Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

TLDR

Artificial intelligence in a smart society demands automated data scheduling and analysis across smart applications, infrastructure, systems, and networks, yet a large gap between training and operational processes necessitates a dedicated method for managing massive data and mining information. The presented method seeks to close this gap by delivering near‑zero‑failure advanced diagnostics for smart management, applicable across Society 5.0 contexts to reduce risk at all management levels while ensuring quality and sustainability. To achieve this, we developed human‑centered applications that support maintenance scheduling, lower training costs, boost production yield, and create a human–machine cyberspace for smart infrastructure design. Trials in 12 international companies show that the approach enables global standardization of operative processes, yielding a self‑learning, near‑zero‑failure intelligent system that guides the selection of next‑generation intelligent manufacturing and smart systems to optimize human–machine interactions, smart maintenance, and education.

Abstract

The implementation of artificial intelligence (AI) in a smart society, in which the analysis of human habits is mandatory, requires automated data scheduling and analysis using smart applications, a smart infrastructure, smart systems, and a smart network. In this context, which is characterized by a large gap between training and operative processes, a dedicated method is required to manage and extract the massive amount of data and the related information mining. The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics (AD) for smart management, which is exploitable in any context of Society 5.0, thus reducing the risk factors at all management levels and ensuring quality and sustainability. We have also developed innovative applications for a human-centered management system to support scheduling in the maintenance of operative processes, for reducing training costs, for improving production yield, and for creating a human–machine cyberspace for smart infrastructure design. The results obtained in 12 international companies demonstrate a possible global standardization of operative processes, leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself. Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions, with the related smart maintenance and education.