The invention provides a resident sharing oriented
community public space intelligent optimization design method and
system, and the method comprises the steps: obtaining real track sets and environment data of different groups, filling a data sparsity blind area through employing a conditional
generative adversarial network, and generating space-time
thermodynamic diagrams of different group behaviors. Then, based on a space-time diagram convolutional network, fusing a grid topology, a thermodynamic diagram and a social association matrix, and analyzing a demand priority map; and
coupling cross-
modal features by adopting a dual-channel
deep learning architecture, and generating
physical design parameters conforming to constraints through a bidirectional attention mechanism. According to the method, the confrontation generation network is used for enhancing data, the physical fidelity of the space-time thermodynamic diagram is improved, and different group behaviors and space-
time data are fused to overcome the priority deviation of
public space design caused by subjective
weight distribution. And finally, fusing a spatial topology gradient and a group behavior
evolution rule by cross-
modal feature fusion, thereby improving
demand response precision and dynamic adaptability of
public space optimization.