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.

中文翻译：

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智慧社会与人工智能：大数据调度与智能维护应用的全球标准方法

摘要 人工智能 (AI) 在智能社会中的实施，其中人类习惯的分析是强制性的，需要使用智能应用程序、智能基础设施、智能系统和智能网络进行自动化数据调度和分析。在这种以训练和操作过程之间存在较大差距为特征的背景下，需要一种专门的方法来管理和提取海量数据以及相关的信息挖掘。这项工作中提出的方法旨在通过用于智能管理的接近零故障的高级诊断 (AD) 缩小这一差距，可在社会 5.0 的任何环境中加以利用，从而减少所有管理级别的风险因素并确保质量和可持续性. 我们还为以人为中心的管理系统开发了创新应用程序，以支持操作流程维护中的调度、降低培训成本、提高产量，以及为智能基础设施设计创建人机网络空间。在 12 家国际公司中获得的结果表明，操作流程可能实现全球标准化，从而设计出能够自我学习和升级的接近零故障的智能系统。我们的新方法为选择新一代智能制造和智能系统以优化人机交互以及相关的智能维护和教育提供了指导。以及为智能基础设施设计创建人机网络空间。在 12 家国际公司中获得的结果表明，操作流程可能实现全球标准化，从而设计出能够自我学习和升级的近乎零故障的智能系统。我们的新方法为选择新一代智能制造和智能系统以优化人机交互以及相关的智能维护和教育提供了指导。以及为智能基础设施设计创建人机网络空间。在 12 家国际公司中获得的结果表明，操作流程可能实现全球标准化，从而设计出能够自我学习和升级的近乎零故障的智能系统。我们的新方法为选择新一代智能制造和智能系统以优化人机交互以及相关的智能维护和教育提供了指导。