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Abstract

A computer-implemented method for recalibrating a trained data-based calibration model for use in a sensor system for measuring one or more physical variables is disclosed. The data-based calibration model is formed as a neural graph network which is trained to output an output vector comprising one or more output variables and a plurality of auxiliary variables, depending on a sensor state graph representing a sensor state. The method includes (i) detecting one or more detection variables relating to the one or more physical variables and one or more state variables indicating one or more environmental influences on the sensor system, (ii) ascertaining a sensor state graph depending on the one or more detection variables and the one or more state variables at a time of detection, (iii) augmenting the sensor state graph, (iv) evaluating the augmented sensor state graph with the data-based calibration model to obtain the output vector, (v) determining a loss depending on the output vector, and (vi) unsupervised training of the calibration model depending on the determined loss.

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First Claim