Probiotic packaging stability control method based on deep learning

By integrating deep learning with multi-source data analysis and time series prediction networks, a closed-loop control system for probiotic encapsulation was established, which solved the instability problem of the encapsulation process in existing technologies and achieved high-precision encapsulation stability control and product consistency.

CN121573283AInactive Publication Date: 2026-02-27QINGDAO TIANTAI YINLEDUO FOOD CO LTD
0 Cites 3 Cited by

Patent Information

Application Number
CN202511738999.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-25
Publication Date
2026-02-27
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Current probiotic encapsulation technology lacks real-time perception and modeling analysis of the dynamic characteristics and microstructural changes during the encapsulation process, resulting in significant fluctuations in the integrity of the encapsulation structure and the viability rate. Traditional control systems cannot achieve adaptive adjustment and are unable to meet the high requirements for stability and consistency.

Method used

By employing a deep learning-based approach and integrating multi-source data analysis, a probiotic encapsulation stability feature space is constructed through time-series feature extraction and microscopic image segmentation. A time-series prediction network is then used for prediction, and a closed-loop control structure of prediction-decision-execution-feedback is established to achieve adaptive adjustment of key parameters.

Benefits of technology

It achieves intelligent modeling and dynamic control of the entire probiotic encapsulation process, improving the controllability of the encapsulation process and product consistency, enhancing the retention rate of live bacteria and the integrity of the encapsulation structure, and adapting to dynamic changes in different process stages.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a probiotic packaging stability control method based on deep learning, and the method comprises the following steps: collecting multi-source data in a packaging process, and carrying out the preprocessing of the multi-source data; performing time sequence feature analysis on the standardized structured data set, and extracting multi-scale time sequence features; performing target segmentation and multi-dimensional structure feature extraction on the standardized microscopic image data set; feature fusion is carried out, a probiotic packaging stability feature space is constructed, and a stable feature vector is generated; inputting a time sequence prediction network, and carrying out time sequence modeling and prediction processing; performing control parameter adjustment and constraint optimization based on a difference value between a prediction result and stability reference data; and executing and updating the packaging control adjustment scheme according to feedback to complete a packaging control closed loop. According to the method, deep learning and multi-source data analysis are fused, intelligent prediction and adaptive control of the probiotic packaging process are realized, and the method has the advantages of high stability and high precision.
Need to check novelty before this filing date? Find Prior Art