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].

全国大江大河洪水预见期从3天延长到10天。 9月29日，国务院新闻办举行高质量完成"十四五"规划系列主题新闻发布会，介绍"十四五"时期水利高质量发展成就。 水利部规划计划司司长张祥伟在发布会上回答第一财经记者提问时表示，数字化、网络化、智能化是未来趋势，也是发展方向。"十四五"以来， 水利部门系统谋划推进数字孪生水利体系建设，也就是数字孪生流域、数字孪生水网、数字孪生水利工程，有效赋能流域防洪、水资源管理与调 配等业务应用，强化了预报预警预演预案"四预"功能，成为推动水利行业新质生产力发展的显著标志。 张祥伟介绍，在推进数字孪生水利体系建设中，重点抓监测感知、数学模型建设和业务应用。在监测感知方面。"十四五"以来，加快构建"天空地 水工"一体化监测感知系统，大力提升对水利物理对象的透彻感知能力，为水利工程安全风险监测防控和数字孪生水利体系构建和运行提供动态实 时信息支持。 在数学模型方面，立足数字孪生水利体系精准映射、虚实交互、实现"四预"功能。加快构建"高保真"数字流场模拟数学模型系统，实现业务"正向 —逆向—正向"推演应用，为水旱灾害防御和流域水工程统一联合调度提供决策支持。 在业务应用方面，坚持从实际需 ...