面向数字孪生维护的可靠性感知调度
Reliability-Aware Scheduling for Digital Twin Maintenance
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中文总结 AI 辅助
针对工业物联网中数字孪生维护的观测调度难题，提出基于集成分歧指标EDI的可靠性感知更新价值调度器R-VoU，在有限通信预算下实现了更低的数字孪生估计误差与综合成本。
中文摘要 AI 辅助
在工业物联网系统中，基于学习的数字孪生（Digital Twin, DT）通过利用分布式设备上报的数据来维护物理过程的数字表示，从而支持远程监控。当上行业务资源有限时，基站无法在每个通信时隙收集所有设备的新观测值，必须决定哪些设备应进行传输。当数字孪生模型训练完成后物理过程发生变化时，该决策会变得具有挑战性。在这种情况下，仅靠近期观测值可能无法保持数字孪生的准确性，因为学习到的模型可能不再匹配底层过程。本文研究如何调度观测请求，以使基站维护的数字孪生保持接近真实的物理过程。我们将集成分歧指标（Ensemble Disagreement Indicator, EDI）定义为一种不确定性度量，由基站根据独立训练的数字孪生预测器产生的估计值之间的差异计算得出。基于EDI，我们提出R-VoU，这是一种可靠性感知更新价值调度器，它优先选择那些预计能最大程度提升数字孪生性能的观测值，该调度器通过基于不确定性和预测数字孪生误差的成本来实现这一目标。数字孪生还会根据接收的观测值与当前预测值之间的差异进行在线调整。对流程制造数据的实验表明，在有限的通信预算下，在仅使用每次调度决策前可用信息的对比调度器中，R-VoU实现了最低的综合成本和数字孪生估计误差。
英文摘要
In Industrial Internet of Things systems, learning-enabled Digital Twins (DTs) support remote monitoring by using data reported by distributed devices to maintain digital representations of physical processes. When uplink resources are limited, a base station cannot collect new observations from every device at every communication slot and must decide which devices should transmit. This decision becomes challenging when the physical process changes after the DT models have already been trained. In such cases, recent observations alone may not keep the DT accurate, because the learned model may no longer match the underlying process. This paper studies how to schedule observation requests so that the DT maintained at the base station remains close to the true physical process. We define the Ensemble Disagreement Indicator (EDI) as an uncertainty measure computed at the base station from the spread among estimates produced by independently trained DT predictors. Building on EDI, we propose R-VoU, a reliability-aware value-of-update scheduler that prioritizes the observations expected to most improve the DT by reducing a cost based on uncertainty and predicted DT error. The DT is further adjusted online using the difference between received observations and current predictions. Experiments on process manufacturing data show that, under limited communication budgets, R-VoU achieves the lowest combined cost and DT estimation error among the compared schedulers that use only information available before each scheduling decision.
