Abstract: To enhance the operational reliability of integrated vacuum interrupter devices under extreme conditions, a real-time process automation （RPA） control engine based on multi-parameter feedback and a composite arc-extinguishing strategy combining magnetic blowing and pulsed airflow is proposed. A closed-loop feedback mechanism is established by constructing a dynamic vacuum threshold classification model, a Kalman filter-based leakage rate compensation algorithm, and a spectral inversion method for contact ablation. A dual-mode cooperative control architecture combining fuzzy PID and deep reinforcement learning ensures millisecond-level precision in opening and closing operations. Coupled with magnetic field gradient focusing and timed airflow matching, a multi-path energy dissipation model for arc plasma is formed. Experimental results show that the vacuum fluctuation range is reduced from ±12.3% to ±4.2%, the arc re-ignition probability drops from 1.5% to 0.3%, and the dielectric recovery strength increases by 27.6%. This study provides theoretical and technical support for the intelligent upgrading of vacuum interrupter devices and is applicable to circuit breaker performance optimization in scenarios such as renewable energy integration and DC distribution networks.

Key words: vacuum switching device, RPA control engine, dynamic arc extinction optimization, multi-parameter feedback, dual-mode cooperative control