CN111311191B

CN111311191B - Method and device for obtaining raw material quality range based on wine product quality range

Info

Publication number: CN111311191B
Application number: CN202010118080.6A
Authority: CN
Inventors: 镇依婷; 杨华; 周康; 刘朔; 刘江蓉; 高婧; 周坚; 尚雪纯
Original assignee: Wuhan Polytechnic University
Current assignee: Wuhan Polytechnic University
Priority date: 2020-02-25
Filing date: 2020-02-25
Publication date: 2023-05-05
Anticipated expiration: 2040-02-25
Also published as: CN111311191A

Abstract

The invention relates to the technical field of wine processing, and discloses a method and a device for obtaining a raw material quality range based on a wine product quality range, wherein the method comprises the steps of obtaining national and industry standard data of wine grape raw materials and wine products, and establishing an index system of the wine grape raw materials and the wine products according to the data; acquiring a test brewing grape raw material according to an index system and a preset sampling mode, and acquiring a test brewing grape raw material sample set; brewing test is carried out on the brewing grape raw materials based on an index system to obtain test grape wine products, and a test grape wine product sample set is collected; establishing a sample database according to the test wine grape raw material sample set and the test wine product sample set; based on a sample database, a data-driven model analysis algorithm of the wine raw material index range is adopted to obtain the wine raw material quality index range. Solves the technical problem of how to quickly and scientifically determine the optimal range of the quality index of the raw material of the wine grapes.

Description

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

Technical Field

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

Background

In the field of wine processing, for brewing wine, as various wine products have different physicochemical properties, the requirements on the quality of wine brewing grape raw materials are different, and the wine products meeting national standards, provinces standards, line standards and enterprise standards are required to be manufactured, so that the optimal quality range of the wine brewing grape raw materials is predicted according to the quality of the wine products, the basis can be provided for enterprises to collect and handle the raw materials, the qualified wine products are brewed under the given production process conditions, the qualification rate of the wine products is improved, the waste of the wine brewing grape raw materials is reduced, the loss is reduced for enterprises, and the profit of enterprises is improved.

At present, enterprises have a plurality of methods for determining which brewing grape raw materials are used for brewing and producing, and generally, by comparing wine products brewed from various brewing grape raw materials according to the sensory feeling of wine brewing specialists, what brewing grape raw materials are put into brewing can be roughly judged to obtain what kind of wine products. However, this method of deciding on the process of wine brewing solely through the organoleptic sensations of the wine brewing specialist presents a certain risk in terms of efficiency and effectiveness, while also presenting a certain space for improvement. For the production planning of enterprises, how to scientifically find the quality range of the wine brewing grape raw material which is most suitable for brewing the grape wine is an urgent problem to be solved.

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 obtaining a raw material quality range based on a wine product quality range, and aims to solve the technical problem of how to quickly and scientifically determine an optimal range of a wine raw material quality index so as to produce a qualified wine product.

To achieve the above object, the present invention provides a method for obtaining a raw material quality range based on a wine product quality range, the method comprising the steps of:

acquiring national standard data and industry standard data of wine brewing grape raw materials and wine products;

establishing an index system of wine brewing grape raw materials and grape wine products according to the national standard data and the industry standard data;

acquiring a test wine grape raw material according to the index system and a preset sampling mode, and acquiring various index values in the test wine grape raw material to generate a wine grape raw material sample set;

carrying out a brewing test on the test wine making grape raw material based on the index system to obtain a test wine product, and collecting all index values in the test wine product to generate a wine product sample set;

Establishing a sample database according to the wine grape raw material sample set and the wine product sample set;

and based on the sample database, acquiring the wine grape raw material quality index range by adopting a data-driven model analysis algorithm of the grape raw material index range.

Preferably, the step of obtaining a test wine product by performing a brewing test on the test wine raw material based on the index system and collecting each index value in the test wine product to generate a wine product sample set specifically comprises the following steps:

acquiring a historical process flow of the wine product according to the index system;

extracting a target process flow of the wine product from the historical process flow according to preset process conditions;

carrying out a brewing test according to the test brewing grape raw material based on the target process flow to obtain a test grape wine product;

and collecting various index values from the test wine products to generate the test wine product sample set.

Preferably, the step of obtaining the wine grape raw material quality index range by adopting a data-driven model analysis algorithm of the wine raw material index range based on the sample database specifically comprises the following steps:

Extracting the wine grape raw material sample set and the wine product sample set from the sample database, setting indexes in the wine grape raw material sample set as prediction variables and setting indexes in the wine product sample set as response variables;

preprocessing the data in the sample database based on the predicted variable and the response variable to obtain processed target data;

predicting the quality index of the wine product based on the target data, and acquiring calculation data in the prediction process;

constructing a brewing grape raw material quality standard optimization model according to the calculation data and a preset optimization target;

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

Preferably, the step of preprocessing the data in the sample database based on the predicted variable and the response variable to obtain processed target data specifically includes:

carrying out standardization processing on a sample set in the sample database based on the prediction variable and the response variable to obtain a standardized wine raw material sample set and a standardized wine product sample set;

Performing main component dimension reduction on the standardized brewing grape raw material sample data in the standardized brewing grape raw material sample set to obtain target brewing grape raw material sample data and a main component factor load matrix;

extracting target wine grape raw material indexes from the target wine grape raw material sample data;

mapping the target brewing grape raw material index into a brewing grape raw material quality index according to the main component factor load matrix;

and taking the target wine grape raw material sample data, the standardized grape product sample set and the wine grape raw material quality index as target data.

Preferably, the step of predicting the quality index of the wine product based on the target data and obtaining the calculation data in the prediction process specifically includes:

generating a training set of a multiple linear regression model according to the target brewing grape raw material sample data and the standardized grape product sample set;

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

extracting target wine product indexes from the standardized wine product sample set, and determining regression functions corresponding to the target wine product indexes based on a preset coefficient matrix;

Determining a goodness of fit according to the regression function;

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

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

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

determining wine product limitation constraint based on the prediction model and a preset target food quality requirement;

correcting the wine product limit constraint according to the fitting goodness to obtain a target wine product limit constraint;

constructing a high-dimensional target space according to a plurality of target brewing grape 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 wine grape raw material limit constraint, the grape product limit constraint and the diffusion factor constraint as constraint conditions;

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

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

converting the brewing grape raw material quality standard optimization model into a single-target brewing grape raw material quality standard optimization model based on a linear weighting method;

performing quartile calculation on the standardized brewing grape raw material sample data according to the single-target brewing grape raw material quality standard optimization model to obtain quartile calculation result data;

determining a regulation and control model according to the quartile calculation result data and the brewing grape raw material quality standard optimization model;

and determining the quality index range of the brewing grape raw material according to the regulation model.

Preferably, the step of determining a regulation model according to the quartile calculation result data and the optimization model of the wine grape raw material quality standard specifically includes:

selecting lower quartile data and upper quartile data from the quartile calculation result data;

calculating a difference value according to the lower quartile data and the upper quartile data;

determining constraint weights according to the difference value and a preset standardization method;

Optimizing the diffusion factor constraint according to the constraint weight and the diffusion factor to obtain a target diffusion factor constraint;

and regulating and controlling the brewing grape raw material quality standard optimization model according to the target diffusion factor constraint to obtain the regulation and control model.

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

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

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

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

In addition, in order to achieve the above object, the present invention also proposes an apparatus for obtaining a raw material quality range based on a wine product quality range, the apparatus comprising:

the data acquisition module is used for acquiring national standard data and industry standard data of the wine brewing grape raw materials and the wine products;

the system establishment module is used for establishing an index system of the wine grape raw material and the wine product according to the national standard data and the industry standard data;

The raw material acquisition module is used for acquiring a test brewing grape raw material according to the index system and a preset sampling mode, and acquiring all index values in the test brewing grape raw material to generate a brewing grape raw material sample set;

the product test module is used for carrying out a brewing test on the brewing grape raw materials through the test wine based on the index system to obtain test grape products, and collecting all index values in the test grape products to generate a grape product sample set;

the sample construction module is used for establishing a sample database according to the wine grape raw material sample set and the wine product sample set;

and the standard determining module is used for acquiring the wine brewing grape raw material quality index range by adopting a data-driven model analysis algorithm of the grape raw material index range based on the sample database.

The invention obtains national standard data and industry standard data of wine brewing grape raw materials and wine products; establishing an index system of wine brewing grape raw materials and grape wine products according to the national standard data and the industry standard data; acquiring a test wine grape raw material according to the index system and a preset sampling mode, and acquiring various index values in the test wine grape raw material to generate a wine grape raw material sample set; carrying out a brewing test on the test wine making grape raw material based on the index system to obtain a test wine product, and collecting all index values in the test wine product to generate a wine product sample set; establishing a sample database according to the wine grape raw material sample set and the wine product sample set; and based on the sample database, acquiring the wine grape raw material quality index range by adopting a data-driven model analysis algorithm of the grape raw material index range. By the method, a sample database is established by combining standard data and test data, the transfer of the color tone and the taste of the wine product to the ingredients and the sweetness of the wine raw material is completed through the sample database, the data-driven model analysis algorithm of the wine raw material index range is adopted to obtain the wine raw material quality index range, the potential of wine raw material input selection to improve the quality of the wine product is exerted, and the technical problem of how to rapidly and scientifically determine the optimal range of the wine raw material quality index so as to produce qualified wine products 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 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 wine product quality range according to the present invention;

FIG. 3 is a representation of a limitation constraint of a wine product in a two-dimensional space in a second embodiment of a method of obtaining a raw material quality range based on a wine product quality range according to the present invention;

FIG. 4 is a flow chart of a third embodiment of a method for obtaining a raw material quality range based on a wine product quality range according to the present invention;

FIG. 5 is a flow chart of a fourth embodiment of a method for obtaining a raw material quality range based on a wine product quality range according to the present invention;

fig. 6 is a block diagram of a first embodiment of an apparatus for deriving a raw material quality range based on a wine product quality range according to the present invention.

The achievement of the object, 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.

An embodiment of the present invention provides a method for obtaining a raw material quality range based on a quality range of a wine product, and 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 quality range of a wine product according to the present invention.

In this embodiment, the method for obtaining the raw material quality range based on the quality range of the wine product includes the following steps:

and step S10, acquiring national standard data and industry standard data of wine brewing grape raw materials and wine products.

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, etc., and may be other electronic devices capable of achieving the same or similar functions and obtaining a raw material quality range based on a wine product quality range. And obtaining a raw material quality range based on the quality range of the wine product, namely obtaining a wine grape raw material index range.

The method comprises the steps of acquiring national standard data and industry standard data of wine brewing grape raw materials and wine products, and carrying out research and theoretical research on the wine brewing grape raw materials and the wine products, wherein the research and theoretical research can determine factors such as the requirements of wine production enterprises on the quality of the wine products and the wine brewing grape raw materials, the mastering of the detection technology of the wine production enterprises on the quality related to the wine products and the wine brewing grape raw materials, and the like, so that indexes of the wine brewing grape raw materials and the wine products can be evaluated. The national standard data and industry standard data can refer to the contents of the national grape wine standard GB15037-2006, the national mountain grape wine standard GBT27586-2011 and the national ice grape wine standard GBT 25504-2010.

Specifically, the wine product index (including but not all indexes below) used for evaluating the wine product in the national standard data and the industry standard data: sensory requirements = { clarity, hue, purity, concentration, quality, purity, concentration, persistence, quality }, physicochemical requirements = { alcoholic strength, total sugar, dry extract, volatile acid }, composition = { anthocyanin, tannin, total phenol, total flavonoids of wine, resveratrol, trans-resveratrol glycoside, cis-resveratrol glycoside, trans-resveratrol, cis-resveratrol, DPPH half-inhibitory volume }.

Wine grape raw material indexes (including but not all indexes below) for evaluating wine grape raw materials in national standard data and industry standard data: total amino acid, single amino acid content, protein, VC content, anthocyanin, fresh weight, tartaric acid, malic acid, citric acid, polyphenol oxidase activity, browning degree, DPPH free radical, total phenol, tannin, total grape flavone, resveratrol, trans-resveratrol glycoside, cis-resveratrol glycoside, trans-resveratrol, cis-resveratrol, flavonol, myricetin, quercetin, kaempferol, isorhamnetin, total sugar, reducing sugar, fructose, glucose, soluble solids, PH, titratable acid, solid acid ratio, dry matter content, ear mass, hundred grain mass, fruit stalk ratio, juice yield, peel mass, peel color.

And step S20, establishing an index system of the wine brewing grape raw material and the wine product according to the national standard data and the industry standard data.

It should be understood that, according to the national standard data and the industry standard data, an index system of the wine making grape raw material and the wine making product is established, wherein the index system comprises a wine making product index for evaluating the wine making product and a wine making grape raw material index for evaluating the wine making grape raw material, and the index system can also comprise other contents without limitation.

And S30, acquiring a test wine grape raw material according to the index system and a preset sampling mode, and acquiring various index values in the test wine grape raw material to generate a wine grape raw material sample set.

It is easy to understand that according to the specific needs of research and theoretical study on the raw materials and products of wine, a raw material area of wine is determined, and all varieties and market pricing of raw materials of wine in the producing area are obtained. The determination principle of the wine grape raw material area is as follows: the variety range of the wine grape raw material for the national standard is nationwide; the variety range of the standard brewing grape raw material for provincial level is the range of the provincial level and the surrounding provincial level; the variety range of the standard wine grape raw material for the enterprise is the range of the supply of the wine grape raw material which is frequently purchased and potential by the enterprise. And setting weights according to factors such as the commonality, cost performance, quality difference and the like of the brewing grape raw materials, and determining sampling and purchasing schemes of the brewing grape raw materials by adopting a layered sampling method so as to obtain the test brewing grape raw materials. According to a layered sampling method, different varieties of samples of the brewing grape raw materials are obtained, t brewing grape raw materials are purchased and used as test brewing grape raw materials, and all index values in the test brewing grape raw materials are collected to generate a brewing grape raw material sample set.

And S40, carrying out a brewing test on the brewing grape raw materials based on the index system to obtain a test grape wine product, and collecting all index values in the test grape wine product to generate a grape wine product sample set.

The step of obtaining a test wine product by carrying out a brewing test on the test wine raw material based on the index system and collecting all index values in the test wine product to generate a wine product sample set specifically comprises the following steps: acquiring a historical process flow of the wine product according to the index system; extracting a target process flow of the wine product from the historical process flow according to preset process conditions; carrying out a brewing test according to the test brewing grape raw material based on the target process flow to obtain a test grape wine product; and collecting various index values from the test wine products to generate the test wine product sample set.

Specifically, the target process flow of the wine product is extracted from the historical process flow according to preset process conditions, wherein the preset process conditions can be selected by enterprises according to needs, and the target process flow can be typical process flows in the industry, such as: cleaning grape, crushing, adding sugar, barreling, primary fermentation, post fermentation, clarifying, distilling, blending and bottling.

And S50, establishing a sample database according to the wine grape raw material sample set and the wine product sample set.

The sample database constructed according to the wine grape raw material index and the wine product index is a high quality database, and comprises { x } ₁ ,x ₂ ,…x _m ，y ₁ ,y ₂ ,…y _n (x) ₁ ,x ₂ ,…x _m The } is a wine grape raw material index that can evaluate the wine grape raw material, { y ₁ ,y ₂ ,…y _n And is a wine product index that allows the assessment of wine products.

The sample database D is:

D _A is basic information data of wine grape raw material, D _M Is an index data set of the raw materials of the wine grapes, namely a sample set of the raw materials of the wine grapes, D _N The method is an index data set of a wine product, namely a wine product sample set, wherein t is t brewing grape raw materials, k is basic information data of the kth brewing grape raw materials, m is index data of the mth brewing grape raw materials, and n is index data of the nth wine product.

Specifically, A ₁ Is numbered A ₁₁ Is the 1 st basic attribute, x of the 1 st brewing grape raw material _tm Is the mth basic attribute of the t-th brewing grape raw material. Basic attributes of the wine grape raw material may include variety of wine grape, production place, etc., wherein the variety of wine grape raw material may include: cabernet Sauvignon, serpentis, pink, meile, sila, and indigo naturalis; the place of origin of the wine grape raw material may include: langerhans, french Longgu, french Bo and French Bo Poly, crotam, california, shandong, bohai Bay, tianjin coast, etc. The wine grape raw material index for evaluating the wine grape raw material and the wine product index for evaluating the wine product are specifically referred to in step S10 of this embodiment.

After the step of establishing the sample database according to the wine grape raw material sample set and the wine product sample set, the method further comprises the step of preprocessing data in the sample database, and checking whether the data in the sample database is in the conditions of data missing, data repetition, obvious data errors and the like. If the problems exist in the sample database, if the filling errors or the non-unification of the measurement units cause that the manual modification is correct, otherwise, the brewing test is carried out again on the data item with the problems, and the brewing grape raw material and the grape wine product index data are collected again to generate a brewing grape raw material sample set and a grape wine product sample set so as to obtain a high-quality sample database.

And step S60, based on the sample database, acquiring the wine brewing grape raw material quality index range by adopting a data-driven model analysis algorithm of the grape raw material index range.

It should be noted that step S60 specifically includes: extracting the wine grape raw material sample set and the wine product sample set from the sample database, setting indexes in the wine grape raw material sample set as prediction variables and setting indexes in the wine product sample set as response variables; preprocessing the data in the sample database based on the predicted variable and the response variable to obtain processed target data; predicting the quality index of the wine product based on the target data, and acquiring calculation data in the prediction process; constructing a brewing grape raw material quality standard optimization model according to the calculation data and a preset optimization target; and determining the range of the quality index of the raw material of the wine grapes according to the optimization model of the quality standard of the raw material of the wine grapes.

The national standard data and industry standard data of the wine brewing grape raw materials and the wine products are obtained; establishing an index system of wine brewing grape raw materials and grape wine products according to the national standard data and the industry standard data; acquiring a test wine grape raw material according to the index system and a preset sampling mode, and acquiring various index values in the test wine grape raw material to generate a wine grape raw material sample set; carrying out a brewing test on the test wine making grape raw material based on the index system to obtain a test wine product, and collecting all index values in the test wine product to generate a wine product sample set; establishing a sample database according to the wine grape raw material sample set and the wine product sample set; and based on the sample database, acquiring the wine grape raw material quality index range by adopting a data-driven model analysis algorithm of the grape raw material index range. By the method, a sample database is established by combining standard data and test data, the transfer of the color tone and the taste of the wine product to the ingredients and the sweetness of the wine raw material is completed through the sample database, the data-driven model analysis algorithm of the wine raw material index range is adopted to obtain the wine raw material quality index range, the potential of wine raw material input selection to improve the quality of the wine product is exerted, and the technical problem of how to rapidly and scientifically determine the optimal range of the wine raw material quality index so as to produce qualified wine products is solved.

As shown in fig. 2, a second embodiment of the method for obtaining a raw material quality range based on a wine product quality range according to the present invention is proposed based on the first embodiment, and the step S60 specifically includes:

step S61, extracting the wine grape raw material sample set and the wine product sample set from the sample database, and setting the index in the wine grape raw material sample set as a prediction variable and the index in the wine product sample set as a response variable.

The method comprises the steps of obtaining a wine raw material sample set and a wine product sample set in the sample database, setting indexes in the wine product sample set as response variables, and setting indexes in the wine raw material sample set as prediction variables, so that constraints on wine product standards can be converted into constraints on the wine raw material standards.

And step S62, preprocessing the data in the sample database based on the predicted variable and the response variable to obtain processed target data.

It should be understood that, based on the predicted variable and the response variable, preprocessing the data in the sample database, and the step of obtaining the processed target data specifically includes:

Firstly, carrying out standardization processing on a sample set in the sample database based on the prediction variable and the response variable to obtain a standardized wine raw material sample set and a standardized wine product sample set. Specifically, a wine raw material sample set and a wine product sample set are extracted from a sample database based on the prediction variable and the response variable, and then the wine raw material sample set and the wine product sample set are subjected to standardization processing respectively, so that a standardized wine raw material sample set and a standardized wine product sample set can be obtained, and the sample set is subjected to standardization processing firstly because the data in the sample set after the standardization processing is more convenient for subsequent use.

And secondly, carrying out main component dimension reduction on the standardized brewing grape raw material sample data in the standardized brewing grape raw material sample set to obtain target brewing grape raw material sample data and a main component factor load matrix. Specifically, standardized brewing grape raw material sample data in a standardized brewing grape raw material sample set are obtained, main component dimension reduction is carried out on the standardized brewing grape raw material sample data, target brewing grape raw material sample data and a main component factor load matrix are obtained, and the purpose of main component dimension reduction is to eliminate unsuitable data, so that the dimension of the data is reduced, the dimension-reduced data meets the training requirement of a follow-up relation model, and the training difficulty is reduced.

And extracting a target wine grape raw material index from the target wine grape raw material sample data, mapping the target wine grape raw material index into a wine grape raw material quality index according to the main component factor load matrix, and taking the target wine grape raw material sample data, the standardized wine product sample set and the wine grape raw material quality index as target data for subsequent calculation and use, wherein the target data comprises the target wine grape raw material sample data, the standardized wine product sample set and the wine grape raw material quality index.

And step S63, predicting the quality index of the wine product based on the target data, and acquiring calculation data in the prediction process.

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

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

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

And thirdly, taking the prediction model and the fitting goodness obtained in the prediction step process as calculation data in the prediction process for subsequent calculation and use, namely, the calculation data in the prediction process comprise 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 wine grape raw material standard to reform the constraint condition, so that the optimized wine grape raw material is ensured to obtain a product conforming to the wine quality standard to the maximum extent, and the reliability of the optimization decision is improved.

And S64, constructing a brewing grape raw material quality standard optimization model according to the calculation data and a preset optimization target.

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

It should be noted that, constraint conditions of the optimization model of the quality standard of the raw material of the wine grape are mainly divided into two types:

A first type of constraint: wine grape raw material limitation constraint. The constraint limits the solving range according to the theoretical reasonable range of index values of the wine grape raw materials.

The second type of constraint: wine preparation restriction constraints. The constraint is that the limitation of the wine product is transferred to the limitation of the wine raw material through the transfer of the relation model of the wine raw material and the wine product, wherein the problem of precision correction of the relation model is also considered. Taking two wine making grape raw material indexes and two wine product indexes as examples to research and construct the principle of wine product limitation constraint, the principle can be popularized to the situation of higher dimensional space.

Establishing wine product limitation constraint:

wherein x is ₁ 、x ₂ For two predicted variables (ingredient and sweetness of the wine grape raw material), y ₁ 、y ₂ For two response variables (hue and mouthfeel of wine products), a predictive model y is trained ₁ ＝f ₁ (x ₁ ，x ₂ )，y ₂ ＝f ₂ (x ₁ ，x ₂ ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein,,

the lower boundary of the color and taste index standard for the wine product is indicated by->

The upper limit of the color and taste index standard of the wine product is shown.

The four solid diagonal lines as in FIG. 3 form the boundary of the wine product limitation constraint, the four diagonal lines circumscribing the region The domain is a wine preparation restriction domain, i.e. a parallelogram region, in which coordinate points (x ₁ ，x ₂ ) The components and sweetness quality indexes of the expressed wine grape raw materials are mapped into wine product indexes through a relation model f under the condition that the first constraint of the wine grape raw materials is not considered, and the wine product indexes obtained after the mapping in theory all meet the wine product constraint.

If the first type constraint is considered again, the wine product constraint domain shown in fig. 3 obtains the quality of the wine raw material conforming to the quality standard of the wine raw material, namely, the target area of the required wine quality range is selected inside the area, and the required target area is a rectangle in the quality range area of the wine raw material. The target area should be a rectangular area containing as many varieties of wine grape raw materials as possible in the wine grape raw material quality area formulated in conformity with the wine grape raw material quality standard.

The rectangular area can be determined by two points: the points of the rectangular area furthest from the origin and closest can be determined accordingly. The optimization model aims at enabling the points farthest from the original point to be farthest and the points closest to the original point to be closest on the premise that all vertexes of the target area are in the area conforming to the quality standard of the wine grape raw material.

For example, two points A and B in FIG. 3 are shown, and the coordinate values of the two points are the optimal modeling results, namely the upper and lower bounds of the wine grape raw material quality index. The rectangular area drawn by the points A and B is the dotted area in fig. 3, and the index value of the raw material of the wine grape indicated by the coordinate points contained in the area is theoretically preferable. The objective of the optimization model is to find the coordinates of the points A and B, and the coordinates of the two points are set as the optimization objective. The set requirements for the optimization objective are described in connection with the rectangle in fig. 3:

(1) The length of each side represents the value range of the wine quality index represented by the side, so each side needs to be as long as possible.

(2) The sum of the rectangular side lengths should also be the largest sought in the feasible region.

(3) Therefore, when the multi-objective is subjected to weighted solution, the optimization weight importance of each index is set according to the discrete characteristic of the quality index, and each weight proportion is intuitively expressed as the ratio of the side length of the dotted rectangle.

And reducing the dimension of the solving space to the index number of the wine grape raw material index set, wherein the wine grape raw material limit constraint and the wine product limit constraint form a feasible region defined by a plurality of hyperplanes, and the modeling optimization solving target is to search a hypercube in the feasible region.

And step S65, determining the quality index range of the brewing grape raw material according to the brewing grape raw material quality standard optimization model.

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

It should be noted that, the main means of optimization is to adjust the weight in the optimization function, so that the samples of the solution conclusion after regulation are covered as much as possible, and the weight can be adjusted by adopting methods such as quartile in the discrete measurement index. Calculating the length of the quartile, obtaining the weight through normalization processing, and substituting the weight into a model of the modeling step to carry out secondary modeling solution. The ratio of each side length of the rectangle obtained by the secondary modeling is the ratio of the dispersion of each index, and then sensitivity analysis is carried out to search the sum of the ranges of each index (the sum of each side length) so as to observe whether the possibility of improvement exists or not, thereby obtaining the most suitable solution.

The secondary modeling is controlled as follows based on the primary modeling:

(1) Substituting the maximum side length in the primary modeling solution conclusion into constraint, and setting the optimization weight of each side length as the dispersion of each index of the sample data.

(2) And (3) setting a reduction step length for the maximum side length in the step (1), and carrying out multiple optimization solutions.

(1) And the step is to optimize parameters based on the primary modeling result and redefine the proportional constraint of each side length. (2) Setting a comparison experiment to analyze sensitivity, and analyzing the change condition of the solution range to obtain an optimal solution. The optimized solution regulated and controlled in the two steps can meet the setting requirement of the optimization target in the modeling step, and the optimized range of each index is obtained.

In this embodiment, the wine preparation sample set and the wine preparation sample set are extracted from the sample database, and the indexes in the wine preparation sample set are set as prediction variables, and the indexes in the wine preparation sample set are set as response variables; preprocessing the data in the sample database based on the predicted variable and the response variable to obtain processed target data; predicting the quality index of the wine product based on the target data, and acquiring calculation data in the prediction process; constructing a brewing grape raw material quality standard optimization model according to the calculation data and a preset optimization target; and determining the range of the quality index of the raw material of the wine grapes according to the optimization model of the quality standard of the raw material of the wine grapes. By means of the method, the data in the sample database are preprocessed to obtain the target data, the calculation data in the process of predicting the quality index of the wine product based on the target data are obtained, transfer of the color tone and the taste of the wine product to the components and sweetness of the wine grape raw material is completed, a wine grape raw material quality standard optimization model is built, the range of the wine grape raw material quality index is determined by building the wine grape raw material quality standard optimization model, the potential of wine grape raw material input selection for improving the quality of the wine product is exerted, and the technical problem that how to quickly and scientifically determine the optimal range of the wine grape raw material quality index under given production process conditions is solved, so that the wine grape raw material can produce qualified wine products is solved.

As shown in fig. 4, a third embodiment of the method for obtaining a raw material quality range based on a wine product quality range according to the present invention is proposed based on the first embodiment, and in the step S62, specifically includes:

step S621, carrying out standardization processing on the sample set in the sample database based on the prediction variable and the response variable to obtain a standardized wine raw material sample set and a standardized wine product sample set.

The brewing grape raw material sample sets D are respectively _M And wine product sample set D _N Carrying out standardization treatment to obtain a standardized wine raw material sample set and a standardized wine product sample set, wherein the calculation formula is as follows:

wherein x is _i ＝(x _1i ；x _2i ；…；x _ti ) (i=1, 2, …, m) is a winery grape raw material sample set;

y _j ＝(y _1j ；y _2j ；…；y _tj ) (j=1, 2, …, n) is a sample set of wine preparations; t brewing grape raw materials;

mean value of

Standard deviation of

The normalized sample set is still denoted as D _M 、D _N 。

And S622, performing main component dimension reduction on the standardized wine grape raw material sample data in the standardized wine grape raw material sample set to obtain target wine grape raw material sample data and a main component factor load moment.

The specific steps of dimension reduction of the specific main components are as follows:

(1) And carrying out Pearson correlation analysis according to the evaluation index of the wine product and the evaluation index of the wine brewing grape raw material. If the indexes have stronger correlation, the main component dimension reduction can be considered, and the quality of the wine product under a given production scene and a target technological process can be predicted by using a multi-element linear model.

The evaluation indexes of the wine products are (including but not all the following) as follows: sensory requirements = { clarity, hue, purity, concentration, quality, purity, concentration, persistence, quality }, physicochemical requirements = { alcoholic strength, total sugar, dry extract, volatile acid }, composition = { anthocyanin, tannin, total phenol, total flavonoids of wine, resveratrol, trans-resveratrol glycoside, cis-resveratrol glycoside, trans-resveratrol, cis-resveratrol, DPPH half-inhibitory volume }.

The evaluation indexes of the wine grape raw materials are (the following are included but not all indexes): total amino acid, single amino acid content, protein, VC content, anthocyanin, fresh weight, tartaric acid, malic acid, citric acid, polyphenol oxidase activity, browning degree, DPPH free radical, total phenol, tannin, total grape flavone, resveratrol, trans-resveratrol glycoside, cis-resveratrol glycoside, trans-resveratrol, cis-resveratrol, flavonol, myricetin, quercetin, kaempferol, isorhamnetin, total sugar, reducing sugar, fructose, glucose, soluble solids, PH, titratable acid, solid acid ratio, dry matter content, ear mass, hundred grain mass, fruit stalk ratio, juice yield, peel mass, peel color.

(2) The main component is used for reducing the dimension. In the principal component analysis PCA flow, m is the dimension of a brewing grape raw material sample set, d is the dimension of data after dimension reduction, d is specified by observing the total variance of the data after dimension reduction and can be explained, and the number of the explained variables is also the number of the explained variables for training the second-stage regression model. After the PCA process of principal component analysis is finished, the eigenvectors with smaller corresponding eigenvalues are discarded, the discarding rule is that if the accumulated variance of the former eigenvalues exceeds 95%, the subsequent eigenvectors can be discarded, and the dimension of the principal component dimension reduction is m-d. The purpose of dimension reduction is to reduce the cost of training calculation and simultaneously make the input training data accord with one of basic assumptions of a multi-element linear model, namely, no correlation exists among interpretation variables.

Finally, reducing dimension of standardized brewing grape raw material sample data to target brewing grape raw material sample data D _d ：

Obtaining a principal component factor load matrix:

step S623, extracting target wine grape raw material indexes from the target wine grape raw material sample data.

And S624, mapping the target wine grape raw material index into a wine grape raw material quality index according to the main component factor load matrix.

The main component factor load matrix is used for calculating s each main component expression, and the main component expressions are used for mapping the target wine grape raw material index into the wine grape raw material quality index. The expression of the main components is:

where i=1, 2, …, m,

represents the i-th raw material quality index, j=1, 2, …, d, coefficient of main component with respect to each raw material quality index ∈>

λ＝(λ ₁ ，λ ₂ ，…，λ _j ) For the d eigenvalues selected.

And step S625, taking the target wine grape raw material sample data, the standardized grape product sample set and the wine grape raw material quality index as target data.

Further, the step S63 includes:

and step S631, generating a training set of a multiple linear regression model according to the target wine grape raw material sample data and the standardized grape product sample set.

It should be understood that brewing with reduced-dimension targetGrape raw material sample data D _d Based on the quality index of the wine product is predicted by using the quality index of the wine grape raw material.

In training the multiple linear regression model of each quality index of the wine product, D is given as _d And standardized wine product sample set D _N Training set (D) constituting multiple linear regression model _d |D _N )。

And step S632, training the multiple linear regression model according to the training set to obtain a prediction model corresponding to the quality index of the wine product.

Step S633, extracting target wine product indexes from the standardized wine product sample set, and determining regression functions corresponding to the target wine product indexes based on a preset coefficient matrix.

Step S634, determining the goodness of fit according to the regression function.

It should be noted that, since each multiple linear regression model is used to predict a single variable, the sample set input each time the least squares solution is applied is:

wherein D is _d Is the target brewing grape raw material sample data after dimension reduction, y _j ＝(y _1j ；y _2j ；…；y _tj ) (j=1, 2, …, n) is a wine product index set.

And (3) ending the algorithm flow, and obtaining a prediction model of the quality index of each wine product. According to the coefficient matrix (θ|ω) ^* ) _n*(m+1) Giving a regression function of the ith wine product index:

where i=1, 2, …, n, j=1, 2, …, m. θ is the principal component factor load matrix, multiple linear regression function f _i (x) Fitting goodness of (2)

Take the value of 0,1]。

Step S635, using the prediction model and the goodness of fit as calculation data in the prediction process.

In this embodiment, the accuracy of the prediction model is ensured by performing data processing on the data in the sample database, and then the quality index of the wine product is predicted by the quality index of the wine raw material, so that the transfer of the quality index of the wine product to the quality index of the wine raw material is completed, and the direct, effective and reasonable establishment of the model is ensured.

As shown in fig. 5, a fourth embodiment of the method for obtaining a raw material quality range based on a wine product quality range according to the present invention is proposed based on the first embodiment, the second embodiment or the third embodiment, in this embodiment, the step S64 includes:

step S641, physical and chemical property data of the wine grape raw material quality corresponding to the wine grape raw material quality index is searched, and the wine grape raw material limitation constraint is determined based on the physical and chemical property data.

It should be understood that the lower and upper limit values x of the criteria determining the various quality indicators of the raw material of the wine grape ^low ，x ^up A vector of decision variables;

wherein,,

it should be noted that, based on the physicochemical properties of the raw material quality of the wine grapes, a first type of constraint is determined: wine grape raw material limitation constraint.

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

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

wherein l _α And u _α Respectively the raw material index x of the wine grapes _α There are m such indicators for the lower and upper bounds of (2).

Step S642, determining wine product limitation constraint based on the prediction model and a preset target food quality requirement.

And step S643, correcting the wine product limit constraint according to the fitting goodness to obtain a target wine product limit constraint.

It should be noted that, based on the prediction model and the preset target food quality requirement, determining a wine product limitation constraint, and correcting the wine product limitation constraint according to the fitness, the step of obtaining the target wine product limitation constraint specifically includes:

(1) Determination of quality standards for wine products

Assuming that the feasible range of physicochemical properties and sensory scores in the wine product quality index is l= (L) ₁ ，L ₂ ，…，L _n )，U＝(U ₁ ，U ₂ ，…，U _n ) Representing the lower and upper bounds of the target wine product quality index, respectively.

(2) Determination of correction factors

To improve the accuracy of the limitation constraint of the wine products, the degree of fit R is used ² And correcting the constraint of the quality standard of the wine product. 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 of each regression function

"correction factor" of (3): />

c. Calculating the upper and lower bounds of the quality index of the wine product to reduce and increase the same size according to the correction amounts of the upper and lower bounds:

[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.

(3) Wine product limitation constraint

The wine product limit constraint is set by the steps of (1) and (2):

wherein k=1, 2, …, n, X _process The feasible region limited by the "wine product limitation constraint",

is the regression function of the kth wine product index, L= (L) ₁ ，L ₂ ，…，L _n )，U＝(U ₁ ，U ₂ ，…，U _n ) Representing the lower and upper bounds of the target wine product quality index, respectively.

Step S644, constructing a high-dimensional target space according to the target wine grape raw material indexes, and setting diffusion factors in the high-dimensional target space.

Step S645, determining a diffusion factor constraint according to the diffusion factor and a preset target area requirement.

The preset target area requirement is a requirement targeting maximization of the target area.

It should be noted that, based on the requirement that the target area is maximized as the target, a third type of constraint is determined: and (3) the diffusion factor constraint, namely setting a diffusion factor in a high-dimensional target space formed by a plurality of target brewing grape 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 raw materials of the wine grape 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 widespread, and the final solution is pursued to cover as much as possible in the wine raw material constraint and the wine product constraint.

Let x be ^low ，x ^up For the lower and upper bounds of the final solution of each raw material, delta is the diffusion factor, and there are m such indices

Step S646 takes the wine grape raw material limit constraint, the wine product limit constraint, and the diffusion factor constraint as constraint conditions.

And step S647, constructing a brewing grape raw material quality standard optimization model according to the constraint conditions and a preset optimization target.

The range of the quality index of the brewing grape raw material solved by the multi-objective optimization model needs to cover a wider range on the premise of meeting constraint conditions, and the maximization of delta needs to be met firstly, which is the primary objective:

max f 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 optimization model of the quality standard of the wine grape raw material is a multi-objective optimized mathematical model, and is specifically expressed as follows:

wherein x is ^low ，x ^up For making wine The standard lower and upper limits of the quality index of the raw materials, delta is the diffusion factor, f is the objective function, l= (L) ₁ ，L ₂ ，…，L _n )，U＝(U ₁ ，U ₂ ，…，U _n ) Respectively representing the lower and upper bounds and X of the quality index of the target wine product _limit Vector space, y, expressed by raw material index range of wine grape meeting requirements _k Is a wine product index set.

Further, the step S65 includes:

and step S651, converting the brewing grape raw material quality standard optimization model into a single-target brewing grape raw material quality standard optimization model based on a linear weighting method.

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 optimization model of the quality standard of the wine grape raw material is converted into a optimization model of the quality standard of the single-target wine grape raw material:

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 wine grape 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 Multiple vertices, two of whichThe coordinates of (a) are respectively

And->

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

And step S652, performing quartile calculation on the standardized brewing grape raw material sample data according to the single-target brewing grape raw material quality standard optimization model to obtain quartile calculation result data.

It should be understood that the third class constraint is optimized using the optimal solution calculated by the single-object orthographic normalization model, and the quartiles of the raw material normalization data are calculated first, that is, the quartiles are used to arrange all the values from small to large and divide them into four equal parts, and the values are located at the three division points.

And step S653, determining a regulation and control model according to the quartile calculation result data and the brewing grape raw material quality standard optimization model.

Further, in the step S653, the method specifically includes:

selecting lower quartile data and upper quartile data from the quartile calculation result data; calculating a difference value according to the lower quartile data and the upper quartile data; determining constraint weights according to the difference value and a preset standardization method; optimizing the diffusion factor constraint according to the constraint weight and the diffusion factor to obtain a target diffusion factor constraint; and regulating and controlling the brewing grape raw material quality standard optimization model according to the target diffusion factor constraint to obtain a regulation and control model.

From the calculation result data, a value at 25% (lower quartile data) and a value at 75% (upper quartile data) are selected, and a difference r= (R) is calculated from the lower quartile data and the upper quartile data ₁ ，r ₂ ，…，r _α )。

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

at this time, the weights still satisfy

But not equal. According to the diffusion factor delta ₀ Adjusting constraints of a third class

Modify it to +.>

Where ρ is a relaxation factor used to scale down the diffusion factor such that the final solution space (hypercube) has delta coordinates 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 brewing grape raw material so as to obtain different solution ranges.

The regulated multi-objective optimization model with priority is as follows:

wherein x is ^low ，x ^up The lower and upper limit values of the standard of each quality index of the wine grape raw material are adopted, delta is a diffusion factor, f is an objective function, and L= (L) ₁ ，L ₂ ，…，L _n )，U＝(U ₁ ，U ₂ ，…，U _n ) Respectively representing the lower and upper bounds and X of the quality index of the target wine product _limit Vector space, y, expressed by raw material index range of wine grape meeting requirements _k Is a wine product index set, and gamma is a floating variable.

Wherein P1 is far greater than P2, the value range of the relaxation factor rho is [0,0.5], namely, the maximum half value of the maximum diffusion factor is used as the floating range of the hypercube coordinate, the step length of 0.05 rho is set, 10 times of solving is carried out, and the most reasonable solution range is obtained by comparing the result.

And step S654, determining the quality index range of the brewing grape raw material according to the regulation and control model.

Further, in the step S654, the method specifically includes:

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

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

wherein γ is a "floating variable", x ^low ，x ^up A lower limit value (lower limit value of the quality index of the wine grape raw material) and an upper limit value (upper limit value of the quality index of the wine grape raw material) which are the standard of each quality index of the wine grape raw material, m indexes, beta _α For updated optimization weights, β' is an order of magnitude greater than β _α Is high.

It should be appreciated that the standard deviation sigma is normalized in terms of data _i Mean value of

i represents what number of raw material quality indexes, and the result x is calculated ^low 、x ^up Performing inverse standardization treatment to obtain the optimized range of each raw material index

Inverse standardization maleThe formula is:

in the embodiment, the standard of the quality index of the raw material of the wine grape is determined by constructing a standardized optimization model of the quality index of the raw material of the wine grape, and in order to contain as many raw material varieties as possible in the formulated variation range of the quality index of the raw material of the wine grape, the structure and parameters of the model of the quality index of the raw material are optimized and adjusted, the standardized optimization model of the raw material of the wine grape is improved to obtain a regulation and control model, and the shape of a hypercube is optimized, so that the most suitable range of the quality index of the raw material of the wine grape of a target product is obtained.

In addition, referring to fig. 6, an apparatus for obtaining a raw material quality range based on a wine product quality range according to an embodiment of the present invention is further provided, where the apparatus for obtaining a raw material quality range based on a wine product quality range includes:

the data acquisition module 10 is used for acquiring national standard data and industry standard data of wine brewing grape raw materials and wine products.

The system establishment module 20 is configured to establish an index system of the wine grape raw material and the wine product according to the national standard data and the industry standard data.

The raw material collection module 30 is configured to obtain a test wine grape raw material according to the index system and a preset sampling manner, and collect each index value in the test wine grape raw material to generate a wine grape raw material sample set.

And the product test module 40 is used for carrying out a brewing test on the brewing grape raw materials based on the index system to obtain a test grape wine product, and collecting various index values in the test grape wine product to generate a grape wine product sample set.

A sample construction module 50 for creating a sample database from the set of wine making raw material samples and the set of wine making samples.

The standard determining module 60 is configured to obtain a wine raw material quality index range by using a data-driven model analysis algorithm of a wine raw material index range based on the sample database.

In this embodiment, the data acquisition module 10 is configured to acquire national standard data and industry standard data of wine grape raw materials and wine products; a system establishment module 20, configured to establish an index system of wine grape raw materials and wine products according to the national standard data and the industry standard data; the raw material collection module 30 is configured to obtain a test wine grape raw material according to the index system and a preset sampling manner, and collect each index value in the test wine grape raw material to generate a wine grape raw material sample set; the product test module 40 is used for carrying out a brewing test on the brewing grape raw materials based on the index system to obtain a test grape wine product, and collecting various index values in the test grape wine product to generate a grape wine product sample set; a sample construction module 50 for creating a sample database from the wine grape raw material sample set and the wine preparation sample set; the standard determining module 60 is configured to obtain a wine raw material quality index range by using a data-driven model analysis algorithm of a wine raw material index range based on the sample database. By the method, a sample database is established by combining standard data and test data, the transfer of the color tone and the taste of the wine product to the ingredients and the sweetness of the wine raw material is completed through the sample database, the data-driven model analysis algorithm of the wine raw material index range is adopted to obtain the wine raw material quality index range, the potential of wine raw material input selection to improve the quality of the wine product is exerted, and the technical problem of how to rapidly and scientifically determine the optimal range of the wine raw material quality index so as to produce qualified wine products is solved.

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

Furthermore, 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 system 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 system. 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 system 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 such 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 a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an electronic device for obtaining a raw material quality range based on a wine quality range, or a network device, etc.) to perform the method according to the embodiments of the present invention.

The foregoing description of the preferred embodiments of the present invention should not be taken as limiting the scope of the invention, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the following description and drawings, or by direct or indirect application to other relevant art(s).

Claims

1. A method of obtaining a raw material quality range based on a wine product quality range, the method comprising:

preprocessing the data in the sample database based on the prediction variable and the response variable to obtain processed target data, wherein the target data comprises a standardized wine grape raw material sample set;

2. The method for obtaining a raw material quality range based on a wine product quality range according to claim 1, wherein the step of obtaining a test wine product by performing a brewing test on the test wine raw material based on the index system and collecting index values of each item in the test wine product to generate a wine product sample set specifically comprises:

3. The method for obtaining a raw material quality range based on a wine product quality range according to claim 1, wherein the step of preprocessing the data in the sample database based on the predicted variable and the response variable to obtain processed target data specifically comprises:

4. A method for deriving a raw material quality range based on a wine preparation quality range as claimed in claim 3 wherein said step of predicting a wine preparation quality index based on said target data and deriving calculation data in the prediction process comprises:

determining a goodness of fit according to the regression function;

5. The method for obtaining a raw material quality range based on a wine product quality range according to claim 4, wherein the step of constructing a wine raw material quality standard optimization model according to the calculation data and a preset optimization target specifically comprises:

6. The method for obtaining a raw material quality range based on a wine product quality range according to claim 1, wherein the step of determining a regulation model according to the quartile calculation result data and the optimization model of the wine raw material quality standard specifically comprises:

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

8. An apparatus for obtaining a raw material quality range based on a wine product quality range, the apparatus comprising:

the sample construction module is used for carrying out a brewing test on the brewing grape raw materials through the test brewing grape raw materials based on the index system to obtain a test grape wine product, collecting all index values in the test grape wine product to generate a grape wine product sample set, and establishing a sample database according to the brewing grape raw material sample set and the grape wine product sample set;

the standard determining module is used for extracting the wine grape raw material sample set and the wine product sample set from the sample database, setting indexes in the wine grape raw material sample set as prediction variables and setting indexes in the wine product sample set as response variables; preprocessing the data in the sample database based on the prediction variable and the response variable to obtain processed target data, wherein the target data comprises a standardized wine grape raw material sample set; predicting the quality index of the wine product based on the target data, and acquiring calculation data in the prediction process; constructing a brewing grape raw material quality standard optimization model according to the calculation data and a preset optimization target; converting the brewing grape raw material quality standard optimization model into a single-target brewing grape raw material quality standard optimization model based on a linear weighting method; performing quartile calculation on the standardized brewing grape raw material sample data according to the single-target brewing grape raw material quality standard optimization model to obtain quartile calculation result data; determining a regulation and control model according to the quartile calculation result data and the brewing grape raw material quality standard optimization model; and determining the quality index range of the brewing grape raw material according to the regulation model.