Research on the Construction of the Yellow River Smart Ice Prevention Application

Abstract

Reliable monitoring and forecasting of large-scale environmental hazards require efficient sensing, robust information transmission, and intelligent analysis of heterogeneous spatiotemporal data. This study proposes a smart ice-prevention framework for the Yellow River based on distributed sensing networks, digital twin technology, and multi-source information fusion. A hierarchical architecture integrating monitoring and perception networks, communication infrastructure, cloud-based computing resources, and digital twin platforms is developed to support real-time acquisition and management of hydrological, meteorological, and ice-condition information. To improve situational awareness and forecasting capability, heterogeneous data from ground sensors, video monitoring systems, unmanned aerial vehicles, and remote-sensing platforms are fused through a multidimensional spatiotemporal data model. Machine-learning-based prediction models, pattern-recognition algorithms, and knowledge-driven reasoning mechanisms are further employed to achieve ice-condition forecasting, early warning generation, and emergency-response support. Experimental deployment demonstrates that the proposed framework significantly enhances monitoring efficiency, forecasting accuracy, and decision-support capability for ice-prevention operations. By integrating distributed sensing, spatiotemporal information fusion, digital twin modeling, and intelligent forecasting, the proposed framework provides an effective methodology for large-scale monitoring systems, environmental sensing networks, and data-driven hazard management in complex dynamic environments.