The invention provides a VAE-ALSTM-based bridge structure state
anomaly detection method and
system, and the method comprises the steps: carrying out the
standardization and sliding window segmentation of vibration
monitoring data, extracting potential features through a variational auto-
encoder, and carrying out the
time sequence prediction and reconstruction in combination with an attention-enhanced long-short-
term memory network, thereby achieving the detection of the abnormal state of a bridge structure. Fusing the prediction error and the
reconstruction error to generate an abnormal
score; a threshold value is automatically set through
quartile distance statistics, and self-adaptive judgment under different bridge types is achieved; when the
score crosses the boundary, real-time alarm is triggered; the
system correspondingly comprises a data preprocessing and sample construction module, a VAE-ALSTM model construction module, a training stage prediction and reconstruction module, an error calculation and anomaly scoring module, a threshold setting module and an anomaly judgment and alarm module. The method and the
system do not need manual threshold parameter adjustment, are high in precision and good in real-time performance, and can be widely applied to the fields of bridge health monitoring, operation and maintenance early warning and the like.