This application relates to a method and
system for risk early warning of geotechnical infrastructure based on a multi-scale damage model. The method includes: acquiring
environmental monitoring data of a target geotechnical area based on a preset monitoring scale; transforming the
environmental monitoring data into a multi-scale damage model through multi-scale
coupling analysis modeling; extracting spatiotemporal features of damage from the multi-scale damage model using a pre-trained
convolutional neural network, and determining risk patterns by matching with a preset risk pattern
library; inputting the spatiotemporal features of damage and risk patterns into a pre-trained risk
diffusion probability prediction model to obtain risk propagation prediction results; constructing a multi-dimensional risk
decision boundary using a
nonlinear classifier based on the risk patterns and risk propagation prediction results; and generating graded early warning instructions through preset dynamic risk decision rules. This method can achieve accurate and dynamic early warning of risks to geotechnical infrastructure, preventing safety accidents caused by multi-scale damage evolution.