认知数字孪生赋能航空装备: 新范式与研究前沿

As core assets in modern military and civil aviation, aviation equipment features high complexity, long service lifecycles, and stringent reliability requirements, posing severe challenges to equipment development and operation maintenance. Digital twin technology provides effective support for the full-lifecycle management of aviation equipment. However, conventional digital twin technologies remain confined to static representation, making them insufficient to support deep cognition, evolutionary prediction, and intervention-oriented decision-making for the complex and dynamic behaviors of equipment throughout its lifecycle. In this context, driven by rapid advances in cognitive intelligence technologies such as large models, knowledge graphs, and causal reasoning, cognitive digital twins are emerging as a new research frontier. Existing studies on cognitive digital twins are largely limited to localized explorations within specific scenarios. Their theoretical system remains underdeveloped, and a unified architecture for complex aviation systems is lacking. In view of these gaps, a novel cognitive digital twin framework for the full-lifecycle management of aviation equipment is proposed, aiming to elucidate the paradigm shift from state mapping to autonomous cognition. First, it reviews the evolution of digital twin technology from “virtual-physical mapping” to “intelligent cognition”, and analyzes the current applications and urgent needs of this transition in the aviation domain. Second, the cognitive digital twin framework is deconstructed into its constituent layers-physical entity, virtual entity, interactive connection, and cognitive embedding. It also provides a detailed explanation of the cognition-enabling mechanisms of cognitive digital twins across the full lifecycle of the equipment. Subsequently, it systematically summarizes the research status and technical pathways of each layer from an implementation perspective. Finally, in response to current challenges faced by the digital twin application of aviation equipment, it explores the integration of emerging technologies including large models, and looks ahead to future trends in constructing a cognitive digital twin system for aviation equipment with deep comprehension, autonomous evolution, and trustworthy decision-making capabilities.