A Virtual Data Service Method Based on Autoregressive Model Optimized by Particle Filter

Aiming at the problems of random loss and fragment loss of monitoring data during the perception and transmission process, a virtual data service method based on autoregressive model optimized by particle filter is proposed. Firstly, the construction of autoregressive model for monitoring data is studied. The historical data are used to estimate the model parameters, and the virtual data are generated by model extrapolating to recover the missing data. Secondly, aiming at the problem that autoregressive model cannot adapt to the change of data trend caused by the state evolution of the monitored equipment, the particle filter is used to optimize the model parameters to improve the adaptability and accuracy of virtual data estimation. Finally, the simulating analysis and experimental results validate the effectiveness of the proposed method.