A Digital Twin and Knowledge Graph Fusion Framework for Industrial Intelligence

In modern industrial systems, Digital Twins (DT) focus on mapping physical states, simulating conditions, and predicting operations. Knowledge Graphs (KG) specialize in structured knowledge representation, cross-entity association, and logical inference. Despite their complementarity, the separation between the data and semantic layers leads to challenges such as limited model reuse, weak semantic interpretation, and a lack of closed-loop control. This paper presents a four-layer DT–KG fusion architecture (Q-Layer) that defines collaborative mechanisms from entity mapping through cognitive decision-making and distills four fusion patterns suited to diverse industrial scenarios. Q-Layer addresses the problem of heterogeneous data silos by establishing a closed loop of perception, semantics, inference, and decision, significantly enhancing system intelligence and adaptability. This framework offers a scalable pathway and theoretical foundation for advancing industrial intelligence.