GNN-Bayesian hybrid framework for probabilistic modeling in smart infrastructure: optimizing decisions with uncertainty quantification

In order to improve the design quality of complex systems and optimize system decisions, a hybrid design method combining graph neural networks with Bayesian optimization is proposed, which maintains the descriptive correlation of the structure and the effective adjustment of model parameters. For a large amount of complex class data, singular value decomposition (SVD) is used to compress the number of features, reduce the amount of invalid information, and improve the simplicity of data expression and the efficiency of computing resources. The experiment included energy dispatch, traffic flow prediction, and industrial monitoring, and processed over 120000 sets of actual data samples, demonstrating strong universality and practical value. This design scheme not only ensures high accuracy, but also achieves a dual improvement in convergence speed and model stability, providing a definite approach and reliable experimental method for studying dynamic environments.