【目的】 高压直流输电（high voltage direct current，HVDC）智能运维面临状态评估精度不足、风险预测前瞻性差及故障诊断实时性低等关键问题，传统方法在多源数据融合、小样本泛化与决策可解释性方面存在局限。本文旨在系统梳理知识图谱与预训练模型融合的技术路径，为构建新一代HVDC智能运维体系提供理论参考。【方法】 首先剖析了HVDC智能运维在状态评估、风险预测与故障诊断三大场景中的技术瓶颈；其次阐述了知识图谱与预训练模型的融合基础及协同机制；最后归纳了融合技术在三大场景中的关键进展与解决方案。【结果】 知识图谱与预训练模型的深度融合有效解决了数据孤岛、小样本泛化弱及决策黑箱等痛点，在多模态特征对齐、时序因果推理及云边协同诊断等方面取得了显著突破。【结论】 当前该领域仍面临知识自动更新、物理机理融合及实时性保障等挑战，未来应重点发展自动化知识工程、物理信息嵌入预训练模型及可信评估体系。

[Objective] Intelligent operation and maintenance （O&M） of high voltage direct current （HVDC） systems face critical challenges， including insufficient accuracy in state assessment， limited predictive foresight in risk assessment， and poor real-time performance in fault diagnosis. Traditional methods are constrained by limitations in multi-source data fusion， small-sample generalization， and decision interpretability. This paper systematically reviews the integration pathways of knowledge graphs and pre-trained models， aiming to provide theoretical references for building a new generation of HVDC intelligent O&M systems. [Methods] First， the technical bottlenecks in three core scenarios—state assessment， risk prediction， and fault diagnosis—are analyzed. Subsequently， the integration fundamentals and synergistic mechanisms of knowledge graphs and pre-trained models are elaborated. Finally， key advancements and solutions applied to these three scenarios are summarized. [Results] The deep integration of knowledge graphs and pre-trained models effectively addresses pain points such as data silos， weak generalization with small samples， and the “black-box” nature of decision-making. Notable breakthroughs have been achieved in multi-modal feature alignment， temporal causal reasoning， and cloud-edge collaborative diagnosis. [Conclusions] However， challenges remain regarding automatic knowledge updating， the integration of physical mechanisms， and real-time assurance. Future research should focus on automated knowledge engineering， physics-informed pre-trained models， and trustworthy evaluation systems.