The invention provides a dam
safety monitoring data
anomaly detection method based on
unsupervised learning, and the method comprises the following steps: (1), obtaining to-be-detected
time series data of a monitoring amount during the operation of a dam, carrying out the normalization
processing of the collected to-be-detected
time series data, performing rolling sampling on the
normalized time sequence data to be detected by adopting a moving sliding window, and establishing a training sample
data set and a
test sample data set; (2) based on a training sample
data set and a
test sample data set long-short memory (LSTM)
recurrent neural network regression prediction model, performing regression prediction on the to-be-detected
time series data, and calculating a residual sequence of the to-be-detected time
series data and the reconstructed sequence data; and (3) establishing an
anomaly detection model based on an isolated forest (iForest)
algorithm, and inputting the residual sequence into the
anomaly detection model to complete real-time detection of the abnormal value of the dam
monitoring data. According to the method, the problem of online intelligent identification of the abnormal value of the
monitoring data in the dam
safety monitoring real-time acquisition process can be solved, the method has high generalization ability and wide application range, the data types acquired by different sensors can be detected, and a large amount of data can be quickly processed.