Research on steam meter calibration in cigarette factory based on machine learning

Abstract

In the current industrial environment, electronic instruments have been widely used, but at the same time, electronic instruments also face many problems, such as zero temperature drift and sensitivity temperature drift caused by temperature changes. This article proposes an algorithm based on software proofreading that combines anomaly detection algorithms with neural networks. It is concluded that this method can indeed proofread instruments and maintain errors within an acceptable range. This method can further improve the accuracy and reliability of the current industry, ensuring process optimization and efficiency improvement.