Agricultural product production and sales data processing method and equipment

Abstract

The invention relates to the technical field of big data, and discloses an agricultural product production and sales data processing method and equipment, and the method comprises the following steps: S01, dividing agricultural products into similar agricultural products and non-similar agricultural products, S02, collecting the production and sales data of the agricultural products in the step S01, S03, carrying out the preprocessing of the production and sales data of the agricultural products in the step S02, and carrying out the processing of the production and sales data of the agricultural products in the step S02. Step S04, completing agricultural product price fluctuation analysis through data mining according to the production and sales data preprocessed in the step S03, and step S05, visually presenting an agricultural product price fluctuation analysis result in the step S04. S06, according to the production and sales data preprocessed in the step S03, association analysis of the same kind of agricultural products and non-same kind of agricultural products is completed through data mining, S07, the association degree of the same kind of agricultural products and non-same kind of agricultural products is judged, S08, analysis of the agricultural product production and sales data is completed, and an enterprise finds potential risks in a supply chain through data analysis. And optimization and adjustment are carried out in time.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and more specifically to a method and device for processing agricultural product production and sales data. Background Art

[0002] In traditional agriculture, farmers' production decisions are often based on subjective analysis or feelings, lacking scientific data support, resulting in a disconnect between production and sales in agricultural production, that is, a certain agricultural product has a good harvest but is difficult to sell, while another agricultural product has a high price due to a shortage of supply. This information asymmetry and decision-making errors have brought great uncertainty to agricultural production. With the continuous maturity of data processing technology, by collecting production data, sales data, consumer feedback data, etc. of agricultural products, and using data analysis tools for in-depth mining and analysis, companies can more accurately understand market demand and consumer preferences, thereby formulating more scientific and reasonable production plans and sales strategies.

[0003] However, agricultural product prices are affected by many factors, including supply and demand, weather conditions, policy regulation, etc. The existing agricultural product production and sales data processing methods have limited early warning capabilities for agricultural product prices, and lack a price monitoring system based on data analysis. When agricultural product prices fluctuate abnormally, timely measures cannot be taken to intervene, and the interests of farmers and consumers cannot be protected. Agricultural products face similar changes in market demand, but existing data processing methods fail to accurately capture such demand changes, resulting in producers being unable to adjust sales strategies in a timely manner based on the correlation between agricultural products. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and device for processing agricultural product production and sales data to solve the problems existing in the above-mentioned background technology.

[0005] The present invention provides the following technical solution: a method for processing agricultural product production and sales data, comprising the following steps:

[0006] Step S01: Classifying agricultural products into similar agricultural products and non-similar agricultural products: Classifying agricultural products into similar agricultural products and non-similar agricultural products based on the types of agricultural products;

[0007] Step S02: Collecting the production and sales data of the agricultural products in step S01: obtaining the production and sales data of various agricultural products through remote sensing technology and questionnaire survey;

[0008] Step S03: pre-processing the production and sales data of the agricultural products in step S02: removing invalid values ​​and abnormal values ​​in the original production and sales data of the agricultural products through data cleaning processing, and unifying the data format of the production and sales data of the agricultural products;

[0009] Step S04: completing the price fluctuation analysis of agricultural products through data mining based on the production and sales data pre-processed in step S03: establishing an agricultural product price fluctuation analysis model through the agricultural product supply coefficient and the market service supply coefficient, completing the price fluctuation analysis of agricultural products;

[0010] Step S05: Visualizing the analysis results of agricultural product price fluctuations in step S04: Visualizing the analysis results of agricultural product price fluctuations to the user terminal, and setting a price fluctuation range, and displaying an early warning when the price fluctuation range is exceeded;

[0011] Step S06: completing the correlation analysis between similar agricultural products and non-similar agricultural products through data mining based on the production and sales data pre-processed in step S03: establishing a correlation analysis model, and calculating the correlation between similar agricultural products and non-similar agricultural products;

[0012] Step S07: Determine the correlation between similar agricultural products and non-similar agricultural products: compare the correlation between similar agricultural products and non-similar agricultural products calculated in step S06 with a preset correlation threshold to determine the correlation between similar agricultural products and non-similar agricultural products;

[0013] Step S08: Complete the analysis of agricultural product production and sales data: receive the warning information in step S05 to conduct warning analysis on agricultural product prices, receive the judgment result in step S07, and generate a correlation analysis report for similar agricultural products and non-similar agricultural products.

[0014] Preferably, in step S01, agricultural products are divided into similar agricultural products and non-similar agricultural products based on their types, wherein the similar agricultural products include crop products, animal husbandry products and fishery products, and the crop products, animal husbandry products and fishery products are represented as non-similar agricultural products.

[0015] Preferably, in step S02, production and sales data of various agricultural products are obtained through remote sensing technology and questionnaire surveys, and the production and sales data include: retail prices of agricultural products in different quarters, production prices of agricultural products in different quarters, agricultural product outputs in different quarters, agricultural product inputs in different quarters, and agricultural product marketing service inputs in different quarters.

[0016] Preferably, in step S03, the specific contents of removing invalid values ​​and abnormal values ​​in the original agricultural product production and sales data through data cleaning processing are: removing duplicate data: checking duplicate records in the data set through unique identifiers, and deleting duplicate records; filling missing values: filling missing values ​​in the data set with the mean; correcting erroneous values: checking erroneous values ​​in the data set by comparing with known data and correcting them.

[0017] Preferably, in step S04, an agricultural product price fluctuation analysis model is established through the agricultural product supply coefficient and the market service supply coefficient, and the specific contents of completing the agricultural product price fluctuation analysis are as follows:

[0018] Step S11: Calculate the supply coefficient of agricultural products in different quarters. The calculation formula is: Where W ax represents the supply coefficient of agricultural products in different quarters, γ represents the elasticity of demand for agricultural products, and γ a represents the price elasticity of agricultural product production, p x represents the retail price of agricultural products in different quarters. If the retail price of agricultural products fluctuates in different quarters, then p x represents the average daily retail price of agricultural products in different quarters, c x represents agricultural product input in different quarters, y x represents the output of agricultural products in different quarters, represents the agricultural product output y in different quarters x Investment in agricultural products x Find partial derivatives;

[0019] Step S12: Calculate the market supply coefficients in different quarters. The calculation formula is: Where W bx represents the market supply coefficient of agricultural products in different quarters, γ b represents the market service price elasticity, d x represents the agricultural product marketing service input in different quarters, Represents the agricultural product output y in different quarters x Investment in agricultural product marketing services x Find partial derivatives;

[0020] Step S13: Establish an agricultural product price fluctuation analysis model to calculate the agricultural product price fluctuation index in different quarters. The calculation formula is: P x =log2(W ax ×p x +W bx ×p x ), where P x Represents the agricultural product price volatility index in different quarters.

[0021] Preferably, in step S05, the agricultural product price fluctuation index of different quarters is visualized and presented to the user terminal, and a price fluctuation range is set to determine whether the agricultural product price fluctuation index of different quarters is within the set price fluctuation range, and an early warning is displayed when the agricultural product price fluctuation index exceeds the price fluctuation range.

[0022] Preferably, in step S06, the specific contents of establishing a correlation analysis model and calculating the correlation between similar agricultural products and non-similar agricultural products are as follows:

[0023] Step S11: Calculate the sales profit of a single agricultural product in different quarters. The calculation formula is: x =p x -h x , where s x represents the sales profit of a single agricultural product in different quarters, p x represents the retail prices of agricultural products in different quarters, h x represents the production price of agricultural products in different quarters. If the production price of agricultural products in different quarters fluctuates, then h x It represents the average daily production price of agricultural products in different quarters;

[0024] Step S12: Create a sales profit amount sequence x},{s x} = {s1, s2, s3, s4}, where s1 represents the sales profit of a single agricultural product in the first quarter, s2 represents the sales profit of a single agricultural product in the second quarter, S3 represents the sales profit of a single agricultural product in the third quarter, and S4 represents the sales profit of a single agricultural product in the fourth quarter;

[0025] Step S13: Establishing a sales profit amount sequence of similar agricultural products {m x},{m x}={m1, m2, m3, m4}, where m1 represents the sales profit of a single agricultural product of the same type in the first quarter, m2 represents the sales profit of a single agricultural product of the same type in the second quarter, m3 represents the sales profit of a single agricultural product of the same type in the third quarter, and m4 represents the sales profit of a single agricultural product of the same type in the fourth quarter;

[0026] Step S14: Establish a sales profit amount sequence of non-similar agricultural products {n x},{n x}={n1, n2, n3, n4}, where n1 represents the sales profit of a single non-similar agricultural product in the first quarter, n2 represents the sales profit of a single non-similar agricultural product in the second quarter, n3 represents the sales profit of a single non-similar agricultural product in the third quarter, and n4 represents the sales profit of a single non-similar agricultural product in the fourth quarter;

[0027] Step S15: Calculate the correlation between similar agricultural products, the calculation formula is: Among them, ε1 represents the correlation between similar agricultural products, min represents the minimum value, max represents the maximum value, and α represents the correlation coefficient between similar agricultural products;

[0028] Step S16: Calculate the correlation between different agricultural products, the calculation formula is: Among them, ε2 represents the correlation between non-similar agricultural products, and β represents the correlation coefficient between non-similar agricultural products.

[0029] Preferably, in step S07, the correlations ε1 and ε2 between similar agricultural products and non-similar agricultural products calculated in step S06 are compared with a preset correlation threshold; if the correlation ε1 between similar agricultural products is greater than or equal to the preset correlation threshold for similar agricultural products, the correlation between similar agricultural products is judged to be high; if the correlation ε1 between similar agricultural products is less than the preset correlation threshold for similar agricultural products, the correlation between similar agricultural products is judged to be low; if the correlation ε2 between non-similar agricultural products is greater than or equal to the preset correlation threshold for non-similar agricultural products, the correlation between non-similar agricultural products is judged to be high; if the correlation ε2 between non-similar agricultural products is less than the preset correlation threshold for non-similar agricultural products, the correlation between non-similar agricultural products is judged to be low.

[0030] Preferably, in step S08, the warning information in step S05 is received to perform a warning analysis on the agricultural product prices, and a supply and demand adjustment strategy is given through market monitoring to strengthen the circulation of agricultural products; the judgment result in step S07 is received, and a correlation analysis report of similar agricultural products and non-similar agricultural products is generated, and the analysis report includes the correlation between similar agricultural products, and the complementarity and substitutability between non-similar agricultural products.

[0031] A device for processing agricultural product production and sales data includes a computer program. When the computer program is run in the memory of a server, a method for processing agricultural product production and sales data is implemented.

[0032] Technical effects and advantages of the present invention:

[0033] The present invention is provided with step S01: dividing agricultural products into similar agricultural products and non-similar agricultural products, step S02: collecting production and sales data of agricultural products in step S01, step S03: preprocessing the production and sales data of agricultural products in step S02, step S04: completing agricultural product price fluctuation analysis through data mining based on the preprocessed production and sales data in step S03, step S05: visualizing the agricultural product price fluctuation analysis result in step S04, step S06: completing the association analysis of similar agricultural products and non-similar agricultural products through data mining based on the preprocessed production and sales data in step S03, step S07: judging the association degree of similar agricultural products and non-similar agricultural products, step S08: completing the analysis of agricultural product production and sales data. In short, a method and device for processing agricultural product production and sales data takes timely measures to intervene in abnormal situations through analysis of agricultural product price fluctuations to protect the interests of farmers and consumers, and timely adjusts the sales strategy according to the association degree of agricultural products through association analysis of similar agricultural products and non-similar agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flowchart of a method for processing agricultural product production and sales data. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The agricultural product production and sales data processing method and device involved in the present invention are not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, the present invention provides a method for processing agricultural product production and sales data, comprising the following steps:

[0037] Step S01: Classifying agricultural products into similar agricultural products and non-similar agricultural products: Classifying agricultural products into similar agricultural products and non-similar agricultural products based on the types of agricultural products;

[0038] Step S02: Collecting the production and sales data of the agricultural products in step S01: obtaining the production and sales data of various agricultural products through remote sensing technology and questionnaire survey;

[0039] Step S03: pre-processing the production and sales data of the agricultural products in step S02: removing invalid values ​​and abnormal values ​​in the original production and sales data of the agricultural products through data cleaning processing, and unifying the data format of the production and sales data of the agricultural products;

[0040] Step S04: completing the price fluctuation analysis of agricultural products through data mining based on the production and sales data pre-processed in step S03: establishing an agricultural product price fluctuation analysis model through the agricultural product supply coefficient and the market service supply coefficient, completing the price fluctuation analysis of agricultural products;

[0041] Step S05: Visualizing the analysis results of agricultural product price fluctuations in step S04: Visualizing the analysis results of agricultural product price fluctuations to the user terminal, and setting a price fluctuation range, and displaying an early warning when the price fluctuation range is exceeded;

[0042] Step S06: completing the correlation analysis between similar agricultural products and non-similar agricultural products through data mining based on the production and sales data pre-processed in step S03: establishing a correlation analysis model, and calculating the correlation between similar agricultural products and non-similar agricultural products;

[0043] Step S07: Determine the correlation between similar agricultural products and non-similar agricultural products: compare the correlation between similar agricultural products and non-similar agricultural products calculated in step S06 with a preset correlation threshold to determine the correlation between similar agricultural products and non-similar agricultural products;

[0044] Step S08: Complete the analysis of agricultural product production and sales data: receive the warning information in step S05 to conduct warning analysis on agricultural product prices, receive the judgment result in step S07, and generate a correlation analysis report for similar agricultural products and non-similar agricultural products.

[0045] In the present embodiment, it should be specifically explained that in the step S01, the agricultural products are divided into similar agricultural products and non-similar agricultural products based on the types of agricultural products. The similar agricultural products include crop products, animal husbandry products and fishery products. The crop products, animal husbandry products and fishery products are represented as non-similar agricultural products.

[0046] In this embodiment, it should be specifically explained that in step S02, production and sales data of various agricultural products are obtained through remote sensing technology and questionnaire surveys, and the production and sales data include: retail prices of agricultural products in different quarters, production prices of agricultural products in different quarters, agricultural product outputs in different quarters, agricultural product inputs in different quarters, and agricultural product marketing service inputs in different quarters.

[0047] In this embodiment, it should be specifically explained that in step S03, the specific content of removing invalid values ​​and abnormal values ​​in the original agricultural product production and sales data through data cleaning processing is: removing duplicate data: checking duplicate records in the data set through unique identifiers, and deleting duplicate records; filling missing values: using the mean to fill missing values ​​in the data set; correcting erroneous values: checking erroneous values ​​in the data set by comparing with known data and correcting them.

[0048] In this embodiment, it should be specifically explained that in step S04, the agricultural product price fluctuation analysis model is established by using the agricultural product supply coefficient and the market service supply coefficient, and the specific content of completing the agricultural product price fluctuation analysis is as follows:

[0049] Step S11: Calculate the supply coefficient of agricultural products in different quarters. The calculation formula is: Where W ax represents the supply coefficient of agricultural products in different quarters, γ represents the elasticity of demand for agricultural products, and γ a represents the price elasticity of agricultural product production, p x represents the retail price of agricultural products in different quarters. If the retail price of agricultural products fluctuates in different quarters, then p x represents the average daily retail price of agricultural products in different quarters, c x represents agricultural product input in different quarters, yx represents the output of agricultural products in different quarters, Represents the agricultural product output y in different quarters x Investment in agricultural products x Find partial derivatives;

[0050] If the retail prices of agricultural products fluctuate in different quarters, then p x It represents the average daily retail price of agricultural products in different quarters, expressed as Where t represents the number of days in different quarters, p xt It indicates the daily retail prices of agricultural products in different quarters;

[0051] Step S12: Calculate the market supply coefficients in different quarters. The calculation formula is: Where W bx represents the market supply coefficient of agricultural products in different quarters, γ b represents the market service price elasticity, d x represents the agricultural product marketing service input in different quarters, Represents the agricultural product output y in different quarters x Investment in agricultural product marketing services x Find partial derivatives;

[0052] Step S13: Establish an agricultural product price fluctuation analysis model to calculate the agricultural product price fluctuation index in different quarters. The calculation formula is: P x =log2(W ax ×p x +W bx ×p x ), where P x represents the price volatility index of agricultural products in different quarters, W ax represents the supply coefficient of agricultural products in different quarters, W bx represents the market supply coefficient of agricultural products in different quarters, p x Represents the retail prices of agricultural products in different quarters.

[0053] In the present embodiment, it should be specifically explained that in step S05, the agricultural product price fluctuation index of different quarters is visualized and presented to the user terminal, and a price fluctuation range is set to determine whether the agricultural product price fluctuation index of different quarters is within the set price fluctuation range. If the agricultural product price fluctuation index is within the set price fluctuation range, it is determined that there is no abnormality in the agricultural product price fluctuation. If the agricultural product price fluctuation index exceeds the price fluctuation range, it is determined that the agricultural product price fluctuation is abnormal and an early warning is displayed.

[0054] In this embodiment, it should be specifically explained that in step S06, the specific contents of establishing the association analysis model and calculating the association between the same agricultural products and the different agricultural products are as follows:

[0055] Step S11: Calculate the sales profit of a single agricultural product in different quarters. The calculation formula is: x =p x -h x , where s x represents the sales profit of a single agricultural product in different quarters, p x represents the retail prices of agricultural products in different quarters, h x represents the production price of agricultural products in different quarters. If the production price of agricultural products in different quarters fluctuates, then h x It represents the average daily production price of agricultural products in different quarters;

[0056] If the production price of agricultural products fluctuates in different quarters, then h x It represents the average daily production price of agricultural products in different quarters, expressed as Where t represents the number of days in different quarters, h xt It indicates the daily production price of agricultural products in different quarters;

[0057] Step S12: Create a sales profit amount sequence x},{s x} = {s1, s2, s3, s4}, where s1 represents the sales profit of a single agricultural product in the first quarter, s2 represents the sales profit of a single agricultural product in the second quarter, s3 represents the sales profit of a single agricultural product in the third quarter, and s4 represents the sales profit of a single agricultural product in the fourth quarter;

[0058] Step S13: Establishing a sales profit amount sequence of similar agricultural products {m x},{m x}={m1, m2, m3, m4}, where m1 represents the sales profit of a single agricultural product of the same type in the first quarter, m2 represents the sales profit of a single agricultural product of the same type in the second quarter, m3 represents the sales profit of a single agricultural product of the same type in the third quarter, and m4 represents the sales profit of a single agricultural product of the same type in the fourth quarter;

[0059] Step S14: Establish a sales profit amount sequence of non-similar agricultural products {n x},{n x}={n1, n2, n3, n4}, where n1 represents the sales profit of a single non-similar agricultural product in the first quarter, n2 represents the sales profit of a single non-similar agricultural product in the second quarter, n3 represents the sales profit of a single non-similar agricultural product in the third quarter, and n4 represents the sales profit of a single non-similar agricultural product in the fourth quarter;

[0060] Step S15: Calculate the correlation between similar agricultural products, the calculation formula is: Among them, ε1 represents the correlation between similar agricultural products, min represents the minimum value, max represents the maximum value, and α represents the correlation coefficient between similar agricultural products;

[0061] Step S16: Calculate the correlation between different agricultural products, the calculation formula is: Among them, ε2 represents the correlation between non-similar agricultural products, and β represents the correlation coefficient between non-similar agricultural products.

[0062] In the present embodiment, it should be specifically explained that in the step S07, the correlation degrees ε1 and ε2 between the same agricultural products and the different agricultural products calculated in the step S06 are compared with the preset correlation threshold value. If the correlation degree ε1 between the same agricultural products is greater than or equal to the preset correlation threshold value of the same agricultural products, the correlation between the same agricultural products is judged to be high; if the correlation degree ε1 between the same agricultural products is less than the preset correlation threshold value of the same agricultural products, the correlation between the same agricultural products is judged to be low; if the correlation degree ε2 between different agricultural products is greater than or equal to the preset correlation threshold value of different agricultural products, the correlation between the different agricultural products is judged to be high; if the correlation degree ε2 between the different agricultural products is less than the preset correlation threshold value of different agricultural products, the correlation between the different agricultural products is judged to be low.

[0063] In this embodiment, it should be specifically explained that in step S08, the warning information in step S05 is received to perform warning analysis on agricultural product prices, and supply and demand adjustment strategies are given through market monitoring to strengthen the circulation of agricultural products; the judgment result in step S07 is received, and a correlation analysis report of similar agricultural products and non-similar agricultural products is generated, and the analysis report includes the correlation between similar agricultural products, and the complementarity and substitutability between non-similar agricultural products.

[0064] In the present embodiment, it is necessary to specifically explain that a device for processing agricultural product production and sales data includes a computer program, and the computer program implements a method for processing agricultural product production and sales data when running in the memory of a server.

[0065] The difference between the present embodiment and the prior art mainly lies in that the present embodiment is provided with step S01: dividing agricultural products into similar agricultural products and non-similar agricultural products, step S02: collecting the production and sales data of the agricultural products in step S01, step S03: pre-processing the production and sales data of the agricultural products in step S02, step S04: completing the price fluctuation analysis of agricultural products through data mining based on the pre-processed production and sales data in step S03, step S05: visualizing the analysis results of the price fluctuation analysis of agricultural products in step S04, step S06: completing the association analysis of similar agricultural products and non-similar agricultural products through data mining based on the pre-processed production and sales data in step S03, step S07: judging the association degree of similar agricultural products and non-similar agricultural products, step S08: completing the analysis of the production and sales data of agricultural products. In short, a method and device for processing agricultural product production and sales data takes timely measures to intervene in abnormal situations through analysis of agricultural product price fluctuations to protect the interests of farmers and consumers, and adjusts the sales strategy in time according to the association degree of agricultural products through association analysis of similar agricultural products and non-similar agricultural products.

[0066] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for processing agricultural product production and sales data, characterized in that: The following steps are involved: Step S01: Classifying agricultural products into similar agricultural products and non-similar agricultural products: Classifying agricultural products into similar agricultural products and non-similar agricultural products based on the types of agricultural products; Step S02: Collecting the production and sales data of the agricultural products in step S01: obtaining the production and sales data of various agricultural products through remote sensing technology and questionnaire survey; Step S03: pre-processing the production and sales data of the agricultural products in step S02: removing invalid values ​​and abnormal values ​​in the original production and sales data of the agricultural products through data cleaning processing, and unifying the data format of the production and sales data of the agricultural products; Step S04: completing the price fluctuation analysis of agricultural products through data mining based on the production and sales data pre-processed in step S03: establishing an agricultural product price fluctuation analysis model through the agricultural product supply coefficient and the market service supply coefficient, completing the price fluctuation analysis of agricultural products; Step S05: Visualizing the analysis results of agricultural product price fluctuations in step S04: Visualizing the analysis results of agricultural product price fluctuations to the user terminal, and setting a price fluctuation range, and displaying an early warning when the price fluctuation range is exceeded; Step S06: completing the correlation analysis between similar agricultural products and non-similar agricultural products through data mining based on the production and sales data pre-processed in step S03: establishing a correlation analysis model, and calculating the correlation between similar agricultural products and non-similar agricultural products; Step S07: Determine the correlation between similar agricultural products and non-similar agricultural products: compare the correlation between similar agricultural products and non-similar agricultural products calculated in step S06 with a preset correlation threshold to determine the correlation between similar agricultural products and non-similar agricultural products; Step S08: Complete the analysis of agricultural product production and sales data: receive the warning information in step S05 to conduct warning analysis on agricultural product prices, receive the judgment result in step S07, and generate a correlation analysis report for similar agricultural products and non-similar agricultural products.

2. A method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S01, agricultural products are divided into similar agricultural products and non-similar agricultural products based on their types. Similar agricultural products include crop products, animal husbandry products and fishery products. Crop products, animal husbandry products and fishery products are represented as non-similar agricultural products.

3. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S02, production and sales data of various agricultural products are obtained through remote sensing technology and questionnaire survey, and the production and sales data include: retail prices of agricultural products in different quarters, production prices of agricultural products in different quarters, outputs of agricultural products in different quarters, inputs of agricultural products in different quarters, and inputs of marketing services for agricultural products in different quarters.

4. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S03, the specific contents of removing invalid values ​​and abnormal values ​​in the original agricultural product production and sales data through data cleaning processing are as follows: removing duplicate data: checking duplicate records in the data set through unique identifiers, and deleting duplicate records; filling missing values: filling missing values ​​in the data set with the mean; correcting erroneous values: checking erroneous values ​​in the data set by comparing with known data and correcting them.

5. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S04, the agricultural product price fluctuation analysis model is established through the agricultural product supply coefficient and the market service supply coefficient, and the specific contents of completing the agricultural product price fluctuation analysis are as follows: Step S11: Calculate the supply coefficient of agricultural products in different quarters. The calculation formula is: Where W ax represents the supply coefficient of agricultural products in different quarters, γ represents the elasticity of demand for agricultural products, and γ a represents the price elasticity of agricultural product production, p x represents the retail price of agricultural products in different quarters. If the retail price of agricultural products fluctuates in different quarters, then p x represents the average daily retail price of agricultural products in different quarters, c x represents agricultural product input in different quarters, y x represents the output of agricultural products in different quarters, Represents the agricultural product output y in different quarters x Investment in agricultural products x Find partial derivatives; Step S12: Calculate the market supply coefficients in different quarters. The calculation formula is: Where W bx represents the market supply coefficient of agricultural products in different quarters, γ b represents the market service price elasticity, d x represents the agricultural product marketing service input in different quarters, Represents the agricultural product output y in different quarters x Investment in agricultural product marketing services x Find partial derivatives; Step S13: Establish an agricultural product price fluctuation analysis model to calculate the agricultural product price fluctuation index in different quarters. The calculation formula is: P x =log2(W ax ×p x +W bx ×p x ), where P x Represents the agricultural product price volatility index in different quarters.

6. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S05, the agricultural product price fluctuation index of different quarters is visualized and presented to the user terminal, and a price fluctuation range is set to determine whether the agricultural product price fluctuation index of different quarters is within the set price fluctuation range. When the agricultural product price fluctuation index exceeds the price fluctuation range, an early warning is displayed.

7. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S06, the specific contents of establishing a correlation analysis model and calculating the correlation between similar agricultural products and non-similar agricultural products are as follows: Step S11: Calculate the sales profit of a single agricultural product in different quarters. The calculation formula is: x =p x -h x , where s x represents the sales profit of a single agricultural product in different quarters, p x represents the retail prices of agricultural products in different quarters, h x represents the production price of agricultural products in different quarters. If the production price of agricultural products in different quarters fluctuates, then h x It represents the average daily production price of agricultural products in different quarters; Step S12: Create a sales profit amount sequence x },{s x } = {s1, s2, s3, s4}, where s1 represents the sales profit of a single agricultural product in the first quarter, s2 represents the sales profit of a single agricultural product in the second quarter, S3 represents the sales profit of a single agricultural product in the third quarter, and S4 represents the sales profit of a single agricultural product in the fourth quarter; Step S13: Establishing a sales profit amount sequence of similar agricultural products {m x },{m x }={m1, m2, m3, m4}, where m1 represents the sales profit of a single agricultural product of the same type in the first quarter, m2 represents the sales profit of a single agricultural product of the same type in the second quarter, m3 represents the sales profit of a single agricultural product of the same type in the third quarter, and m4 represents the sales profit of a single agricultural product of the same type in the fourth quarter; Step S14: Establish a sales profit amount sequence of non-similar agricultural products {n x },{n x }={n1, n2, n3, n4}, where n1 represents the sales profit of a single non-similar agricultural product in the first quarter, n2 represents the sales profit of a single non-similar agricultural product in the second quarter, n3 represents the sales profit of a single non-similar agricultural product in the third quarter, and n4 represents the sales profit of a single non-similar agricultural product in the fourth quarter; Step S15: Calculate the correlation between similar agricultural products, the calculation formula is: Among them, ε1 represents the correlation between similar agricultural products, min represents the minimum value, max represents the maximum value, and α represents the correlation coefficient between similar agricultural products; Step S16: Calculate the correlation between different agricultural products, the calculation formula is: Among them, ε2 represents the correlation between non-similar agricultural products, and β represents the correlation coefficient between non-similar agricultural products.

8. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In the step S07, the correlations ε1 and ε2 between the same agricultural products and different agricultural products calculated in the step S06 are compared with the preset correlation threshold. If the correlation ε1 between the same agricultural products is greater than or equal to the preset correlation threshold, the correlation between the same agricultural products is judged to be high. If the correlation ε1 between the same agricultural products is less than the preset correlation threshold, the correlation between the same agricultural products is judged to be low. If the correlation ε2 between different agricultural products is greater than or equal to the preset correlation threshold, the correlation between the different agricultural products is judged to be high. If the correlation ε2 between the different agricultural products is less than the preset correlation threshold, the correlation between the different agricultural products is judged to be low.

9. The method for processing agricultural product production and sales data according to claim 1, characterized in that: In step S08, the warning information in step S05 is received to conduct a warning analysis on the agricultural product prices, and a supply and demand adjustment strategy is given through market monitoring to strengthen the circulation of agricultural products; the judgment result in step S07 is received, and a correlation analysis report of similar agricultural products and non-similar agricultural products is generated, and the analysis report includes the correlation between similar agricultural products, and the complementarity and substitutability between non-similar agricultural products.

10. A data processing device for agricultural product production and sales, characterized in that: The agricultural product production and sales data processing device includes a computer program, and when the computer program is run in the memory of the server, it implements the agricultural product production and sales data processing method described in any one of claims 1 to 9.

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