Design of a Real-Time Monitoring and Pollution Early Warning System for Industrial Flue Gas Emissions Based on Embedded Edge Computing

Real-time environmental monitoring in industrial plants requires rapid signal processing, reliable data transmission, and accurate anomaly detection under complex operating conditions. To address the limitations of conventional centralized monitoring systems, this study proposes an industrial flue-gas monitoring and early-warning framework based on embedded edge computing. A distributed sensing architecture integrating multi-sensor arrays, edge computing nodes, and cloud-assisted management is developed to enable local real-time processing and low-latency response. Multi-sensor fusion algorithms are employed to improve measurement reliability for pollutant concentration, flow rate, and environmental parameters, while lightweight intelligent models deployed at edge nodes perform anomaly detection and trend prediction. A cloud–edge collaborative mechanism is further introduced to support continuous model optimization and distributed network management. The modular architecture incorporates wireless Mesh networking and fault-tolerant data synchronization, enhancing system robustness in environments affected by electromagnetic interference and unstable communication conditions. Compared with conventional centralized systems, the proposed framework significantly improves response speed, monitoring accuracy, and early-warning capability while reducing network dependence and deployment complexity. The proposed architecture provides an effective engineering solution for distributed sensing, intelligent monitoring, and real-time information processing in large-scale industrial environments.