A digital twin (DT) contains a set of virtual models of real systems that are synchronized to their physical counterparts. This enables quick experimentation, simulating the consequences of decisions in real time. However, the DT's accuracy depends on timely updates that maintain alignment with the real system. We can distinguish between: (i) pull-updates, which follow a request from the DT to the sensors, to decrease its drift from the physical state; (ii) push-updates, which contain anomalies and are sent proactively by the sensors. In this work, we devise a push-pull scheduler (PPS) to integrate the two types of updates and dynamically allocate resources. Our scheme strikes a balance in the trade-off between DT alignment in normal conditions and anomaly reporting, reducing model drift by over 20% with respect to state-of-the-art solutions, while maintaining the same anomaly detection guarantees, as well as reducing the worst-case anomaly detection age of incorrect information (AoII) from 70 ms to 30 ms under the same drift constraint.

翻译：数字孪生包含真实系统的虚拟模型集合，这些模型与其物理对应物保持同步。这使快速实验成为可能，可实时模拟决策后果。然而，数字孪生的精度取决于维持与真实系统对齐的及时更新。我们可区分两种更新方式：(i) 拉取更新，遵循数字孪生对传感器的请求，以降低其与物理状态的漂移；(ii) 推送更新，包含异常信息并由传感器主动发送。本研究设计了一种推拉调度器来整合两种更新类型并动态分配资源。该方案在正常条件下的数字孪生对齐与异常报告之间取得平衡，相较现有最优方案将模型漂移降低20%以上，同时保持同等异常检测保障，并在相同漂移约束下将最坏情况下的异常检测错误信息时效从70毫秒降至30毫秒。