Abstract
When the manufacturing industry is dealing with information technology, it has to face a large number of parameters and frequent adjustments. How to correctly import and maintain it has always been a huge challenge. Once the setting is wrong, it will bring losses, ranging from poor products that require maintenance, heavy work or scrapping, and at worst, resulting in production line shutdown, reduced factory productivity, delayed shipments and other adverse consequences. In order to improve this problem, this study uses the data in the approval form of a customized label set by an electronic manufacturer and use artificial intelligence models to find out the hidden rules behind a large number of customized labels, through data processing and model building. Model and parameter experiments are used to improve the effectiveness of artificial intelligence models, and for the problem of time characteristics but uneven distribution of data, the method of cyclic testing is adopted to increase the diversity of the test set. The results of this paper, we integrate each stage and an auxiliary decision-making is established. When the user's setting is inconsistent with the predicted result, a warning will be displayed to speed up the operation process, reduce the scope of confirmation, and ultimately reduce the error rate, thereby improving the problem, reducing scrap and production line shutdown to improve factory productivity. In the statics, the accuracy rate of new recruits was only 80%. The accuracy rate of the artificial intelligence model can be increased to 95%. The number of stoppages is reduced from 4 times per month to 1 time per month. Under full capacity, this assistance the decision-making system can reduce loss cost.
Intelligent credit scoring system using knowledge management
