Research On Data Completion For Digital Twins

With the development of the next generation of industrial intelligence and information systems,digital twin technology,as a way to simulate,predict,and optimize physical systems and processes,has become a core driving force and an emerging research hotspot for various fields in the future.Digital twins are an accurate mapping of physical objects in the real world throughout their entire lifecycle,with real-time continuity,and their construction and update depend on the two processes of data source collection and transmission.However,due to factors such as device resource limitations,data privacy issues,and communication transmission failures,digital twins face the risks of missing data and high latency,which cannot meet the requirements for precision and real-timeness of digital twins.If no corresponding data completion mechanism is applied,insufficient feature information and incorrect conclusions will result,making it impossible to construct and update digital twins that accurately correspond to physical objects.This article focuses on the actual scene of digital twins,building and updating digital twins through complete data of physical objects,and explores the need for different data completion requirements in different offline and online scenarios during data synchronization.The main work and innovation of this article are as follows:Against the backdrop of the online data flow scene of digital twin,a dynamic Bayesian network-based online data completion scheme was designed.Firstly,in view of the complex and time-consuming problem of learning Bayesian networks,a hybrid parallel search algorithm based on simulated annealing and greedy algorithms was proposed to complete the Bayesian network structure.Then,the EM algorithm was used to complete the parameter learning and construct a dynamic Bayesian network for completion.Finally,based on the data completion,the efficiency evaluation was used to generate corresponding improvement plans and feedback was used to update the model parameters in the dynamic Bayesian network,optimizing the accuracy of the model’s completion effect real-time.Through comparison experiment analysis,it was shown that the designed scheme was more effective than existing Bayesian completion methods in improving the precision of data completion by 11%,and the efficiency and fit of structure learning were improved by about 50% and 19%,respectively.The real-time accuracy of data completion was controlled within a range of 30-40 ms,ensuring the precision and real-trimness of information interaction.For the offline data stream scenario of digital twin system,an offline data completion scheme based on improved temporal sequence and shape-shifting long short-term memory network is designed to deeply explore the temporal signatures and relational features among different dimensional attributes within the digital twin data stream,and use the fused temporal features to complement the missing data streams to ensure the accuracy and consistency of the data for the whole system.Through comparison experiments with other completion algorithms,the completion scheme designed in this thesis improves the completion accuracy by 5% with Mogrifier-LSTM,indicating that the proposed scheme can be adapted to the completion scenario of offline data streams in digital twin systems,and ensures the high accuracy and consistency of data completion.Finally,a comparison of the designed online/offline completion schemes is carried out to show the reasonableness of the online/offline scenarios delineated in this thesis.