Vehicle‐Pile‐Network Digital Twin Based on Adaptive Consensus Mechanism of Unity Data‐Driven Model

Although the vehicle‐pile‐network Digital Twin technology has been gradually applied in intelligent transportation and energy Internet, there are obvious shortcomings in the existing research. Specifically, some DT models adopt a fixed parameter configuration. When the traffic flow fluctuates, the virtual mapping deviation is too large, and the deep coupling of the data‐driven model and the adaptive consensus mechanism is not realized. Therefore, this study adopts the collaborative application of Unity real time simulation technology data driven modeling and adaptive consensus mechanism (URT‐DDM & ACM). It builds the vehicle‐pile‐network DT modeling and multi‐node collaborative application technology. Innovatively, it builds a series of optimization formulas, such as multi‐source data fusion and mapping accuracy calibration for collaborative architecture, to address the shortcomings of poor dynamic adaptability in existing technology. The experiment is carried out in the Unity simulation environment. As a result, the virtual model mapping deviation is controlled at 6.8% ± 0.4%, which is 37.3% lower than the existing instance. The multi‐node consensus delay is reduced to 72 ± 5 ms, which is 64% lower than the existing technology on average. The comprehensive performance, robustness, and practicability indexes are all above 0.92, which is 9.6%–16.3% higher than the new combination technology after 2023. This study effectively realizes high‐precision mapping, efficient consensus and real‐time simulation of vehicle‐pile‐network DT. Consequently, it provides major technical support for its engineering application, and helps integrate deeply into the intelligent transportation and energy Internet.