Application of Digital Twin Technology in Intelligent Inspection of Reservoir Areas
数字孪生技术在水库库区智能巡检中的应用

摘要

水库库区覆盖范围广、地质环境复杂、安全要素众多，传统巡检方式在实时性、全面性和精准性方面存在明显短 板。数字孪生技术通过构建物理库区的高保真虚拟映射体，能够融合多源异构监测数据，实现库区状态实时感知、异 常精准辨识和巡检任务动态优化。本文立足水库库区“大坝—库岸—水域”协同巡检的需求特点，系统梳理了数字孪 生技术在库区智能巡检中的应用架构，重点论述了多源数据融合与实时映射、巡检路径与任务动态规划、视觉与点云 智能缺陷识别、异常场景仿真推演等关键技术，并设计了日常巡查、专项检查与应急巡查相衔接的闭环业务模式。 

Abstract

Reservoir areas usually cover wide spatial ranges, involve complex geological environments, and contain numerous safety-related factors. Traditional inspection methods have obvious shortcomings in real-time performance, comprehensiveness, and accuracy. By constructing a high-fidelity virtual mapping of the physical reservoir area, digital twin technology can integrate multi-source heterogeneous monitoring data and realize real-time perception of reservoir-area conditions, accurate identification of abnormalities, and dynamic optimization of inspection tasks. Based on the requirements of coordinated inspection involving “dam–reservoir bank–water area” systems, this paper systematically reviews the application architecture of digital twin technology in intelligent inspection of reservoir areas. It focuses on key technologies such as multi-source data fusion and real-time mapping, dynamic planning of inspection routes and tasks, intelligent defect identification based on visual data and point clouds, and simulation-based deduction of abnormal scenarios. In addition, it designs a closed-loop business model that connects routine inspection, special inspection, and emergency inspection.