System and method for dynamically monitoring growth trend of microorganisms in fruit wine fermentation tank

By deploying sensors and image acquisition devices inside the fruit wine fermentation tank, and combining multi-channel convolutional neural networks and long short-term memory network models, real-time monitoring and prediction of microbial growth trends inside the fermentation tank were achieved. This solved the problems of low efficiency and slow response in traditional methods, and improved the quality and batch stability of fruit wine products.

CN120890495AInactive Publication Date: 2025-11-04HUBEI YAORONG PAPAYA BIOTECHNOLOGY DEV CO LTD
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Patent Information

Application Number
CN202511014001.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-23
Publication Date
2025-11-04
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional microbial monitoring methods are inefficient and have a slow response time, making it difficult to achieve real-time online monitoring of microbial growth in fruit wine fermentation tanks. This makes it impossible to grasp the dynamic changes in the microbial community in a timely manner, affecting the quality and batch stability of fruit wine products.

Method used

Multiple types of sensors are deployed inside the fruit wine fermentation tank to collect dynamic environmental data and images. Combined with multi-channel convolutional neural networks and long short-term memory network models, the growth trend of microorganisms is monitored and predicted in real time.

Benefits of technology

It enables intelligent control of the fruit wine fermentation process, improves the efficiency and accuracy of microbial monitoring, and can provide timely warnings of abnormal conditions, ensuring product quality and production stability.

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Abstract

The invention relates to the technical field of microbial fermentation, in particular to a system and method for dynamically monitoring the growth trend of microorganisms in a fruit wine fermentation tank. The method comprises the following steps: arranging a pH sensor, a dissolved oxygen sensor and a temperature sensor in a fermentation tank, collecting dynamic environmental data, and extracting an extreme value turn-back frequency to represent environmental volatility; liquid level images are collected through a camera, bacterial colony features are recognized in combination with a convolutional neural network model, and dynamic adjustment of the collection frequency is achieved based on image local energy features; arranging a gas sensor at a metabolic gas exhaust port, and monitoring the concentrations of CO2, H2S, acetic acid and ethanol gas in real time; a multi-channel convolutional neural network is constructed by fusing multi-source data to extract high-order features, accurate prediction of the microbial growth trend is further realized by using a long-short-term memory network, and support is provided for intelligent control and quality regulation and control in the fermentation process.
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