AI 中文总结
本文针对数字孪生在分布式环境中的问题，提出联邦认知数字孪生架构，结合联邦与认知，通过本地和全局孪生体在边缘到云连续体上分布智能，集成分布式自主性与认知推理，提升复杂分布式CPS的可扩展性、响应能力和决策能力。
AI 中文摘要
数字孪生（DT）越来越多地被用于通过物理资产与其数字对应物之间的持续交互来监测、分析和优化网络物理系统（CPS）。然而，当前的DT架构通常依赖集中式和整体式设计，在智能城市等分布式环境中导致可扩展性、延迟和弹性问题。此外，它们对语义集成和高级推理的支持有限，降低了基于DT的决策有效性。近期关于联邦数字孪生（FDT）的研究通过将复杂系统分解为相互作用的孪生体解决了可扩展性问题，但仍在很大程度上把智能集中在云组件中。同时，认知数字孪生（CDT）通过语义推理、可解释性和人工智能驱动的决策支持增强了DT，但通常难以集成到分布式架构中。本文提出了一种联邦认知数字孪生（FCDT）架构，将联邦和认知结合在统一方法中。该架构通过提供实时监测和轻量级认知能力的本地孪生体以及执行系统级推理、模拟和协调的全局孪生体，在边缘到云连续体上分布智能。通过将分布式自主性与认知推理相结合，该方法提高了复杂分布式CPS中的可扩展性、响应能力和决策能力。
英文摘要
Digital Twins (DTs) are increasingly adopted to monitor, analyze, and optimize Cyber-Physical Systems (CPSs) through continuous interaction between physical assets and their digital counterparts. However, current DT architectures often rely on centralized and monolithic designs, leading to scalability, latency, and resilience issues in distributed environment such as smart cities. Moreover, they provide limited support for semantic integration and high-level reasoning, reducing the effectiveness of DT-based decision-making. Recent studies on Federated Digital Twins (FDTs) have addressed scalability by decomposing complex systems into interacting twins, but they still largely centralize intelligence in cloud components. In parallel, Cognitive Digital Twins (CDTs) enhance DTs with semantic reasoning, explainability, and AI-driven decision support, yet they are typically difficult to integrate into distributed architectures. This paper proposes a Federated Cognitive Digital Twin (FCDT) architecture that combines federation and cognition within a unified approach. The architecture distributes intelligence across the edge-to-cloud continuum through local twins, which provide real-time monitoring and lightweight cognitive capabilities, and global twins, which perform system-level reasoning, simulation, and coordination. By integrating distributed autonomy with cognitive reasoning, the proposed approach improves scalability, responsiveness, and decision-making in complex distributed CPSs
