A Cloud-Based Platform for Big Data-Driven CPS Modeling of Robots

Abstract

This paper proposes an improved cyber-physical systems (CPS) architecture for a smart robotic factory based on an industrial cloud platform driven by big data based on the traditional CPS architecture. This paper uses the architecture analysis and design language to model and design a total of three scales for the underlying cell-level robot, the system-level robot shop, and the overall robotic smart factory CPS, respectively, to complete the conceptual scheme for building a robotic smart factory from a local to an overall CPS system. Using the advantages of cloud computing and combining robotic CPS with cloud computing, an architecture for an industrial management system for CPS cloud computing is proposed. Base based distributed storage architecture with Storm based distributed real-time processing architecture. In terms of modeling, the advantages and disadvantages of using AADL, structural analysis, and design language, and modelers, a physical device modeling language, are combined to analyze the advantages and disadvantages of architecture analysis design language (AADL) for modeling CPS and propose a CPS analysis and design based on AADL and applicable to it. The paper also investigates the use of LeNet models for state identification in the HSV color space. The algorithm was verified on a self-built power equipment indicator dataset with a 100% detection rate and 99.8% state recognition accuracy after four consecutive frames of fusion detection. Simulink simulation of the trolley was carried out in terms of a cell-level robotic trolley CPS system to demonstrate the effectiveness of the design of a robotic CPS system driven by soaring data based on the industrial cloud platform proposed in this paper.