Page 73 - 2025年第56卷第9期

（12）：201-204，210.
［ 18］ 马超，李武峰，陈羽飞，等 . Chat GPT 类大语言模型赋能电力标准数字化转型的核心技术、技术特征及应用

展望［J］ 高电压技术，2025，51（4）：1727-1746.
.
［ 19］ 冯钧，畅阳红，陆佳民，等 . 基于大语言模型的水工程调度知识图谱的构建与应用［J］ 计算机科学与探索，
.
2024，18（6）：1637-1647.
［ 20］ 冯钧，吕志鹏，范振东，等 . 基于大语言模型辅助的防洪调度规则标签设计方法［J］ 水利学报，2024，55
.
（8）：920-930.
.
［ 21］ 文森，钱力，胡懋地，等 . 基于大语言模型的问答技术研究进展综述［J］ 数据分析与知识发现，2024，8
（6）：16-29.
［ 22］ ZHU J， JIANG J， YANG M. ChatGPT and environmental research［J］ Environmental Science & Technology，
.
2023，57（46）：17667-17670.
.
［ 23］ 王乔晨，吴振刚，刘虎 . 大语言模型应用的安全与隐私问题综述［J］ 工业信息安全，2024（5）：40-45.
.
［ 24］ 付志远，陈思宇，陈骏帆，等 . 大语言模型安全的挑战与机遇［J］ 信息安全学报，2024，9（5）：26-55.
［ 25］ ALI S，ABUHMED T，EL-SAPPAGH S，et al. Explainable Artificial Intelligence（XAI）：What we know and what
.
is left to attain Trustworthy Artificial Intelligence［J］ Information Fusion，2023，99：101805.
.
［ 26］ BAI J，BAI S，CHU Y，et al. Qwen technical report［J］ ArXiv，2023. https：/arxiv/org/abs/2309. 16609.
/
［ 27］ 李可佳，胡学先，陈越，等 . 基于主成分分析和函数机制的差分隐私线性回归算法［J］ 计算机科学，2023，
.
50（8）：342-351.

Standardization of reservoir groups operating rules and analysis of their
characteristics using large language models

1，2 3 4

REN Kang ，ZHANG Rui ，HUANG Qiang

（1. Hubei Key Laboratory of Construction and Management in Hydropower Engineering，China Three Gorges University，Yichang 443002，

China；2. College of Hydraulic & Environmental Engineering，China Three Gorges University，Yichang 443002，China；

3. Changjiang Survey Planning Design and Research Limited Co.，Wuhan 430079，China；

4. College of Water Conservancy and Hydropower，Xi’an University of Technology，Xi’an 710048，China）

Abstract：Reservoir operating rules are often described in natural language，outlining operating principles，objec⁃

tives，and constraints. However，these descriptions typically suffer from inconsistencies in format，terminological dis⁃

crepancies，and a lack of clear structure，which present significant challenges for the standardization and digital pro⁃

cessing of these rules. This study focuses on the controlled reservoir groups in the Yangtze River Basin and utilizes a
locally deployed large language model to standardize the text-based flood control operating rules into well-structured
decision tree representations. A method to measure the complexity of these operating rules was proposed by analyzing
decision tree depth and nodes，thereby uncovering the relationship between key decision variables，their complexity，

and the characteristics of reservoir groups. The results indicate that decision tree-based operating rules provide a

more intuitive and user-friendly framework for operating，in contrast to traditional text descriptions. Significant differ⁃
ences in operating rule complexity were observed across different reservoirs，though the correlation with most charac⁃

teristic variables of the reservoir groups was found to be low. While spatial clustering patterns in reservoir group fea⁃

tures were identified，their connection to decision complexity remained weak. The joint operating plan for the con⁃
trolled reservoir groups in the Yangtze River Basin should prioritize top-level design to minimize inconsistencies in

operating rules across reservoirs，thus advancing the standardization and digitization of these rules.

Keywords：reservoir groups；joint operation；large language model；decision tree；feature analysis

（责任编辑：耿庆斋）
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