Digital risk detection technology for power system data transmission under multi-source data fusion
In the process of multi-source data communication,it mainly relies on the frequency domain feature to detect the ab-normal data transmission signal,and ignores the time domain feature,which makes the detection result of data transmission anomaly miss rate high.Therefore,the abnormal detection technology of system data transmission under multi-source data fusion is proposed.Considering the characteristics of distributed data transmission in the process of multi-source data fusion,a transmission data acquisi-tion model including database,sensor combination and data storage security components is established.The data transmission anomaly sequence of each channel is obtained,and the data transmission sequence is reconstructed.The time-domain and frequence-domain features are extracted from the reconstructed sequences,and the feature-scale correlation coefficients of different data transmission cy-cles are deduced,so as to detect abnormal data transmission traffic.Analyze the data flow containing abnormal communication traffic,establish DOM(document object model)tree structure,obtain data transmission anomaly index through node quantitative evaluation,and output risk detection results.The experimental results show that after the application of the new data transmission anomaly detec-tion technology,the maximum missed detection rate is only 8.05％,which can more accurately identify the risk of multi-source data transmission.
multi-source data fusiontime domain featuredata transmissionreconfiguration processingabnormal flowsafety index
万方数据
维普
