Anomaly detecting method in sequence of control segment of automation equipment using graph autoencoder

A method of training an anomaly detecting model using a plurality of pieces of graph data, the method comprising:

• (a) inputting one piece of graph data that has not yet been input, among the plurality of pieces of graph data, to a graph neural network (GNN) AutoEncoder calculating a probability of each edge as input data;
• (b) calculating a difference value (hereinafter, “edge difference value”) between an edge probability value of reconstructed data output by the GNN AutoEncoder and an edge value of the input data;
• (c) calculating an average value (hereinafter, “positive edge loss”) of a positive edge and an average value (hereinafter, “negative edge loss”) of a negative edge using the edge difference value, and calculating an edge prediction loss value of the reconstructed data by summing the positive edge loss and the negative edge loss;
• (d) retraining the GNN AutoEncoder until the edge prediction loss value is minimized;
• and
• (e) repeatedly executing the operations (a) to (d) when there remains graph data that has not yet been input, among the plurality of pieces of graph data.