AI-Empowered Intelligent Mechanical and Transportation Systems: Perception, Diagnosis, Decision-Making, and Collaboration

With the rapid development of new-generation artificial intelligence, advanced perception, digital twin, and autonomous decision-making technologies, mechanical equipment and transportation systems are accelerating toward intelligence, autonomy, collaboration, and green development. Intelligent mechanical and transportation systems are widely applied in high-end manufacturing, aerospace, rail transportation, road transport, smart logistics, energy equipment, and major engineering fields. Their operational status, reliability, safety, and decision-making efficiency directly relate to the high-quality development of modern industrial systems and comprehensive transportation systems.

Complex mechanical equipment and transportation systems typically feature multi-source information coupling, dynamic operating environments, complex failure mechanisms, diverse task constraints, and high safety requirements. Traditional methods relying on empirical rules and single models are insufficient to address state perception, fault diagnosis, performance prediction, operational optimization, and autonomous decision-making under complex working conditions. AI technology can extract key features from multi-modal, multi-scale, and strong-noise data, and through data-driven, mechanism-driven, and data-mechanism fusion modeling, provide new theoretical foundations and technical pathways for mechanical equipment health management, transportation system operation control, and complex system collaborative optimization.

This forum targets doctoral students and young scholars in intelligent mechanical engineering, mechanical engineering, transportation engineering, aerospace, control science, computer science, and related interdisciplinary fields. It aims to build an open, cutting-edge, and cross-disciplinary academic exchange platform, showcasing the latest research achievements of AI-empowered mechanical equipment and transportation systems, discussing key scientific problems and technical challenges in intelligent perception, condition diagnosis, predictive maintenance, autonomous decision-making, collaborative control, and engineering applications, and promoting in-depth exchange and cooperation among different disciplinary directions.

随着新一代人工智能、先进感知、数字孪生与自主决策技术的快速发展，机械装备与载运系统正加速向智能化、自主化、协同化和绿色化方向演进。智能机械与载运系统广泛服务于高端制造、航空航天、轨道交通、道路运输、智慧物流、能源装备及重大工程等领域，其运行状态、可靠性、安全性和决策效率直接关系到现代工业体系与综合交通系统的高质量发展。

复杂机械装备和载运系统通常具有多源信息耦合、运行环境动态变化、故障机理复杂、任务约束多样以及安全要求高等特点。传统依赖经验规则和单一模型的方法难以充分应对复杂工况下的状态感知、故障诊断、性能预测、运行优化与自主决策问题。人工智能技术能够从多模态、多尺度和强噪声数据中提取关键特征，并通过数据驱动、机理驱动以及数据—机理融合建模，为机械装备健康管理、载运系统运行控制和复杂系统协同优化提供新的理论基础与技术路径。

本论坛面向智能机械、机械工程、交通运输工程、航空航天、控制科学、计算机科学及相关交叉领域的博士研究生和青年学者，旨在搭建开放、前沿、交叉的学术交流平台，集中展示人工智能赋能机械装备与载运系统研究的最新成果，探讨智能感知、状态诊断、预测维护、自主决策、协同控制及工程应用中的关键科学问题与技术挑战，促进不同学科方向之间的深度交流与合作。