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（mmWave）多跳集成接入与回程（IAB）网络中，移动障碍物引起的动态阻塞对网络的鲁棒性和连续性构成严重威胁。数据包复制（PD）虽能通过路径分集提升可靠性，但不可避免地使流量负载加倍，导致严重拥塞和信息年龄（AoI）恶化。为解决这一可靠性-拥塞权衡问题，我们在多跳IAB场景中构建了一个优化问题，旨在满足严格队列稳定约束的同时最小化平均AOI。利用Lyapunov优化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，提出了一种弹性与新鲜度感知调度（RFAS）算法。仿真结果表明，在易阻塞环境中，RFAS通过维持高于95%的数据包交付率（PDR）显著优于基线方法。关键在于，它在硬缓冲区约束下严格保证队列稳定性，而基线方法则会出现缓冲区溢出。此外，在高频流量场景中，RFAS相比基线将网络负载不均衡性降低19%。这证实了RFAS作为实时工业控制环路的稳健且可持续解决方案的有效性。