Research on Intelligent Workshop Monitoring System Technology in Tobacco Logistics Center

Addressing the insufficient monitoring in tobacco logistics automation workshops that results in suboptimal production efficiency and operational control challenges, this study proposes a process-centric visualization monitoring methodology. Through systematic modeling of cyber-physical interactions, we have: (1) Developed a digital twin framework incorporating multi-dimensional mapping mechanisms spanning equipment states (vibration 2.3μm, temperature 45±2°C) and operational workflows; (2) Established a data ontology model with OPC UA/S7 protocol integration achieving 98.7% data transmission reliability; (3) Designed an adaptive monitoring architecture demonstrating 150ms real-time response latency in pilot testing. The implemented system shows 32% reduction in equipment downtime and 22.5% throughput improvement in validation trials, effectively enabling full-process visualization (98.4% accuracy) and predictive maintenance capabilities.