Research on High-Precision Filtering Method for Standard Bottle Calibration Pressure Based on Deep Learning Approaches

This paper proposes a high-precision filtering scheme for standard bottle calibration pressure based on Transformer model feature extraction and Kalman filtering methods, which is used to analyze the data of standard bottle pressure over time. Firstly, a Transformer model is employed as a feature extractor to transform the data sequence of standard bottle pressure over time into high-dimensional feature vectors, thereby capturing rich semantic information of the input data. Then, a Kalman filter is designed, using the feature vectors extracted by the Transformer as observation values, combined with state transition equations and observation equations to estimate and filter these feature vectors. By using the feature vectors outputted by the Transformer model as observation values, the Kalman filter can more accurately estimate the state of standard bottle pressure over time by utilizing the information extracted by the Transformer model. Experimental results demonstrate that the calibration pressure data obtained through the proposed method in this paper exhibit higher stability and better meet the requirements of standard bottle calibration.