A Software Application Layer Interface Data Analysis Method Based on Quantum K-nearest Neighbor Classification Algorithm
Due to the scattered distribution of software application layer interface data,when analyzing data to determine interface status,the corresponding reliability is difficult to guarantee.To this end,a software application layer interface data analysis method based on quantum K-nearest neighbor classification algorithm is proposed.After mapping the characteristics of interface data to quantum bits,encode specific interface data using quantum states;During the analysis phase,cross validation is used to determine the optimal K value,and choose the Manhattan distance as the benchmark to measure the similarity between data points,set the center of classification as the target state parameter for software application layer interface data analysis,complete data analysis based on the distance between the interface data of the quantization software application layer and the state classification center.The test results indicate that,the design method has a high degree of fit between the analysis results of software application layer interface status and monitoring values.