Problem
Research questions and friction points this paper is trying to address.
Cognitive Digital Twins
Digital Twin Systems
Cognitive Capabilities Integration
Task-oriented Operation
Knowledge-driven
From State Synchronization to Cognitive Self-Evolution: An Operational Architecture for Cognitive Digital Twins
🤖 AI Summary
为解决认知能力系统性融入数字孪生架构的问题，本文提出了一种四层认知数字孪生架构，通过物理、数字孪生、认知及任务层构建自进化闭环操作流程。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cognitive Digital Twins
self-evolving closed operational loop
task-specific cognitive models
semantic communication
closed-loop synchronization
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