Abstract: Disclosed are a product quality incident early warning method and system based on a convolutional neural network, the method includes: obtaining product quality information, determining product quality compliance, and issuing an early warning for a quality incident; inspecting product quality to obtain production-related parameters and appearance parameters, which are used to determine a production benefit value and a finished product qualification rate of the product, respectively, thereby determining a product quality compliance value; comparing the product quality compliance value with a preset quality compliance threshold to screen out quality anomaly index information of the product, and constructing a product quality anomaly convolutional neural network model to issue an early warning for the quality incident, such that product quality information can be obtained in a more accurate and rapid manner, allowing for quicker and more efficient identification of a product quality incident.

Abstract: The present invention relates to the data updating technology, disclosing a method for constructing a quality evaluation data updating model based on a convolutional neural network including: collecting historical data of product quality evaluation from an original quality evaluation model, determining the update frequency of quality evaluation data of original model; obtaining the latest quality evaluation data for data update of model based on the historical data of product quality evaluation; establishing a first data sample set and a second data sample set to update the model; and conducting model performance testing on the original model with such updated data to determine the effectiveness evaluation results of the data updates. The present invention updates the data samples of the quality evaluation model by determining the data update frequency of the model, and improves the performance and accuracy of the model by evaluating the effectiveness of the data updates.

Abstract: Disclosed is a method for determining a number of product quality incidents based on a convolutional neural network, the method includes the following steps: building an appearance quality dataset, building a quality evaluation model, and analyzing a number of product quality incidents; a preset model and a preset optimized model are trained and validated according to the built appearance quality dataset to obtain initial quality evaluation models, which are filtered to obtain a quality evaluation model according to model performance coefficient and comprehensive model coefficient, product appearance defect data are obtained according to appearance defect data detected and obtained by the quality evaluation model, appearance defect ratio and significant appearance defect ratio of the products under quality inspection are obtained according to the product appearance defect data, and a number of product quality incidents of the products under quality inspection is finally obtained according to an incident occur

Abstract: The present invention is related to the field of quality control technology, specifically disclosing a classification method for product quality accidents and its system based on convolutional neural networks. The method consists of: collecting the sample data of product quality accidents, and grading the severity of accidents for each product with the quality accident; then extracting the image feature elements of various products with quality accidents and a valid feature element screening is performed; after that, a product quality accident classification model can be generating through training various valid feature elements and then the product quality accident is classified. The present invention can reduce the subjective errors due to human judgment, and thus the objectivity and consistency of the quality accident assessment results can be improved.