A Dynamic Risk Assessment Model for Intelligent Chemical Production Processes Based on Extenics Theory

With the continuous development of intelligent manufacturing technologies, the integration of production systems, automation of control systems, and digital information networking have become core trends in the chemical industry. However, the domestic chemical sector comprises a vast number of enterprises with complex production processes, facing various safety hazards that pose challenges to accident risk management. This paper addresses the issue of dynamic risk assessment in typical production processes within the chemical industry by proposing a comprehensive risk assessment model based on extension theory. This model innovatively incorporates the impact of information attacks on production processes into the traditional risk assessment framework, establishing a multidimensional risk indicator system that includes factors related to personnel, processes, equipment, environment, and management. A novel weight calculation method-Extension Analytic Hierarchy Process (EAHP)-is introduced, effectively reducing the influence of human factors on weight determination. The research findings provide a scientific basis for accident risk management in chemical enterprises and offer effective support for safety management decision-making in complex environments. This study contributes to enhancing the safety and operational efficiency of the chemical industry, aligning with the national strategy for sustainable development and the promotion of advanced manufacturing practices.