Dynamic Accident Monitoring and Early Warning Technology for Chemical Production Processes Based on Risk Situation Awareness

Dynamic early warning technology for accidents is an important means to enhance safety management efficiency, particularly significant in chemical production processes. This paper explores risk situation awareness as the core of accident early warning technology, emphasizing its effective predictive capability regarding safety status changes supported by massive monitoring data. A risk mutation model for chemical production processes based on risk entropy theory is proposed, which includes the construction of a risk entropy indicator system and its weighting. Furthermore, a three-tier accident monitoring and early warning mechanism is established, comprising conventional warning, dynamic perception warning, and system-level warning. Through real-time monitoring and dynamic analysis of the concentration of toxic and hazardous substances and safety parameters (such as temperature, pressure, liquid level, etc.), potential accident information is identified, enabling dynamic assessment of the production processes in chemical enterprises. This mechanism aims to detect abnormal changes early, thereby providing proactive warnings for chemical companies, enhancing safety management levels, and reducing the risk of accidents.