A Method for Extracting High-Quality Core Data from Edge Computing Nodes

Abstract

Intelligent factory has the characteristics of wide data sources, high data dimensions, and strong data relevance. Intelligent factories need to make different decisions for different needs, so they need to efficiently analyze these data and explore the inherent laws contained in them. At the same time, the increasing amount of data brings various burdens to the network infrastructure between users and smart devices. For the above needs, this paper proposes a tension-based heterogeneous data fusion model in the edge computing layer, which represents the multisource heterogeneous data in the industrial scene as a tensor model, and uses the incremental decomposition algorithm to extract high-quality core data. The model reduces the data flow between the data center and the central cloud while retaining the core data set. Experiments show that the approximate tensor reconstructed from the tensor with 15% core data can guarantee 90% accuracy.