多源异构数据边缘融合信息化监测仿真

Information Monitoring Simulation for Edge Fusion of Multi-Source Heterogeneous Data

由于对信息数据的需求量越来越大,为了能够获得全面、准确的信息数据,需要对各种信息数据有效融合,并实现精准监测。因此,提出一种基于边缘计算的多源异构数据融合信息化监测方法。采用小波阈值去噪方法估计并消除多源异构数据中的线性误差,完成数据预处理。通过边缘计算与PCA-DS推理方法,构建多源异构数据融合架构,完成数据融合处理。将融合处理后的数据输入到隐半马尔可夫模型中,实现多源异构数据融合信息化监测。实验结果表明,所提方法对数据监测实时性保持在95%以上,在数据融合信息化监测中,获取的监测结果精度高。

Due to the increasing demand for information data,it is necessary to effectively integrate information data and achieve accurate monitoring.Therefore,an informative monitoring method of multi-source heterogeneous data fusion based on edge computing was proposed.Firstly,the wavelet threshold de-noising method was adopted to estimate and eliminate the linear error in multi-source heterogeneous data,thus completing the data preprocessing.Then,the edge computing and PCA-DS reasoning methods were used to build an architecture of multi-source heterogeneous data fusion and thus to complete data fusion.Moreover,the fused data was input into the hidden semi-Markov model.Finally,the informative monitoring for multi-source heterogeneous data fusion was achieved.Experimental results show that the real-time performance of the proposed method for data monitoring is more than 95%,and the accuracy of the monitoring result is high.