Research on Collaborative Interaction Model of Digital Twin Virtual Training for Multi-Terminals

This paper proposes a multi-user collaborative interaction virtual training model for the distribution network field, aiming to improve the task execution efficiency, response speed and stability of the system by optimizing the collaborative interaction mechanism between multiple terminals. The model supports four main types of collaboration: synchronous in the same place, asynchronous in the same place, synchronous in different places and asynchronous in different places, to adapt to the collaborative operation needs in different scenarios, especially complex tasks such as inspection and maintenance in the distribution network field. In order to increase the cooperative efficiency of multi-terminal, a hybrid deep reinforcement learning algorithm is proposed, which combines the merits of deep learning and reinforcement learning, smart scheduling, dynamic allocation of resources, and automatic optimization of cooperative strategies. Through this algorithm system, it can make adaptive adjustments according to the real-time status of each terminal (such as CPU usage, network delay, etc.), optimize load balancing, and significantly reduce delays and data loss in the interaction process. Experimental results show that the proposed algorithm has significant advantages over traditional methods in a multi-terminal collaborative environment. The response time is shortened by more than 30% compared with traditional methods, the collaborative efficiency is improved by 40%, the data synchronization consistency is maintained at more than 98%, and the task completion rate is increased by 15%. In addition, the model supports multimodal interaction (such as gestures, voice, eye movement, brain waves, etc.), providing users with a more convenient and immersive virtual training experience.