清华大学学报（自然科学版）2026，Vol.66Issue(4)：702-711,10.DOI:10.16511/j.cnki.qhdxxb.2026.26.014
大型水电工程数据共享智能体构建方法与应用
Construction method and application of a data-sharing agent for large-scale hydropower projects
摘要
Abstract
[Objective]Large-scale hydropower projects generate substantial amounts of heterogeneous data dispersed across design,construction,and supervision units.The interoperability among stakeholders is suboptimal due to the heterogeneity of data structures and professional contexts.Consequently,information sharing remains inefficient.Existing studies have typically focused on specific data types or lifecycle stages,lacking a unifying framework to facilitate comprehensive,full-cycle data sharing.To address this issue,this study proposes the development of a data-sharing agent tailored to the needs of hydropower engineering.The proposed agent is designed to accommodate structured,semi-structured,and unstructured data,and it integrates external tools such as time-series databases,knowledge graphs,and text vector databases.This integration enables accurate,on-demand data retrieval.By enhancing the tool-learning capabilities of large language models,the agent bridges data silos,enhances cross-domain collaboration,and lays a solid technical foundation for intelligent construction in complex hydropower projects.[Methods]The research commences with a systematic analysis of data-sharing requirements across the full lifecycle of hydropower projects,encompassing time-series monitoring data,technical documentation,and parametric design files.Based on this analysis,a comprehensive agent framework is designed to support multi-modal data interoperability.To ensure its practicality,a supporting tool system is constructed that integrates intelligent modules for database retrieval,knowledge graph querying,and rule-based inference.Furthermore,an action-planning dataset comprising over 4 000 samples is developed to train the agent in decision-making and tool invocation.Two versions of the DeepSeek-R1-Distill-Qwen model(1.5 B and 7.0 B parameters)are fine-tuned using this dataset to enhance structured parameter extraction,multi-step reasoning,and action planning capabilities.To assess performance,a benchmark testing dataset comprising hundreds of real-world business queries derived from hydropower project workflows is established and manually annotated to ensure fairness and reproducibility.[Results]Experimental results demonstrated that the fine-tuned models substantially improved planning and reasoning performance.A comparative analysis revealed that the 1.5 B and 7.0 B models achieved 270％and 104％improvements in planning accuracy,respectively,compared with their pre-fine-tuning counterparts.On the business query test set,the overall output accuracies were 65.83％and 90.83％,respectively,thereby confirming a significant enhancement in model reliability and practical utility through fine-tuning.Notably,the 7.0 B model consistently outperformed the smaller version,highlighting the larger model's capacity to handle complex,multi-step reasoning tasks.A practical deployment of the agent-based data-sharing platform was conducted for a real hydropower project in a representative watershed.Under static and structured data-sharing conditions,the agent maintained an average response time of less than 20 s.Conversely,dynamic monitoring scenarios involving high-frequency data streams exhibited average latencies exceeding 30 s,with peaks exceeding 60s under intensive analytical loads.[Conclusions]This study proposes a comprehensive framework for constructing a data-sharing agent that effectively addresses critical challenges in current hydropower data-sharing practices,particularly in high-altitude,data-scarce environments.By aligning agent design with engineering-specific requirements and integrating a highly refined large language model with a domain-oriented tool ecosystem,the proposed method significantly enhances the efficiency,intelligence,and semantic interoperability of data sharing.The agent reduces cross-disciplinary access barriers,improves system responsiveness,and supports knowledge-driven decision-making.The results from field applications confirm its considerable potential for practical implementation in intelligent construction platforms.Furthermore,the findings of this study provide a scalable,generalizable technical foundation for the future development of data-driven management and intelligent decision-support systems in complex hydropower projects.关键词
水电工程/智能体/数据共享/工具学习/知识图谱Key words
hydropower projects/agent/data sharing/tool learning/knowledge graph分类
建筑与水利引用本文复制引用
杨晨,罗一鸣,许后磊,夏勇,张志伟,林鹏..大型水电工程数据共享智能体构建方法与应用[J].清华大学学报（自然科学版）,2026,66(4):702-711,10.基金项目
2023年云南省工程研究中心创新能力建设和提升专项项目 （）
云南省数字水工程技术创新中心项目(202305AK340003) （202305AK340003）
流域梯级水电站物联网信息融合技术、设备与平台研发项目(DJ-HXGG-2022-03) （DJ-HXGG-2022-03）
中国电建集团成都勘测设计研究院有限公司科研项目(WRQ202311151) （WRQ202311151）
