A Hybrid Intelligent Risk Assessment Method Based on Multi-level Fuzzy Control

A substantial portion of industrial risk research has predominantly emphasized health management, frequently overlooking the essential step of risk assessment. Since assessment precedes management, its accuracy is pivotal to the effectiveness of health management. To bridge this gap, this paper introduces a hybrid intelligent risk assessment algorithm. First, a risk assessment index system is formulated based on the specific conditions of the industrial production line. Subsequently, an adaptive assignment modeling method leveraging the Sparrow Search Algorithm (SSA) is employed to calculate the risk weights. Using these combined weights, the risk level of each workstation on the production line is assessed by a cloud model based on multilevel fuzzy control. Experimental validation and comparative analysis confirm the efficacy of the proposed method. Compared to alternative approaches, this method demonstrates superior accuracy in identifying and managing risks, thereby ensuring more relevant and actionable results. A practical case study of a body-in-white welding production line in an automotive company was also conducted, and the validity of the methodology was further confirmed by the actual data obtained.