从状态同步到认知自我进化：认知数字孪生的操作架构

中文摘要 AI 辅助

随着数字孪生（DT）系统从状态同步向面向任务和知识驱动的操作演进，认知数字孪生（CDT）作为将认知能力融入孪生操作的一种扩展而出现。现有的CDT研究往往侧重于特定的使能技术，如学习模块、知识图谱和大语言模型，而对认知如何系统性地集成到DT架构中提供的见解有限。为解决这一问题，本文提出了一种四层CDT架构，包括物理层、数字孪生层、认知层和任务层。所提出的架构建立了一个跨越这四层的自进化闭环操作回路，其中物理状态被同步为数字表示，认知通过知识、记忆和注意力构建任务特定的认知模型，并在实际约束下生成任务级决策。操作反馈进一步优化认知经验，并更新数字表示中的关系和注释，使后续的任务解释、启动和推理能够随系统操作而演进。基于该框架，刻画了两种代表性操作模式：用户请求驱动的认知和自驱动认知。我们进一步讨论了与语义通信、知识查询、任务编排和闭环同步相关的关键使能机制和部署挑战。一项轻量级仿真研究说明了在有限语义信息下可靠的闭环任务可行性，以及通过累积任务经验提高的操作效率。所提出的框架为未来CDT系统的设计和开发提供了结构化基础。

英文摘要

As Digital Twin (DT) systems evolve beyond state synchronization toward task-oriented and knowledge-driven operation, Cognitive Digital Twins (CDTs) have emerged as an extension that incorporates cognitive capabilities into twin operation. Existing CDT studies often focus on specific enabling techniques, such as learning modules, knowledge graphs, and large language models, while providing limited insight into how cognition can be systematically integrated into DT architectures. To address this issue, this paper proposes a four-layer CDT architecture consisting of the physical layer, digital-twin layer, cognitive layer, and task layer. The proposed architecture establishes a self-evolving closed operational loop spanning these four layers, in which physical states are synchronized into digital representations, cognition constructs task-specific cognitive models through knowledge, memory, and attention, and task-level decisions are generated under practical constraints. Operational feedback further refines cognitive experience and updates relationships and annotations in the digital representation, enabling subsequent task interpretation, initiation, and reasoning to evolve with system operation. Based on this framework, two representative operation modes are characterized: user-request-driven cognition and self-driven cognition. We further discuss key enabling mechanisms and deployment challenges associated with semantic communication, knowledge querying, task orchestration, and closed-loop synchronization. A lightweight simulation study illustrates reliable closed-loop task feasibility under limited semantic information and improved operational efficiency through accumulated task experience. The proposed framework provides a structured foundation for the design and development of future CDT systems.

发表机构

• Concordia University（康考迪亚大学）

机构由 AI 辅助整理，请以论文原文为准。