AI 中文总结
本文针对无人机边缘网络的模型更新与资源分配问题，提出LYRA框架，通过OSDR触发更新、SASS同步及李雅普诺夫引导的离散强化学习算法，提升语义保真度与资源效率，性能优于基线。
AI 中文摘要
在部署分层视觉模型处理关键任务时，无人机（UAV）边缘系统必须自适应更新模型，以在低级环境损坏下维持推理可靠性。然而，现有研究忽略了模型更新的最优时机、依赖实时专家标签的不实用性，以及无人机显著的带宽和能量约束。本文提出一种联合模型更新调度与资源分配框架，旨在最大化无人机边缘智能系统的长期语义保真度和资源效率。为解决无标签语义评估的挑战，我们将在线语义分歧率（OSDR）公式化为及时触发更新的代理指标，从而实现细粒度的感知敏感型结构同步（SASS）。此外，为克服混合动作空间中的维数灾难并有效约束长期能量预算，我们提出一种李雅普诺夫引导的离散强化学习算法，该算法执行动作空间降维并将约束转化为虚拟队列稳定性问题。基于真实流量轨迹的实验结果表明，所提框架在满足长期能量预算的前提下，在语义恢复效率和更新触发精度上始终优于代表性基线，且在动态环境损坏场景中可将平均风险积压最多降低33.3%。
英文摘要
While deploying hierarchical vision models to process mission-critical tasks, UAV edge systems must adaptively update the models to sustain inference reliability under low-level environmental corruption. However, existing work has overlooked the optimal timing for model updates, the impracticality of relying on real-time expert labels, and the significant bandwidth and energy constraints of UAVs. This paper proposes a joint model update scheduling and resource allocation framework, aiming to maximize long-term semantic fidelity and resource efficiency of UAV edge intelligence systems. To address the challenge of label-free semantic evaluation, we formulate the Online Semantic Disagreement Rate (OSDR) as a proxy for timely update triggering, thereby enabling fine-grained Sensitivity-Aware Structural Synchronization (SASS). Furthermore, to overcome the curse of dimensionality in hybrid action spaces and effectively bound long-term energy budgets, we propose a Lyapunov-guided discrete reinforcement learning algorithm that performs action space dimensionality reduction and transforms constraints into virtual queue stability problems. The reported experimental results, based on real traffic traces, demonstrate that the proposed framework consistently outperforms representative baselines in semantic recovery efficiency and update triggering precision, by satisfying long-term energy budget and by reducing average risk backlog by up to 33.3\% in the dynamic environmental corruption scenario.
