DLUP: A Deep Learning Utility Prediction Scheme for Solid-State Fermentation Services in IIoT

At present, solid-state fermentation (SSF) is mainly controlled by artificial experience, and the product quality yield are not stable. Therefore, predicting of SSF great significance for improving utility SSF. In this article, we propose a deep learning prediction (DLUP) scheme in Industrial Internet Things, including parameters collection process. Furthermore, novel edge-rewritable Petri net to model process further verify their soundness. More importantly, DLUP combines generating ability least squares generative adversarial network with fully connected neural realize (usually use alcohol concentration) Experiments show that proposed method predicts concentration more accurately than other joint methods. addition, our article provides evidences setting ratio raw materials proper temperature through numerical analysis.