The rapid development of large-model technology offers a new technical pathway for the intelligent transformation of the water conservancy sector. To address key challenges in water conservancy informatization

including numerous policies and regulations

heterogeneous data formats

siloed systems

and high barriers to the use of professional models

this study explores the systematic integration of large-model technology into the full workflow of water conservancy operations and develops a comprehensive knowledge service platform integrating intelligent QA

convenient tool invocation

and automated document generation. The framework establishes a four-layer logical architecture

with the Data-Model Foundation Layer serving as the data and model base

the Technology-Driven Layer providing common capabilities such as localized deployment and knowledge retrieval

the Function Implementation Layer performing intelligent scheduling and closed-loop optimization

and the Business Application Layer directly addressing core water conservancy operations. This study systematically describes four key technologies: large-model selection and local deployment

vector-free knowledge retrieval

modular MCP transformation of legacy applications

and agent-assisted task decomposition

thereby addressing challenges related to information security

knowledge utilization

system integration

and complex problem solving. Analysis of typical application scenarios in Sichuan Province

including the thematic water conservancy map

construction project management

and water storage and conservation

shows that the framework can effectively improve the automation and intelligence of water conservancy operations

with strong practicality and potential for broader application. It provides a feasible

systematic technical solution for the digital transformation of the water conservancy sector.