Risk identification of PPP mode in stadiums and gymnasiums by ant colony algorithm

Abstract

Aiming at the problem that the PPP model of stadiums and gymnasiums can not realize multi-dimensional risk analysis and the global risk identification ability is poor, an ant colony algorithm is proposed. Firstly, k clustering is used to cluster PPP mode data. Then, the ant colony algorithm is analyzed stochastically, and the risk set of PPP mode is obtained, which lays the foundation for later risk identification. Finally, through the random fusion function, the PPP mode risk set based on the path profile is constructed, and the optimal identification path is obtained, so as to improve the identification accuracy of PPP mode risk. MATLAB simulation shows that the ant colony algorithm proposed in this paper is superior to the risk clustering method in the overall risk identification accuracy and identification time of PPP mode. Therefore, the ant colony algorithm proposed in this paper can be used to identify the risk of PPP mode, to meet the needs of the construction of stadiums and gymnasiums.