CN111292006B

CN111292006B - Method and device for obtaining raw material quality range based on yellow wine product quality range

Info

Publication number: CN111292006B
Application number: CN202010118101.4A
Authority: CN
Inventors: 尹明君; 周康; 周坚; 杨华; 刘朔; 刘江蓉; 高婧; 周胜玲
Original assignee: Wuhan Polytechnic University
Current assignee: Wuhan Polytechnic University
Priority date: 2020-02-25
Filing date: 2020-02-25
Publication date: 2023-05-23
Anticipated expiration: 2040-02-25
Also published as: CN111292006A

Abstract

The invention relates to the technical field of yellow wine processing, and discloses a method and a device for obtaining a raw material quality range based on a yellow wine product quality range, wherein the method comprises the following steps: obtaining yellow wine quality investigation data and yellow wine quality research data, obtaining a yellow wine raw material sample set and a yellow wine product sample set according to the data, performing stepwise regression processing, predicting the yellow wine product quality index, and constructing a yellow wine raw material quality standard optimization model according to calculation data in the prediction process to determine the yellow wine raw material quality index range. Therefore, by firstly obtaining a yellow wine raw material sample set and a yellow wine product sample set, and then constructing a yellow wine raw material quality standard optimization model, determining the range of the yellow wine raw material quality index according to the model, the technical problem of how to determine the optimal range of the yellow wine raw material quality index, so that the yellow wine raw material can produce qualified yellow wine products under given production process conditions is solved.

Description

Method and device for obtaining raw material quality range based on yellow wine product quality range

Technical Field

The invention relates to the technical field of yellow wine processing, in particular to a method and a device for obtaining a raw material quality range based on a yellow wine product quality range.

Background

In the field of yellow wine processing, the requirements of diversified yellow wine products on the quality of yellow wine raw materials are different, and yellow wine products meeting national standards, provinces, lines and enterprises are required to be manufactured, so that the optimal quality range of the yellow wine raw materials is predicted according to the quality of the yellow wine products, the basis can be provided for enterprises to collect and handle the raw materials, the qualified target products are produced under the given production process conditions, the qualification rate is improved, the waste is reduced, the loss is reduced for enterprises, and the profit of the enterprises is improved.

At present, enterprises have a plurality of methods for determining the production raw materials of the yellow wine products, for example, according to production experience or simple comparison test, the yellow wine products which are obtained by putting the yellow wine raw materials into production can be roughly determined, but the method for making decisions on the production flow of the yellow wine products according to the production experience has a certain improvement space in efficiency. For the production planning of enterprises, how to scientifically find the most suitable yellow wine raw material quality range for producing target yellow wine products is a great difficult problem.

The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.

Disclosure of Invention

The invention mainly aims to provide a method and a device for acquiring a raw material quality range based on a yellow wine product quality range, which aim at solving the technical problem of how to determine the optimal range of yellow wine raw material quality indexes so that the yellow wine raw material can produce qualified yellow wine products under given production process conditions.

In order to achieve the above object, the present invention provides a method for obtaining a raw material quality range based on a yellow wine product quality range, the method for obtaining a raw material quality range based on a yellow wine product quality range includes the following steps:

obtaining yellow wine quality investigation data and yellow wine quality investigation data;

constructing an index system of yellow wine raw materials and yellow wine products according to the yellow wine quality research data and the yellow wine quality research data;

extracting a yellow wine making process from the yellow wine quality research data, and selecting a target yellow wine making process from the yellow wine making processes;

determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process;

respectively carrying out standardization treatment on the yellow wine raw material sample set and the yellow wine product sample set to obtain a standardized yellow wine raw material sample set and a standardized yellow wine product sample set;

Stepwise regression processing is carried out on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, and a regression equation is obtained;

predicting the quality index of the yellow wine product according to the regression equation, and acquiring calculation data in the prediction process;

constructing a yellow wine raw material quality standard optimization model according to the calculated data and a preset optimization target;

and determining the quality index range of the yellow wine raw material according to the yellow wine raw material quality standard optimization model.

Preferably, the determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process specifically includes:

determining yellow wine raw material indexes and yellow wine product indexes according to the index system;

extracting the variety of the yellow wine raw materials and the basic information of the yellow wine raw materials from the investigation data;

selecting a target yellow wine raw material variety from the yellow wine raw material varieties according to the basic information of the yellow wine raw material;

searching a target sample yellow wine raw material index corresponding to the target yellow wine raw material variety according to the yellow wine raw material index;

constructing a yellow wine raw material sample set according to the target sample yellow wine raw material index;

processing yellow wine raw materials corresponding to the target yellow wine raw material variety according to the target yellow wine manufacturing process to obtain a target yellow wine product;

And constructing a yellow wine product sample set according to the yellow wine product index and the target yellow wine product.

Preferably, the step-by-step regression processing is performed on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, and before the regression equation is obtained, the method further includes:

extracting target yellow wine raw material indexes from the standardized yellow wine raw material sample set, and extracting target yellow wine product indexes from the standardized yellow wine product sample set;

performing multiple collineation analysis processing on the target yellow wine raw material index to obtain a variance expansion coefficient corresponding to the target yellow wine raw material index;

comparing the variance expansion coefficient with a preset coefficient threshold;

and if the variance expansion coefficient is larger than the preset coefficient threshold value, executing the step of stepwise regression processing on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set to obtain a regression equation.

Preferably, the step regression processing is performed on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set to obtain a regression equation, which specifically includes:

constructing a regression model according to the target yellow wine raw material index and the target yellow wine product index;

Extracting yellow wine raw material sample set dimensions from the standardized yellow wine raw material sample set, and extracting yellow wine product sample set dimensions from the standardized yellow wine product sample set;

and determining a regression equation according to the dimension of the yellow wine raw material sample set, the dimension of the yellow wine product sample set and the regression model.

Preferably, the predicting the quality index of the yellow wine product according to the regression equation, and obtaining the calculation data in the prediction process specifically includes:

generating a multiple linear regression model corresponding to the quality index of the yellow wine product;

training the multiple linear regression model according to the regression equation to obtain a prediction model corresponding to the yellow wine product quality index;

extracting target yellow wine product indexes from the standardized yellow wine product sample set, and determining regression functions corresponding to the target yellow wine product indexes;

determining a goodness of fit according to the regression function;

and taking the prediction model and the goodness of fit as calculation data.

Preferably, the building of the yellow wine raw material quality standard optimization model according to the calculated data and the preset optimization target specifically includes:

searching physicochemical property data of the yellow wine raw material quality corresponding to the yellow wine raw material quality index, and determining the limitation constraint of the yellow wine raw material based on the physicochemical property data;

Determining a yellow wine product limit constraint based on the prediction model and a preset target yellow wine product quality requirement;

correcting the limiting constraint of the yellow wine product according to the goodness of fit to obtain a target limiting constraint of the yellow wine product;

constructing a high-dimensional target space according to a plurality of target yellow wine raw material indexes, and setting diffusion factors in the high-dimensional target space;

determining a diffusion factor constraint according to the diffusion factor and a preset target area requirement;

taking the yellow wine raw material limit constraint, the target yellow wine product limit constraint and the diffusion factor constraint as constraint conditions;

and constructing a yellow wine raw material quality standard optimization model according to the constraint conditions and a preset optimization target.

Preferably, the determining the range of the yellow wine raw material quality index according to the yellow wine raw material quality standard optimization model specifically includes:

converting the yellow wine raw material quality standardized model into a single target quality standard optimization model;

obtaining result data of the single target quality standard optimization model;

optimizing the diffusion factor constraint according to the result data to obtain a target diffusion factor constraint;

regulating and controlling the yellow wine raw material quality standard optimization model according to the target diffusion factor constraint to obtain a regulating and controlling model;

And determining the quality index range of the yellow wine raw material according to the regulation and control model.

Preferably, the determining the yellow wine raw material quality index range according to the regulation model specifically includes:

calculating a lower limit value of the yellow wine raw material quality index and an upper limit value of the yellow wine raw material quality index according to the regulation model;

performing inverse standardization treatment on the lower limit value of the yellow wine raw material quality index and the upper limit value of the yellow wine raw material quality index;

and determining the quality index range of the yellow wine raw material according to the processing result.

In addition, in order to achieve the above object, the present invention also provides a device for obtaining a raw material quality range based on a yellow wine product quality range, the device for obtaining a raw material quality range based on a yellow wine product quality range includes:

the data acquisition module is used for acquiring yellow wine quality investigation data and yellow wine quality investigation data;

the system construction module is used for constructing an index system of the yellow rice wine raw material and the yellow rice wine product according to the yellow rice wine quality investigation data and the yellow rice wine quality investigation data;

the preparation process module is used for extracting a yellow wine preparation process from the yellow wine quality research data and selecting a target yellow wine preparation process from the yellow wine preparation processes;

The sample set determining module is used for determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process;

the data processing module is used for respectively carrying out standardized processing on the yellow wine raw material sample set and the yellow wine product sample set to obtain a standardized yellow wine raw material sample set and a standardized yellow wine product sample set;

the gradual regression module is used for carrying out gradual regression processing on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set to obtain a regression equation;

the index prediction module is used for predicting the quality index of the yellow wine product according to the regression equation and obtaining calculation data in the prediction process;

the model construction module is used for constructing a yellow wine raw material quality standard optimization model according to the calculation data and a preset optimization target;

and the range determining module is used for determining the range of the yellow wine raw material quality index according to the yellow wine raw material quality standard optimizing model.

Preferably, the index prediction module is further configured to generate a multiple linear regression model corresponding to the yellow wine product quality index;

the index prediction module is further used for training the multiple linear regression model according to the regression equation to obtain a prediction model corresponding to the yellow wine product quality index;

The index prediction module is further used for extracting target yellow wine product indexes from the standardized yellow wine product sample set and determining regression functions corresponding to the target yellow wine product indexes;

the index prediction module is further used for determining a goodness of fit according to the regression function;

the index prediction module is further used for taking the prediction model and the fitting goodness as calculation data.

According to the method for acquiring the raw material quality range based on the yellow wine product quality range, yellow wine quality investigation data and yellow wine quality investigation data are acquired; constructing an index system of yellow wine raw materials and yellow wine products according to the yellow wine quality research data and the yellow wine quality research data; extracting a yellow wine making process from the yellow wine quality research data, and selecting a target yellow wine making process from the yellow wine making processes; determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process; respectively carrying out standardization treatment on the yellow wine raw material sample set and the yellow wine product sample set to obtain a standardized yellow wine raw material sample set and a standardized yellow wine product sample set; stepwise regression processing is carried out on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, and a regression equation is obtained; predicting the quality index of the yellow wine product according to the regression equation, and acquiring calculation data in the prediction process; constructing a yellow wine raw material quality standard optimization model according to the calculated data and a preset optimization target; and determining the quality index range of the yellow wine raw material according to the yellow wine raw material quality standard optimization model. Therefore, by firstly obtaining a yellow wine raw material sample set and a yellow wine product sample set, and then constructing a yellow wine raw material quality standard optimization model, determining the range of the yellow wine raw material quality index according to the model, the technical problem of how to determine the optimal range of the yellow wine raw material quality index, so that the yellow wine raw material can produce qualified yellow wine products under given production process conditions is solved.

Drawings

FIG. 1 is a flow chart of a first embodiment of a method for obtaining a raw material quality range based on a yellow wine product quality range according to the present invention;

FIG. 2 is a flow chart of a second embodiment of a method for obtaining a raw material quality range based on a yellow wine product quality range according to the present invention;

FIG. 3 is a flow chart of a third embodiment of a method for obtaining a raw material quality range based on a yellow wine product quality range according to the present invention;

fig. 4 is a schematic functional block diagram of a first embodiment of an apparatus for obtaining a quality range of a raw material based on a quality range of a yellow wine product according to the present invention.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Referring to fig. 1, fig. 1 is a flow chart of a first embodiment of a method for obtaining a raw material quality range based on a yellow wine product quality range according to the present invention.

In a first embodiment, the method for obtaining the raw material quality range based on the yellow wine product quality range comprises the following steps:

and S10, obtaining yellow wine quality investigation data and yellow wine quality investigation data.

It should be noted that, the execution body of the embodiment may be a computing service device having functions of data processing, program running and network communication, for example, a smart phone, a tablet computer, a personal computer, or other devices that can achieve the same or similar functions and obtain a raw material quality range based on a quality range of a yellow wine product, which is not limited in this embodiment, and in this embodiment, an apparatus that obtains a raw material quality range based on a quality range of a yellow wine product is taken as an example.

It should be understood that the yellow wine quality investigation data is various data on yellow wine raw materials and yellow wine products collected according to big data, and the yellow wine quality investigation data is data collected during the process of researching and testing the yellow wine raw materials and the yellow wine products.

It should be understood that the yellow wine raw material indexes include: regional, rice variety, rice moisture, protein, fat, raw starch, amylose, amylopectin, rice organoleptic score, yield, peak viscosity, lowest viscosity, final viscosity, attenuation value, retrogradation value, gelatinization temperature.

The indexes of the yellow wine product comprise: yellow wine type, total sugar, PH, total acid, amino acid nitrogen, caO, alcohol content, non-sugar solid, appearance, aroma, taste, style and total score.

And S20, constructing an index system of the yellow wine raw material and the yellow wine product according to the yellow wine quality investigation data and the yellow wine quality investigation data.

It should be noted that, according to the yellow wine quality investigation data and each item of data in the yellow wine quality investigation data, including but not limited to the above listed data, an index system of the yellow wine raw material and the yellow wine product may be constructed, and the index system contains the yellow wine raw material index capable of evaluating the yellow wine raw material and the yellow wine product index capable of evaluating the yellow wine product.

And step S30, extracting a yellow wine making process from the yellow wine quality research data, and selecting a target yellow wine making process from the yellow wine making processes.

It should be understood that multiple yellow wine making processes can be extracted from the yellow wine quality research data, and the most typical yellow wine making process is taken as the target yellow wine making process through screening the universality and advancement of the yellow wine making process.

It should be understood that the manufacturing process of the target yellow wine can be as follows: glutinous rice, crushing, liquefying, saccharifying, inoculating, fermenting, filtering and obtaining a finished product, wherein the process comprises the following main points: crushing the polished glutinous rice, sieving with a 40-mesh sieve, weighing 215g in a 1000mL beaker or conical flask, and adding 538mL of distilled water according to a material-water ratio of 1:2.5. Adding high temperature resistant alpha-amylase into 15U/g dry glutinous rice after mixing uniformly, treating for 90min in a water bath at 95 ℃, regulating pH to 4.5-5.0 by lactic acid after cooling, adding saccharifying enzyme into 150U/g dry glutinous rice, and treating for 30min in a water bath at 65 ℃ under continuous stirring. And (3) filling the mash into a fermentation bottle while the mash is hot, cooling, inoculating yeast, fermenting, filtering, canning and pasteurizing to obtain a finished yellow wine product. Inoculating active dry yeast for brewing wine according to 1.0X107/mL, fermenting in biochemical incubator at 25deg.C for 3 days, sampling to determine yeast number in fermented mash, cooling to 20deg.C, fermenting for 7 days, and determining alcohol content and flavor substance content.

And S40, determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process.

Further, the step S40 includes:

determining yellow wine raw material indexes and yellow wine product indexes according to the index system; extracting a yellow wine raw material variety and yellow wine raw material basic information from the investigation data, wherein the yellow wine raw material basic information comprises yellow wine raw material quality information; selecting a target yellow wine raw material variety from the yellow wine raw material varieties according to the basic information of the yellow wine raw material; searching a target sample yellow wine raw material index corresponding to the target yellow wine raw material variety according to the yellow wine raw material index; constructing a yellow wine raw material sample set according to the target sample yellow wine raw material index; processing yellow wine raw materials corresponding to the target yellow wine raw material variety according to the target yellow wine manufacturing process to obtain a target yellow wine product; and constructing a yellow wine product sample set according to the yellow wine product index and the target yellow wine product.

It should be noted that, the yellow wine raw material variety and the yellow wine raw material basic information are extracted from the investigation data, the yellow wine raw material basic information includes but is not limited to yellow wine raw material quality information, the most suitable yellow wine raw material variety is selected as the target yellow wine raw material variety according to the information, the yellow wine raw material corresponding to the target yellow wine raw material variety is used as the sample, so that t sample yellow wine raw materials can be obtained, and the index corresponding to the sample yellow wine raw material is the target sample yellow wine raw material index.

It should be noted that the steps for determining the variety of the target yellow wine raw material can be as follows:

(1) and obtaining all varieties and quality information of the yellow wine raw materials in the determined area according to the requirements. Determination of the region: for the national standard, the variety range of the yellow wine raw material is nationwide; for provincial standards, the variety range of the yellow wine raw material is the range of the provincial and the surrounding provincial parts; for the enterprise standard, the variety range of the yellow wine raw material is the range of frequent purchasing and potential raw material supply of the enterprise.

(2) And setting weights according to factors such as quality difference of the raw materials, and determining sampling and purchasing schemes of the yellow wine raw materials by adopting a layered sampling method.

(3) Sampling varieties of yellow wine raw materials according to a layered sampling method to obtain 5 yellow wine raw materials: glutinous rice, polished round-grained rice, long-grained rice, millet and millet, and a plurality of samples are collected for each variety for subsequent experiments.

(4) And (5) counting basic information of yellow rice wine raw materials:

wherein A is ₁ Is numbered A ₁₁ The 1 st basic attribute of the 1 st yellow wine raw material, namely the region of glutinous rice, A _5m For the m-th basic attribute of millet, m corresponds to the sequence of the yellow wine raw material quality indexes, 1 is the region, if m=4, A _5m Represents the protein of maize. It should be noted that the index of the yellow wine raw material quality index is not applicable to the record of certain yellow wine raw material varieties.

(5) Performing measurement experiments on a plurality of samples of 5 yellow wine raw materials, collecting index values of quality standards of various varieties, and obtaining a yellow wine raw material quality data set D of the plurality of samples _M ：

Wherein x is ₁₁ An index value of 1 st index of 1 st yellow wine raw material, such as a first sample of glutinous rice. X is x _tm An index value of the mth index of the t-th yellow rice wine raw material, such as the 3 rd sample of polished round-grained rice.

Carrying out production experiments on t yellow wine raw material samples according to a selected target yellow wine making process to obtain t yellow wine products, and collecting quality index values of the t yellow wine products to obtain a yellow wine product sample set D _N The method comprises the following steps:

wherein y is ₁₁ An index value of 1 st index of 1 st yellow wine product, such as yellow wine type of yellow wine produced by glutinous rice product first sample, y _tm Is the index value of the mth index of the mth yellow wine product, such as the alcoholic strength of the yellow wine produced by the second sample of polished round-grained rice.

According to the basic information D of yellow wine raw material _A Yellow wine raw material quality data set D _M Sample set D of yellow wine products _N The database D is established as follows:

wherein matrix D _A ^* Is D _A Copy the row vectors and increase the column vector dimension results, and ensure D _A ^* And D _M The same yellow wine raw material variety in the row vector table with the same row number can be understood as D _A ^* Is given to D _M Is labeled.

And then, performing simple data processing on the data in the database, and checking whether the data in the database has data missing, data repetition, obvious data errors and the like.

And S50, respectively carrying out standardization treatment on the yellow wine raw material sample set and the yellow wine product sample set to obtain a standardized yellow wine raw material sample set and a standardized yellow wine product sample set.

The yellow wine raw material sample set D _M Sample set D of yellow wine products _N The calculation formula for the standardized processing is as follows:

wherein x is _i ＝(x _1i ；x _2i ；…；x _ti ) (i=1, 2, …, 8) is a yellow wine raw material sample set, y _j ＝(y _1j ；x _2j ；…；y _tj ) (j=1, 2, …, 7) is a sample set of yellow wine products, t yellow wine raw materials, average

Standard deviation of

The standardized yellow wine raw material sample set and the standardized yellow wine product sample set after standardization are still marked as D _M 、D _N 。

Step S60, stepwise regression processing is carried out on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, and a regression equation is obtained.

The yellow wine raw material sample set and the yellow wine product sample set in the sample database are obtained, indexes in the yellow wine product sample set are set as response variables, and indexes in the yellow wine raw material sample set are set as prediction variables, so that constraint on the yellow wine product standard can be converted into constraint on the yellow wine raw material standard.

It should be understood that the step of stepwise regression processing on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set specifically comprises the following steps:

constructing a regression model according to the target yellow wine raw material index and the target yellow wine product index; extracting yellow wine raw material sample set dimensions from the standardized yellow wine raw material sample set, and extracting yellow wine product sample set dimensions from the standardized yellow wine product sample set; and determining a regression equation according to the dimension of the yellow wine raw material sample set, the dimension of the yellow wine product sample set and the regression model.

It will be appreciated that the data can be screened by stepwise regression, so that the interpretation variables retained in the model are important and have no severe multiple collinearity, and the subsequent steps can be made more accurate.

And step S70, predicting the quality index of the yellow wine product according to the regression equation, and acquiring calculation data in the prediction process.

The step of predicting the yellow wine product quality index based on the target data specifically includes:

firstly, generating a training set of a multiple linear regression model according to target yellow wine raw material sample data and a standardized yellow wine product sample set, training the multiple linear regression model according to the training set to obtain a prediction model corresponding to the yellow wine product quality index, wherein the prediction model is used for predicting the yellow wine product quality index according to the yellow wine raw material quality index, and determining the limitation constraint of the yellow wine product through the prediction model in the follow-up step.

And then, extracting target yellow wine product indexes from the standardized yellow wine product sample set, determining a regression function corresponding to the target yellow wine product indexes based on a preset coefficient matrix, and determining the goodness of fit according to the regression function.

And finally, taking the prediction model and the fitting goodness obtained in the prediction step process as calculation data for subsequent calculation and use, namely, the calculation data comprises the prediction model and the fitting goodness.

It should be understood that, in order to solve the precision problem, a correction factor based on regression prediction precision is introduced for the constraint of the yellow wine raw material standard to reform the constraint condition, so that the optimized yellow wine raw material can ensure that a product conforming to the yellow wine quality standard is obtained to the maximum extent, and the credibility of the optimization decision is improved.

And S80, constructing a yellow wine raw material quality standard optimization model according to the calculated data and a preset optimization target.

The preset optimization target is to set optimization of the raw material quality index standard as a target.

The constraint conditions of the yellow wine raw material quality standard optimization model include:

a first type of constraint: and (5) limiting and restraining the yellow wine raw materials. The constraint limits the solving range according to the reasonable range of index values of the yellow wine raw materials.

The second type of constraint: the restriction of the yellow wine product. The constraint is that the limitation on the yellow wine product is transferred to the limitation on the yellow wine raw material through the transmission of the relation model of the yellow wine raw material and the yellow wine product, wherein the problem of precision correction of the relation model is also required to be considered. The principle of constructing the restriction constraint of the yellow wine products is researched by taking two yellow wine raw material indexes and two yellow wine product indexes as examples, and the principle can be popularized to the situation of higher dimensional space.

Third class of constraints: diffusion factor confinement. And (5) constructing a yellow wine raw material quality standard optimization model according to the constraint conditions.

And step S90, determining the quality index range of the yellow wine raw material according to the yellow wine raw material quality standard optimization model.

It should be noted that, the yellow wine raw material quality standard optimization model may be optimized to obtain a regulation model, and then the yellow wine raw material quality index range is determined according to the regulation model, which should be understood that the index range is the index standard, and in this embodiment, represents the same meaning.

It should be understood that the purpose of optimizing the yellow wine raw material quality standard optimization model is to enable the conclusion to include the yellow wine raw material quality index range of more varieties of yellow wine raw materials.

In the embodiment, the yellow wine quality investigation data and the yellow wine quality investigation data are obtained; constructing an index system of yellow wine raw materials and yellow wine products according to the yellow wine quality research data and the yellow wine quality research data; extracting a yellow wine making process from the yellow wine quality research data, and selecting a target yellow wine making process from the yellow wine making processes; determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process; respectively carrying out standardization treatment on the yellow wine raw material sample set and the yellow wine product sample set to obtain a standardized yellow wine raw material sample set and a standardized yellow wine product sample set; stepwise regression processing is carried out on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, and a regression equation is obtained; predicting the quality index of the yellow wine product according to the regression equation, and acquiring calculation data in the prediction process; constructing a yellow wine raw material quality standard optimization model according to the calculated data and a preset optimization target; and determining the quality index range of the yellow wine raw material according to the yellow wine raw material quality standard optimization model. Therefore, by firstly obtaining a yellow wine raw material sample set and a yellow wine product sample set, and then constructing a yellow wine raw material quality standard optimization model, determining the range of the yellow wine raw material quality index according to the model, the technical problem of how to determine the optimal range of the yellow wine raw material quality index, so that the yellow wine raw material can produce qualified yellow wine products under given production process conditions is solved.

In an embodiment, as shown in fig. 2, a second embodiment of the method for obtaining a raw material quality range based on a yellow wine product quality range according to the present invention is provided based on the first embodiment, and before the step S60, the method further includes:

step S501, extracting target yellow wine raw material indexes from the standardized yellow wine raw material sample set, and extracting target yellow wine product indexes from the standardized yellow wine product sample set.

And step S502, performing multiple collineation analysis processing on the target yellow wine raw material index to obtain a variance expansion coefficient corresponding to the target yellow wine raw material index.

Step S503, comparing the variance expansion coefficient with a preset coefficient threshold.

And step S504, if the variance expansion coefficient is larger than the preset coefficient threshold, executing the step of stepwise regression processing on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set to obtain a regression equation.

Before stepwise regression, it is necessary to determine whether stepwise regression is required, extract a target yellow wine raw material index from a standardized yellow wine raw material sample set, perform multiple collinear analysis on the target yellow wine raw material index to obtain a variance expansion coefficient VIF corresponding to the target yellow wine raw material index, and compare the variance expansion coefficient with a preset coefficient threshold, and if the variance expansion coefficient is greater than the preset coefficient threshold, perform stepwise regression.

In a specific implementation, the preset coefficient threshold value can be 10, based on SPSS multiple collineation analysis, if VIF is more than 10 between target yellow wine raw material indexes, stepwise regression processing is performed, the method is used for the quality of yellow wine products in a given scene, and the multiple collineation analysis is performed on each target yellow wine raw material index to obtain the following results:

wherein amylose, the rising value VIF are all greater than 10, and multiple collinearity exists.

Further, the step S60 includes:

It should be noted that the basic idea of stepwise regression is: the variables are introduced into the model one by one, F test is carried out after each interpretation variable is introduced, t test is carried out on the selected interpretation variables one by one, and when the originally introduced interpretation variable becomes no longer obvious due to the introduction of the later interpretation variable, the originally introduced interpretation variable is deleted. To ensure that only significant variables are included in the regression equation before each new variable is introduced. This is an iterative process until neither significant explanatory variables are selected into the regression equation nor insignificant explanatory variables are removed from the regression equation. To ensure that the resulting set of interpretation variables is optimal. Meanwhile, the interpretation variables finally remained in the model are important and have no serious multiple collinearity through stepwise regression.

In a specific implementation, the step of stepwise regression is specifically:

(1) Establishing regression model according to yellow wine raw material index and yellow wine product index

y _j ＝ω ₀ +ωx ^T +ε

Wherein ω= (ω) ₁ ，ω ₂ ，…，ω _m )，x ^T ＝(x ₁ ，x ₂ ，…，x _m ) J=1, 2, … n, m is the yellow wine raw material sample set dimension, n is the yellow wine product sample set dimension. And the value of the F test statistic is recorded as

Taking the maximum value +.>

I.e.

For a given significance level α, the corresponding threshold value is noted as F ⁽¹⁾ ，

Will->

Introducing regression model, record I ₁ To select a variable index set.

(2) Establishing yellow wine product Y and raw material subset

M-1 in total. Calculating the statistical magnitude of the regression coefficient F test of the variable, noted +.>

Selecting the largest one, recordingIs->

I.e.

For a given significance level α, the corresponding threshold value is noted as F ⁽²⁾ ，

Will->

A regression model is introduced. Otherwise, the variable introduction process is terminated.

(3) According to the index subset of yellow wine raw materials

Training the product to obtain a stepwise regression equation.

In the embodiment, the data can be screened by stepwise regression processing, so that the interpretation variable remained in the model is important and has no serious multiple collinearity, the subsequent steps can be more accurate, and the calculation accuracy is improved.

In an embodiment, as shown in fig. 3, a third embodiment of the method for obtaining a raw material quality range based on a yellow wine product quality range according to the present invention is provided based on the first embodiment or the second embodiment, in this embodiment, the step S70 is described based on the first embodiment, and includes:

and step 701, generating a multiple linear regression model corresponding to the yellow wine product quality index.

And step S702, training the multiple linear regression model according to the regression equation to obtain a prediction model corresponding to the yellow wine product quality index.

And step 703, extracting target yellow wine product indexes from the standardized yellow wine product sample set, and determining regression functions corresponding to the target yellow wine product indexes.

Step S704, determining the goodness of fit according to the regression function.

Step S705, using the prediction model and the goodness of fit as calculation data.

The method includes the steps of generating a multiple linear regression model corresponding to the quality index of the yellow wine product, and training the multiple linear regression model according to a regression equation to obtain a prediction model corresponding to the quality index of each yellow wine product. Regression function for index of each target yellow wine product:

Wherein i=1, 2, …, m; j=1, 2, …, n. θ is the coefficient, f of the multiple linear regression function _i (x) Fitting goodness R of (2) _i ² The value is 0,1]。

Further, the step S80 includes:

step S801, physical and chemical property data of the yellow wine raw material quality corresponding to the yellow wine raw material quality index is searched, and the limitation constraint of the yellow wine raw material is determined based on the physical and chemical property data.

The lower and upper limit values x of the standard for determining each quality index of yellow wine raw material ^low 、x ^up A vector of decision variables, wherein

It should be understood that physical and chemical property data of the yellow wine raw material quality corresponding to the yellow wine raw material quality index is searched, and a first type constraint is determined based on the physical and chemical property data: and (5) limiting and restraining the yellow wine raw materials.

The yellow wine raw material limiting constraint is a yellow wine raw material index range meeting the requirements put into practical production, and the vector space represented by the yellow wine raw material index range is:

X _limit ＝{x|l _α ≤x _α ≤u _α ，α＝1，2，…，m}

step S802, determining the limitation constraint of the yellow wine product based on the prediction model and the preset target yellow wine product quality requirement.

It should be noted that, the preset target yellow wine product quality requirement is also set by the user according to the actual situation, which is not limited in this embodiment.

Based on the prediction model and the preset target yellow wine product quality requirement, determining a second class constraint: the restriction of the yellow wine product.

Assume that the feasible range of the yellow wine quality index is L= (L) ₁ ，L ₂ ，…，L _n )，U＝(U ₁ ，U ₂ ，…，U _n ) Respectively represent the lower boundary and the upper boundary of the yellow wine quality index.

And step 803, correcting the limiting constraint of the yellow wine product according to the goodness of fit to obtain the limiting constraint of the target yellow wine product.

In order to improve accuracy of the restriction of the yellow wine product, the restriction of the yellow wine product is corrected according to the goodness of fit, and the restriction of the target yellow wine product is obtained.

Goodness of fit obtained for the prediction stage

The specific correction method comprises the following steps:

a calculating delta _k ＝L _k -U _k ，Δ _k Is the range initial value.

b calculating the goodness of fit R of each regression function _i ² "correction factor" of (3):

c, calculating the upper and lower boundaries of the quality index of the yellow wine product, and carrying out equal-size reduction and increase according to the correction amounts of the upper and lower boundaries:

[L′ _j ，U′ _j ]＝[L _j +0.5*Δ _j *∈ _j ，U _j -0.5*Δ _j *∈ _j ]

where j=1, 2, …, n, represents the quality standard at which the kth product index is established, and 0.5 represents the upper and lower bound correction amounts are equally distributed between the upper and lower bounds.

And (3) setting a limiting constraint of the yellow wine product:

where k=1, 2, …, n, X _process. The feasible domain limited by the yellow wine product limitation constraint.

Step S804, a high-dimensional target space is constructed according to a plurality of target yellow wine raw material indexes, and diffusion factors are set in the high-dimensional target space.

And S805, determining a diffusion factor constraint according to the diffusion factor and a preset target area requirement.

It should be noted that, the preset target area requirement may be a target area maximization, and the third type constraint is determined based on the target area maximization requirement: diffusion factor confinement.

Setting a diffusion factor in a high-dimensional target space formed by a plurality of yellow wine raw material indexes, and substituting the diffusion factor into a linear weighting method target to meet the following requirements:

(1) The range is reasonable, so that the range of the yellow wine raw material is ensured to be a high-credibility range solution given under the condition of comprehensively considering the prediction errors of various products.

(2) The range is the most extensive, and the final solution is pursued to cover as much coverage as possible in the yellow wine raw material limit constraint and the yellow wine product limit constraint.

Let x be ^low 、x ^up For the final solution of the yellow wine raw materials, the lower and upper bounds are obtained, delta is a diffusion factor, and 9 indexes are provided

And step S806, taking the yellow wine raw material limit constraint, the yellow wine product limit constraint and the diffusion factor constraint as constraint conditions.

And S807, constructing a yellow wine raw material quality standard optimization model according to the constraint conditions and a preset optimization target.

It should be noted that, setting an objective function, and solving the food raw material quality index range through a multi-objective optimization model, covering a wider range on the premise of meeting constraint conditions, and first meeting the maximization of delta, which is the primary objective:

maxf ₁ ＝δ

and the final solution upper and lower bounds are to meet the maximum and minimum, respectively, with the following secondary objectives:

in summary, the yellow wine raw material quality standard optimization model is a multi-objective optimized mathematical model, and specifically expressed as follows:

P ₁ max f ₁ ＝δ

/>

further, the step S90 includes:

and step S901, converting the yellow wine raw material quality standardization model into a single target quality standard optimization model.

It should be noted that, by using a linear weighting method, a weight value with an order difference is set according to the priority of the target and the importance of the target of the same level, and the raw material quality standard optimization model is converted into a single target raw material quality standard optimization model:

wherein beta is _α The weights of the indexes are equal

Beta' is the weight of the diffusion factor, and the general ratio beta is calculated when the proper solution is obtained _α One and two orders of magnitude greater.

In a high-dimensional space constructed by all raw material index variables, X _limit Limiting the basic value range of the variable of each dimension of the space to form a hypercube space, and X _process A super-dimensional space with irregular shapes is constructed by linear functions of the dimensional variables and L, U. These two spaces have two cases of inclusion or partial overlap in the high-dimensional space. The objective of setting the objective function is to find a hypercube in the overlapping region of the two high-dimensional spaces, the hypercube having 2 ^m A plurality of vertexes, wherein the coordinates of two points are respectively

And->

The two coordinates already contain all vertex coordinate information of the hypercube, so that all food raw material quality standard ranges can be determined only by determining the two coordinates.

Step S902, obtaining result data of the single target quality standard optimization model.

And step S903, optimizing the diffusion factor constraint according to the result data to obtain a target diffusion factor constraint.

And step S904, regulating and controlling the yellow wine raw material quality standard optimization model according to the target diffusion factor constraint to obtain a regulating and controlling model.

It should be noted that the regulation stage is divided into three parts: determining the size of the regulation range, determining a regulation model and obtaining the quality standard of the special yellow wine raw material. The specific implementation steps of the regulation stage are as follows.

Step 1: and determining the size of the regulation and control range.

(1) The quartile of the raw material standardized data is calculated, namely, the quartile is used for arranging all the numerical values from small to large and dividing the numerical values into four equal parts, and the numerical values are positioned at three dividing points.

(2) The value at 25% (called lower quartile) and the value at 75% (called upper quartile) are selected to calculate the difference r= (R) ₁ ，r ₂ ，…，r _α )。

(3) Calculating new solving weights and constraint weights of all decision variables by using a min-max standardization method:

β _α ＝r _α /∑ _α r _α

at this time, the weights still satisfy

But not equal.

(4) According to the diffusion factor delta ₀ Adjusting constraints of a third class

Modifying it into

Where ρ is a "relaxation factor" for narrowing the diffusion factor found in the third stage such that the final solution space (hypercube) has a coordinate of δ ₀ * The relaxation space of ρ, in which each vertex can adjust for variations. And gamma is a floating variable and is used for controlling the final solution of each index to float according to the discrete characteristics of each quality index of the actual food raw material so as to obtain different solution ranges.

Step 2: and determining a regulation model.

The regulated double-layer multi-objective optimization model is as follows:

P1：max f ₁ ＝γ

wherein P1 is far greater than P2, the value range of the 'relaxation factor' rho is [0,0.05], namely, the maximum half value of the maximum diffusion factor is taken as the floating range of the hypercube coordinate, the step length of 0.05 rho is set, 10 times of solving are carried out, and the most reasonable solution range is obtained by comparing the result.

Step 3: and calculating a regulation model.

When solving by using a linear weighting method, substituting new solving weights of all decision variables, and multi-targeting as a single target:

wherein beta is _α For updated optimization weights, β' is an order of magnitude greater than β _α Is high.

And step S905, determining the quality index range of the yellow wine raw material according to the regulation and control model.

Further, the step S905 includes:

calculating a lower limit value of the yellow wine raw material quality index and an upper limit value of the yellow wine raw material quality index according to the regulation model; performing inverse standardization treatment on the lower limit value of the yellow wine raw material quality index and the upper limit value of the yellow wine raw material quality index; and determining the quality index range of the yellow wine raw material according to the processing result.

It should be noted that the range of the yellow wine raw material quality index is determined by adopting a conclusion inverse standardization mode. Standard deviation sigma according to data normalization process _i Mean value of

i represents what number of raw material quality indexes, and the result x is calculated ^low 、x ^up (yellow wine raw material)A lower limit value of the quality index and an upper limit value of the quality index of the yellow wine raw material) to obtain an optimized range of each raw material index>

The inverse normalization formula is:

it should be understood that by using the data-driven model analysis method formulated by the yellow wine raw material quality index, the yellow wine raw material index corresponding to the index of each yellow wine product and the range value of each raw material index (the yellow wine raw material index which can guide the production is screened from the yellow wine raw material sample set and the optimal range of the indexes is determined) are summarized, the quality index range of the yellow wine raw material is obtained, the yellow wine raw material is controlled by the quality standard of the yellow wine raw material, and the yellow wine meeting the standard of the yellow wine product can be produced and processed under the same production process.

In the embodiment, the yellow wine raw material quality index range is determined by constructing a yellow wine raw material quality standardization optimizing model, and in order to contain as many raw material varieties as possible in the specified change range of the yellow wine raw material quality index, the structure and parameters of the raw material quality index model are optimized and adjusted, the yellow wine raw material quality standardization optimizing model is improved to obtain a regulation and control model, and the shape of a hypercube is optimized, so that the most suitable yellow wine raw material quality index range of a target product is obtained.

In addition, referring to fig. 4, an embodiment of the present invention further provides a device for obtaining a raw material quality range based on a yellow wine product quality range, where the device for obtaining a raw material quality range based on a yellow wine product quality range includes:

the data acquisition module 10 is used for acquiring yellow wine quality investigation data and yellow wine quality investigation data;

the system construction module 20 is used for constructing an index system of the yellow wine raw material and the yellow wine product according to the yellow wine quality investigation data and the yellow wine quality investigation data;

a preparation process module 30, configured to extract a yellow wine preparation process from the yellow wine quality study data, and select a target yellow wine preparation process from the yellow wine preparation processes;

A sample set determining module 40, configured to determine a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process;

the data processing module 50 is configured to perform standardized processing on the yellow wine raw material sample set and the yellow wine product sample set respectively, so as to obtain a standardized yellow wine raw material sample set and a standardized yellow wine product sample set;

a stepwise regression module 60, configured to perform stepwise regression processing on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, so as to obtain a regression equation;

the index prediction module 70 is configured to predict the yellow wine product quality index according to the regression equation, and obtain calculation data in the prediction process;

the model construction module 80 is configured to construct a yellow wine raw material quality standard optimization model according to the calculation data and a preset optimization target;

the range determining module 90 is configured to determine a range of yellow wine raw material quality indexes according to the optimization model of yellow wine raw material quality standards.

In one embodiment, the sample set determining module 40 is further configured to determine a yellow wine raw material index and a yellow wine product index according to the index system; extracting the variety of the yellow wine raw materials and the basic information of the yellow wine raw materials from the investigation data; selecting a target yellow wine raw material variety from the yellow wine raw material varieties according to the basic information of the yellow wine raw material; searching a target sample yellow wine raw material index corresponding to the target yellow wine raw material variety according to the yellow wine raw material index; constructing a yellow wine raw material sample set according to the target sample yellow wine raw material index; processing yellow wine raw materials corresponding to the target yellow wine raw material variety according to the target yellow wine manufacturing process to obtain a target yellow wine product; and constructing a yellow wine product sample set according to the yellow wine product index and the target yellow wine product.

In an embodiment, the device for obtaining the raw material quality range based on the yellow wine product quality range further comprises a regression confirmation module for extracting a target yellow wine raw material index from the standardized yellow wine raw material sample set and extracting a target yellow wine product index from the standardized yellow wine product sample set; performing multiple collineation analysis processing on the target yellow wine raw material index to obtain a variance expansion coefficient corresponding to the target yellow wine raw material index; comparing the variance expansion coefficient with a preset coefficient threshold; and if the variance expansion coefficient is larger than the preset coefficient threshold value, executing the step of stepwise regression processing on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set to obtain a regression equation.

In one embodiment, the stepwise regression module 60 is further configured to construct a regression model based on the target yellow wine raw material index and the target yellow wine product index; extracting yellow wine raw material sample set dimensions from the standardized yellow wine raw material sample set, and extracting yellow wine product sample set dimensions from the standardized yellow wine product sample set; and determining a regression equation according to the dimension of the yellow wine raw material sample set, the dimension of the yellow wine product sample set and the regression model.

In one embodiment, the index prediction module 70 is further configured to generate a multiple linear regression model corresponding to the quality index of the yellow wine product; training the multiple linear regression model according to the regression equation to obtain a prediction model corresponding to the yellow wine product quality index; extracting target yellow wine product indexes from the standardized yellow wine product sample set, and determining regression functions corresponding to the target yellow wine product indexes; determining a goodness of fit according to the regression function; and taking the prediction model and the goodness of fit as calculation data.

In an embodiment, the model building module 80 is further configured to find physicochemical property data of the yellow wine raw material quality corresponding to the yellow wine raw material quality index, and determine a yellow wine raw material limitation constraint based on the physicochemical property data; determining a yellow wine product limit constraint based on the prediction model and a preset target yellow wine product quality requirement; correcting the limiting constraint of the yellow wine product according to the goodness of fit to obtain a target limiting constraint of the yellow wine product; constructing a high-dimensional target space according to a plurality of target yellow wine raw material indexes, and setting diffusion factors in the high-dimensional target space; determining a diffusion factor constraint according to the diffusion factor and a preset target area requirement; taking the yellow wine raw material limit constraint, the target yellow wine product limit constraint and the diffusion factor constraint as constraint conditions; and constructing a yellow wine raw material quality standard optimization model according to the constraint conditions and a preset optimization target.

In one embodiment, the range determining module 90 is further configured to convert the yellow wine raw material quality standardization model into a single target quality standard optimization model; obtaining result data of the single target quality standard optimization model; optimizing the diffusion factor constraint according to the result data to obtain a target diffusion factor constraint; regulating and controlling the yellow wine raw material quality standard optimization model according to the target diffusion factor constraint to obtain a regulating and controlling model; and determining the quality index range of the yellow wine raw material according to the regulation and control model.

In one embodiment, the range determining module 90 is further configured to calculate a lower limit value of a yellow wine raw material quality index and an upper limit value of a yellow wine raw material quality index according to the regulation model; performing inverse standardization treatment on the lower limit value of the yellow wine raw material quality index and the upper limit value of the yellow wine raw material quality index; and determining the quality index range of the yellow wine raw material according to the processing result.

Other embodiments or specific implementation methods of the device for obtaining the quality range of the raw materials based on the quality range of the yellow wine product according to the present invention may refer to the above method embodiments, and will not be described herein again.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in an estimator readable storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing an intelligent device (which may be a mobile phone, estimator, device for obtaining a raw material quality range based on a yellow wine quality range, air conditioner, or device for obtaining a raw material quality range based on a yellow wine quality range by a network, etc.) to perform the method described in the embodiments of the present invention.

The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims

1. The method for obtaining the raw material quality range based on the yellow wine product quality range is characterized by comprising the following steps of:

constructing a yellow wine raw material quality standard optimization model according to the calculation data and a preset optimization target, wherein the calculation data comprises a prediction model and a fitting goodness;

Determining the quality index range of the yellow wine raw material according to the yellow wine raw material quality standard optimization model;

the yellow wine raw material quality standard optimization model is constructed according to the calculated data and a preset optimization target, and specifically comprises the following steps:

2. The method for obtaining a raw material quality range based on a yellow wine product quality range according to claim 1, wherein determining a yellow wine raw material sample set and a yellow wine product sample set according to the index system and the target yellow wine manufacturing process specifically comprises:

3. The method for obtaining a raw material quality range based on a yellow wine product quality range according to claim 1, wherein the step-by-step regression processing is performed on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set, and before obtaining the regression equation, the method further comprises:

4. The method for obtaining a raw material quality range based on a yellow wine product quality range according to claim 3, wherein the step-by-step regression processing is performed on the yellow wine raw material quality index based on the standardized yellow wine raw material sample set to obtain a regression equation, specifically comprising:

5. The method for obtaining a raw material quality range based on a yellow wine product quality range according to claim 1, wherein predicting the yellow wine product quality index according to the regression equation and obtaining the calculation data in the prediction process specifically comprises:

determining a goodness of fit according to the regression function;

and taking the prediction model and the goodness of fit as calculation data.

6. The method for obtaining a raw material quality range based on a yellow wine product quality range according to claim 1, wherein the determining a yellow wine raw material quality index range according to the yellow wine raw material quality standard optimization model specifically comprises:

obtaining result data of the single target quality standard optimization model;

7. The method for obtaining a raw material quality range based on a yellow wine product quality range according to claim 6, wherein determining a yellow wine raw material quality index range according to the regulation model specifically comprises:

8. The device for obtaining the raw material quality range based on the yellow wine product quality range is characterized by comprising:

the index prediction module is used for predicting the quality index of the yellow wine product according to the regression equation and obtaining calculation data in the prediction process, wherein the calculation data comprises a prediction model and a fitting goodness;

the range determining module is used for determining the range of the yellow wine raw material quality index according to the yellow wine raw material quality standard optimizing model;

the model construction module is also used for searching physicochemical property data of the yellow wine raw material quality corresponding to the yellow wine raw material quality index and determining the limitation constraint of the yellow wine raw material based on the physicochemical property data; determining a yellow wine product limit constraint based on the prediction model and a preset target yellow wine product quality requirement; correcting the limiting constraint of the yellow wine product according to the goodness of fit to obtain a target limiting constraint of the yellow wine product; constructing a high-dimensional target space according to a plurality of target yellow wine raw material indexes, and setting diffusion factors in the high-dimensional target space; determining a diffusion factor constraint according to the diffusion factor and a preset target area requirement; taking the yellow wine raw material limit constraint, the target yellow wine product limit constraint and the diffusion factor constraint as constraint conditions; and constructing a yellow wine raw material quality standard optimization model according to the constraint conditions and a preset optimization target.

9. The apparatus for obtaining a raw material quality range based on a yellow wine product quality range according to claim 8, wherein the index prediction module is further configured to generate a multiple linear regression model corresponding to a yellow wine product quality index;