This invention relates to a dual-track
transition system and instruction self-evolution method for intelligent operation and maintenance (O&M) in small and medium-sized enterprises (SMEs), belonging to the field of industrial IoT intelligent O&M technology. The
system includes: seamless access to legacy O&M platforms via an API gateway and IoT
message queue, providing
data source semantic query services and supporting the discovery of callable interfaces using
natural language; an optional multimodal
perception layer generating structured semantic events; a human-
machine collaborative interaction platform comprising five windows; instructions generated manually or by a lightweight decision-making model based on multi-event, multi-indicator context, with effectiveness feedback and context jointly constituting training samples for optimizing the decision-making model; automatic triggering of replay
record generation at key nodes, supporting explicit user activation of events to capture high-value scenarios, achieving sample correction and instruction strategy self-evolution. This invention is suitable for the gradual intelligent
upgrade of resource-constrained SMEs, solving the problem that SMEs cannot deploy advanced AI model-based intelligent operation and maintenance systems.