Informatization management system and method for high-quality wine brewing process

Abstract

The invention relates to the technical field of wine brewing process informatization, in particular to an informatization management system and method for a high-quality wine brewing process, and the system comprises a data collection module which collects multi-source data, and specifically and quantitatively detects the concentration of saccharomyces cerevisiae through a quantitative PCR technology; the data communication module is used for filtering and cleaning the multi-source data, dynamically adjusting the sampling frequency according to the brewing stage and then encrypting and uploading the multi-source data; the cloud service management module is used for storing data and process parameters in a classified manner by adopting a hybrid architecture, analyzing a trend through a time sequence prediction model, executing graded early warning based on an HACCP principle and a parameter deviation degree, and generating an adjustment suggestion in combination with a process knowledge graph; and the user interaction module provides man-machine interaction and receives a user instruction. Through informatization management of the whole wine brewing process, the consistency of product quality, the refinement level of production management and the real-time prevention and control capability of quality safety risks can be improved.

Description

Technical Field

[0001] This invention relates to the field of information technology in winemaking processes, and in particular to an information management system and method for a high-quality winemaking process. Background Technology

[0002] Early winemaking relied primarily on the winemaker's experience and sensory judgment. The entire process, from grape cultivation and raw material selection to fermentation and aging, was mainly based on qualitative assessments, lacking objective data support. Traditional production records were mostly in paper form, resulting in isolated information that was difficult to trace and analyze effectively. This led to significant human factors influencing quality, marked quality fluctuations, difficulties in passing on techniques, and low precision and consistency in product quality control.

[0003] With the development of the Internet of Things (IoT) and sensor technology, the wine industry has begun to introduce automated equipment to monitor single parameters such as temperature and humidity, achieving initial data collection in some production stages. However, these technological applications are fragmented, often limited to simple monitoring of individual stages, failing to form a closed-loop data system covering the entire process from raw materials to fermentation and aging. The systems operate independently, lacking collaborative analysis and intelligent decision-making capabilities. The ability to perceive and control key quality indicators such as microbial activity and changes in flavor compounds during fermentation is insufficient, and process adjustments rely heavily on the experience and judgment of technical personnel.

[0004] Currently, existing wine production management systems still face technical bottlenecks: insufficient data collection dimensions, lack of rapid and specific online detection methods for key microbial indicators such as wine yeast concentration, existing solutions do not have customized models designed for the specificities of winemaking, and general parameter systems are difficult to adapt to the process requirements of different production regions and varieties; shallow data utilization, failing to deeply integrate multi-source data with winemaking process knowledge, and failing to achieve quality trend early warning through predictive models, with quality and safety monitoring mostly remaining at the level of simple data comparison; low level of decision-making intelligence, with most systems only having data display functions, unable to automatically generate accurate process adjustment suggestions based on real-time data and process model comparison results, and insufficient data storage and analysis capabilities, making it difficult to predict the changing trends of key quality indicators through time-series predictive models, resulting in delayed process adjustments.

[0005] Chinese invention patent CN119560049A discloses a method and system for real-time monitoring of wine fermentation tanks based on artificial intelligence. This invention collects spectral data of fermentation broth through near-infrared spectroscopy, combines grey relational analysis to screen key control features, and uses a BP neural network to establish a total sugar content prediction model to achieve real-time monitoring of total sugar content in the fermentation tank. However, this invention does not cover the full-process data collection of planting environment and raw material quality, specific detection of microbial concentration, HACCP-based graded early warning and intelligent process adjustment suggestions, or differentiated process models for different grape varieties.

[0006] Therefore, this invention discloses an information management system and method for a high-quality wine brewing process. Summary of the Invention

[0007] The purpose of this invention is to address the problems of crude quality control, fragmented data, and delayed decision-making in the prior art by proposing an information management system and method for a high-quality wine brewing process.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: an information management system for a high-quality wine brewing process, comprising:

[0009] The data acquisition module is used to collect multi-source data in real time throughout the entire winemaking process, including vineyard environmental parameters, grape raw material quality indicators, and fermentation liquid physicochemical indicators.

[0010] The data communication module is used to process and upload the collected multi-source data;

[0011] The cloud service management module is used to receive and process multi-source data uploaded by the data communication module. The cloud service management module includes:

[0012] Data storage unit, used for classifying and storing the multi-source data and preset process model parameters;

[0013] The big data analytics unit is used for trend prediction of key quality indicators.

[0014] The quality and safety monitoring unit is used to output early warning information based on the degree of data deviation according to HACCP principles.

[0015] Intelligent decision-making unit, used to generate process adjustment suggestions by combining process knowledge graph;

[0016] The user interaction module is used to display multi-source data, prediction curves of key quality indicators, early warning information and process adjustment suggestions, and to receive user commands.

[0017] An information management method for the high-quality winemaking process, comprising:

[0018] S1 collects multi-source data from the entire winemaking process, including vineyard environmental parameters, grape raw material quality indicators, and fermentation liquid physicochemical indicators.

[0019] S2 preprocesses the collected multi-source data, adjusts the sampling frequency, and uploads it through a secure transmission mechanism;

[0020] S3 classifies and stores the uploaded multi-source data, predicts the trend of key quality indicators based on the preset process model, executes hierarchical early warning through data comparison, and generates process adjustment suggestions in combination with process knowledge graph;

[0021] S4 displays multi-source data, key quality indicator prediction curves, early warning information, and process adjustment suggestions through an interactive interface, and receives user commands.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] This invention addresses the problem of poor adaptability of existing general information systems to different varieties and fermentation stages through a specific design for winemaking processes, enabling precise matching and dynamic optimization of process parameters.

[0024] This invention deeply integrates HACCP principles with a multi-level early warning mechanism, breaking through the limitations of existing technologies that only perform simple threshold comparisons and passive prompts. It achieves pre-event early warning, in-event graded prevention and control, and proactive handling of quality risks, thereby reducing the incidence of quality accidents.

[0025] This invention introduces quantitative PCR technology for specific quantitative detection of key microorganisms such as Saccharomyces cerevisiae, and combines process knowledge graphs and multi-objective optimization algorithms to generate process adjustment suggestions. This solves the shortcomings of existing technologies, such as insufficient accuracy in microbial monitoring, delayed process adjustment, and reliance on human experience, and can improve the scientific nature and accuracy of decision-making.

[0026] This invention constructs a multi-source data chain and traceability system covering the entire process of planting, raw materials, fermentation, and aging, achieving closed-loop management from data collection and intelligent analysis to decision feedback. This meets the core requirements of modern wine production for transparent and refined management throughout the entire process. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the system configuration provided in an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of the method flow provided in an embodiment of the present invention. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an information management system and method for a high-quality wine brewing process proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0033] The following description, in conjunction with the accompanying drawings, details the specific solution of the information management system and method for a high-quality wine brewing process provided by this invention.

[0034] Example

[0035] An embodiment of the present invention provides an information management system for a high-quality winemaking process, comprising:

[0036] The data acquisition module is used to collect multi-source data in real time throughout the entire winemaking process, including vineyard environmental parameters, grape raw material quality indicators, and fermentation liquid physicochemical indicators.

[0037] The data communication module is used to process and upload the collected multi-source data;

[0038] The cloud service management module is used to receive and process multi-source data uploaded by the data communication module. The cloud service management module includes:

[0039] Data storage unit, used for classifying and storing multi-source data and preset process model parameters;

[0040] The big data analytics unit is used for trend prediction of key quality indicators.

[0041] The quality and safety monitoring unit is used to output early warning information based on the degree of data deviation according to HACCP principles.

[0042] Intelligent decision-making unit, used to generate process adjustment suggestions by combining process knowledge graph;

[0043] The user interaction module is used to display multi-source data, prediction curves of key quality indicators, early warning information and process adjustment suggestions, and to receive user commands.

[0044] Please refer to Figure 1This is a schematic diagram of the system configuration provided in an embodiment of the present invention.

[0045] I. Data Acquisition Module:

[0046] The data acquisition module includes:

[0047] The planting environment data acquisition unit is equipped with soil sensors and a weather station to collect environmental parameters of the vineyard, including soil temperature and humidity, soil pH value and regional meteorological data.

[0048] The raw material quality acquisition unit integrates a spectral detection device to obtain grape raw material quality indicators, including sugar content, acidity, and pesticide residue indicators.

[0049] The fermentation process acquisition unit, a multi-parameter sensor array deployed inside the fermenter, is used to monitor the physicochemical properties of the fermentation broth in real time, including the temperature, pH value, sugar content, alcohol content, and microbial concentration data of the fermentation broth.

[0050] Fermentation process acquisition unit, wherein:

[0051] The concentration of microorganisms in the fermentation broth was quantitatively analyzed using quantitative PCR technology, including the concentration of Saccharomyces cerevisiae.

[0052] Quantitative PCR technology enables the specific quantitative detection of Saccharomyces cerevisiae concentration by using specific primer pairs for Saccharomyces cerevisiae.

[0053] It should be noted that the planting environment data acquisition unit is responsible for monitoring key environmental factors that affect grape growth. Soil sensors can be installed in an insert or buried manner to monitor the real-time condition of the soil layer where grape roots are located. Small automatic weather stations can monitor regional meteorological data such as temperature, humidity, light intensity, precipitation and wind speed in the vineyard.

[0054] The collected environmental data is transmitted to the data communication module via LoRa. Through long-term monitoring of these environmental parameters, the system can provide data support for precise irrigation, fertilization, and pest and disease control in vineyards, ensuring the quality of raw materials from the source.

[0055] The raw material quality acquisition unit is used to perform rapid, non-destructive testing of key quality indicators of grapes before harvesting and processing. It integrates a near-infrared spectroscopy detection device. A near-infrared spectrometer is installed on the grape conveyor belt or sorting table. When a grape sample passes through the detection window, the spectrometer emits near-infrared light and its diffuse reflectance spectrum is collected. This technology avoids the time-consuming and labor-intensive nature of traditional testing methods, enabling high-throughput screening and grading of raw material quality, providing crucial input for subsequent winemaking process decisions.

[0056] The fermentation process acquisition unit is crucial for monitoring the core biochemical reaction process of brewing. It is deployed inside the fermentation tank or in the connected circulation pipeline and uses a multi-parameter sensor array to monitor the physicochemical indicators of the fermentation liquid in real time.

[0057] The multi-parameter sensor array of the fermentation process acquisition unit can be evenly distributed at different depths and radial positions in the fermenter to ensure real-time monitoring of physicochemical indicators such as temperature, pH value, sugar content, and alcohol content in various areas of the fermentation broth. These parameters are core signals reflecting the fermentation process. For example, temperature fluctuations can affect yeast activity, and changes in sugar content are directly related to alcohol conversion efficiency.

[0058] Quantitative PCR technology uses specific primer pairs targeting specific microorganisms to achieve specific and highly sensitive quantitative detection of Saccharomyces cerevisiae concentration. Its workflow is as follows:

[0059] A micro-sample of fermentation broth is periodically extracted from the fermenter using an automated sampling system; microbial nucleic acids are then extracted from the samples.

[0060] Quantitative real-time PCR amplification was performed using pre-designed specific primers.

[0061] By analyzing the amplification curve, the initial concentration of the target microorganism in the sample can be accurately calculated.

[0062] The fermentation process acquisition unit can acquire comprehensive data in real time, including the temperature, pH value, sugar content, alcohol content, and specific microbial concentration of the fermentation broth, providing an unprecedented data dimension for accurately controlling the fermentation process and timely detecting anomalies.

[0063] II. Data Communication Module:

[0064] The data communication module includes:

[0065] The edge preprocessing unit is used to filter, clean, and standardize the collected multi-source data, and adjust the sampling frequency of the multi-source data according to the brewing stage.

[0066] The secure transmission unit is used to achieve secure and stable uploading of multi-source data through encryption protocols and redundant transmission mechanisms.

[0067] The edge preprocessing unit adjusts the sampling frequency of multi-source data according to the brewing stage, wherein:

[0068] Increase the sampling frequency during the logarithmic phase of alcoholic fermentation;

[0069] Reduce the sampling frequency during the stable period of alcoholic fermentation or the aging stage.

[0070] It should be noted that the edge preprocessing unit is deployed in the edge computing gateway or industrial controller close to the data acquisition point. It is responsible for performing preliminary processing on the multi-source raw data transmitted from the data acquisition module to improve data quality and optimize transmission efficiency.

[0071] Preprocessing operations include:

[0072] Data filtering: Removes obvious abnormal jump values ​​and invalid data packets caused by momentary sensor failure or external interference;

[0073] Data cleaning: The data is smoothed and filtered using the moving average method to eliminate random noise and ensure data stability;

[0074] Data standardization: Transform sensor data from different manufacturers and with different dimensions into standard engineering values ​​with unified units and dimensions, and encapsulate them into unified JSON or XML format data packets for easy cloud parsing.

[0075] The edge preprocessing unit features an adaptive sampling frequency adjustment function, which can dynamically adjust the sampling frequency of the data acquisition module according to a preset brewing process stage model or received cloud instructions, thereby achieving efficient utilization of system resources. During the logarithmic phase of alcoholic fermentation (a stage where yeast activity is vigorous, biochemical reactions are intense, and parameters change rapidly), the relevant sensors are controlled to increase the sampling frequency from once per hour to once every 10-15 minutes to capture rapid changes in key parameters and provide high-time-resolution data for accurate monitoring. During the stable phase of alcoholic fermentation or the aging stage (a stage where reactions are mild and parameter changes are slow), the unit will reduce the sampling frequency to once every 2-4 hours, thereby significantly reducing redundant data and saving network bandwidth and cloud storage space.

[0076] The secure transmission unit is responsible for reliably and securely uploading the pre-processed data to the cloud service management platform. It establishes a secure encrypted channel with the cloud via HTTPS, encrypts data packets during transmission, prevents data from being stolen or tampered with during transmission, and ensures the confidentiality and integrity of the data.

[0077] The secure transmission unit employs a redundant transmission mechanism to ensure connection stability and data reliability. This mechanism includes:

[0078] Automatic retransmission: When the network is briefly interrupted or data packets are lost, the data is cached locally and automatically retransmitted after the network is restored;

[0079] Multi-network link backup: The device can simultaneously support wired networks and 4G / 5G wireless networks, and automatically switch to the backup link when the main link fails to ensure uninterrupted data transmission.

[0080] III. Cloud Service Management Module:

[0081] The cloud service management module includes:

[0082] The data storage unit adopts a hybrid storage architecture, combining a time-series database to store multi-source data, a relational database to store process parameter data of the preset process model, and uses distributed storage technology to achieve reliable data backup.

[0083] The big data analysis unit uses a time-series prediction model to predict the changing trends of key quality indicators, including sugar content, acidity, alcohol content, and microbial concentration collected by the data acquisition module.

[0084] The quality and safety monitoring unit compares multi-source data with process parameter data from a preset process model and outputs early warning information based on the degree of deviation.

[0085] If the deviation is less than 10%, no warning will be triggered;

[0086] A warning alert is triggered when the deviation is 10%-30%.

[0087] An emergency warning is triggered when the deviation exceeds 30%.

[0088] The intelligent decision-making unit generates process adjustment suggestions based on a process knowledge graph and a multi-objective optimization algorithm.

[0089] The pre-defined process model includes a benchmark system of process parameters built based on different grape varieties and production area characteristics, including:

[0090] Parameters for fermentation temperature range, sulfur dioxide control range, and aging period for red grape varieties;

[0091] Fermentation temperature range, clarification treatment parameters, and stability control indicators for white grape varieties.

[0092] It should be noted that the data storage unit adopts a hybrid storage architecture to address the needs of efficient storage and retrieval of multi-source heterogeneous data. This architecture combines the use of time-series databases and relational databases.

[0093] The time series database uses InfluxDB: it is used to efficiently store and manage massive amounts of time series data from the data acquisition module, such as temperature and pH values ​​collected every second. This type of database is optimized for timestamped data and is very suitable for storing and quickly querying time-stamped sensor data.

[0094] Relational databases using MySQL are used to store structured data such as process parameters, user information, and equipment metadata for pre-defined process models. These databases are adept at handling data with complex relationships and requiring transaction guarantees.

[0095] The data storage unit is based on a distributed storage architecture and redundancy mechanism to achieve data backup and disaster recovery, preventing data loss due to single point of failure, thereby ensuring the security and high availability of system data.

[0096] The big data analytics unit is responsible for mining stored historical and real-time data to predict future trends. It uses an LSTM (Long Short-Term Memory) network to predict the changing trends of key quality indicators. Through these predictions, the system can anticipate whether the fermentation process is normal, whether there are any stagnation or abnormal risks, providing operators with forward-looking decision-making support and transforming passive response into proactive intervention.

[0097] The quality and safety monitoring unit, based on HACCP principles, performs real-time online quality control of the brewing process. It compares multi-source data with the benchmark values ​​of process parameters from a preset process model, calculates the deviation of the current measured value from the standard value, and outputs graded early warning information based on the degree of deviation.

[0098] When the deviation is less than 10%, it is considered a normal process fluctuation and will not trigger an early warning.

[0099] When the deviation reaches 10%-30%, it is determined that there is a risk of deviation, triggering a warning yellow alert, prompting operators to pay attention to changes in this parameter;

[0100] When the deviation exceeds 30%, it is judged as a serious abnormality that may affect batch quality. An emergency red alert is immediately triggered, and relevant personnel are notified to take manual or automatic intervention measures.

[0101] The process knowledge graph constructs a complex network of relationships between grape varieties, raw material indicators, process parameters, environmental factors, and final quality, digitizing and structuring the winemaker's experience and process knowledge.

[0102] When the intelligent decision-making unit receives an early warning from the quality and safety monitoring unit or a risk prediction from the big data analysis unit, it activates the genetic algorithm. With wine quality as the core objective, it comprehensively considers multiple factors such as the current fermentation state and process constraints, searches for the optimal path in the knowledge graph, and generates specific and actionable process adjustment suggestions.

[0103] The preset process model is an expert knowledge base stored in the system, which serves as a benchmark for comparison and decision-making. For red grape varieties, the benchmark system may include a higher fermentation temperature range, a precise sulfur dioxide control range, and the required aging period parameters; for white grape varieties, the benchmark system may include a lower fermentation temperature range, specific clarification treatment parameters, and stability control indicators.

[0104] IV. User Interaction Module:

[0105] The user interaction module includes:

[0106] The visualization display sub-unit displays multi-source data, prediction curves of key quality indicators, early warning information, and process adjustment suggestions generated by the intelligent decision-making unit in real time via mobile terminals;

[0107] The instruction interaction subunit receives user confirmation of warning alerts, intervention instructions for emergency alerts, and execution instructions for process adjustment suggestions, and records the entire process operation log.

[0108] The traceability management subunit generates a full-process batch report that includes multi-source data, early warning records, process adjustment trajectories, and changes in key quality indicators.

[0109] It should be noted that the visualization sub-unit is responsible for transforming system data into intuitive graphical information, providing users with real-time monitoring views through desktop dashboards or mobile apps. These views include:

[0110] Real-time data dashboard: Displays the current values ​​of vineyard environmental parameters, raw material quality indicators, and various physicochemical indicators in the fermentation tank in real time in numerical and format.

[0111] Trend prediction curve: Displays historical data and future prediction trend curves of key quality indicators generated by the big data analysis unit in the form of line charts, etc., so that users can grasp the direction of the fermentation process at a glance.

[0112] Warning information panel: Displays alerts and emergency warnings issued by the quality and safety monitoring unit in yellow, red, and via push notifications;

[0113] Process adjustment suggestions: Clearly display process adjustment suggestions generated by the intelligent decision-making unit, such as "suggest cooling" or "suggest adding nutrients".

[0114] The instruction interaction subunit is responsible for receiving decisions and instructions made by the user based on system information. It is the core of the system for achieving human-machine collaboration. This unit provides a series of interaction interfaces, allowing users to:

[0115] Confirming a warning indicates that you are aware of the risk.

[0116] In response to emergency warnings, intervention commands can be issued. For example, after reviewing system suggestions, users can manually approve or modify and issue a "shut down refrigerant valve" command to the actuator.

[0117] The system responds to process adjustment suggestions generated by the intelligent decision-making unit, allowing users to choose to adopt and implement, reject, or modify and implement them.

[0118] All user operations, including the operator, operation time, and operation content, are automatically recorded by the system and generate an unalterable full-process operation log.

[0119] The traceability management subunit is the ultimate embodiment of the system's data value. For each batch of wine, this unit can generate a complete digital batch report with a single click, including:

[0120] Raw data: All multi-source data from vineyard soil data to the end of fermentation;

[0121] Event Log: Records of all warnings that occurred during the brewing process and their handling;

[0122] Process trajectory: All process adjustment instructions and timestamps executed manually or automatically by the system;

[0123] Quality Changes: Curves showing changes in key quality indicators.

[0124] Please refer to Figure 2 This is a schematic diagram of the method flow provided in an embodiment of the present invention.

[0125] An information management method for the high-quality winemaking process, comprising:

[0126] S1 collects multi-source data from the entire winemaking process, including vineyard environmental parameters, grape raw material quality indicators, and fermentation liquid physicochemical indicators.

[0127] S2 preprocesses the collected multi-source data, adjusts the sampling frequency, and uploads it through a secure transmission mechanism;

[0128] S3 classifies and stores the uploaded multi-source data, predicts the trend of key quality indicators based on the preset process model, executes hierarchical early warning through data comparison, and generates process adjustment suggestions in combination with process knowledge graph;

[0129] S4 displays multi-source data, key quality indicator prediction curves, early warning information, and process adjustment suggestions through an interactive interface, and receives user commands.

[0130] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An information management system for a high-quality winemaking process, characterized in that, include: The data acquisition module is used to collect multi-source data in real time throughout the entire winemaking process, including vineyard environmental parameters, grape raw material quality indicators, and fermentation liquid physicochemical indicators. The data communication module is used to process and upload the collected multi-source data; The cloud service management module is used to receive and process multi-source data uploaded by the data communication module. The cloud service management module includes: Data storage unit, used for classifying and storing the multi-source data and preset process model parameters; The big data analytics unit is used for trend prediction of key quality indicators. The quality and safety monitoring unit is used to output early warning information based on the degree of data deviation according to HACCP principles. Intelligent decision-making unit, used to generate process adjustment suggestions by combining process knowledge graph; The user interaction module is used to display multi-source data, prediction curves of key quality indicators, early warning information and process adjustment suggestions, and to receive user commands.

2. The information management system for the high-quality wine brewing process according to claim 1, characterized in that, The data acquisition module includes: The planting environment data acquisition unit is equipped with soil sensors and a weather station to collect environmental parameters of the vineyard, including soil temperature and humidity, soil pH value and regional meteorological data. The raw material quality acquisition unit integrates a spectral detection device to obtain grape raw material quality indicators, including sugar content, acidity, and pesticide residue indicators. The fermentation process acquisition unit, a multi-parameter sensor array deployed inside the fermenter, is used to monitor the physicochemical properties of the fermentation broth in real time, including the temperature, pH value, sugar content, alcohol content, and microbial concentration data.

3. The information management system for the high-quality wine brewing process according to claim 2, characterized in that, The fermentation process acquisition unit, wherein: The concentration of microorganisms in the fermentation broth was quantitatively analyzed using quantitative PCR technology, including the concentration of Saccharomyces cerevisiae. The quantitative PCR technology uses specific primer pairs for Saccharomyces cerevisiae to achieve specific quantitative detection of Saccharomyces cerevisiae concentration.

4. The information management system for the high-quality wine brewing process according to claim 1, characterized in that, The data communication module includes: An edge preprocessing unit is used to filter, clean, and standardize the collected multi-source data, and adjust the sampling frequency of the multi-source data according to the brewing stage. The secure transmission unit is used to achieve secure and stable uploading of the multi-source data through encryption protocols and redundant transmission mechanisms.

5. The information management system for the high-quality wine brewing process according to claim 4, characterized in that, The edge preprocessing unit adjusts the sampling frequency of the multi-source data according to the brewing stage, wherein: During the logarithmic phase of alcoholic fermentation, increase the sampling frequency; Reduce the sampling frequency during the stable period of alcoholic fermentation or the aging stage.

6. The information management system for the high-quality wine brewing process according to claim 1, characterized in that, The cloud service management module includes: The data storage unit adopts a hybrid storage architecture, combining a time-series database to store multi-source data and a relational database to store process parameter data of a preset process model, and uses distributed storage technology to achieve reliable data backup. The big data analysis unit predicts the changing trends of key quality indicators through a time-series prediction model. These key quality indicators include sugar content, acidity, alcohol content, and microbial concentration collected by the data acquisition module. The quality and safety monitoring unit compares multi-source data with process parameter data from a preset process model, and outputs early warning information based on the degree of deviation in the comparison, wherein: If the deviation is less than 10%, no warning will be triggered; A warning alert is triggered when the deviation is 10%-30%. An emergency warning is triggered when the deviation exceeds 30%. The intelligent decision-making unit generates process adjustment suggestions based on a process knowledge graph and a multi-objective optimization algorithm.

7. The information management system for the high-quality wine brewing process according to claim 6, characterized in that, The preset process model includes a process parameter benchmark system built based on different grape varieties and production area characteristics, including: Parameters for fermentation temperature range, sulfur dioxide control range, and aging period for red grape varieties; Fermentation temperature range, clarification treatment parameters, and stability control indicators for white grape varieties.

8. The information management system for the high-quality wine brewing process according to claim 1, characterized in that, The user interaction module includes: The visualization display sub-unit displays multi-source data, prediction curves of key quality indicators, early warning information, and process adjustment suggestions generated by the intelligent decision-making unit in real time via mobile terminals; The instruction interaction subunit receives user confirmation of warning alerts, intervention instructions for emergency alerts, and execution instructions for process adjustment suggestions, and records the entire process operation log. The traceability management subunit generates a full-process batch report that includes multi-source data, early warning records, process adjustment trajectories, and changes in key quality indicators.

9. An information management method for a high-quality winemaking process, used to implement the information management system for the high-quality winemaking process as described in claim 1, characterized in that, include: S1 collects multi-source data from the entire winemaking process, including vineyard environmental parameters, grape raw material quality indicators, and fermentation liquid physicochemical indicators. S2 preprocesses the collected multi-source data, adjusts the sampling frequency, and uploads it through a secure transmission mechanism; S3 classifies and stores the uploaded multi-source data, predicts the trend of key quality indicators based on the preset process model, executes hierarchical early warning through data comparison, and generates process adjustment suggestions in combination with process knowledge graph; S4 displays multi-source data, key quality indicator prediction curves, early warning information, and process adjustment suggestions through an interactive interface, and receives user commands.

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