  At present, SOILD-STATE Fermentation (SSF) is mainly controlled by artificial
experience, and the product quality and yield are not stable. Accurately
predicting the quality and yield of SSF is of great significance for improving
human food security and supply. In this paper, we propose an Intelligent
Utility Prediction (IUP) scheme for SSF in 5G Industrial Internet of Things
(IoT), including parameter collection and utility prediction of SSF process.
This IUP scheme is based on the environmental perception and intelligent
learning algorithms of the 5G Industrial IoT. We build a workflow model based
on rewritable petri net to verify the correctness of the system model function
and process. In addition, we design a utility prediction model for SSF based on
the Generative Adversarial Networks (GAN) and Fully Connected Neural Network
(FCNN). We design a GAN with constraint of mean square error (MSE-GAN) to solve
the problem of few-shot learning of SSF, and then combine with the FCNN to
realize the utility prediction (usually use the alcohol) of SSF. Based on the
production of liquor in laboratory, the experiments show that the proposed
method is more accurate than the other prediction methods in the utility
prediction of SSF, and provide the basis for the numerical analysis of the
proportion of preconfigured raw materials and the appropriate setting of cellar
temperature.
