This invention discloses a method for identifying the edge of micro-motion damage in ancient building structures based on lightweight
deep learning, belonging to the field of cultural heritage protection. The method includes: micro-
motion perception fusion, employing a high-sensitivity
accelerometer and micro-displacement
laser measurement to achieve dual-mode
perception of "dynamic mode + static deformation";
signal preprocessing; lightweight time-
frequency analysis network identification, generating time-frequency spectra from acceleration samples through
continuous wavelet transform, inputting them into the lightweight time-
frequency analysis network LTFANet, and outputting
modal parameters and variation characteristics; crack propagation
trend analysis; structural damage index calculation, fusing multi-source information to calculate a physically interpretable structural damage index (SDI); graded early warning, pushing early warning information based on a four-level threshold
system; and early warning
verification. This invention achieves high-precision
perception and real-time edge identification of micro-damage in ancient buildings, with high early warning accuracy, and is applicable to immovable cultural relics such as ancient pagodas and wooden structures.