Multisource Heterogeneous Data Fusion Methods Driven by Digital Twin on Basis of Prophet Algorithm

Abstract

With the development of intelligent manufacturing and the wider application of the Internet of Things (IoT), it is crucial to fuse heterogeneous sensor data from multiple sources. However, the current data fusion methods still have problems, such as low accuracy of fused data, insufficient data integrity, poor data fusion efficiency, and poor scalability of fusion methods. In response to these issues, this article explores a multisource heterogeneous data fusion method based on the Prophet algorithm digital twin drive to improve the fusion effect of sensor data and provide more support for subsequent decision-making. The article first used curve and sequence alignment to extract data features and then analyzed the trend of data changes using the Prophet algorithm. Afterward, this article constructed a digital twin model to provide analytical views and data services. In conclusion, this paper used tensor decomposition to merge text and image data from sensor data. Deep learning algorithms and Kalman filtering techniques were also examined to confirm the efficacy of data fusion under the Prophet algorithm. The experimental results showed that after fusing the data using the Prophet algorithm, the average accuracy can reach 92.63%, while the average resource utilization at this time was only 9.97%. The results showed that combining Prophet with digital twin technology can achieve higher accuracy, fusion efficiency, and better scalability. The research in this paper can provide new ideas and means for the fusion and analysis of heterogeneous data from multiple sources.