Detection Method of Large Industrial CT Data Transmission Information Anomaly Based on Association Rules

In the abnormal detection of large industrial CT data transmission information, the network is unstable and vulnerable to noise interference, resulting in the unstable output energy of the ray source, making the detection accuracy of data transmission information abnormal low. To solve this problem, a large industrial CT data transmission information anomaly detection method based on association rules is designed. Through association rule mining algorithm, the data transmission information of large-scale industrial CT is analyzed, and the association rules are obtained by introducing interest threshold. The improved Apriori algorithm is adopted to improve the accuracy of association rule mining. According to the results of association rule mining, the nonlinear wavelet transform threshold denoising algorithm based on the improved threshold function is used to denoise the information data. By calculating the abnormal probability of information entropy in data flow and sliding window, the abnormal detection of data transmission information is realized. Experimental results show that the proposed method has high detection accuracy and short average anomaly detection time.