Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

Abstract

In order to effectively control the cost risk of power grid construction projects, the author proposes a cost risk management system for power grid construction projects based on the fuzzy clustering algorithm. The system introduces the fuzzy clustering maximum tree algorithm, and by constructing a mathematical model, combined with the empirical analysis, the key risk factors in the cost risk of power grid construction projects are identified. Through the analysis, it can be concluded that the key risks in the cost risk of power grid construction projects are the planning risks of infrastructure projects, the research risks of infrastructure projects, and the cost risks of infrastructure projects. Experimental results show that combined with expert experience and the actual situation of power grid engineering, the classification result at threshold λ = 0.785 becomes more realistic. At this λ level, 6 risk factors are grouped into 4 categories as follows: class I x 1 , x 2 , x 4 , class II x 3 , class III x 5 , and class IV x 6 . Through research, the identification of key risks can enable project managers to control the cost of power grid construction projects, targeted, so that risks can be minimized and investment returns can be improved.