Self-service equipment intelligent operation and maintenance and predictive maintenance system based on large model

Abstract

The application belongs to the technical field of intelligent equipment maintenance, and particularly relates to a self-service equipment intelligent operation and maintenance and predictive maintenance system based on a large model, which comprises a multi-modal full-life-cycle perception collection layer, an edge-cloud collaborative large model hybrid inference engine, an intelligent decision and global resource scheduling center, an operation and maintenance full-process closed-loop management and control and model evolution module, a knowledge-enhanced operation and maintenance support library and a full-link security guarantee unit. The application can accurately identify fault precursors, predict performance degradation trends and cross-equipment fault propagation risks, completely change the operation and maintenance mode from 'after-maintenance and passive response' to 'prior-prediction and active maintenance', effectively reduce equipment downtime, reduce service losses caused by faults, break through the limitations of traditional operation and maintenance modes, realize full-life-cycle accurate control and active prediction, and significantly improve operation and maintenance foresight.

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