This invention discloses a real-
time inversion method for seepage edge analysis in dams based on a
physical information neural network, belonging to the field of water conservancy
engineering safety monitoring technology. The method includes: collecting and preprocessing multi-source
monitoring data on seepage pressure, temperature, and deformation; constructing a seepage-temperature-deformation
coupling mechanism model; constructing a
physical information neural network, embedding the partial differential equations of seepage
mechanics as physical constraints into the
loss function, and using hard constraint boundary conditions; lightweighting the model and deploying it on
edge computing nodes to achieve real-time
seepage field inversion; and performing streamline tracing and comprehensive anomaly index calculation based on the inversion results to identify abnormal seepage paths and provide graded early warnings. This invention achieves technical effects such as an inversion accuracy R²=0.97, low equation residuals, short single
inversion time (seconds), and anomaly identification accuracy of 94%, and can be widely applied to seepage
safety monitoring of reservoirs, dams, dikes, and
tailings ponds.