Optimized intelligent data management framework for a cyber-physical system for computational applications

Abstract

AbstractData management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.