The invention provides a tailing pond abnormal
data monitoring method based on an improved Transform, and belongs to the technical field of tailing pond
safety monitoring. Adding a time convolutional network before the self-attention mechanism, performing fine modeling on different
time sequence associations by adopting a
branch structure to obtain an optimized attention mechanism, and constructing a detection model based on the optimized attention mechanism and the time convolutional network; and training the constructed detection model based on the reconstruction
loss function and in combination with a maximum and minimum value strategy, calculating an abnormal
score based on the trained detection model, and identifying an abnormal point in the abnormal
score. By introducing a time convolutional
network component to replace an absolute position coding mechanism in a traditional Transform model, complex long-term dependency in
time sequence data is successfully captured, effective fusion of
global time sequence information is realized, and the accuracy of
anomaly detection is improved. And secondly, a self-attention mechanism module is optimized, the recognition capability of the model on an abnormal mode is enhanced, and the
detection performance is further improved.