The invention provides an
anomaly detection method and
system based on Bi-LSTM and ST-GCN, and the method comprises the steps: constructing a space-time joint modeling frame for bridge health monitoring through the staged fusion of a bidirectional long-short time memory neural network (Bi-LSTM) and a space-time diagram
convolutional neural network (ST-GCN) based on a point-line-plane three-stage detection mechanism for a tied
arch bridge; the method comprises the following steps: extracting
time sequence characteristics of a cable force meter through Bi-LSTM, identifying a single-sensor hardware fault, separating shared characteristics and sensor private characteristics from multi-sensor
time sequence data, and enhancing sensor identity distinction degree and inhibiting interference factors such as temperature through decoupling comparison loss; the sensor private features are used as node features in an ST-GCN neural network, spatial positions among different sensors are identified by adopting a time attention mechanism through ST-GCN, spatial topological association and long-term
time sequence dependence among the sensors are captured, and connection probability anomaly under a static
adjacency relation is analyzed; and finally, the identified spatial position is analyzed and self-adaptive early warning is carried out, and a multi-node collaborative abnormal region is positioned through a connected domain
algorithm, so that local damage and global
abnormality are accurately positioned. The function of monitoring whether the bridge structure is damaged or not is achieved. Compared with a traditional
machine learning
database, more experimental parameters and a larger experimental
data set are selected; the defect of low prediction precision of traditional
machine learning is avoided, and the method has very high accuracy and reliability.