Research on Risk Early Warning of Industrial Product Quality and Safety Based on Multiple Algorithms

In order to realize efficient early warning of industrial product quality and safety risks, a risk assessment model based on the Bert model-improved sparrow search algorithm-least squares support vector machine (BERT-ISSA-LSSVM) algorithm is proposed. First, the Bert model is utilized to extract risk features from industrial product quality and safety related data, and quantify and classify risks based on risk features and risk assessment criteria. Secondly, in order to further improve the early warning efficiency and realize the accurate risk classification of massive industrial product quality and safety data, the LSSVM algorithm is used to draw the separating hyperplane, and the optimization of LSSVM parameter settings is realized based on the ISSA algorithm. Finally, the superiority of the proposed model is verified by comparing the ISSA algorithm, Whale Optimization Algorithm (WOA), Particle Swarm Optimization Algorithm (PSO) and SSA. The results show that BERT-ISSA-LSSVM has high accuracy in risk level classification, with an improvement of about 2% to 5% compared to both the LSSVM algorithm as well as the random forest (RF) algorithm. Therefore, the model can effectively extract industrial product quality and safety risk characteristic data, accurately classify the risk level, and improve the stability and reliability of risk warning.