Digital twin technologies help practitioners simulate, monitor, and predict undesirable outcomes in-silico, while avoiding the cost and risks of conducting live simulation exercises. Virtual reality (VR) based digital twin technologies are especially useful when monitoring human Patterns of Life (POL) in secure nuclear facilities, where live simulation exercises are too dangerous and costly to ever perform. However, the high-security status of such facilities may restrict modelers from deploying human activity sensors for data collection. This problem was encountered when deploying MetaPOL, a digital twin system to prevent insider threat or sabotage of secure facilities, at a secure nuclear reactor facility at Oak Ridge National Laboratory (ORNL). This challenge was addressed using an agent-based model (ABM), driven by anecdotal evidence of facility personnel POL, to generate synthetic movement trajectories. These synthetic trajectories were then used to train deep neural network surrogates for next location and stay duration prediction to drive NPCs in the VR environment. In this study, we evaluate the efficacy of this technique for establishing NPC movement within MetaPOL and the ability to distinguish NPC movement during normal operations from that during a simulated emergency response. Our results demonstrate the success of using a multi-layer perceptron for next location prediction and mixture density network for stay duration prediction to predict the ABM generated trajectories. We also find that NPC movement in the VR environment driven by the deep neural networks under normal operations remain significantly different to that seen when simulating responses to a simulated emergency scenario.

翻译：数字孪生技术能够帮助从业者在虚拟环境中模拟、监测并预测不良结果，同时避免开展实景模拟演练所需的高昂成本与风险。在核安保设施中监测人员行为模式时，基于虚拟现实的数字孪生技术尤为有效，因为此类设施的实景演练既极度危险又成本高昂。然而，这类设施的高安保等级可能限制建模人员部署用于数据采集的人员活动传感器。在橡树岭国家实验室某安保核反应堆设施部署MetaPOL系统（一种用于防范内部威胁或破坏安保设施的数字孪生系统）时，我们便遇到了此问题。本研究通过采用基于智能体的建模方法，结合设施人员行为模式的案例证据生成合成移动轨迹，从而应对该挑战。这些合成轨迹随后用于训练深度神经网络代理模型，以实现下一位置预测与停留时长预测，进而驱动虚拟现实环境中的非玩家角色。本研究评估了该技术在MetaPOL系统中建立非玩家角色移动行为的有效性，以及区分常规运行与模拟应急响应期间非玩家角色移动模式的能力。实验结果表明：采用多层感知机进行下一位置预测，结合混合密度网络进行停留时长预测，能够有效预测基于智能体模型生成的轨迹。同时发现，在常规运行状态下由深度神经网络驱动的虚拟现实非玩家角色移动模式，与模拟应急场景响应时的移动模式存在显著差异。