Аннотация
Industrial facilities operating with toxic and explosive gases require continuous monitoring systems capable not only of detecting threshold exceedances but also of anticipating hazardous trends. Conventional IoT-based gas monitoring solutions are primarily limited to real-time data acquisition and alarm triggering, which restricts their ability to prevent incidents proactively. This study presents the architecture of an intelligent predictive analytics system for gas environment monitoring that integrates sensor-series IoT gas analyzers with advanced data analytics. The proposed system is built on domestically developed SENSOR-Mine gas analyzers supporting LoRaWAN and Wi-Fi communication, centralized data storage in MS SQL Server, machine learning–based analytics implemented in Python, and a web-based visualization platform using ASP.NET MVC. Time-series forecasting models and anomaly detection algorithms are jointly employed to analyze gas concentration dynamics and identify potentially dangerous situations at early stages. Experimental validation using carbon monoxide measurements demonstrates the practical applicability of the proposed architecture for industrial safety monitoring. The presented approach provides a scalable foundation for intelligent gas environment monitoring systems aimed at reducing industrial risks and improving worker protection.