IDENTIFICATION OF KEY FACTORS OF FIRE RISK OF OIL DEPOT BASED ON FUZZY CLUSTERING ALGORITHM

With the rapid development of the national economy, the demand for oil is increasing. In order to meet the increasing energy demand, China has established a number of oil depot in recent years, whose largest capacity reaching up to tens of millions of cubic meters. Due to the flammable and explosive nature of the stored medium, the risk of fire in the oil depot area has increased dramatically as the tank capacity of the storage tank area has increased. The intensification of the oil depot and the development of large-scale oil storage tanks have brought convenience to the national oil depot, but also brought many catastrophic consequences. In recent years, there have been many fires and explosions in the oil depot, causing major casualties and property losses, which seriously endangered the ecological environment and public safety. Based on the constructed oil depot fire risk index system, the fuzzy C -means algorithm (FCM) and fuzzy maximum support tree clustering algorithm is introduced. Through the two fuzzy clustering mathematical models, key factors in the established index system are identified. Firstly, the expert scoring method is used to evaluate the indicators in the oil depot fire risk index system, and the importance degree evaluation matrix of oil depot fire risk factors is constructed through the fuzzy analysis of expert comments. Then, the fuzzy C -means algorithm (FCM) and the fuzzy clustering tree algorithm are used to cluster the various risk indicators, and the key factors of the oil depot fire risk are identified. Through the comparative analysis and cross -validation of the results of the two fuzzy clustering methods, the accuracy of the recognition results is ensured. Finally, using an oil depot as a case study, it is found that passive fire prevention capability and emergency rescue capability are key factors that need to be paid attention to in the oil depot fire risk index. The fuzzy clustering algorithm used in this paper can digitize the subjective comments of experts, thus reducing the influence of human subjective factors. In addition, by using two fuzzy clustering algorithms to analyze and verify the key factors of the oil depot fire risk, the reliability of the clustering results is guaranteed. The identification of key factors can enable managers to predict high-risk factors in advance in the fire risk prevention and control process of the oil depot, so as to adopt corresponding preventive measures to minimize the fire risk in the oil depot, and ensure the safety of the operation of the oil depot.