Research on Multi-objective Engineering Project Resource Optimal Allocation and Schedule Collaborative Management Model Based on NSGA-Ⅲ Algorithm

Abstract

This study focuses on the impact of climate change on agricultural production. By comprehensively analyzing the temperature fluctuation and crop yield data in the past decade, and the significant correlation between temperature rise and crop yield, a generative adversarial network model of multi-objective optimization strategy is proposed, which is dedicated to the prediction of safety accident risks in architectural engineering. By optimizing the architecture of GAN, the model enhances its adaptability and effectiveness in practical engineering risk prediction scenarios. The experimental results show that compared with the traditional prediction model, the accuracy rate of this model in safety risk prediction of large-scale construction projects is as high as 92%, far exceeding the accuracy rate of the traditional model of 78%. The model also shows good predictive ability on key performance indicators such as recall rate and F1 score, reaching 90% and 86%, respectively. It can effectively prove the significant advantages of the model based on multi-objective optimization GAN in the field of safety incident risk prediction in architectural engineering.