Intelligent detection method based on graph neural network

Abstract

The invention provides an intelligent detection method based on a graph neural network. The intelligent detection method comprises the following steps of collecting data, preprocessing the data, building a network model, carrying out pre-training and transfer learning, carrying out predicting and performing casual inspection verification to perfect a whole prediction system. Compared with manual detection, the method has the advantages that the component detection accuracy and efficiency are improved, the interference of human factors on detection is reduced, and the labor cost and the detection cost are reduced. Compared with a traditional machine learning method, the method has the advantages that the graph neural network does not require that the composition form of the data must have agood spatial relationship, that is to say, the graph neural network has a neatly arranged matrix form, and the feature that the graph neural network can accept unstructured input significantly improves the expression ability of the model. Compared with a convolutional neural network method, the graph neural network can better learn the logic relationship of each element, so that the generalization ability of the model is improved. In the learning process of the network, each node is responsible for spreading own information and integrating information of neighbor nodes, so that the logic normal form of data is learned and mastered.

Description

technical field [0001] The invention belongs to the technical field of intelligent detection, and in particular relates to an intelligent detection method based on a graph neural network. Background technique [0002] Component inspection is an essential link in the industrialized assembly line. At present, the processing process, assembly process and transmission process have reached the standard of full automation. However, due to its particularity, the detection process has not yet reached the fully automated standard. [0003] In order to ensure product quality, each component needs to undergo strict functional testing before leaving the factory. At present, most of the debugging equipment in the workshop has the function of automatically or semi-automatically executing the debugging process, but the debugging results still need manual judgment. [0004] Taking the intelligent detection of components such as elevator traction motors as an example, the current componen...

AI Technical Summary

Problems solved by technology

[0005](1) It takes a long time to manually judge the debugging results, and it is easy to cause misjudgment and missed judgment

[0006](2) It takes a lot of time and money to train qualified inspectors

[0007](3) There is no process of recording and saving debugging information, which is not traceable and maintainable

Due to the inevitable presence of destructive testing in the testing process, collecting enough testing samples requires destroying the same number of components, which will cause great losses

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