Opinion dynamics is a discipline that studies the formation, evolution and dissemination processes of individual or group opinions as well as the laws of their interactions. Opinion evolution models are the core research tools in the field of opinion dynamics, aiming to explore the evolution mechanism of individual opinions in social networks under interactive influences through mathematical modeling. Traditional opinion evolution models mostly adopt synchronous update mechanisms, ignoring individual differences, while existing asynchronous update studies cannot reveal the triggering motivations of individual asynchronous updates in large-scale networks. To solve this problem, this paper proposes an asynchronous update mechanism, which can explain the triggering conditions and influencing factors of individual asynchronous updates to describe individual heterogeneity. In addition, the convergence analysis of the proposed model is carried out, and it is found that the model converges exponentially fast in strongly connected networks, and the steady-state values of individuals are determined by their initial private opinions. Finally, simulation experiments are conducted to verify the effectiveness of the model and the correctness of the theoretical analysis.