数字孪生水利工程的建设重点和实践应用

Core Insights - The article discusses the construction of digital twin hydraulic engineering, focusing on data foundation, monitoring perception, data integration, intelligent model development, and precise decision support, which collectively enhance flood prevention, water resource scheduling, and engineering management [1][2]. Group 1: Data Foundation - A high-precision multi-dimensional modern surveying benchmark system is established, integrating satellite positioning and computer information technologies, which resolves issues related to spatial data standardization and fusion [3]. - The construction includes L3-level high-precision data foundations and LOD3.0 BIM models for hydraulic structures, enabling real-time dynamic physical mapping of hydraulic projects [3]. Group 2: Monitoring Enhancement - The enhancement of monitoring capabilities is crucial for the efficient operation of digital twin projects, utilizing satellite remote sensing, drones, and 5G technologies to create a dynamic monitoring and intelligent perception system [4]. - New monitoring systems are established for hydraulic structures, including online flow measurement systems and non-contact intelligent real-time detection technologies [4]. Group 3: Data Governance - Data governance and resource integration are essential for maximizing data value, involving the unification of data standards and the creation of a data directory to meet business needs [5]. - A grid-based storage and integrated management model is developed, covering all elements of hydraulic engineering for efficient data support in subsequent intelligent model development [5]. Group 4: Intelligent Model Development - Diverse intelligent analysis models are developed for water resource management, including rainfall-runoff models and structural stress analysis models, which support precise operations of hydraulic structures [7]. - The models aim to enhance flood management and optimize the scheduling of hydraulic structures, contributing to the overall efficiency of water resource management [7]. Group 5: Decision Support - Business applications are optimized to support precise decision-making, utilizing hydraulic models for flood evolution analysis and water resource scheduling [8]. - A smart management platform is constructed to facilitate the application of BIM technology throughout the lifecycle of hydraulic projects, promoting intelligent and refined management practices [8]. Group 6: Conclusion - The integration of IoT, big data, and AI technologies in digital twin hydraulic engineering promotes comprehensive data sharing and infrastructure upgrades, significantly enhancing flood prevention and water resource optimization [9]. - This initiative is strategically important for modernizing the governance system and capabilities in watershed management [9].