Water resources authorities attach great importance to the development of digital twins system for water resources management and have proposed developing a series of demonstration application scenarios based on large language model(LLM) frameworks. However

the application of LLMs in water conservancy operations still faces challenges such as insufficient technical adaptability

data governance difficulties

and system integration barriers

which hinder their deeper integration into digital twin systems. To address these issues

this paper proposes a technical framework centered on intelligent agents and systematically describes implementation pathways for LLMs in digital twins systems for water resources management in terms of knowledge-base development

workflow orchestration

and scenario implementation. Within the Digital Twin Pearl River initiative

the flood-control AI assistant developed using this framework provides intelligent QA on water conservancy knowledge

data querying

and automated report generation

effectively improving the efficiency of flood-control decision-making and operational processing and offering practical experience that can inform broader and deeper application of LLMs in digital twin water conservancy systems.