OIDM：一种面向工业信息物理系统的基于可观测性的智能分布式边缘感知方法
OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical Systems
AI总结：
针对工业信息物理系统随机调度中可观测性研究不足的问题，提出基于可观测性的智能分布式边缘感知方法OIDM，结合深度强化学习与可观测性概率界优化感知性能，经热轧板坯温度估计仿真验证有效。
摘要：
工业信息物理系统（ICPS）将工业场景中的物理过程与计算、通信技术相融合。在边缘计算技术的支撑下，调度大规模传感器实现高效分布式感知具备可行性。在感知过程中，可观测性是获取完整系统状态的关键，考虑到无线通信中的不确定因素，随机调度更具适用性，但现有工作对随机调度中的可观测性研究较为有限。针对这一问题，我们提出了基于可观测性的智能分布式边缘感知方法OIDM。该方法采用深度强化学习（DRL）优化感知精度与能效，基于系统实现可观测性的能力，在可观测性与传感器成功传输数量之间建立关联，提出了可观测性判据的新型线性近似，并推导了可观测性的概率界。此外，这些概率界还指导动作空间设计，在随机调度中实现概率可观测性保障。最后，我们将所提方法应用于工业热轧过程的板坯温度估计，仿真结果验证了其有效性。
英文摘要：
Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness.
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