基于多模型协同的数字孪生奎屯河工程安全“分析–预警–处置”技术研究

Research on the “Analysis-Warning-Disposal” Technology of Digital Twin Kuitun River Engineering Safety Project Based on Multi-Model Collaboration

摘要: 在全球气候变化导致极端水文事件愈发频繁的背景下，奎屯河作为区域内至关重要的水资源载体，其工程安全正面临着严峻挑战。传统的工程安全管理模式已难以适应复杂工况的需求，亟待引入新型技术手段。本研究构建了基于多模型协同的数字孪生平台，通过有机集成水文预报模型、水动力仿真模型以及结构力学分析模型等多种专业模型，构建了“分析–预警–处置”的全链条技术体系。该体系借助物联网传感网络(如水位计、流量计、应力计、位移计)与多源数据融合技术，实现了对工程运行状态(水文、结构响应等)的实时感知；通过结合深度学习算法(如CNN、LSTM)构建智能预警模型，显著提升了安全隐患预警的精度；并依托虚拟仿真技术，对不同处置方案(如工程抢险、调度预案)的效果进行模拟预演，优化处置决策。奎屯河工程的实证应用结果表明，该技术体系使洪水预报效率提升了40%，预警响应时间缩短至15分钟以内，为水利工程安全管理提供了新型的数字化范式。本研究不仅为奎屯河工程的安全运行提供了坚实的技术保障，也为同类水利工程的安全管理提供了可借鉴的经验和技术参考。

Abstract: In the context of the increasing frequency of extreme hydrological events caused by global climate change, the engineering safety of Kuitun River, as a vital water resource carrier in the region, is facing severe challenges. The traditional engineering safety management mode is difficult to adapt to the needs of complex working conditions, and innovative technical means need to be introduced urgently. This study has developed a digital twin platform based on multi-model collaboration, integrating various specialized models such as hydrological forecasting models, hydrodynamic simulation models, and structural mechanics analysis models to create a comprehensive technical system for “Analysis-Warning-Disposal”. This system utilizes an Internet of Things (IoT) sensor network (including water level gauges, flow meters, strain gauges, and displacement meters) and multi-source data fusion technology to achieve real-time perception of the engineering operational status (such as hydrology and structural responses). By incorporating deep learning algorithms (such as CNN and LSTM) to build intelligent warning models, the accuracy of safety hazard warnings has been significantly improved. Additionally, relying on virtual simulation technology, the effects of different disposal plans (such as emergency rescue and scheduling plans) are simulated and rehearsed to optimize decision-making in disposal. The empirical application results of the Kuitun River project show that the technical system improves the flood forecasting efficiency by 40% and shortens the early warning response time to less than 15 minutes, which provides an innovative digital paradigm for the safety management of water conservancy projects. This study not only provides a solid technical guarantee for the safe operation of the Kuitun River project, but also provides a reference experience and technical reference for the safety management of similar water conservancy projects.