Automatic Calibration and Optimization Methods of Instruments based on Artificial Intelligence

In modern industry, the demand for high-precision instruments is urgent. However, traditional calibration methods are inefficient and prone to errors. The current methods are affected by issues such as prolonged time, high workload, and inherent limitations in accuracy. To address these challenges, this article proposes an AI based automatic calibration framework. Using a hybrid architecture that combines Convolutional Neural Networks (CNN) and Long Term Memory Networks (LSTM), it combines the ability to extract spatial features (from CNN) with time series modeling (from LSTM), combining visual recognition with the processing of time related data. The model was optimized by continuously collecting and training data to achieve a relative error of ⩽ 0.12% within the range of 0-1MPa, and its applicability to a large number of instruments was demonstrated. The experimental results show that the accuracy has been improved, the robustness in different scenarios has been enhanced, and the calibration cost has been reduced.