基于多智能体的数字孪生及其在工业中应用的综述

数字孪生是一种将物理实体数字化的技术,通过建立虚拟的数字孪生模型模拟实际的物理过程,以便进行模拟仿真、数据分析和优化设计等操作.鉴于此,分析数字孪生技术在复杂工业生产中的发展历程和研究现状,并重点讨论其概念、国家相关重点研究的政策,以及数字孪生使能技术在各行业的应用.主要途径是分析和综述基于多智能体的数字孪生、基于数字孪生的设计、制造和运维、数字孪生的集成在智能制造中的应用相关的研究成果.此外,提出高炉连续生产数字孪生方案和大飞机多智能体离散制造方案,高炉模型包括成分场大模型和增量学习小模型,该模型可以为数字孪生在复杂流程工业中的应用提供带有增量补偿的机理与计算机视觉相结合的解决方案.在复杂工业制造中,数字孪生和多智能体技术可以提高生产效率和质量,减少能源消耗和废品产生,同时也能够降低复杂度、安全风险和成本.

A review of multi-agent-based digital twins and its application in industry

Digital twins involve the transfer of a physical entity into a virtual model, enabling the simulation, analysis, and optimization of physical products, processes, operations, or systems. This paper aims to explore the current state of research and development in digital twins and their applications within complex industrial processes. It examines concepts, government development strategies, and ongoing applications. The primary approach of this study involves analyzing research findings related to the status of multi-agent based digital twinning, as well as the design, manufacturing, and operation and maintenance of digital twins in intelligent manufacturing. Furthermore, this paper presents a digital twin scheme for continuous production in a blast furnace, along with a multi-agent discrete manufacturing scheme for large aircrafts. The digital twin of the blast furnace incorporates a comprehensive component field model and a small incremental learning model. It leverages computer vision to implement an incremental compensation mechanism within the complex process industry. In the realm of complex industrial manufacturing, digital twins and multi-agent technologies have the potential to enhance production efficiency and quality, reduce energy consumption and waste generation, while also mitigating complexity, safety risks, and costs.