Wild edible mushroom price prediction method and system

Abstract

The invention provides a wild edible mushroom price prediction method. The method comprises the following steps: S1, data acquisition: acquiring meteorological data in the first 1-20 days and wild edible mushroom price data in the first 1-5 days; s2, carrying out dimensionless processing; s3, substituting a regression model; s4, comparing and checking; and S5, giving a prediction result. The invention also provides a wild edible mushroom price prediction system. Based on a regression modeling mode, the method can achieve the preliminary estimation of the price of the wild edible mushrooms through observable meteorological data, thereby carrying out the trend prediction of the price of the wild edible mushrooms more accurately, timely and instructively, and forcefully promoting the deep research of the wild mushroom edible industry in Yunnan province.

Description

Technical Field

[0001] The invention relates to a method and system for predicting the price of wild edible mushrooms, belonging to the technical field of data processing systems or methods specially suitable for prediction purposes. Background Art

[0002] Yunnan wild edible fungi are wild-collected commodities. Compared with other agricultural products, their sales models are mostly outside the normal sales channels, and the statistics of production and output value have always been mainly estimated. Since the 1980s, Yunnan wild edible fungi have gradually entered the commercialization stage. As of today, Yunnan wild edible fungi have become a huge industry covering 129 counties in the province and more than 10 million people. However, in-depth research on the wild edible fungi industry in Yunnan Province is very lacking, and even related research is rare.

[0003] The core issue of the research on the wild edible fungi industry is the output and price of wild edible fungi. However, the price fluctuation of wild edible fungi is affected by multiple factors such as output, consumer consumption willingness, consumer disposable income, major force majeure factors, etc., involving multiple disciplines such as environment, ecology, economy, and logistics. It is not easy to make a relatively accurate, timely and guiding trend forecast of the price of wild edible fungi. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a wild edible fungus price prediction method and system. The wild edible fungus price prediction method and system are based on regression modeling, which can achieve a preliminary estimate of the wild edible fungus price through observable meteorological data, thereby providing guidance for trend forecasting of the wild edible fungus price.

[0005] The present invention is achieved through the following technical solutions.

[0006] The present invention provides a method for predicting wild edible mushroom prices, comprising the following steps:.

[0007] S1. Obtain data: Obtain meteorological data from 1 to 20 days ago and wild edible mushroom price data from 1 to 5 days ago; S2. Dimensionless processing: Dimensionless processing of meteorological data and wild edible mushroom price data; S3. Substitution into the regression model: Substitute the meteorological data of the previous 5 to 20 days into the regression model to calculate the predicted price data of wild edible mushrooms in the previous 1 to 5 days; S4 comparison test: compare the wild mushroom price data and the wild mushroom price forecast data 1 to 5 days before, determine whether the error exceeds the preset threshold, then return to step S1; S5. Give the forecast result: Substitute the meteorological data of the previous 1 to 5 days into the regression model to calculate the forecast price data of wild edible mushrooms in the next 1 to 5 days.

[0008] The regression model is constructed by using the multivariate linear regression method, with many years of historical data of meteorological data as input items and many years of historical data of wild edible mushroom prices as corresponding output items.

[0009] The meteorological data include daily average temperature, daily precipitation, and daily sunshine hours.

[0010] The dimensionless processing operation is to divide the current data by the highest value of the current data type in the year, and the obtained ratio value is the result value.

[0011] The regression method is a multivariate linear stepwise regression method.

[0012] When the regression model is constructed, the regression model is also tested by the standard coefficient test, including the calculation of the model regression validity F test value, the regression factor coefficient, the standardized regression coefficient and the regression factor significance t test value. While testing the regression model, the multicollinearity test of the explanatory factor, the model residual variance test and the autocorrelation test are also performed. If the autocorrelation DW test fails, the autocorrelation coefficient is calculated. If the autocorrelation coefficient is less than the preset value (weak autocorrelation), the Cochran-Orcott iteration method is used to calculate the autocorrelation regression model; if the autocorrelation coefficient is greater than the preset value (strong autocorrelation), the first-order difference method is used to calculate the autocorrelation regression model, and then the calculated autocorrelation regression model replaces the regression model and re-checks the distribution coefficient; if the distribution coefficient check passes, the regression model is determined. The preset value of the autocorrelation is 0.8.

[0013] The present invention also provides a wild edible mushroom price prediction system, comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the wild edible mushroom price prediction method as described above.

[0014] The beneficial effect of the present invention is that based on the regression modeling method, a preliminary estimate of the price of wild edible fungi can be achieved through observable meteorological data, so as to more accurately, timely and instructively forecast the trend of wild edible fungi prices, thereby effectively promoting in-depth research on the wild edible fungi industry in Yunnan Province. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1It is a schematic diagram of a process of at least one embodiment of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described below, but the scope of protection claimed is not limited to the description.

[0017] A first embodiment of the present invention relates to Figure 1 A wild edible mushroom price prediction method is shown, comprising the following steps:.

[0018] S1. Obtain data: Obtain meteorological data from 1 to 20 days ago and wild edible mushroom price data from 1 to 5 days ago; S2. Dimensionless processing: Dimensionless processing of meteorological data and wild edible mushroom price data; S3. Substitution into the regression model: Substitute the meteorological data of the previous 5 to 20 days into the regression model to calculate the predicted price data of wild edible mushrooms in the previous 1 to 5 days; S4 comparison test: compare the wild mushroom price data and the wild mushroom price forecast data 1 to 5 days before, determine whether the error exceeds the preset threshold, then return to step S1; S5. Give the forecast result: Substitute the meteorological data of the previous 1 to 5 days into the regression model to calculate the forecast price data of wild edible mushrooms in the next 1 to 5 days.

[0019] The second embodiment of the present invention is substantially the same as the first embodiment, mainly in that it is further optimized that the regression model is constructed by using a multivariate linear regression method with many years of historical data of meteorological data as input and many years of historical data of wild edible mushroom prices as corresponding output.

[0020] The meteorological data include daily average temperature, daily precipitation, and daily sunshine hours.

[0021] The dimensionless processing operation is to divide the current data by the highest value of the current data type in the year, and the obtained ratio value is the result value.

[0022] The multiple linear regression method is a multiple stepwise regression method.

[0023] The third embodiment of the present invention is substantially the same as the first embodiment, mainly in that it is further preferred that when the regression model is constructed, the regression model is tested by using a standard coefficient test, including calculating the model regression validity F test value, the standardized regression coefficient, and the factor significance t test value, etc. While testing the regression model, the multicollinearity test of the explanatory factors, the model residual heterovariance test and the autocorrelation test are also performed. If the autocorrelation DW test fails, the autocorrelation coefficient is calculated. If the autocorrelation coefficient is less than the preset value (weak autocorrelation), the Cochran-Orcott iteration method is used to calculate the autocorrelation regression model; if the autocorrelation coefficient is greater than the preset value (strong autocorrelation), the first-order difference method is used to calculate the autocorrelation regression model, and then the calculated autocorrelation regression model replaces the regression model and re-checks the distribution coefficient; if the distribution coefficient check passes, the regression model is determined. The preset value of the autocorrelation is 0.8.

[0024] A fourth embodiment of the present invention relates to a wild edible mushroom price prediction system, comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the first to third embodiments described above.

Claims

1. A method for predicting the price of wild edible mushrooms, characterized in that: The following steps are involved: S1. Obtain data: Obtain meteorological data from 1 to 20 days ago and wild edible mushroom price data from 1 to 5 days ago; S2. Dimensionless processing: Dimensionless processing of meteorological data and wild edible mushroom price data; S3. Substitution into the regression model: Substitute the meteorological data of the previous 5 to 20 days into the regression model to calculate the predicted price data of wild edible mushrooms in the previous 1 to 5 days; S4 comparison test: compare the wild mushroom price data and the wild mushroom price forecast data 1 to 5 days before, determine whether the error exceeds the preset threshold, then return to step S1; S5. Give the forecast result: Substitute the meteorological data of the previous 1 to 5 days into the regression model to calculate the forecast price data of wild edible mushrooms in the next 1 to 5 days.

2. The wild edible mushroom price prediction method according to claim 1, characterized in that: The regression model is constructed by using the multivariate linear regression method, with many years of historical data of meteorological data as input items and many years of historical data of wild edible mushroom prices as corresponding output items.

3. The wild edible mushroom price prediction method according to claim 1, characterized in that: The meteorological data include daily average temperature, daily precipitation, and daily sunshine hours.

4. The wild edible mushroom price prediction method according to claim 1, characterized in that: The dimensionless processing operation is to divide the current data by the highest value of the current data type in the year, and the obtained ratio value is the result value.

5. The wild edible mushroom price prediction method according to claim 2, characterized in that: The multiple linear regression method is a multiple linear stepwise regression method.

6. The wild edible mushroom price prediction method according to claim 2, characterized in that: When constructing the regression model, the regression model is also tested by means of standard coefficient test, including calculating the model regression validity F test value, regression factor coefficient, standardized regression coefficient and regression factor significance t test value, etc.

7. The wild edible mushroom price prediction method according to claim 6, characterized in that: While testing the regression model, multicollinearity test of explanatory factors, model residual heterovariance test and autocorrelation test are also performed.

8. The wild edible mushroom price prediction method according to claim 7, characterized in that: If the autocorrelation DW test fails, the autocorrelation coefficient is calculated. If the autocorrelation coefficient is less than the preset value (weak autocorrelation), the Cochran-Orcot iterative method is used to calculate the autocorrelation regression model; if the autocorrelation coefficient is greater than the preset value (strong autocorrelation), the first-order difference method is used to calculate the autocorrelation regression model, and then the calculated autocorrelation regression model replaces the regression model and re-checks the distribution coefficient; if the distribution coefficient check passes, the regression model is determined.

9. The wild edible mushroom price prediction method according to claim 8, characterized in that: The preset value of the autocorrelation is 0.

8.

10. A wild edible mushroom price prediction system, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the wild edible mushroom price prediction method as described in any one of claims 1 to 9.

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