In industrial millimeter-wave (mmWave) multi-hop Integrated Access and Backhaul (IAB) networks, dynamic blockages caused by moving obstacles pose a severe threat to robust and continuous networks. While Packet Duplication (PD) enhances reliability by path diversity, it inevitably doubles the traffic load, leading to severe congestion and degraded Age of Information (AoI). To navigate this reliability-congestion trade-off, we formulated an optimization problem in a multi-hop IAB scenario that minimizes the average AOI while satisfying strict queue stability constraints. We utilize Lyapunov optimization to transform the long-term stochastic optimization problem into tractable deterministic sub-problems. To solve these sub-problems efficiently, we propose a Resilient and Freshness-Aware Scheduling (RFAS) algorithm. Simulation results show that in blockage-prone environments, RFAS significantly outperforms baselines by maintaining a Packet Delivery Ratio (PDR) above 95\%. Crucially, it strictly guarantees queue stability under hard buffer constraints, whereas baselines suffer from buffer overflows. Furthermore, RFAS reduces the network load imbalance by 19\% compared to the baseline in high-frequency traffic scenarios. This confirms RFAS as a robust and sustainable solution for real-time industrial control loops.

翻译：在工业毫米波多跳集成接入与回传网络中，移动障碍物导致的动态阻塞对网络的鲁棒性与连续性构成严重威胁。分组复制技术通过路径分集提升了可靠性，但不可避免地使流量负载加倍，导致严重拥塞并劣化信息年龄。为权衡可靠性与拥塞矛盾，我们在多跳IAB场景中构建了优化问题，旨在满足严格队列稳定性约束的同时最小化平均信息年龄。利用李雅普诺夫优化方法，将长期随机优化问题转化为可处理的确定性子问题。为高效求解这些子问题，我们提出了弹性与信息新鲜度感知调度算法。仿真结果表明，在易阻塞环境中，该算法在保持分组投递率高于95%方面显著优于基线方法。关键在于，该算法在严格缓冲区约束下能切实保证队列稳定性，而基线方法则出现缓冲区溢出问题。此外，在高频流量场景中，该算法将网络负载不均衡度较基线降低了19%。这证实了该算法可作为实时工业控制回路的鲁棒且可持续的解决方案。