This invention discloses a method for updating and feedback mechanisms based on personalized profiles of
bus drivers, belonging to the field of intelligent transportation and driving behavior analysis technology. The method first constructs an initial
safety profile of the driver, including baseline physiological characteristics, operating habits, and historical violation records. During vehicle operation, driving behavior events, physiological states, and environmental
context data are collected in real time via the onboard terminal. Combined with
risk assessment results from an
edge server, behavioral deviation and risk contribution factors are dynamically calculated. Based on a time-decay weighted and
incremental learning strategy, key dimensions in the profile, such as fatigue sensitivity, distractibility tendency, and aggressive driving index, are updated online. Simultaneously, the profile update results and early warning handling effects form a
feedback loop, used to optimize
risk assessment model thresholds, adjust early warning strategy priorities, and push personalized safety improvement suggestions to the driver. When the profile
stability index falls below a preset threshold, a profile recalibration process is triggered. This invention achieves
adaptive evolution of
driver safety profiles and synergistic optimization of
system strategies, significantly improving the accuracy and effectiveness of
bus operation safety management and intervention.