A Cloud-Edge Collaborative Framework for Adaptive Quality Prediction Modeling in IIoT

With the rapid development of cloud computing, edge computing, and deep learning technologies, the implementation of soft sensor modeling within a cloud-edge collaboration architecture shows great potential and plays a pivotal role in achieving consistent and high accuracy for industrial processes. However, traditional soft sensor models encounter significant challenges such as difficulties in the extraction of spatial correlation features, updating, and model mismatch. For this reason, this article proposes a soft sensor modeling method for adaptive quality prediction based on cloud-edge collaboration. The method first designs a basic model for the cloud and edge, which is a variable reordering-based convolutional neural network (VRCNN) for nonneighboring feature learning. Then, it deploys a deep training model and shallow fine-tuning prediction model in the cloud and edge, respectively. After that, the adaptive cloud-edge collaborative modeling is achieved through parameter collaboration, data collaboration, and inference collaboration between cloud and edge models. The proposed method is validated through comparative experiments on two tasks of the industrial hydrocracking process.