Abstract: In the wave of digital transformation,the anomaly detection and repair of business processes are crucial for ensuring the operational efficiency and decision-making quality of enterprises.Meanwhile,higher requirements are put forward for its detection and repair technologies.Traditional anomaly detection methods can no longer meet the needs of real-time monitoring and adaptive adjustment of current business processes.Most of the existing methods focus on static analysis and do not fully consider the complexity and variability of the business environment,so it is difficult to adapt to the needs of dynamic changes in processes.Based on this,this paper innovatively proposes the AAHM.This method improves the accuracy of anomaly detection and the effectiveness of repair through dynamic parameter adjustment and real-time data feedback.To verify the effectiveness of this me-thod,four groups of real event logs are used for simulation in the experiment.The results show that this method can effectively identify and repair abnormal behaviors and restore the normal execution of business processes through feature vector completion and behavior repair strategies.In addition,through post hoc test analysis of the experimental results,the effectiveness and rationality of the proposed method are further verified.

Key words: Process mining, Anomaly detection, Adaptive method, Abnormal repair, Post hoc test