Digital Twins (DT) have become crucial to achieve sustainable and effective
smart urban solutions. However, current DT modelling techniques cannot support
the dynamicity of these smart city environments. This is caused by the lack of
right-time data capturing in traditional approaches, resulting in inaccurate
modelling and high resource and energy consumption challenges. To fill this
gap, we explore spatiotemporal graphs and propose the Reinforcement
Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN).
By doing so, our study contributes to advancing Green Cities and showcases
tangible benefits in accuracy, synchronisation, resource optimization, and
energy efficiency. As a result, we note the spatiotemporal graphs are able to
offer a consistent accuracy and 55% higher querying performance when
implemented using graph databases. In addition, our model demonstrates
right-time data capturing with 20% lower overhead and 25% lower energy
consumption.

数字孪生（DT）对于实现可持续和有效的智慧城市解决方案至关重要，但现有的 DT 建模技术无法支持智慧城市环境的动态性，本研究探讨了时空图，并提出了基于强化学习的自适应孪生（RL-AT）机制，通过深度 Q 网络（DQN）的方法来填补该空白，该研究在精确性、同步性、资源优化和能源效率方面做出了贡献，时空图模型能够以更一致的精确性提供 55% 更高的查询性能，此外，我们的模型还证明了较低的开销和节能消耗率，实现了合理时间的数据获取，开销下降了 20%，能源消耗下降了 25%。