Abstract: This study addresses the data standardization challenges in digital twin systems for irrigation districts. By integrating five critical data dimensions—foundational data, monitoring data, operational management data, geospatial data, and cross-sectoral shared data—a three-tiered standardization framework is proposed, encompassing data classification, naming and encoding protocols, and data format/content specifications. The framework establishes a hierarchical data classification system, formulates standardized naming rules with structured encoding schemes, and defines unified data formats and metadata requirements. Through systematic implementation, the multidimensional value of the standardization system is demonstrated, including enhanced interoperability in heterogeneous data integration, ensured spatial-topological consistency, and improved model parameter compatibility. The research develops a universal methodology for water conservancy data governance, providing a reusable technical standard system for digital twin platform development in irrigation districts. This work contributes to advancing the digital transformation of water infrastructure by offering scalable solutions for system integration and intelligent management.