为解决目前电网应急体系在应对台风灾害时, 存在过度依赖主观经验和知识整合能力较弱等问题, 该文提出一种融合大语言模型的台风场景下电网应急知识图谱构建方法, 并利用台风灾害历史数据和应急预案资料构建知识图谱, 以辅助台风场景下电网的应急决策。该文首先采用双向编码器表征法-双向长短期记忆深度学习模型, 从非结构化文本中抽取关键实体及其关系; 随后, 采用可视化技术提升模型输出结果的可解释性, 并结合判别式方法和大语言模型进行知识融合。研究结果表明：该文所提方法的知识融合准确率较传统方法提升10.11%;在知识图谱应用阶段, 知识融合的可靠性和丰富度等指标显著提升, 可生成融合历史案例和应急预案的辅助决策方案。该文融合知识图谱与大语言模型, 有效解决了知识碎片化问题, 研究结果可为电力系统在台风灾害下的应急管理提供参考。

Objective: Typhoons, characterized by sudden onset, extensive geographic impact, and considerable destructive power, pose recurring threats to the stability and safety of power grid systems, particularly in China's coastal regions. As extreme weather events become more frequent due to climate change, conventional emergency management approaches are inadequate. These methods often suffer from fragmented knowledge sources, inefficient information extraction, and limited support for intelligent decision-making. Hence, this article proposes an integrated technical framework that combines knowledge graphs with large language models (LLMs). This study aimed to improve risk perception, enhance decision-making accuracy, and bolster emergency response effectiveness in typhoon-triggered power grid incidents. Zhejiang Province, a coastal area frequently impacted by typhoons, was selected as the demonstration case for the framework. Methods: The framework of knowledge graph construction included the design of two graph types: one derived from accident reports and the other derived from emergency plans. The accident-based knowledge graph was structured according to the Triangular Framework for Public Security Science and Technology at the schema layer. It organized knowledge into three primary dimensions: emergency events, affected infrastructure, and corresponding emergency management strategies. Meanwhile, the emergency-plan-based knowledge graph was structured based on the electric power production life cycle, covering key stages such as power transmission, power transformation, power distribution, power utilization, and energy storage. Both graphs worked together to support emergency planning. The system employed a hybrid approach at the data processing layer that integrated BERT with a bidirectional long short-term memory network. This hybrid model performed named entity recognition and relationship extraction. The extracted entities and relationships were visualized to improve model interpretability, enabling domain experts to validate and understand the underlying information. An enhanced discriminative similarity algorithm was introduced in the knowledge fusion process. Initially, cosine similarity and Pearson correlation filtered out low-relevance entity pairs. High-similarity entities were then semantically validated using the LLM, ensuring accurate fusion and reducing erroneous entity alignments. Experimental results showed a 10.11% improvement in accuracy compared to conventional methods. The final knowledge graphs were stored in the Neo4j graph database, which supported interactive visualization and real-time query functionalities. The system enabled intelligent reasoning for handling real-world disaster scenarios in the application stage. Using the Cypher query language, this study conducted a fuzzy query based on disaster descriptions. Relevant information was retrieved from the knowledge graph as a structured knowledge base. The ECO-STAR prompting template was used to guide the model in generating targeted risk analyses and emergency recommendations. Results: A case study was conducted in Zhejiang Province to validate the proposed framework. The results showed that integrating knowledge graphs and LLMs improved semantic precision. The integration also enhanced the relevance of decision support outputs. In addition, it reduced hallucination phenomena that often occurred when general-purpose LLMs were applied in specialized domains. Conclusions: This study highlights the value of leveraging LLMs in constructing an emergency knowledge graph for power grids during typhoons. The proposed method offers a scalable, intelligent solution for managing power grid emergencies during typhoons and serves as a valuable reference for enhancing the disaster resilience of energy systems.