The present application belongs to the field of
software system performance
bottleneck analysis and detection, and relates to a
software system performance
bottleneck detection method based on intelligent operation and maintenance. The performance
bottleneck detection method is as follows: first, performance data is acquired; second, performance
data entry items are extracted to
complete data preprocessing; third,
test data is subjected to data two-group bottleneck clustering to obtain bottleneck time point marking division; fourth, training data is trained to obtain
model architecture parameters and complete input data representation mapping; fifth,
test data is trained to complete representation mapping with the training data; and sixth, the performance bottleneck detection result corresponding to the
test data is obtained. The present application has low requirements for
performance index data itself, a wider applicable scope, can effectively help complete
system performance bottleneck analysis and interpretation by means of
artificial intelligence, and has good applicability and robustness.