Intelligent fermentation factory design: large-scale IoT perception, multi-source big data analysis

The management of intelligent fermentation factories requires a combination of multiple software and hardware, based on a comprehensive and refined perception of manufacturing elements such as people, machines, materials, methods, and environment of the enterprise, and the use of large-scale IoT perception, multi-source big data analysis, and other technological means to achieve full process and integrated fermentation management of production, management, and scientific decision-making.

Optimize the process flow and modularize the fermentation system design. Efficient data collection to improve production capabilities. Remote data maintenance and monitoring of equipment operation status. Workshop zoning inventory, real-time updating of inventory data. 

Material access management, real-time environmental monitoring, to prevent human errors. Pre fault prediction, mid event alarm warning, and post event intelligent analysis.

The main functions of an intelligent fermentation factory include controlling the production process, monitoring production status, recording production data, conducting fault detection and prediction, and completing artificial intelligence analysis based on big data and the Internet of Things.

Automated wireless data collection and updates are carried out for various operational processes such as arrival, feeding, discharging, allocation, material transfer, relocation, and inventory counting to ensure the speed and accuracy of data input in material management. 

This ensures that the enterprise can timely and accurately grasp production operation data, and reasonably maintain and control production planning. Through scientific coding, it is also convenient to effectively manage the batch and shelf life of materials.

The intelligent fermentation factory can add various sensors and accessories according to fermentation needs through wireless transmission network. 

Fully integrate various systems, achieve modular and iterative factory upgrades, invest in multiple applications at once, and avoid duplicate investments.

At present, there are problems such as a single risk prediction method and insufficient comprehensive use of information in the fermentation industry. Bailun proposes an intelligent fermentation fault alarm risk prediction analysis method. 

Design a fermentation fault scanning and risk assessment system model based on the pre-set risk structure points of fermentation equipment. 

Improve the full process risk protection architecture for fermentation data collection and processing, remote client analysis, and fault prediction algorithms. Realize immediate monitoring of user faults and improve production processes.