The Strategy of Digital Twin Convergence Service based on Metavers

Abstract

The Advanced and radical development of IT technology and artificial intelligence technology have made it possible to develop advanced services Digital Twin, Metaverse, Metatwin-verse, etc using Artificial Intelligence(AI). The results induced from AI present the correct solution when AI performs accurate study and analysis. Specifically, real situations reflecting complex relationships between objects, results from real situations have to be adaptive to convergence situations and then it should be possible to draw conclusions and make decisions that are not limited to specific situations. So, it is essential to conduct AI based study and analysis by considering these real world characteristics to provide digital twin services based on metaverse. Recently, there are many studies on Graph Neural Network(GNN) and services applied to GNN for learning the relationship between objects detected in real situations. Accordingly, this paper proposes a metaverse-based Digital Twin Convergence Service(DTCS) including spatial elements strategy that is possible to draw accurate conclusions in a changing convergence situation. DTCS is able to conduct causal reasoning and association learning between objects considering directions and distances change characteristics between objects and this is possible to make correct solution and decision making in the process of simulation and analysis of digital twin. In that DTCS proceeds by considering distance and changing angle between objects, this overcomes the limitation of existing GNN which only considers the degree of association or assigns the same parameters to connected objects. DTCS would be possible to expand the advanced services of Metatwinverse in that it is possible to have robust learning based conclusions in real-time changing convergence situations.