Research on the Dynamic Safety Supervision Model of Chemical Enterprise Based on Machine Learning

This paper first analyzes the accident data in China's chemical industry from 2018 to 2022, revealing that although the number of accidents and the mortality rate have decreased, the accident rate remains high, highlighting the urgency of strengthening safety supervision methods. To address this, a dynamic supervision indicator system covering multiple key dimensions such as production load, alarm conditions, operation quantity, and maintenance status has been constructed. This indicator system can capture key factors related to safety risks through multidimensional monitoring of enterprises' daily work safety status. The study uses the entropy weight method to objectively weight each indicator and employs K-means and DBSCAN clustering algorithms to classify enterprise data. While the entropy weight method provides an intuitional basis for feature weighting, further optimization is needed to improve sample discrimination. The K-means algorithm effectively distinguishes different risk states of enterprises, such as shutdown and full-load production, and reasonably classifies enterprises into three categories: special control, key attention, and general supervision. In contrast, the DBSCAN algorithm performs better in identifying high-risk enterprises but is limited in finely distinguishing between special control and key attention enterprises. The final results show that the K-means algorithm performs best when combined with the indicator system for dynamic supervision, particularly in accurately identifying low-risk and generally supervised enterprises. By establishing a comprehensive dynamic supervision indicator system and applying machine learning algorithms, this paper provides a new technological path for safety supervision of hazardous chemical enterprises, demonstrating the potential of intelligent supervision in enhancing accident prevention and risk control levels.