The Application of Cluster Analysis Algorithm in Supply Chain Risk Identification

Abstract

The risk control model of the power supply chain system is established. A fault information identification method based on fuzzy clustering is proposed. This method fully considers the power grid’s characteristics and uses terrible data. A risk assessment model based on fuzzy set theory is established by the COWA operator weight method and grey cluster evaluation method. The security risk identification model of power grid enterprises uses insufficient data. The security risk identification data are normalized and classified. Empirical analysis determines various risk factors that may appear in power projects. The applicability and feasibility of the index system and evaluation model are verified.