水利学报2025，Vol.56Issue(12)：1556-1569,14.DOI:10.13243/j.cnki.slxb.20250261
基于大语言模型的水库调度知识图谱智能构建
Intelligent construction of reservoir operation knowledge graphs based on large language models
摘要
Abstract
Under the dual influence of climate change and human activities,reservoir operation faces challenges such as the proliferation of multi-source heterogeneous data and the weakening of knowledge correlations,making tra-ditional knowledge management models inadequate for supporting refined decision-making.To address the issues of knowledge fragmentation and lack of semantic associations in reservoir operation,this paper proposes an intelligent method for constructing knowledge graphs by integrating Large Language Models(LLMs)and deep learning tech-niques.Firstly,a multi-dimensional knowledge system covering hydrological elements,engineering attributes,opera-tion methods,constraints,and optimization models is constructed.A knowledge extraction framework for unstruc-tured texts is designed,which employs dynamic encoding of textual semantic features.The Bert-BiLSTM-CRF model is utilized to identify entity boundaries in reservoir operation texts,and an attention mechanism is incorporated to enhance the extraction of specialized entities such as operation methods and models.Secondly,a relation extraction strategy based on semantic role labeling and dependency parsing is proposed.Conflict resolution rules for reservoir operation knowledge are established to address the challenge of cross-literature entity alignment.A reservoir opera-tion knowledge graph,built using materials from core domestic journal literature and other sources,contains 1,590 entities and 922 relation groups.The entity recognition accuracy and recall rates reach 97.38%and 97.96%,respec-tively,with the F1 score improving by 13.29%and 13.02%compared to the traditional BiLSTM-CRF and BiLSTM-CNN models.Application results demonstrate that the knowledge graph effectively reveals the topological associations within reservoir operation knowledge,supporting applications such as knowledge reasoning and question answering.It can serve as a scalable knowledge hub for the intelligent operation of river basin reservoir groups.The construction paradigm offers theoretical reference for the development of digital twin systems in water resources.关键词
水库调度/知识图谱/大语言模型/知识抽取/知识驱动Key words
reservoir operation/knowledge graph/large language model/knowledge extraction/knowledge-driven分类
建筑与水利引用本文复制引用
冯仲恺,林腾,牛文静,肖洋,杨涛,唐洪武..基于大语言模型的水库调度知识图谱智能构建[J].水利学报,2025,56(12):1556-1569,14.基金项目
国家自然科学基金项目(52379009,52441901,U2240209) （52379009,52441901,U2240209）
江苏省自然科学基金优秀青年基金项目(BK20240189) （BK20240189）
北京江河水利发展基金会—水利青年科技英才资助项目(JHYC202310) （JHYC202310）
水灾害防御全国重点实验室自主研究项目(5240152E2) （5240152E2）
江苏省科技智库计划项目(JSKX0225047) （JSKX0225047）
