Digital Twin-Enabled Governance Modernization and Engineering–Industrial Safety Risk Prevention in a Large-Scale Irrigation District: Evidence and Pathways from the Qinglinghe Irrigation District, China

摘要

The digital transformation of large-scale irrigation districts hinges not on the accumulation of devices or three-dimensional visualization, but on restructuring water supply scheduling, engineering maintenance, water-use services, and safety prevention and control systems through real-time data, specialized models, and institutional rules. Employing literature review and case analysis, this paper synthesizes research on digital twins and smart irrigation, draws on materials concerning the modernization of the Qinglinghe Irrigation District and the comprehensive agricultural water price reform, and constructs a five-dimensional analytical framework of “data–model–operations–safety–institutions.” The findings indicate that the district has established foundations in trunk infrastructure, flow measurement facilities, and water rights and pricing mechanisms, yet its development remains constrained by insufficient terminal-level sensing, inconsistent data standards, weak coupling between models and operations, disconnection between safety prevention and digital platforms, and inadequate cross-county coordination and long-term operation and maintenance mechanisms. Accordingly, pathways are proposed in line with demand-driven, layered, and iterative principles: improving integrated sensing and a unified data foundation; prioritizing high-frequency operational applications; establishing human–machine collaborative control together with a tiered closed-loop system for engineering safety prevention; and safeguarding sustained operation through organizational, human-resource, financial, and institutional arrangements. Only when digital twins are embedded in structures of authority and responsibility and in daily operations—while safety is held as the bottom line—can they be translated into genuine irrigation governance capacity.