Abstract: The present disclosure discloses a master pattern generation method which is a major pattern in a repeated cycle by analyzing programmable logic controller (PLC) logic, and a method for training a model that may analyze an error of a cycle using the generated master pattern. The master pattern generation method and the training method for a cycle analysis model according to the present disclosure are different from the related art in that the methods are a technology of processing a machine control language (low-level language) that is difficult for humans to analyze and converting the machine control language into an analyzable language (high-level language), i.e., a machine language processing (MLP)-based technology that analyzes the executed machine language (a language that controls a machine) with a computer and can be understood by humans.

Abstract: A method, of training an anomaly detecting model using a plurality of pieces of graph data, includes: (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.

Abstract: Disclosed is a method of analyzing a programmable logic controller (PLC) logic to detect whether an anomaly that deviates from a standard pattern occurs in a repeated cycle. After modeling and patterning an operation pattern of automation equipment and processes with a graph, an anomaly detecting model capable of detecting whether a pattern is abnormal may be constructed as a graph AutoEncoder model. By detecting the change in the process pattern, it is possible to early detect the anomaly of the equipment and processes.

Abstract: The present disclosure discloses a master pattern generation method which is a major pattern in a repeated cycle by analyzing programmable logic controller (PLC) logic, and a method for training a model that may analyze an error of a cycle using the generated master pattern. The master pattern generation method and the training method for a cycle analysis model according to the present disclosure are different from the related art in that the methods are a technology of processing a machine control language (low-level language) that is difficult for humans to analyze and converting the machine control language into an analyzable language (high-level language), i.e., a machine language processing (MLP)-based technology that analyzes the executed machine language (a language that controls a machine) with a computer and cats be understood by humans.