Research on probiotic activity monitoring and predictive maintenance algorithm based on industrial big data

Aiming at the difficulties of activity monitoring and equipment maintenance in probiotic fermentation process, this study proposed a comprehensive solution based on industrial big data. By collecting multi-source heterogeneous data (such as environmental parameters, microbial metabolic indicators, equipment operation status, etc.) in the fermentation process, a probiotic activity monitoring model and equipment fault prediction algorithm are constructed to realize real-time monitoring and predictive maintenance of the fermentation process. Data preprocessing, feature engineering and machine learning technology are used to optimize the model accuracy, and the effectiveness of the algorithm is verified by experiments. The results show that the scheme can significantly improve the monitoring accuracy of probiotic activity, reduce the failure rate of equipment, and provide theoretical and technical support for intelligent production of probiotic industry.