain the model (4-23) as follows:

[0047] Among them, , , , ,

[0048] The solution of the S5 goal programming model, based on the Monte Carlo method, simulates the uncertainties of the expected sales volume, yield per mu, planting cost, and selling price through random sampling. The essence of the problem is simulated through a large number of random experiments. The specific algorithm steps are as follows: (1) Based on the above analysis, implement the probability distribution sampling of the uncertainties of the expected sales volume, yield per mu, planting cost, and selling price.

[0049] (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 (2.1) Determine the encoding method of the chromosome In this embodiment, the chromosome uses integer encoding. 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 an encoding method makes each chromosome represent the planting arrangement of a crop on different lands, in different years, and seasons.

[0050] (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 per season. By looping a sufficient number of times, generate a specified number of integer arrays to form the initial population.

[0051] (2.3) Determine the fitness function In this embodiment, only maximizing the profit is the optimization goal. 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. 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.

[0052] (2.4) Screening of excellent individuals The method for screening excellent individuals is based on the evaluation and selection of the fitness function. The specific process is as follows: First, for each individual in the population, that is, the solution, it is judged whether it meets the constraint conditions of the problem. For those that do not meet, their fitness is set to negative infinity. Then, calculate the individuals that meet the constraint conditions and calculate their fitness, that is, calculate the profit according to 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.

[0053] (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.

[0054] 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.

[0055] 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.

[0056] (2.6) Setting relevant parameters of the genetic algorithm Set the population size to 100 to ensure a sufficient population scale. The larger the population scale, the easier it is to find the optimal solution. Set the mutation rate to 0.1. Setting a relatively large mutation rate can increase the diversity of the population and avoid premature convergence to a local optimal solution. Set the maximum number of genetic generations to 100 to ensure that the optimization results converge sufficiently. The above steps are implemented using Python.

[0057] (3) Examine and verify the simulation results.

[0058] (4) According to the initial simulation conditions, set relevant parameters, return to the first step, and perform iteration.

[0059] (5) Obtain the final maximum profit and planting plan.

[0060] The calculated maximum profit is 42,935,084.30 yuan. Considering that the complementary relationship between crops increases the crop yield, and the relationship between the expected sales volume, sales price, and planting cost restricts each other, resulting in an increase in the expected final profit, which demonstrates the rationality of the model. The agricultural planting strategies for the maximum benefits from 2024 to 2030 are shown in Tables 5 to 11 below.

[0061] Table 5 Agricultural planting plan for 2024

[0062] Table 6 Agricultural Planting Plan for 2025

[0063] Table 7 Agricultural Planting Plan for 2026

[0064] Table 8 Agricultural Planting Plan for 2027

[0065] Table 9 Agricultural Planting Plan for 2028

[0066] Table 10 Agricultural Planting Plan for 2029

[0067] Table 11 Agricultural Planting Plan for 2030

[0068] This embodiment provides a planting planning method based on supply - demand cost and crop complementarity, which has the following advantages: (1) It can comprehensively consider the sales volume, profit of agricultural crops and the limitation of land resources, reduce waste and maximize the income of farmers.

[0069] (2) For different sales situations, the model considers two kinds of unsalable situations: one is that the unsalable part is completely wasted, and the other is that the unsalable part is sold at 50% of the price. This makes the optimization plan more flexible and more adaptable.

[0070] (3) By using the genetic algorithm to optimize the planting plan, it considers the interaction of multiple variables, such as land type, crop type, sales price, etc., and can effectively find the optimal planting plan.

[0071] This embodiment can effectively reduce the waste phenomenon in agricultural crop planting, reduce the impact of uncertain factors on agricultural income, and maximize the economic benefits of farmers by adopting the linear programming optimization method and simulation calculation. Compared with the traditional method, this embodiment has significant advantages in improving resource utilization rate, reducing costs and optimizing sales prediction, and is especially suitable for long - term agricultural production planning between 2024 and 2030.

[0072] This embodiment provides a planting planning system based on supply - demand cost and crop complementarity, including: Supply-demand cost and complementarity relationship analysis module, which is used to construct the relationship between the per-mu yield of other types of crops and the complementarity index based on the differential comparison method, and construct a linear regression model between the expected sales volume, sales price and planting cost of crops; Goal programming module construction and correction module, which is used to determine the decision variables of agricultural planting profit, and based on the decision variables, combined with the relationship between the per-mu yield of other types of crops and the complementarity index, and the linear regression model for correction to construct a goal programming model. The goal programming model includes an objective function and constraint conditions, and the objective function is to maximize agricultural planting income; Agricultural planting strategy planning module, which is used to solve the goal programming model by using a genetic algorithm to obtain the agricultural planting strategy with the maximum income, and realize the planting planning method based on supply-demand cost and crop complementarity.

[0073] The division of modules in the embodiments of the present invention is illustrative, 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 can be integrated in a processor, or can exist independently physically, or two or more modules can be integrated in one module. The above integrated modules can be implemented in the form of hardware or in the form of software function modules.

[0074] In this embodiment, a computer device is also provided. The computer device 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 suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow 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 supply-demand cost and crop complementarity.

[0075] 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 a storage space, and this storage space stores the operating system of the terminal. And, in this storage space, there are 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 supply-demand cost and crop complementarity in the above embodiment.

[0076] 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 supply-demand cost and crop complementarity in the above embodiment.

[0077] 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 complete hardware embodiment, a complete 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.

[0078] 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.

[0079] 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 work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes Figure 1 or blocks Figure 1 specified in one or more of the processes and / or blocks.

[0080] 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, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes Figure 1 or blocks Figure 1 specified in one or more of the blocks.

[0081] 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: modifications or equivalent replacements can still be made to the specific embodiments of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A planting planning method based on supply and demand costs and crop complementarity, characterized in that: The following steps are involved: Based on the difference comparison method, the relationship between the per-acre yield of other types of crops and the complementarity index is constructed, and a linear regression model between the expected sales volume, sales price and planting cost of the crops is constructed; Determine the decision variables of agricultural planting profit, and construct a target programming model based on the decision variables, in combination with the relationship between the per-acre yield of other types of crops and the complementarity index, and the linear regression model for correction, wherein the target programming model includes an objective function and constraints, and the objective function is to maximize the agricultural planting income; A genetic algorithm is used to solve the target programming model to obtain an agricultural planting strategy with maximum benefits, thereby realizing a planting planning method based on supply and demand costs and crop complementarity.

2. A planting planning method based on supply and demand costs and crop complementarity according to claim 1, characterized in that: The relationship between the per-acre yield of other types of crops and the complementarity index is as follows: in, is the complementary index of whether the land contains legume mycelium to the per-acre yield of the j-th crop, is the per-acre yield of the jth crop when the land does not contain legume mycelium, is the per-acre yield of the jth crop when the land contains legume mycelium, t is the year, i is the land number, is the crop type, for In the Planting the first The per-acre yield of crops.

3. A planting planning method based on supply and demand costs and crop complementarity according to claim 1, characterized in that: The linear regression model between the expected sales volume, sales price and planting cost of the crop is as follows: in, for Year Expected sales of the crop, 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 sales price of crops.

4. A planting planning method based on supply and demand costs and crop complementarity according to claim 1, characterized in that: The decision variables include expected sales volume, per-acre yield, crop planting cost and sales price.

5. A planting planning method based on supply and demand costs and crop complementarity according to claim 1, characterized in that: The target programming model includes: in, , , , , Among them, among them, 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, is the complementary index of whether the land contains legume mycelium to the per-acre yield of the j-th crop, for In the Planting the first The per-acre yield of crops.

6. A planting planning method based on supply and demand costs and crop complementarity 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 supply and demand costs and crop complementarity, characterized in that: include: The module for analyzing the relationship between supply and demand costs and complementarity is used to construct the relationship between the per-acre yield of other types of crops and the complementarity index based on the difference comparison method, and to construct a linear regression model between the expected sales volume, sales price and planting cost of crops; A target planning module construction and correction module is used to determine the decision variables of agricultural planting profits, based on the decision variables, combined with the relationship between the per-acre yield of other types of crops and the complementarity index, and the linear regression model for correction to construct a target planning model, the target planning model includes an objective function and constraints, and the objective function is to maximize the agricultural planting benefits; The agricultural planting strategy planning module is used to solve the target planning model using a genetic algorithm to obtain the agricultural planting strategy with the maximum benefit, thereby realizing a planting planning method based on supply and demand costs and crop complementarity.

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 supply and demand costs and crop complementarity 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 supply and demand costs and crop complementarity 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 supply and demand costs and crop complementarity as described in any one of claims 1 to 6 are implemented.

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