With the rapid advancement of unmanned aerial vehicle (UAV) technology, high-speed UAV swarms are increasingly applied in low-altitude complex environments. However, this also poses challenges, as traditional routing protocols struggle to cope with highly dynamic network conditions. Based on the Rainbow deep Q-network (DQN) deep reinforcement learning model, this paper suggests an intelligent routing strategy for high-speed UAVs with an emphasis on communication assurance in high-speed and highly dynamic scenarios and enhancing the capacity of UAV nodes to make autonomous, decentralized decisions. Stable factor is also designed to evaluate the future stability of communication links, enabling UAVs to autonomously make adaptive decisions based on the latest network state. According to experimental results, the high speed intelligent routing scheme suggested in this study reduces the average end-to-end delay and the per-hop delay by more than 23% when compared to traditional routing protocols, while maintaining a packet delivery ratio of more than 85% under all evaluated velocity settings. In high-frequency communication scenarios, the packet delivery ratio is improved by more than 15% on average, effectively meeting the requirements of high-speed UAV networks for stability and communication efficiency.

A prediction technique based on multi-task learning with scalar long short-term memory (MTL-sLSTM) was presented to solve the problem of remaining useful life (RUL) prediction for lithium-ion batteries in low-altitude economy vehicles under multi-condition coupling. Firstly, a heterogeneous input layer integrates multi-dimensional time-series data from diverse flight conditions. Then, a hierarchical stacked sLSTM structure serves as a shared feature extractor, enabling deep cross-scale feature integration and the capture of common nonlinear degradation patterns. Finally, by hard-coding task IDs to dynamically modulate network weights, the multi-task learning mechanism adaptively identifies each operating condition’s unique aging behavior while simultaneously promoting knowledge transfer across domains for independent RUL estimation. According to experimental results, MTL-sLSTM achieves a 60.7%–92.3% reduction in root mean square error (RMSE) on the eVTOL dataset, outperforming six temporal approaches, including the attention mixture of experts (AttMoE) and dual-channel LSTM (Dual-LSTM). This validates the effectiveness of the multi-task learning mechanism in enhancing the generalization capability of degradation features and improving prediction accuracy under complex operating conditions.