Multisource heterogeneous data integration and real-time analysis technology for cigarette production digital workshop

This study proposes a comprehensive framework for multi-source heterogeneous data integration and real-time analysis in cigarette production digital workshops, addressing critical challenges in dynamic industrial environments. By combining edge-cloud collaboration, adaptive semantic reconciliation, and lightweight stream processing, the framework achieves efficient fusion of heterogeneous data from IoT sensors, PLC systems, and quality inspection units. A hybrid modeling approach integrating Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) networks captures spatiotemporal dependencies in production data, reducing defect prediction errors by 12.8% compared to conventional methods. Experimental results demonstrate 94.7% cross-source anomaly detection accuracy with sub-200ms latency, alongside tangible operational improvements: 63% reduction in unplanned downtime, 15.2% energy savings in drying processes, and 2.1 tons/month reduction in material waste. The system exhibits scalability across product categories, enhancing parameter optimization cycles by 22% for traditional cigarettes and batch consistency by 18%fornovel tobacco products. While demonstrating efficacy in real-world production scenarios, limitations in ultra-high-frequency data processing highlight directions for future research, including quantum-enhanced algorithms and 5G-Advanced-enabled digital twins. This work establishes a foundational architecture for intelligent manufacturing in process industries, bridging the gap between real-time analytics and autonomous decision-making.