【摘要】 地下压缩空气储能（compressed air energy storage，简称CAES）作为新型储能技术的重要分支，凭借其大规模、长时效、绿色环保等优势，正成为支撑新型电力系统的重要技术方向。然而，CAES地下工程普遍面临地质条件复杂、多物理场耦合显著与注采调控频繁等挑战，传统方法在建模精度与运行效率方面存在明显局限。近年来，人工智能（artificial intelligence，简称AI）技术凭借其强大的非线性建模与数据驱动能力，为地下CAES系统的选址识别、结构预测、智能运行与风险预警等提供了新思路。通过文献计量与知识图谱分析，系统梳理了AI在CAES地下工程领域的研究现状与典型应用案例，包括选址与地质建模、储能库智能建造、腔体稳定性预测、注采运行优化、多物理耦合建模与安全监测预警等方面，发现该方向当前仍处于起步阶段，缺乏系统性研究框架。在总结已有研究的基础上，进一步提出了物理引导建模、多源数据融合与智能平台集成等关键发展建议，以期为推动地下CAES工程的智能化发展与我国“双碳”目标的实现提供理论支撑与技术参考。更多还原

【Abstract】 As a key branch of emerging energy storage technologies, underground compressed air energy storage(CAES) is gaining increasing attention for its advantages in large-scale capacity, long-duration storage, and environmental sustainability, making it a crucial support for new power systems. However, underground CAES projects often face challenges such as complex geological conditions, significant multi-physical field coupling, and frequent injection-production cycles, where traditional methods show clear limitations in modeling accuracy and operational efficiency. In recent years, artificial intelligence(AI) technologies, with their powerful nonlinear modeling and data-driven capabilities, have offered novel approaches to intelligent site selection, structural prediction, system operation, and risk warning in underground CAES. This paper employs bibliometric analysis and knowledge mapping techniques to systematically review the current state of AI applications in underground CAES, covering typical scenarios such as site selection and geological modeling, intelligent cavern construction, stability prediction, injection-production optimization, multiphysical coupling modeling, and safety monitoring. The findings reveal that research in this field remains in its early stages, lacking a comprehensive and systematic framework. Based on existing studies, this paper proposes several key directions for future development, including physics-informed modeling, multi-source data integration, and the construction of intelligent engineering platforms, aiming to provide theoretical insights and technical references for advancing the intelligent development of underground CAES and supporting the realization of China’s dual carbon goals.更多还原