To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode.

为了在瞬息万变的复杂世界中完成日益艰巨而艰巨的任务，将开发具有更高灵活性和适应性的智能工业产品。数字孪生（DT）带来了一种可能的手段，因为它能够根据收到的来自其物理部分的“反馈”提供候选行为调整。但是，这种候选调整是确定性的，因此缺乏灵活性和适应性。为了解决这个问题，本文提出了扩展的概念-进化数字孪生（EDT）和基于EDT的智能工业产品开发新模式。借助我们建议的EDT，可以通过监督学习建立更精确的物理世界近似模型，在此基础上，可以通过强化学习进行在多个网络空间中通过并行仿真对最优策略进行协作探索。因此，可以通过基于机器学习（例如监督学习和强化学习）的自我进化为工业产品带来更大的灵活性和适应性。为了初步验证我们提出的方法的有效性，我们进行了案例研究。实验结果充分证实了我们基于EDT的开发模式的有效性。为了初步验证我们提出的方法的有效性，我们进行了案例研究。实验结果充分证实了我们基于EDT的开发模式的有效性。为了初步验证我们提出的方法的有效性，我们进行了案例研究。实验结果充分证实了我们基于EDT的开发模式的有效性。