基于生成式人工智能的水库调度应用研究OA

This study focuses on the application of reservoir operation regulations to guide reservoir management,addressing the challenges of efficient and accurate retrieval and intelligent reasoning when dealing with complex operational guidelines.Traditional retrieval approaches,such as expert regulation libraries and knowledge graphs,exhibit limitations in handling complex regulations,including insufficient retrieval accuracy,weak reasoning capabilities,and the lack of natural language interaction,which fail to meet the requirements of modern reservoir operation decision-making.To address these issues,this paper develops a retrieval-augmented generation(RAG)system and decision-making framework for complex reservoir operation regulations based on large language models(LLMs).By leveraging high-dimensional vector processing techniques,an efficient vectorization method for handling reservoir regulations is proposed,establishing a domain-specific knowledge base.Prompt engineering tailored to regulatory characteristics is designed,and logical reasoning capabilities are enhanced through chain-of-thought(CoT)and code-first strategies.A knowledge base system is implemented using the open-source ChatGLM4 model,employing efficient retrieval and information-matching mechanisms.Through the integration of vector databases and prompt engineering,deep injection of regulatory knowledge is achieved,significantly improving retrieval accuracy,efficiency,and reasoning performance.Experimental results demonstrate that,compared with traditional methods,the LLM-based retrieval-augmented approach achieves superior performance across multiple evaluation metrics,with an average answer similarity score of 0.94,answer relevance of 0.90,answer correctness of 0.75,and contextual precision of 0.92,providing an intelligent and high-precision pathway for applying reservoir operation regulations in practical reservoir management.