Highland barley wine quality management method and related equipment

Abstract

The invention provides a quality management method of highland barley wine and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining target highland barley wine information, a training sample set and target user information; processing the target highland barley wine information to generate quality inspection information, target feature information and attribute information of the target highland barley wine; processing the target user information to generate order use information of the target user; training a preset highland barley wine production quality early warning model based on the training sample set to generate a target highland barley wine production quality early warning model; processing the quality inspection information based on the target highland barley wine production quality early warning model to generate defective rate information of the target highland barley wine; processing the attribute information of the target highland barley wine and the order use information of the target user to generate a highland barley wine storage impact factor; and processing the defective rate information of the target highland barley wine and the highland barley wine storage impact factor to generate product grading information of the target highland barley wine.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a quality management method for highland barley wine and related equipment. Background Art

[0002] Highland barley wine is an ancient traditional alcoholic beverage in ethnic minority areas such as Tibet, Sichuan Tibetan areas, and Qinghai. It is made from highland barley and fermented with koji, and is mostly handmade at home. In recent years, with the development of industrialization, traditional fermented highland barley wine has gradually moved towards factory production, the market scope is also gradually expanding, and consumers have higher and higher requirements for the quality of fermented highland barley wine. In the production process of winemaking, the brewing process of highland barley wine involves multiple links such as the production of koji blocks, fermentation of mash, picking wine, storing wine, and blending. Each link involves key factors that can affect the quality of winemaking, and ultimately affects the quality of the finished highland barley wine.

[0003] In the traditional highland barley wine brewing process, each production link is based on the production workers' manual judgment of the key factors affecting the quality of the wine. However, this production method relies on the subjective judgment of the production workers, which requires extremely high experience and quality of the production workers and relies too much on human resources. At the same time, relying on manual experience to make judgments, it is impossible to make scientific experience summaries, which affects the stability of the production quality of highland barley wine brewing and easily leads to poor quality of the produced highland barley wine.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide a quality management method and related equipment for highland barley wine, which at least overcomes the problems existing in the prior art to a certain extent, and predicts the production quality and storage influencing factors of highland barley wine by analyzing the production information, user order information and quality inspection information of highland barley wine, combined with a deep learning model. Finally, according to the defective rate and storage environment influencing factors, the product classification information of highland barley wine is determined to achieve comprehensive quality management of highland barley wine.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a quality management method for highland barley wine is provided, comprising: obtaining target highland barley wine information, a training sample set and target user information, wherein the training sample set comprises highland barley wine information of other production batches that matches the target highland barley wine information; processing the target highland barley wine information to generate quality inspection information, target feature information and attribute information of the target highland barley wine, wherein the quality inspection information comprises the work authority information and production operation information of the operator, the target feature information is used to characterize the production batch information of the target highland barley wine, and the attribute information of the target highland barley wine is used to characterize the storage method of the target highland barley wine; processing the target user information to generate order usage information of the target user, wherein the order usage information of the target user is used to characterize the target user The method comprises the steps of: obtaining a preset highland barley wine production quality warning model that matches the target feature data, wherein the preset highland barley wine production quality warning model is set based on the target highland barley wine information received within a preset time period; training the preset highland barley wine production quality warning model based on the training sample set to generate a target highland barley wine production quality warning model; processing the quality inspection information based on the target highland barley wine production quality warning model to generate defective rate information of the target highland barley wine; processing the attribute information of the target highland barley wine and the order purpose information of the target user to generate a highland barley wine storage influencing factor; processing the defective rate information of the target highland barley wine and the highland barley wine storage influencing factor to generate product grading information of the target highland barley wine.

[0008] In one embodiment of the present application, the target barley wine information is processed to generate quality inspection information of the target barley wine, target characteristic information and attribute information of the target barley wine, including: processing the target barley wine information to generate production information of the target barley wine, wherein the production information of the target barley wine includes production process parameter information and product parameter information of the target barley wine; processing the production process parameter information and the product parameter information of the target barley wine to generate work authority information of the operator, production operation information and production batch information of the target barley wine; processing the production batch information of the target barley wine to generate preset storage information of the target barley wine; acquiring real-time storage information of the target barley wine; processing the real-time storage information of the target barley wine based on the preset storage information of the target barley wine to generate attribute information of the target barley wine.

[0009] In one embodiment of the present application, the target user information is processed to generate order purpose information of the target user, including: processing the target user information to generate order information of the target user; processing the order information of the target user to generate production information of the target barley wine, wherein the production information of the target barley wine includes the number of target barley wines to be produced, the target production time of the target barley wine, and the customized production information of the target barley wine; processing the number of target barley wines to be produced, the target production time of the target barley wine, and the customized production information of the target barley wine to generate the order purpose information of the target user for characterizing the purpose information of the target user's order to purchase barley wine this time.

[0010] In one embodiment of the present application, the preset highland barley wine production quality early warning model is trained based on the training sample set to generate a target highland barley wine production quality early warning model, including: preprocessing the training sample set to generate a preset training set with identification information, wherein the identification information is used to characterize that there is an abnormality in the production process or storage environment of highland barley wine; obtaining highland barley wine production influencing factors and highland barley wine storage influencing factors based on highland barley wine information of different production batches; and analyzing the production influencing factors and storage influencing factors of highland barley wine based on a deep learning model. The storage influencing factors of highland barley wine are processed to generate highland barley wine abnormal association values; the highland barley wine abnormal association values ​​are processed based on risk classification rules to generate risk abnormality indicators; the preset training set with identification information is classified based on the risk abnormality indicators to generate a training set and a test set; the preset highland barley wine production quality early warning model is processed based on the training set and the test set to generate a target highland barley wine production quality early warning model; the method also includes a calculation formula for calculating the highland barley wine abnormal association value, and the calculation formula is: Among them, X i and Y i are the observed values ​​of the two variables, and is their average value.

[0011] In one embodiment of the present application, the quality inspection information is processed based on the target barley wine production quality early warning model to generate the defective rate information of the target barley wine, including: processing the quality inspection information based on the target barley wine production quality early warning model to generate the historical production process information of the target barley wine, wherein the historical production process information of the target barley wine includes the process production information of the target barley wine and the production quality information corresponding to each process production information; processing the process production information of the target barley wine and the production quality information corresponding to each process production information to generate the defective rate information of the target barley wine, wherein the defective rate information of the target barley wine is used to characterize that the production process information of the target barley wine is abnormal.

[0012] In one embodiment of the present application, the attribute information of the target barley wine and the order usage information of the target user are processed to generate a barley wine storage influencing factor, including: processing the attribute information of the target barley wine to generate a storage environment influencing factor of the target barley wine, wherein the storage environment influencing factor of the target barley wine is used to characterize the preset quality information of the target barley wine; processing the storage environment influencing factor of the target barley wine based on the order user information of the target user to generate a barley wine storage influencing factor, wherein the barley wine storage influencing factor is used to characterize the influencing factors affecting the expected quality information of the target barley wine.

[0013] In one embodiment of the present application, the defective rate information of the target barley wine and the barley wine storage influencing factor are processed to generate product grading information of the target barley wine, including: processing the defective rate information of the target barley wine to generate preset product grade information of the target barley wine, wherein the preset product grade information of the target barley wine is used to characterize the preset product grade of the current production batch of barley wine; processing the barley wine storage influencing factor to generate real-time quality information of the target barley wine; processing the preset product grade information of the target barley wine based on the real-time quality information of the target barley wine to generate product grading information of the target barley wine.

[0014] Another aspect of the present application is a highland barley wine quality management device, characterized in that it includes: an acquisition module for acquiring target highland barley wine information, a training sample set and target user information, wherein the training sample set includes highland barley wine information of other production batches that matches the target highland barley wine information; acquiring a preset highland barley wine production quality early warning model that matches the target feature data, wherein the preset highland barley wine production quality early warning model is set based on the target highland barley wine information received within a preset time period; a processing module for processing the target highland barley wine information to generate quality inspection information, target feature information and attribute information of the target highland barley wine, wherein the quality inspection information includes the operator's work authority information and production operation information, and the target feature information is used to characterize the production batch of the target highland barley wine. The target barley wine is processed to generate the target user's order purpose information, wherein the target user's order purpose information is used to characterize the purpose information of the target user's order for purchasing barley wine; the preset barley wine production quality early warning model is trained based on the training sample set to generate the target barley wine production quality early warning model; the quality inspection information is processed based on the target barley wine production quality early warning model to generate the defective rate information of the target barley wine; the attribute information of the target barley wine and the target user's order purpose information are processed to generate the barley wine storage influencing factor; the defective rate information of the target barley wine and the barley wine storage influencing factor are processed to generate the product grading information of the target barley wine.

[0015] According to another aspect of the present application, an electronic device is provided, characterized in that it comprises: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to implement the above-mentioned highland barley wine quality management method by executing the executable instructions.

[0016] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a second processor, the above-mentioned highland barley wine quality management method is implemented.

[0017] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that the computer program implements the above-mentioned quality management method for highland barley wine when executed by a third processor.

[0018] The present application provides a quality management method and related equipment for highland barley wine. The server analyzes the production information, user order information and quality inspection information of highland barley wine, and combines the deep learning model to predict the production quality and storage influencing factors of highland barley wine. Through the preset model and risk classification rules, the training set and test set are generated to build a highland barley wine production quality early warning model. Finally, according to the defective rate and storage environment influencing factors, the product classification information of highland barley wine is determined to achieve comprehensive quality management.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flow chart showing a method for quality management of highland barley wine provided in one embodiment of the present application is shown;

[0021] Figure 2 A schematic structural diagram of a highland barley wine quality management device provided in one embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0023] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0025] Combine the following Figure 1 To describe the quality management method of highland barley wine according to the exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0026] In one embodiment, the present application also proposes a quality management method and related equipment for highland barley wine. Figure 1 The following schematically shows a flow chart of a method for quality management of highland barley wine according to an embodiment of the present application. Figure 1 As shown, the method is applied to a server, comprising:

[0027] S101, obtaining target highland barley wine information, training sample set and target user information.

[0028] In one embodiment, assuming that a certain batch of target highland barley wine is to be analyzed and evaluated, the target highland barley wine information may include the production time, production location, source of raw materials, brewing process parameters and other specific contents of this batch of highland barley wine. The training sample set may be the highland barley wine information of other production batches that match the target batch. For example, if the target highland barley wine is produced in September 2024, then the training sample set may include relevant information of highland barley wine produced in other similar time periods such as August and July 2024, which may cover the alcohol content, taste characteristics, chemical composition analysis results, etc. of each batch.

[0029] The target user information may include the age, gender, occupation, purchase channel, consumption habits, etc. of the users who purchased this batch of highland barley wine. By obtaining this information, we can better understand user needs and market feedback, and provide reference for the production and sales of highland barley wine.

[0030] S102, processing the target highland barley wine information to generate quality inspection information, target feature information and attribute information of the target highland barley wine.

[0031] In one embodiment, the target highland barley wine information is processed to generate the production information of the target highland barley wine, wherein the production information of the target highland barley wine includes the production process parameter information and product parameter information of the target highland barley wine. First, various data about this batch of highland barley wine in the production process are collected. For example, temperature, pressure and other data are obtained from the sensors on the production line, and the time nodes and operation details of each process are extracted from the operation records. For the production process parameter information, the following contents may be included: Fermentation process: The fermentation time is 30 days, the fermentation temperature is controlled between 25°C and 30°C, and a specific yeast strain is used for fermentation. Distillation process: The distillation pressure is a specific value, and the number of distillations is two to ensure the purity and taste of the wine. Aging process: The aging time is 12 months, and the aging environment requires stable temperature and moderate humidity. Product parameter information may cover: Alcohol content: For example, different specifications such as 42 degrees or 53 degrees. Capacity: It can be 500 ml, 1000 ml, etc. Color: It presents a clear and transparent amber color. Aroma: It has a strong aroma of highland barley and unique complex aromas such as fruity and floral aromas. By integrating and analyzing these data, the production information of the target highland barley wine can be generated, providing an important basis for subsequent quality control and market promotion.

[0032] The production process parameter information and product parameter information of the target highland barley wine are processed to generate the operator's work authority information, production operation information and production batch information of the target highland barley wine. Assuming that the applicant has a batch of production process parameter information and product parameter information of the target highland barley wine, the production process parameter information may include: fermentation time is 20 days, fermentation temperature is about 28 ° C, distillation temperature is 90 ° C, etc. The product parameter information may have an alcohol content of 46 degrees and a capacity of 750 ml. First, by analyzing the production process parameter information, the skill requirements of operators for different processes can be determined. For example, the fermentation process requires operators to have the ability to monitor temperature and time, which may correspond to specific work permissions, such as technical workers in the fermentation workshop have the right to adjust the parameters of the fermentation equipment. If the fermentation time deviates from the standard 20 days, by querying the operation record, it can be determined which operator is responsible for this link, thereby generating production operation information. At the same time, according to the time sequence of production and specific coding rules, the production batch information of the target highland barley wine can be determined. For example, according to the month and serial number, the batch information of the 100th batch of highland barley wine produced in September can be expressed as "202409-100". If the production process parameters and product parameters of this batch of wine are different from those of previous batches, the work permissions of operators can be adjusted by analyzing this information to ensure the stability of the production process and the controllability of quality. In short, by processing the production process parameter information and product parameter information, the work permission information of operators, production operation information and production batch information of target highland barley wine can be effectively generated, providing strong support for the production management of highland barley wine.

[0033] The production batch information of the target barley wine is processed to generate the preset storage information of the target barley wine, the real-time storage information of the target barley wine is obtained, and the real-time storage information of the target barley wine is processed based on the preset storage information of the target barley wine to generate the attribute information of the target barley wine. Assume that the production batch of the target barley wine is "20240924-01". By processing this production batch information, the preset storage information can be generated in combination with the previous production experience and the characteristics of the barley wine. For example, according to the storage requirements of the barley wine produced in the batch time in the past, the preset storage information is determined as follows: it needs to be stored in an environment with a temperature of 15°C to 20°C and a humidity of 50% to 60%, avoid direct sunlight, and keep good ventilation. This is because within this temperature and humidity range, barley wine can be better aged to maintain its taste and quality.

[0034] The real-time storage information of the target highland barley wine can be obtained by installing temperature sensors, humidity sensors, surveillance cameras and other equipment in the storage warehouse. For example, at a certain moment, the real-time storage information detected by the sensor is: the temperature is 18°C, the humidity is 55%, the storage environment is not exposed to direct sunlight, and the ventilation is good. By processing the real-time storage information based on the preset storage information, the attribute information of the target highland barley wine can be generated. In this example, since the real-time storage information is basically consistent with the preset storage information, it can be concluded that the attribute information of the target highland barley wine is: the storage environment is good and the quality of the wine is stable. If at a certain moment, the temperature sensor detects that the storage temperature has risen to 25°C, which exceeds the preset storage temperature range, then after processing, the attribute information may become: the storage temperature is too high, which may have a certain impact on the quality of the wine, and the storage environment needs to be adjusted in time. In this way, by processing the production batch information and monitoring and analyzing the real-time storage information, the storage status and quality attributes of the target highland barley wine can be understood in time, providing a scientific basis for the storage management of highland barley wine.

[0035] Specifically, suppose the applicant has a batch of target barley wine. First, the quality inspection information contains the operator's work authority information, such as Zhang San is authorized to perform filling operations, Li Si is authorized to perform quality inspections, etc.; the production operation information may be that the fermentation process is carried out in a certain time period, and the temperature is controlled within a specific range, etc. If the target characteristic information is "batch 202409", it is used to characterize the production batch information of the target barley wine. If the attribute information of the target barley wine is "stored at room temperature", it characterizes the storage method of the target barley wine.

[0036] When processing the target highland barley wine information, the production information of the target highland barley wine may be generated by integrating various data sources. For example, the production process parameter information may be fermentation time of 15 days, distillation temperature of 80 degrees Celsius, etc.; the product parameter information may be alcohol content of 52 degrees, capacity of 500 ml, etc. Then, the production process parameter information and product parameter information are further processed. By analyzing this information, the work authority information of the operator can be determined, such as judging which position is operated by the specific process parameters; at the same time, the production operation information, that is, the specific production steps and methods, can also be obtained; the production batch information of the target highland barley wine can also be generated, such as determining which batch it belongs to based on factors such as time and production line. When processing the production batch information of the target highland barley wine, the preset storage information of the target highland barley wine is generated based on previous experience and regulations. For example, "the 202409 batch of highland barley wine should be stored in a dry, ventilated and room temperature environment."

[0037] The real-time storage information of the target barley wine is obtained through sensors and other devices, such as the temperature and humidity of the current storage environment. Finally, the real-time storage information is processed based on the preset storage information of the target barley wine. If the preset storage information is room temperature storage, and the real-time storage temperature is too high, it can be judged that the attribute information of the target barley wine does not meet the requirements and needs to be adjusted.

[0038] S103: Process the target user information to generate order usage information of the target user.

[0039] In one embodiment, the target user information is processed to generate the target user's order information, and the target user's order information is processed to generate the target barley wine's pending production information, wherein the target barley wine's pending production information includes the target barley wine's pending production number, the target production time of the target barley wine, and the customized production information of the target barley wine. The target user's pending production number, the target production time of the target barley wine, and the customized production information of the target barley wine are processed to generate the target user's order purpose information for characterizing the target user's purpose information for placing an order to purchase barley wine. The target user's order purpose information is used to characterize the target user's purpose information for placing an order to purchase barley wine.

[0040] For example, suppose there is a target user named Wang Wu who places an order to buy highland barley wine on an e-commerce platform. When processing the target user information, Wang Wu's personal information, such as name, contact information, delivery address, etc., will be collected, and his purchase behavior and preferences will also be recorded. By integrating this information, the target user's order information can be generated. For example, Wang Wu's order information may include: purchasing 10 bottles of highland barley wine, selecting a specific packaging style, and requesting that it be shipped as soon as possible.

[0041] By processing Wang Wu's order information, the production information of the target barley wine can be generated. 1. The number of target barley wines to be produced: According to Wang Wu's order, the number of barley wines to be produced is determined to be 10 bottles. 2. The target production time of the target barley wine: If Wang Wu requires delivery as soon as possible, combined with the production process and current production progress, the target production time may be determined to complete production within three days after the order is placed. 3. Customized production information of the target barley wine: If Wang Wu notes in the order that a specific blessing needs to be printed on the packaging of the wine, this is customized production information.

[0042] By processing the target barley wine's number of units to be produced, target production time, and customized production information, we can infer the target user's order purpose information. For example, since Wang Wu purchased a large number of barley wines with customized packaging requirements, and the target production time was tight, we can infer that the purpose of his order to purchase barley wine this time is to give it as a gift to important customers or to use it on special occasions. If Wang Wu purchased a small number of units and had no special requirements, it may be used for his daily drinking. Through such a processing flow, we can better understand the needs of target users and provide more accurate services for production and sales.

[0043] S104, obtaining a preset highland barley wine production quality early warning model that matches the target characteristic data.

[0044] In one implementation, it is assumed that there is now a highland barley wine production enterprise. During the production process, the enterprise will collect various target feature data, such as the fermentation time, distillation temperature, alcohol content and other parameters of the current production batch of highland barley wine. When it is necessary to predict and warn the production quality of this batch of highland barley wine, the system will obtain a preset highland barley wine production quality warning model that matches these target feature data. For example, if the target feature data of the current batch of highland barley wine shows that the fermentation time is long and the distillation temperature is high, the system will automatically match a prediction and warning model suitable for this combination of production parameters. The preset highland barley wine production quality warning model is set based on the target highland barley wine information received within a preset time period. For example, in the past month, the company has collected a large amount of production information of different batches of highland barley wine, including parameters of each production process, quality test results of the final product, etc. By analyzing and modeling these data, a model that can predict the quality of highland barley wine based on production parameters can be established. If it is found within this month that when the fermentation time is within a specific range and the distillation temperature is within a certain interval, the taste and quality of the produced highland barley wine are good, and quality problems may occur outside this range, then the prediction and early warning model established based on these data can predict whether the quality of this batch of highland barley wine is likely to meet the standards in subsequent production when similar production parameters are received, and issue early warnings in time when quality problems may occur, so that the company can take corresponding adjustment measures.

[0045] S105, training the preset highland barley wine production quality early warning model based on the training sample set to generate a target highland barley wine production quality early warning model.

[0046] In one embodiment, the training sample set is preprocessed to generate a preset training set with identification information, wherein the identification information is used to characterize the presence of abnormalities in the production process or storage environment of highland barley wine. Assume that a highland barley wine production enterprise has collected a large number of production and storage data of different batches of highland barley wine as a training sample set. During the preprocessing process, these data are carefully checked and analyzed. For example, it is found that the fermentation temperature of some batches of highland barley wine is too high during the production process, exceeding the normal range, or the humidity in the storage environment is too high, which may affect the quality of the wine. At this time, identification information is added to these data to indicate that there is an abnormality in its production process or storage environment. After processing, a preset training set with identification information is generated, such as a batch of highland barley wine is identified as "abnormal production process: fermentation temperature is too high".

[0047] The production influencing factors and storage influencing factors of highland barley wine are obtained based on the information of highland barley wine of different production batches. Assume that the company has multiple different production batches of highland barley wine. For production batch A: It is observed that a new fermentation process is used in the production process of this batch, and the fermentation time is extended by two days compared with previous batches. Then "new fermentation process and extended fermentation time" can be used as a production influencing factor. At the same time, the highland barley raw materials used in this batch come from different origins. Highland barley from different origins may have differences in composition, which affects the quality of the wine, so "highland barley raw material origin" also becomes a production influencing factor. For production batch B: In the distillation process of this batch, the pressure parameters of the distillation equipment are adjusted. Then "distillation equipment pressure adjustment" is the production influencing factor. In addition, during the production process of this batch, the operator performed some special operation steps, such as increasing the frequency of stirring. "Special operation steps (increasing the stirring frequency)" is also recorded as a production influencing factor.

[0048] Let's look at the storage conditions of different batches of highland barley wine. For storage batch C: This batch of highland barley wine is stored in a relatively humid warehouse. Higher humidity may affect the quality of the wine, so "high humidity in the storage environment" is a storage influencing factor. The storage temperature is also relatively high, exceeding the temperature range suitable for storage. "High storage temperature" becomes another storage influencing factor. For storage batch D: This batch of highland barley wine is stored near the window and is easily exposed to direct sunlight. "The storage location is susceptible to direct sunlight" is a storage influencing factor. At the same time, the ventilation conditions of the warehouse are not very good. "Poor ventilation conditions" are also recorded as a storage influencing factor. By analyzing the information of highland barley wine from different production batches, various production influencing factors and storage influencing factors can be obtained. These factors will provide important basis for the subsequent evaluation of the quality and risks of highland barley wine.

[0049] Based on the deep learning model, the production influencing factors of highland barley wine and the storage influencing factors of highland barley wine are processed to generate abnormal correlation values ​​of highland barley wine. Assume that the applicant has multiple different batches of highland barley wine, and some production influencing factors and storage influencing factors have been identified. Production influencing factors may include: the use of new fermentation strains, the fermentation time is extended by one day, the distillation temperature is 5 degrees Celsius higher than the standard, etc. Storage influencing factors may include: storage environment humidity is 65% (higher than the suitable humidity range), storage temperature is 30 degrees Celsius (higher than the suitable temperature range), storage location is close to vibrating equipment, etc.

[0050] The applicant inputs these influencing factors into the deep learning model. This model can be a neural network trained with a large amount of data, which can learn the complex relationship between different influencing factors and abnormal quality of highland barley wine. The model first extracts features from the input influencing factors. For example, for the factor of a new fermentation strain, the model may extract some features related to the strain, such as the metabolic characteristics of the strain, the effect on the fermentation product, etc. For numerical influencing factors such as temperature and humidity, numerical normalization processing may be performed. Then, the model transmits and calculates information through multiple layers of neurons. Each layer of neurons performs weighted summation and nonlinear transformation on the input information, and gradually extracts more advanced feature representations. After a series of calculations, the model finally outputs a numerical value, which is the abnormal correlation value of highland barley wine. The calculation method of the abnormal correlation value will be explained in the subsequent content, and will not be repeated here.

[0051] If the abnormal correlation value of a batch of highland barley wine is high, it means that this batch of highland barley wine is greatly affected by the production and storage influencing factors, and the possibility of quality abnormalities is high. For example, there is a batch of highland barley wine, in which a new fermentation strain is used in the production process and the fermentation time is prolonged, and the storage environment has high humidity and high temperature. After model processing, a higher abnormal correlation value is obtained, such as 0.8 (assuming that the value range is between 0 and 1, the larger the value, the higher the possibility of abnormality). This prompts the manufacturer to conduct more stringent quality inspection and monitoring of this batch of highland barley wine to prevent quality problems. On the contrary, if the abnormal correlation value of a batch of highland barley wine is low, it means that this batch of highland barley wine is less affected by the influencing factors, and the possibility of quality abnormalities is low. For example, the influencing factors of another batch of highland barley wine are all within the normal range, and the abnormal correlation value output by the model is 0.2. The company can proceed with the subsequent sales and circulation links relatively assured.

[0052] Based on the risk classification rules, the abnormal correlation values ​​of highland barley wine are processed to generate risk abnormality indicators. Based on the risk abnormality indicators, the preset training set with identification information is classified and processed to generate training sets and test sets. It is assumed that the applicant has obtained the abnormal correlation values ​​of different batches of highland barley wine through the deep learning model. The risk classification rules can be set as follows: if the abnormal correlation value is between 0 and 0.3, it is a low risk; between 0.3 and 0.6 is a medium risk; and above 0.6 is a high risk. For example, the abnormal correlation value of a batch of highland barley wine is 0.2. According to the risk classification rules, this is a low risk, and the generated risk abnormality indicator can be "low risk: the production and storage processes are relatively stable, and the possibility of quality abnormalities is small." If the abnormal correlation value of another batch of highland barley wine is 0.7, it is a high risk, and the generated risk abnormality indicator may be "high risk: there are major problems in the production or storage links, and quality risks need to be focused on."

[0053] The preset training set with identification information contains a large amount of pre-processed highland barley wine data, and the identification information indicates whether there are abnormalities in its production process or storage environment. These data are classified and processed according to the risk abnormality index. For example, a part of all batch data marked as low risk is randomly selected as part of the training set, and a part of the batch data of high risk and medium risk is also selected in a certain proportion and added to the training set. For the test set, another part of the data can be extracted from the batch data of high risk, medium risk and low risk respectively. This ensures that the test set can fully cover highland barley wine data of different risk levels. For example, there are 1,000 batches of highland barley wine data, of which 600 batches are low risk, 300 batches are medium risk, and 100 batches are high risk. The applicant can randomly select 400 batches from the low-risk batches and put them into the training set, and select 80 batches and 240 batches from the high-risk and medium-risk batches respectively and put them into the training set. For the test set, the remaining 200 batches can be extracted from the low-risk batches, and 20 batches and 60 batches can be extracted from the high-risk and medium-risk batches, respectively. The training set and test set generated in this way can be used to train and evaluate the preset highland barley wine production quality early warning model to improve the accuracy and reliability of the model.

[0054] The preset highland barley wine production quality early warning model is processed based on the training set and the test set to generate the target highland barley wine production quality early warning model. The preset highland barley wine production quality early warning model is trained with the training set, and the parameters of the model are continuously adjusted so that it can better predict the quality risk of highland barley wine based on the production and storage information. Then, the trained model is tested with the test set. If the accuracy of the model meets the requirements, the target highland barley wine production quality early warning model is obtained. This model can quickly predict the quality risk of the new batch of highland barley wine in subsequent production based on the production and storage information, and issue an early warning to help enterprises take timely measures to ensure the quality of highland barley wine.

[0055] The method also includes a calculation formula for calculating the abnormal correlation value of highland barley wine, and the calculation formula is:

[0056]

[0057] Among them, X i and Y i are the observed values ​​of the two variables, and is their average value.

[0058] The following is an example of this calculation formula: Assume that the applicant has a set of production data and storage data of highland barley wine, with the production data as variable X and the storage data as variable Y, with a total of N = 5 data points. For example, the production data X are X1 = 10 (production temperature), X2 = 12, X3 = 8, X4 = 11, and X5 = 9, then the average value of the production data is The stored data Y are Y1=20 (storage humidity), Y2=22, Y3=18, Y4=21, Y5=19, and the average value of the stored data

[0059] First calculate the numerator:

[0060]

[0061] The sum of the numerators is 0+4+4+1+1-10.

[0062] Then calculate the denominator part:

[0063]

[0064] The sum is 10, and the square root is

[0065] Then calculate the denominator part:

[0066]

[0067] The sum is 10, and the square root is

[0068] Finally, calculate the abnormal correlation value In practical applications, this abnormal correlation value can reflect the relationship between the production data and storage data of highland barley wine, thus helping to determine the possibility of quality problems in highland barley wine. For example, if this value is high, it may mean that the mutual influence between production and storage factors is large, and the quality risk is relatively high.

[0069] S106, processing the quality inspection information based on the target highland barley wine production quality early warning model to generate defective rate information of the target highland barley wine.

[0070] In one embodiment, the quality inspection information is processed based on the target highland barley wine production quality early warning model to generate the historical production process information of the target highland barley wine, wherein the historical production process information of the target highland barley wine includes the process production information of the target highland barley wine and the production quality information corresponding to each process production information. The process production information of the target highland barley wine and the production quality information corresponding to each process production information are processed to generate the defective rate information of the target highland barley wine, wherein the defective rate information of the target highland barley wine is used to characterize that the production process information of the target highland barley wine is abnormal.

[0071] Assume that the applicant has a target highland barley wine production quality early warning model and a batch of highland barley wine quality inspection information. First, the quality inspection information is processed based on the model to generate the historical production process information of the target highland barley wine, which includes the specific production information of each process, such as the fermentation process at a certain time, how long the fermentation time is; the distillation process at another time, what is the distillation temperature, etc.; it also includes the production quality information corresponding to the production information of each process, such as the content of a certain component in the fermented wine, the flavor characteristics of the wine after distillation, etc. Then, the process production information of the target highland barley wine and the production quality information corresponding to each process are further processed to generate the defective rate information of the target highland barley wine. For example, by analyzing the historical production process information, it is found that in a certain production batch, the fermentation process time is longer than normal, and the content of a certain impurity in the wine produced by this batch is detected to exceed the standard range, while other processes and quality information are basically normal. Then it can be considered that the production process information of this batch is abnormal, and the defective rate information calculated based on these data may be high. For example, another batch of highland barley wine had a deviation in temperature control during the distillation process, which resulted in the taste and aroma of the wine not meeting the expected quality standards, which would also increase the defective rate of the batch. Based on this defective rate information, the company can trace back to the specific production process to find out the possible causes of quality problems, so as to take corresponding measures to improve and optimize the production process, reduce the defective rate, and improve the overall quality of highland barley wine.

[0072] Taking the production of a batch of highland barley wine as an example, its historical production process information is as follows: Process production information: Raw material processing: A specific variety of highland barley is selected, and impurities are removed and screened to make perfect grains. Soaking grain: Soak in hot water at a temperature of 70°C for 19 hours. Steaming grain: Steaming at a pressure of 0.1MPa for 3 minutes during the initial steaming; adding warm water at 55°C to stew the grain for 10 minutes; steaming at a steam pressure of 0.05MPa for 18 minutes during the re-steaming. Cooling: Take out the re-steamed highland barley and cool it to 28°C. Adding koji: The amount of koji added is 10.5% of the total weight of the highland barley after cooling. Fermentation: Fermentation is carried out at 23°C in the fermentation room for 28 days. Distillation: Use specific distillation equipment and processes for distillation. Production quality information corresponding to each process: The water content of highland barley after soaking reaches the appropriate range. After steaming, the highland barley is cooked and not rotten, and the indicators of various components are normal. During the fermentation process, various monitoring indicators are stable, and the acidity and sugar content of the fermented grains are in line with expectations. The alcohol content and flavor substance content of the distilled liquor meet the corresponding standards. After processing and analyzing these process production information and production quality information, it is calculated that the defective rate of this batch of highland barley wine is low, indicating that its production process is normal, there are no obvious abnormalities, and the product quality is good. However, if in another batch, for example, the fermentation process takes too long, causing the acidity of the fermented grains to exceed the normal range, or the temperature of the distillation process is too high, causing a large loss of certain flavor substances, thereby affecting the taste and quality of the wine. Then, when processing these abnormal process production information and corresponding production quality information, the calculated defective rate will be high, indicating that the production process information of this batch is abnormal, and further reasons need to be found and improved. For example, it may be necessary to adjust the fermentation conditions and control the fermentation time; or optimize the distillation process to ensure temperature stability. In this way, by continuously monitoring and analyzing the production process and quality information, and using the defective rate information to feedback problems in the production process, it is helpful to continuously improve the production quality of highland barley wine.

[0073] S107, processing the attribute information of the target highland barley wine and the order usage information of the target user to generate a highland barley wine storage influencing factor.

[0074] In one embodiment, the attribute information of the target highland barley wine is processed to generate the storage environment influencing factor of the target highland barley wine, wherein the storage environment influencing factor of the target highland barley wine is used to characterize the preset quality information of the target highland barley wine. Assume that the applicant has a batch of target highland barley wine and processes its attribute information. If the attribute information of the target highland barley wine indicates that its storage method is "normal temperature, dry, and light-proof storage". Then by analyzing these attribute information, some storage environment influencing factors can be determined. For example, temperature stability is a storage environment influencing factor. If the storage environment temperature fluctuates greatly, it may affect the quality of highland barley wine. Humidity is also an influencing factor. Excessive humidity may cause damage to the packaging of the wine or change in the quality of the wine. Light intensity is also an influencing factor, because strong light exposure may cause chemical reactions in certain components of the wine, thereby affecting the quality of the wine. Therefore, these storage environment influencing factors are used to characterize the preset quality information of the target highland barley wine, that is, if these influencing factors can be controlled well, it can ensure that the highland barley wine maintains good quality during storage. For example, if the temperature of this batch of target barley wine is maintained between 20℃ and 25℃, the humidity is controlled at 50% to 60%, and direct sunlight is avoided during storage, then it can be considered that these storage environment influencing factors are in a relatively ideal state, which is conducive to maintaining the preset quality of the barley wine.

[0075] Based on the target user's order user information, the storage environment influencing factors of the target highland barley wine are processed to generate highland barley wine storage influencing factors, among which the highland barley wine storage influencing factors are used to characterize the influencing factors that affect the expected quality information of the target highland barley wine. Assume that the target user's order user information shows that the user requires to receive highland barley wine as soon as possible and intends to store the wine in his basement. Then, the environmental characteristics of the basement will become the influencing factors of highland barley wine storage. For example, the basement may be poorly ventilated, which will affect the air circulation and thus affect the storage of highland barley wine. If the basement is relatively humid, then humidity will become an important storage influencing factor, which may pose a greater threat to the quality of highland barley wine. In addition, if the temperature of the basement is unstable, it will also affect the quality of highland barley wine. Therefore, these highland barley wine storage influencing factors are used to characterize the influencing factors that affect the expected quality information of the target highland barley wine. According to these influencing factors, enterprises can provide users with corresponding storage suggestions, or take some measures during packaging and transportation to minimize the impact of the storage environment on the quality of highland barley wine. For example, desiccant can be added to the packaging to cope with humid environments, or users can be reminded to pay attention to ventilation and temperature control.

[0076] S108, processing the defective rate information of the target highland barley wine and the highland barley wine storage influencing factor to generate product grading information of the target highland barley wine.

[0077] In one embodiment, the defective rate information of the target highland barley wine is processed to generate the preset product grade information of the target highland barley wine, wherein the preset product grade information of the target highland barley wine is used to characterize the preset product grade of the highland barley wine of the current production batch. Assuming that the applicant has a batch of target highland barley wine, the preset product grade information is generated by processing its defective rate information. If the defective rate of this batch of highland barley wine is low, such as only 2%. This shows that the quality of this batch of highland barley wine is relatively stable during the production process, and there are relatively few products with problems. According to the preset standard, its preset product grade information can be set to the "high quality" grade. This means that under normal circumstances, this batch of highland barley wine is expected to be a high-quality product when production is completed. For example, a batch of highland barley wine is tested after production, and all indicators are good and the defective rate is very low. According to the product grade classification standard of the enterprise, the batch is determined to be a "high quality" preset product grade, which means that if there are no problems in the subsequent storage and other links, this batch of wine can be marketed as a high-end product.

[0078] Process the factors affecting the storage of highland barley wine to generate the real-time quality information of the target highland barley wine. Processing the factors affecting the storage of highland barley wine can generate the real-time quality information of the target highland barley wine. Assume that during the storage of a batch of highland barley wine, it is found that the temperature and humidity of the storage environment are high, which are storage influencing factors. By analyzing the impact of these influencing factors on the quality of highland barley wine, the real-time quality information of the batch of highland barley wine can be obtained. For example, due to the high temperature and high humidity storage environment, the taste of the wine may become sour and astringent, the aroma may be weakened, and the alcohol content may also change. For example, a batch of highland barley wine was originally stored in a more suitable environment, but due to unexpected circumstances, the temperature of the storage environment rose to more than 30°C and the humidity reached 70%. After testing and analysis, it was found that the taste and aroma of the wine had declined to a certain extent. At this time, it can be determined that the real-time quality information of the batch of highland barley wine is "the quality is affected to a certain extent".

[0079] Based on the real-time quality information of the target barley wine, the preset product grade information of the target barley wine is processed to generate the product grading information of the target barley wine. Based on the real-time quality information of the target barley wine, the preset product grade information is processed to generate the product grading information of the target barley wine. If the storage methods of the same batch of barley wine are different, the final product grading will also be different. For example, one part of the same batch of barley wine is stored in an environment with suitable temperature, humidity and good ventilation, and the other part is stored in an environment with high temperature and high humidity. The barley wine stored in a good environment has its real-time quality information basically consistent with the preset product grade information, and may still remain at the "high quality" level; while the barley wine stored in a bad environment, due to the influence of storage influencing factors, the quality is reduced, and the product grade may be reduced to the "ordinary" level. Assume that a batch of high-quality barley wine with a preset product grade of "high quality" is stored in two parts. One part is stored in a professional wine cellar in strict accordance with the storage requirements. After re-testing, the quality is still good, and the product grade remains "high quality". The other part was stored in the basement, and the temperature and humidity were not well controlled, which led to a decline in quality. After evaluation, the product grade was downgraded to "medium". This shows that the same batch of highland barley wine will produce different product grading results due to different storage methods.

[0080] The present invention obtains target highland barley wine information, training sample set and target user information by a server, and the training sample set contains other production batch highland barley wine information matching the target highland barley wine information. The target highland barley wine information is processed to generate production information, including production process parameters and product parameter information; then the operator's work authority, production operation and production batch information are generated from this; preset storage information is generated through the production batch information, and the attribute information of the target highland barley wine, that is, the storage mode, is obtained by combining the real-time storage information. The target user information is processed to generate order information, and then the information to be produced is obtained, and finally the order use information of the target user is determined.

[0081] Next, a preset highland barley wine production quality warning model matching the target feature data is obtained, which is set based on the target highland barley wine information within a preset time period. By preprocessing the training sample set, the production and storage influencing factors are obtained, and the highland barley wine abnormal correlation value is generated using the deep learning model. The risk abnormality index is obtained by processing the risk classification rules, and then the training set and test set are generated by classification. The preset model is processed to obtain the target highland barley wine production quality warning model.

[0082] The model is used to process quality inspection information to obtain historical production process information, and then generate defective rate information. The target highland barley wine attribute information is processed to obtain the storage environment influencing factor, and the highland barley wine storage influencing factor is generated in combination with the target user order information. Finally, the defective rate information is processed to obtain the preset product grade information, and the product grading information of the target highland barley wine is determined in combination with the real-time quality information generated by the storage influencing factor. If the same batch of highland barley wine is stored in different ways, the product grading will also be different. This method realizes comprehensive quality management of highland barley wine through multi-faceted information processing.

[0083] In one embodiment, if Figure 2 As shown, the present application also provides a quality management device for highland barley wine, comprising:

[0084] The acquisition module 201 is used to acquire target highland barley wine information, a training sample set and target user information, wherein the training sample set includes highland barley wine information of other production batches that matches the target highland barley wine information; acquire a preset highland barley wine production quality early warning model that matches the target feature data, wherein the preset highland barley wine production quality early warning model is set based on the target highland barley wine information received within a preset time period;

[0085] The processing module 202 is used to process the target highland barley wine information to generate quality inspection information, target feature information and attribute information of the target highland barley wine, wherein the quality inspection information includes the work authority information and production operation information of the operator, the target feature information is used to characterize the production batch information of the target highland barley wine, and the attribute information of the target highland barley wine is used to characterize the storage method of the target highland barley wine; the target user information is processed to generate the order usage information of the target user, wherein the order usage information of the target user is used to characterize the target user's current Purpose information of placing an order to purchase highland barley wine; training the preset highland barley wine production quality early warning model based on the training sample set to generate a target highland barley wine production quality early warning model; processing the quality inspection information based on the target highland barley wine production quality early warning model to generate defective rate information of the target highland barley wine; processing the attribute information of the target highland barley wine and the order purpose information of the target user to generate a highland barley wine storage influencing factor; processing the defective rate information of the target highland barley wine and the highland barley wine storage influencing factor to generate product grading information of the target highland barley wine.

[0086] The present application embodiment provides an electronic device, such as Figure 3As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303. The first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and the first processor 300 executes the quality management method of highland barley wine provided in any of the aforementioned embodiments of the present application when running the computer program.

[0087] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0088] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the first processor 300 executes the program after receiving an execution instruction. The quality management method for highland barley wine disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0089] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0090] The electronic device provided in the above-mentioned embodiment of the present application and the quality management method of highland barley wine provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0091] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium 401 stores a computer program, and when the computer program is read and executed by the second processor 402, the quality management method of highland barley wine as described above is implemented.

[0092] The computer-readable storage medium provided in the above-mentioned embodiment of the present application and the quality management method of highland barley wine provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0093] Each embodiment in the present application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the quality management method, electronic device, electronic device, and readable storage medium embodiment for evaluating highland barley wine, since it is basically similar to the above-mentioned embodiment of the quality management method for highland barley wine, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the quality management method for highland barley wine.

[0094] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A quality management method for highland barley wine, characterized in that: include: Obtaining target highland barley wine information, a training sample set, and target user information, wherein the training sample set includes highland barley wine information of other production batches that matches the target highland barley wine information; Processing the target highland barley wine information to generate quality inspection information, target feature information and attribute information of the target highland barley wine, wherein the quality inspection information includes work authority information and production operation information of the operator, the target feature information is used to characterize the production batch information of the target highland barley wine, and the attribute information of the target highland barley wine is used to characterize the storage mode of the target highland barley wine; Processing the target user information to generate order purpose information of the target user, wherein the order purpose information of the target user is used to represent the purpose information of the target user placing an order to purchase highland barley wine; Acquire a preset highland barley wine production quality early warning model that matches the target characteristic data, wherein the preset highland barley wine production quality early warning model is set based on the target highland barley wine information received within a preset time period; Training the preset highland barley wine production quality early warning model based on the training sample set to generate a target highland barley wine production quality early warning model; Processing the quality inspection information based on the target highland barley wine production quality early warning model to generate defective rate information of the target highland barley wine; Processing the attribute information of the target highland barley wine and the order usage information of the target user to generate a highland barley wine storage influencing factor; The defective rate information of the target highland barley wine and the highland barley wine storage influencing factor are processed to generate product grading information of the target highland barley wine.

2. The method according to claim 1, characterized in that The target highland barley wine information is processed to generate quality inspection information, target feature information and attribute information of the target highland barley wine, including: Processing the target highland barley wine information to generate production information of the target highland barley wine, wherein the production information of the target highland barley wine includes production process parameter information and product parameter information of the target highland barley wine; Processing the production process parameter information and the product parameter information of the target highland barley wine to generate the operator's work authority information, production operation information and production batch information of the target highland barley wine; Processing the production batch information of the target highland barley wine to generate preset storage information of the target highland barley wine; Obtain the real-time storage information of the target highland barley wine; The real-time storage information of the target highland barley wine is processed based on the preset storage information of the target highland barley wine to generate the attribute information of the target highland barley wine.

3. The method according to claim 1, characterized in that The target user information is processed to generate order usage information of the target user, including: Processing the target user information to generate order information of the target user; Processing the order information of the target user to generate the target highland barley wine to be produced information, wherein the target highland barley wine to be produced information includes the number of target highland barley wines to be produced, the target production time of the target highland barley wine and the customized production information of the target highland barley wine; The number of target highland barley wines to be produced, the target production time of the target highland barley wine and the customized production information of the target highland barley wine are processed to generate the order purpose information of the target user for characterizing the purpose information of the target user's order for purchasing highland barley wine.

4. The method according to claim 2, characterized in that The preset highland barley wine production quality early warning model is trained based on the training sample set to generate a target highland barley wine production quality early warning model, including: Preprocessing the training sample set to generate a preset training set with identification information, wherein the identification information is used to characterize that there is an abnormality in the production process or storage environment of highland barley wine; Based on the information of highland barley wine produced in different batches, the production influencing factors and storage influencing factors of highland barley wine are obtained respectively; Processing the production influencing factors of the highland barley wine and the storage influencing factors of the highland barley wine based on the deep learning model to generate an abnormal correlation value of highland barley wine; Processing the abnormal correlation value of highland barley wine based on the risk classification rule to generate a risk abnormality indicator; Classify the preset training set with identification information based on the risk anomaly index to generate a training set and a test set; Processing the preset highland barley wine production quality early warning model based on the training set and the test set to generate a target highland barley wine production quality early warning model; The method also includes a calculation formula for calculating the abnormal correlation value of highland barley wine, and the calculation formula is: Among them, X i and Y i are the observed values ​​of the two variables, and is their average value.

5. The method according to claim 4, characterized in that The quality inspection information is processed based on the target highland barley wine production quality early warning model to generate defective rate information of the target highland barley wine, including: Processing the quality inspection information based on the target highland barley wine production quality early warning model to generate historical production process information of the target highland barley wine, wherein the historical production process information of the target highland barley wine includes process production information of the target highland barley wine and production quality information corresponding to each process production information; The process production information of the target highland barley wine and the production quality information corresponding to each process production information are processed to generate defective rate information of the target highland barley wine, wherein the defective rate information of the target highland barley wine is used to characterize that the production process information of the target highland barley wine is abnormal.

6. The method according to claim 5, characterized in that The attribute information of the target highland barley wine and the order usage information of the target user are processed to generate a highland barley wine storage influencing factor, including: Processing the attribute information of the target highland barley wine to generate a storage environment impact factor of the target highland barley wine, wherein the storage environment impact factor of the target highland barley wine is used to characterize the preset quality information of the target highland barley wine; The storage environment influencing factor of the target highland barley wine is processed based on the order user information of the target user to generate a highland barley wine storage influencing factor, wherein the highland barley wine storage influencing factor is used to characterize the influencing factors that affect the expected quality information of the target highland barley wine.

7. The method according to claim 6, characterized in that The defective rate information of the target highland barley wine and the highland barley wine storage influencing factor are processed to generate product grading information of the target highland barley wine, including: Processing the defective rate information of the target highland barley wine to generate preset product grade information of the target highland barley wine, wherein the preset product grade information of the target highland barley wine is used to characterize the preset product grade of the highland barley wine of the current production batch; Processing the highland barley wine storage influencing factors to generate real-time quality information of the target highland barley wine; The preset product grade information of the target highland barley wine is processed based on the real-time quality information of the target highland barley wine to generate product grading information of the target highland barley wine.

8. A quality management device for highland barley wine, characterized in that: The device comprises: An acquisition module is used to acquire target highland barley wine information, a training sample set and target user information, wherein the training sample set includes highland barley wine information of other production batches that matches the target highland barley wine information; acquire a preset highland barley wine production quality early warning model that matches the target feature data, wherein the preset highland barley wine production quality early warning model is set based on the target highland barley wine information received within a preset time period; a processing module, for processing the target highland barley wine information, generating quality inspection information, target characteristic information and attribute information of the target highland barley wine, wherein the quality inspection information includes the work authority information and production operation information of the operator, the target characteristic information is used to characterize the production batch information of the target highland barley wine, and the attribute information of the target highland barley wine is used to characterize the storage mode of the target highland barley wine; processing the target user information, generating order usage information of the target user, wherein the order usage information of the target user is used to characterize the target user's downloading The method comprises the steps of: processing the target barley wine production quality early warning model and processing the target barley wine production quality early warning model to generate the target barley wine defective rate information; processing the target barley wine property information and the target user's order purpose information to generate the barley wine storage influencing factor; processing the target barley wine defective rate information and the barley wine storage influencing factor to generate the target barley wine product grading information.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the quality management method of highland barley wine described in any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the quality management method for highland barley wine described in any one of claims 1 to 7 is implemented.

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