Method and system for predicting crop industry demand quantity based on Poisson distribution

Abstract

The invention discloses a method and system for predicting crop industry demand based on Poisson distribution, and the method comprises the steps: obtaining the historical selling price of crops in a preset time period, carrying out the Poisson distribution calculation based on the historical selling price, and determining a price distribution coefficient; predicting the selling price of the crops in the future time period based on the price distribution coefficient; obtaining a historical population number of population in a preset time period, and performing Poisson distribution calculation based on the historical population number to determine a population distribution coefficient; predicting the population number in a future time period based on the population distribution coefficient, and calculating a population growth index based on the population number; and determining the industrial demand quantity of the crop based on the selling price and the population growth index in the future time period. According to the invention, a basis can be provided for analyzing the food safety and food consumption demand state in China and aiding decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of crop analysis, and more specifically, to a method and system for predicting industrial demand for crops based on Poisson distribution. Background Art

[0002] Food security is an important foundation for national security. Only by ensuring basic self-sufficiency in food can we take the initiative in food security and thus control the overall situation of economic and social development. The key to food security is to be able to ensure the supply of basic food during wars or disasters. Therefore, it is necessary to do a good job in the work of grain production and the supply of important agricultural products.

[0003] Therefore, a method for predicting the industrial demand for crops based on Poisson distribution is needed. Summary of the invention

[0004] The present invention proposes a method and system for predicting the industrial demand of crops based on Poisson distribution, so as to solve the problem of how to determine the industrial demand of crops.

[0005] In order to solve the above problem, according to one aspect of the present invention, a method for predicting the industrial demand of crops based on Poisson distribution is provided, the method comprising:

[0006] Obtaining historical selling prices of crops within a preset time period, and performing Poisson distribution calculation based on the historical selling prices to determine a price distribution coefficient;

[0007] Predicting the selling price of the crop in a future time period based on the price distribution coefficient;

[0008] Obtaining the historical population size within a preset time period, and performing Poisson distribution calculation based on the historical population size to determine the population distribution coefficient;

[0009] Predicting the population size in a future time period based on the population distribution coefficient, and calculating a population growth index based on the population size;

[0010] Determine the industrial demand for crops based on the selling price and population growth index in the future time period.

[0011] Preferably, when performing Poisson distribution calculation, the method supplements samples based on the bootstrap method and performs Johnson transformation, then determines the distribution form of the samples through the P value and AD value, and determines the range of the distribution coefficient based on the distribution form.

[0012] Preferably, predicting the selling price of crops in a future time period based on the price distribution coefficient comprises:

[0013] YDcy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ),

[0014] Among them, YD cy,t+1 YD is the selling price of crops in the t+1 period; cy,t is the selling price of the crop in time period t; β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t; θ is the correction period constant; x t is the crop price growth factor in year t.

[0015] Preferably, the step of determining the industrial demand for crops based on the selling price and population growth index in a future time period comprises:

[0016] LqCy y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t ,

[0017] Among them, GY y,t is the industrial demand in year t, LnGY y,t YD is the industrial demand index; cy,t is the selling price of agricultural raw materials, lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Calculate constants for agricultural products; α y,h Calculate constants for industry processing; α y,x is the population constant; t is the time, and y is the crop type.

[0018] According to another aspect of the present invention, a system for predicting industrial demand for crops based on Poisson distribution is provided, wherein the system comprises:

[0019] A price distribution calculation unit, used to obtain the historical selling prices of the crops within a preset time period, and perform Poisson distribution calculation based on the historical selling prices to determine the price distribution coefficient;

[0020] a price prediction unit, used for predicting the selling price of the crop in a future time period based on the price distribution coefficient;

[0021] A population distribution calculation unit, used to obtain the historical population size of the population within a preset time period, and perform Poisson distribution calculation based on the historical population size to determine the population distribution coefficient;

[0022] A population prediction unit, used to predict the population size in a future time period based on the population distribution coefficient, and calculate a population growth index based on the population size;

[0023] The industrial demand determination unit is used to determine the industrial demand of crops based on the selling price and the population growth index in a future time period.

[0024] Preferably, the price distribution calculation unit and the population distribution calculation unit, when performing Poisson distribution calculation, supplement samples based on the bootstrap system and perform Johnson transformation, and then determine the distribution form of the samples through the P value and AD value, and determine the range of the distribution coefficient based on the distribution form.

[0025] Preferably, the price prediction unit predicts the selling price of the crop in a future time period based on the price distribution coefficient, including:

[0026] YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ),

[0027] Among them, YD cy,t+1 YD is the selling price of crops in the t+1 period; cy,t is the selling price of the crop in time period t; β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t; θ is the correction period constant; x t is the crop price growth factor in year t.

[0028] Preferably, the industrial demand determination unit determines the industrial demand of crops based on the selling price and population growth index in a future time period, including:

[0029] LqCy y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t ,

[0030] Among them, GY y,t is the industrial demand in year t, LnGY y,tYD is the industrial demand index; cy,t is the selling price of agricultural raw materials, lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Calculate constants for agricultural products; α y,h Calculate constants for industry processing; α y,x is the population constant; t is the time, and y is the crop type.

[0031] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any step of a method for predicting industrial demand for crops based on Poisson distribution.

[0032] According to another aspect of the present invention, the present invention provides an electronic device, including:

[0033] The computer-readable storage medium described above; and

[0034] One or more processors are used to execute the program in the computer-readable storage medium.

[0035] The present invention provides a method and system for predicting the industrial demand of crops based on Poisson distribution, including: obtaining the historical selling price of crops in a preset time period, and performing Poisson distribution calculation based on the historical selling price to determine the price distribution coefficient; predicting the selling price of crops in a future time period based on the price distribution coefficient; obtaining the historical population of the population in a preset time period, and performing Poisson distribution calculation based on the historical population to determine the population distribution coefficient; predicting the population in a future time period based on the population distribution coefficient, and calculating the population growth index based on the population; determining the industrial demand of crops based on the selling price and population growth index in the future time period. The present invention is based on the industrial consumption data of crops in previous years, sorts out the factors that mainly affect industrial consumption, constructs a calculation formula, and then constructs the coverage of factors affecting future industrial demand based on historical data, and predicts the industrial demand of crops using the Poisson distribution model, which provides a basis for analyzing my country's food security and food consumption demand status and assisting decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0037] Figure 1 It is a flowchart of a method 100 for predicting the industrial demand of crops based on Poisson distribution according to an embodiment of the present invention;

[0038] Figure 2 It is an overall flow chart of the calculation of the crop industry demand according to an embodiment of the present invention;

[0039] Figure 3 Schematic diagram of the structure of a system 300 for predicting industrial demand for crops based on Poisson distribution according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0041] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0042] The present invention deeply understands the consumption structure and characteristics of grain varieties, analyzes and selects key variables that affect grain consumption demand, takes corn industrial consumption as a starting point, uses reasonable methods to predict industrial consumption, provides effective information service content, and provides effective services for stabilizing food security in my country.

[0043] Figure 1 FIG. 1 is a flow chart of a method 100 for predicting the demand of crop industry based on Poisson distribution according to an embodiment of the present invention. Figure 1 As shown, the method for predicting the industrial demand of crops based on Poisson distribution provided by the embodiment of the present invention is based on the industrial consumption data of crops in previous years, sorts out the factors that mainly affect industrial consumption, constructs a calculation formula, and then constructs the coverage of factors affecting future industrial demand based on historical data. The industrial demand of crops is predicted by using the Poisson distribution model, which provides a basis for analyzing my country's food security and food consumption demand status and assisting decision-making. The method 100 for predicting the industrial demand of crops based on Poisson distribution provided by the embodiment of the present invention starts from step 101. In step 101, the historical selling price of crops in a preset time period is obtained, and Poisson distribution calculation is performed based on the historical selling price to determine the price distribution coefficient.

[0044] Preferably, when performing Poisson distribution calculation, the method supplements samples based on the bootstrap method and performs Johnson transformation, then determines the distribution form of the samples through the P value and AD value, and determines the range of the distribution coefficient based on the distribution form.

[0045] In step 102, the selling price of the crops in a future period is predicted based on the price distribution coefficient.

[0046] Preferably, predicting the selling price of crops in a future time period based on the price distribution coefficient comprises:

[0047] YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ),

[0048] Among them, YD cy,t+1 YD is the selling price of crops in the t+1 period; cy,t is the selling price of the crop in time period t; β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t; θ is the correction period constant; x t is the crop price growth factor in year t.

[0049] In step 103, the historical population size within a preset time period is obtained, and a Poisson distribution calculation is performed based on the historical population size to determine the population distribution coefficient.

[0050] In step 104, the population size in a future time period is predicted based on the population distribution coefficient, and a population growth index is calculated based on the population size.

[0051] In step 105, the industrial demand for the crop is determined based on the selling price and the population growth index in the future time period.

[0052] Preferably, the step of determining the industrial demand for crops based on the selling price and population growth index in a future time period comprises:

[0053] LqCy y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t ,

[0054] Among them, GY y,t is the industrial demand in year t, LnGY y,tYD is the industrial demand index; cy,t is the selling price of agricultural raw materials, lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Calculate constants for agricultural products; α y,h Calculate constants for industry processing; α y,x is the population constant; t is the time, and y is the crop type.

[0055] In the present invention, the calculation formula of corn industry demand is:

[0056] LqCy y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t

[0057] Among them: GY y,t is industrial demand (thousand tons), LnGY y,t YD is the industrial demand index; cy,t is the selling price of agricultural raw materials (yuan / kg), lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Set as agricultural product calculation constant; α y,h Set as industry processing calculation constant; α y,x Set as a population calculation constant; t represents time, and y represents species (this patent represents corn).

[0058] Poisson distribution is suitable for describing the number and probability of random events occurring within a unit of time (or space). cy,t is the selling price of agricultural raw materials, YX cy,t The population growth number is used to construct a formula to estimate the crop industry volume.

[0059] Combination Figure 2 As shown, the steps of constructing the calculation formula using the Poisson distribution method are as follows:

[0060] Step 1: Fit the existing data and find the most suitable distribution model based on the P and AD values ​​of different distributions. In this example, the Johnson transformation of the Poisson distribution.

[0061] Step 2: Based on the known overall distribution, take multiple bootstrap samples from the original sample and use these samples to make statistical inferences on the parameters of the overall distribution, including the following:

[0062] (1) Repeated sampling technique is used to extract a certain number of samples from the original sample. This process is repeated sampling.

[0063] (2) Calculate the statistic T to be estimated based on the drawn samples.

[0064] (3) Repeat the above process 1000 times to obtain 1000 statistics T.

[0065] Calculate the sample variance of the above 1000 statistics T to estimate the variance of statistic T.

[0066] Therefore, in the process of constructing the formula, it is necessary to eliminate fluctuations and process abnormal data through periodic functions. After adding sine and cosine functions respectively, the influence of fluctuations is eliminated, and the final calculation formula is as follows:

[0067] YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t )

[0068] Among them, YD cy,t+1 is the t+1 selling price, YD cy,t is the selling price in year t, β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t, and θ is the correction period constant. (Both the cosine function and the sine function are trigonometric functions, which are one of the basic elementary functions. They are functions with angle as the independent variable and the coordinates of the intersection of the terminal side of any angle with the unit circle or their ratio as the dependent variable. They can also be equivalently defined by the lengths of various line segments related to the unit circle. Trigonometric functions are basic mathematical tools for studying periodic phenomena. In mathematical analysis, trigonometric functions are also defined as solutions to infinite series or specific differential equations, allowing their values ​​to be extended to any real value, or even complex value. The role here is to eliminate the periodicity of consumer prices).

[0069] In the present invention, the above method is selected to construct the population size and raw material selling price model: this is because when statistically analyzing the historical data from 2000 to 2021, it was found that the population size and selling price model has a certain degree of cyclical fluctuation in time, and has an overall weak growth trend.

[0070] In this invention, taking corn as an example, the Poisson distribution theory in statistics and probability is used to construct a calculation scheme suitable for the key influencing factors of corn industrial demand. The calculation scheme for predicting corn industrial consumption is as follows:

[0071] Step 1: Enter the corn selling price from 2000 to 2021 and confirm the distribution.

[0072] Among them, based on more than 20 years of historical data from 2000 to 2021, open source Matlab was used to confirm the β selling price, the cosine function of the γ1 selling price, the sine function of the γ2 selling price, and θ is the range of the correction period constant.

[0073] Table 1: Estimated range of corn agricultural product selling price data

[0074]

[0075] Step 2: Substitute the range of β, the range of γ1, the range of γ2, and the range of θ into the formula to calculate the result.

[0076] Substituting the above results into YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ) calculated the selling price of corn agricultural products from 2022 to 2035, and the results are as follows:

[0077] Table 2: Estimated data of corn agricultural product sales price from 2022 to 2035

[0078]

[0079] Step 3: Confirm the population range and population increment coefficient

[0080] Calculation formula 3: RK c,t+1 =βx t +γ1cos(θx t )+γ2sin(θx t )

[0081] Among them, RK c,t+1 is the national population change index in year t+1, x t is the national population growth factor in year t, β is the coefficient of the national population growth factor in year t, γ1 is the coefficient of the cosine function of the national population growth factor in year t, γ2 is the coefficient of the sine function of the national population growth factor in year t, and θ is the correction period constant.

[0082] 1) Input the population data from 2000 to 2021, convert it into a population increment index, and calculate the range of β, the range of γ1, the range of γ2, and the range of θ;

[0083] 2) Substitute the above confirmed range into Poisson distribution formula 3 to calculate the population index from 2022 to 2035, and forwardly estimate the population growth.

[0084] Table 3: Estimated data of per capita growth index from 2022 to 2035

[0085]

[0086]

[0087] 3) Based on the coefficient, clarify the population size from 2022 to 2035

[0088] Table 4: Population data from 2022 to 2023

[0089] years Population (10,000 people) 2022 141212.45 2023 141581.25 2024 141934.31 2025 142272.27 2026 142595.74 2027 142905.30 2028 143201.53 2029 143484.96 2030 143756.13 2031 144015.54 2032 144263.69 2033 144501.04 2034 144728.04 2035 144945.00

[0090] Step 4: Confirm the population range and population increment coefficient

[0091] Substitute the above calculation results of the corn agricultural product sales price and population coefficient from 2022 to 2035 into LnGY y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t Calculate the industrial demand for corn from 2022 to 2035.

[0092] Figure 3 FIG. 3 is a schematic diagram of a system 300 for predicting industrial demand for agricultural crops based on Poisson distribution according to an embodiment of the present invention. Figure 3 As shown, the system 300 for predicting the industrial demand of crops based on Poisson distribution provided by the embodiment of the present invention includes: a price distribution calculation unit 301, a price prediction unit 302, a population distribution calculation unit 303, a population prediction unit 304 and an industrial demand determination unit 305.

[0093] Preferably, the price distribution calculation unit 301 is used to obtain the historical selling prices of the crops within a preset time period, and perform Poisson distribution calculation based on the historical selling prices to determine the price distribution coefficient.

[0094] Preferably, the price prediction unit 302 is used to predict the selling price of the crops in a future time period based on the price distribution coefficient.

[0095] Preferably, the population distribution calculation unit 303 is used to obtain the historical population size of the population within a preset time period, and perform Poisson distribution calculation based on the historical population size to determine the population distribution coefficient.

[0096] Preferably, the population prediction unit 304 is used to predict the population size in a future time period based on the population distribution coefficient, and calculate a population growth index based on the population size.

[0097] Preferably, the industrial demand determination unit 305 is used to determine the industrial demand of crops based on the selling price and the population growth index in a future time period.

[0098] Preferably, the price distribution calculation unit and the population distribution calculation unit, when performing Poisson distribution calculation, supplement samples based on the bootstrap system and perform Johnson transformation, and then determine the distribution form of the samples through the P value and AD value, and determine the range of the distribution coefficient based on the distribution form.

[0099] Preferably, the price prediction unit 302 predicts the selling price of the crop in a future time period based on the price distribution coefficient, including:

[0100] YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ),

[0101] Among them, YD cy,t+1 YD is the selling price of crops in the t+1 period; cy,t is the selling price of the crop in time period t; β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t; θ is the correction period constant; x t is the crop price growth factor in year t.

[0102] Preferably, the industrial demand determination unit 305 determines the industrial demand of crops based on the selling price and population growth index in the future time period, including:

[0103] LqCy y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t ,

[0104] Among them, GY y,tis the industrial demand in year t, LnGY y,t YD is the industrial demand index; cy,t is the selling price of agricultural raw materials, lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Calculate constants for agricultural products; α y,h Calculate constants for industry processing; α y,x is the population constant; t is the time, and y is the crop type.

[0105] The system 300 for predicting the crop industry demand based on Poisson distribution in the embodiment of the present invention corresponds to the method 100 for predicting the crop industry demand based on Poisson distribution in another embodiment of the present invention, and will not be described in detail herein.

[0106] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any step of a method for predicting industrial demand for crops based on Poisson distribution.

[0107] According to another aspect of the present invention, the present invention provides an electronic device, including:

[0108] The computer-readable storage medium described above; and

[0109] One or more processors are used to execute the program in the computer-readable storage medium.

[0110] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0111] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise therein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of said means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

[0112] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0113] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting industrial demand for crops based on Poisson distribution, characterized in that: The method comprises: Obtaining historical selling prices of crops within a preset time period, and performing Poisson distribution calculation based on the historical selling prices to determine a price distribution coefficient; Predicting the selling price of the crop in a future time period based on the price distribution coefficient; Obtaining the historical population size within a preset time period, and performing Poisson distribution calculation based on the historical population size to determine the population distribution coefficient; Predicting the population size in a future time period based on the population distribution coefficient, and calculating a population growth index based on the population size; Determine the industrial demand for crops based on the selling price and population growth index in the future time period.

2. The method according to claim 1, characterized in that When performing Poisson distribution calculation, the method supplements samples based on the bootstrap method and performs Johnson transformation, then determines the distribution form of the samples through the P value and AD value, and determines the range of the distribution coefficient based on the distribution form.

3. The method according to claim 1, characterized in that Predicting the selling price of crops in a future time period based on the price distribution coefficient includes: YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ), Among them, YD cy,t+1 YD is the selling price of crops in the t+1 period; cy,t is the selling price of the crop in time period t; β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t; θ is the correction period constant; x t is the crop price growth factor in year t.

4. The method according to claim 1, characterized in that: The method of determining the industrial demand for crops based on the selling price and population growth index in the future time period includes: LnGY y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t , Among them, GY y,t is the industrial demand in year t, LnGY y,t YD is the industrial demand index; cy,t is the selling price of agricultural raw materials, lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Calculate constants for agricultural products; α y,h Calculate constants for industry processing; α y,x is the population constant; t is the time, and y is the crop type.

5. A system for predicting industrial demand for agricultural crops based on Poisson distribution, characterized in that: The system comprises: A price distribution calculation unit, used to obtain the historical selling prices of the crops within a preset time period, and perform Poisson distribution calculation based on the historical selling prices to determine the price distribution coefficient; a price prediction unit, used for predicting the selling price of the crop in a future time period based on the price distribution coefficient; A population distribution calculation unit, used to obtain the historical population size of the population within a preset time period, and perform Poisson distribution calculation based on the historical population size to determine the population distribution coefficient; A population prediction unit, used to predict the population size in a future time period based on the population distribution coefficient, and calculate a population growth index based on the population size; The industrial demand determination unit is used to determine the industrial demand of crops based on the selling price and the population growth index in a future time period.

6. The system according to claim 5, characterized in that The price distribution calculation unit and the population distribution calculation unit, when performing Poisson distribution calculation, supplement samples based on the bootstrap system and perform Johnson transformation, then determine the distribution form of the samples through the P value and AD value, and determine the range of the distribution coefficient based on the distribution form.

7. The system according to claim 5, characterized in that The price prediction unit predicts the selling price of the crop in a future time period based on the price distribution coefficient, including: YD cy,t+1 =βYD cy,t +γ1cos(θx t )+γ2sin(θx t ), Among them, YD cy,t+1 YD is the selling price of crops in the t+1 period; cy,t is the selling price of the crop in time period t; β is the coefficient of the selling price in year t, γ1 is the coefficient of the cosine function of the selling price in year t, γ2 is the coefficient of the sine function of the selling price in year t; θ is the correction period constant; x t is the crop price growth factor in year t.

8. The system according to claim 5, characterized in that The industrial demand determination unit determines the industrial demand of crops based on the selling price and the population growth index in the future time period, including: LnGY y,t =α y,d *lnYD y,t +α y,h *lnYH y,t +α y,x *lnYX y,t , Among them, GY y,t is the industrial demand in year t, LnGY y,t YD is the industrial demand index; cy,t is the selling price of agricultural raw materials, lnYD cy,t It is the agricultural product selling price index; cy,t Industrial Demand Index; YX cy,t Population growth, lnYX cy,t is the population growth index. y,d Calculate constants for agricultural products; α y,h Calculate constants for industry processing; α y,x is the population constant; t is the time, and y is the crop type.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

10. An electronic device, characterized in that: include: The computer readable storage medium as claimed in claim 9; as well as One or more processors are used to execute the program in the computer-readable storage medium.

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