| Abstract:The safe operation and efficient management of water conservancy projects are of vital importance for national infrastructure construction and disaster prevention and mitigation. Affected by global climate change, there is an urgent need for real-time monitoring and early warning of water conservancy facilities. Unmanned aerial vehicle (UAV) technology has provided a new approach for this, but the system faces complex trade-offs between accuracy, latency, and energy consumption, which has become a key challenge in improving the intelligence level of monitoring. This paper conducts research on this joint optimization problem: Based on the characteristics of water conservancy monitoring tasks, a task classification scheme guided by AI reasoning accuracy is proposed; a joint optimization model considering comprehensive accuracy loss, processing delay, and battery consumption is established; a two-stage optimization algorithm for model matching and resource optimization is designed, which can obtain an approximate optimal solution within polynomial time. The core contribution of this paper is to build a task classification and resource allocation framework, establish a multi-objective optimization model, design an efficient convergence algorithm, and verify the effectiveness of the scheme through simulation. Experiments show that in emergency monitoring scenarios, the delay of key tasks is reduced by 42%, the accuracy is improved by 28%, the battery life of the UAV is extended by 35%, and the computing resource usage is reduced by 45% under the same monitoring quality, and it can adapt to the fluctuations of multiple tasks, providing reliable technical support for intelligent water conservancy monitoring. |
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