Automobile body welding process quality score prediction method and device based on LSTM (Long Short Term Memory) model

The invention relates to an automobile body welding process quality score prediction method and device based on an LSTM model, and the method comprises the steps: collecting welding spot quality sample data, including welding spot information parameters, environment parameters, welding equipment parameters and quality score parameters; carrying out training set and test set division on the selected feature parameters, carrying out feature relative importance analysis through an ensemble learning method, and extracting feature parameters with relatively high correlation; and carrying out data analysis and prediction on the extracted characteristic parameter data set through an LSTM deep learning model to obtain a quality scoring result of each welding process of the welding spots. According to the method, an LSTM deep learning prediction model is established, and through the characteristic that the deep neural network memorizes long sequence data, the defects that a traditional prediction model is prone to falling into a local minimum value, low in convergence speed, poor in generalization and the like are overcome, so that accurate prediction of the quality score of the automobile body welding process is achieved.