摘要
地下车站内部设施通过物理连接、功能依赖和信息交互紧密耦合,这种耦合关系在洪水侵袭中呈现显著的级联效应,一旦某一关键设施受损,便可引发系统性风险。现有洪水脆弱性评估方法将各个设施视为独立单元,忽略了设施间的耦合作用关系和风险传导机制,难以准确刻画洪水对地下车站设施的破坏路径。因此,本文利用知识图谱(KG)语义关联和大语言模型(LLMs)的上下文推理能力,提出了一种耦合LLMs-KG的地下车站设施洪水脆弱性级联效应分析方法。首先,构建“对象-行为-状态”三域关联的地下车站设施知识图谱;其次,建立洪水演进-设施构件耦合的元胞自动机计算模型;然后,利用知识图谱约束大语言模型实现地下车站设施洪水脆弱性评估和级联效应推理;最后,选取北京市大兴区某大型地下车站为研究对象,结合DeepSeek-R1系列模型开展案例分析。结果表明,本文方法能够准确识别洪水作用下地下车站设施空间、功能属性变化及传播路径,推理过程具有良好的稳健性与可解释性。与专家预设基准级联路径相比,本文方法在节点匹配率和顺序匹配度方面呈现较高的准确性与逻辑一致性,相关成果能够为地下车站洪水针对性应急策略制定和系统韧性提升提供重要科学支撑。
Underground station facilities are tightly coupled through physical connections,functional dependencies,and information interactions.During flood events,such coupling relationships can result in cascading failures,where damage to a critical facility may trigger systemic risks.Existing flood vulnerability assessment methods often regard facilities as isolated units,ignoring the coupling effects and risk transmission mechanisms,making it difficult to accurately characterize the damage propagation paths.This paper proposes a flood vulnerability cascading analysis method for underground station facilities by integrating knowledge graphs(KGs)and large language models(LLMs).First,a three-domain knowledge graph consisting of“object-behavior-state”is constructed to represent facility relationships.Second,a cellular automaton model is developed to simulate flood evolution coupled with facility component interactions.Third,flood vulnerability assessment and cascading effect inference are performed by constraining the LLMs with the knowledge graph.Finally,a large-scale underground station in Daxing District,Beijing,is selected as a case study,along with the DeepSeek-R1 series model,for experimental analysis.The results show that the proposed method can effectively identify spatial and functional changes of facilities under flood scenarios and reveal risk propagation paths.The reasoning process exhibits strong robustness and high interpretability.Compared with expert-defined benchmark cascade paths,the method achieves higher accuracy and logical consistency in terms of node matching rates and sequence matching accuracy.The findings provide theoretical support and technical reference for the formulation of emergency strategies and the enhancement of system resilience in underground stations.
作者
李维炼
冉晴晴
党沛
朱军
朱庆
张恒
LI Weilian;RAN Qingqing;DANG Pei;ZHU Jun;ZHU Qing;ZHANG Heng(Faculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 611756,China;China Railway Design Corporation,Tianjin 300308,China)
出处
《测绘学报》
北大核心
2026年第1期154-168,共15页
Acta Geodaetica et Cartographica Sinica
基金
国家重点研发计划(2024YFC3015404)
国家自然科学基金(42571503,42201446,42201445)
中国博士后科学基金(2024T170742,2025M770237)
国家资助博士后研究人员计划(GZC20232185)
中央高校基本科研业务费专项(2682024CX095)。
关键词
地下车站
洪水脆弱性
级联效应
知识图谱
大语言模型
underground station
flood vulnerability
cascading effect
knowledge graph
large language models
