Intelligent Root Cause Judgment Method for Network Optimization Based on Post-term Self-learning Decision Tree

The large-scale application of 5G network brings new challenges for centralized optimization work. This paper introduces a self-learning wireless network intelligent root cause method based on LTE and NR networks, which is mainly applied to the root cause analysis and positioning in the process of centralized network optimization analysis, and concentrates superior resources to solve the basic problems affecting network deterioration. This method is mainly based on expert experience and work order cases, excavates the influence degree of various wireless problems R and indicator K found in daily cell, and adopts the root cause judgment algorithm of multi-factor quantification weight calculation such as backward learning, probability factor, time-domain heartbeat correlation, and variable Pearson correlation for automatic root cause analysis and proposal output