This invention relates to the field of
gas analyzer calibration technology, specifically to a method for reducing calibration errors. The method includes collecting historical calibration data and current environmental parameters using a
calibration error reduction device to construct a dataset. The
calibration error reduction device includes an integrated probe, an
environmental simulation section, and an analysis section. The integrated probe is entirely housed within the
environmental simulation section. A
machine learning model is trained using the dataset to obtain a parameter
recommendation model that correlates parameters with calibration effectiveness. Based on the parameter
recommendation model,
calibration gas is input into the
environmental simulation section, and the internal and
external pressure values of the environmental
simulation section are measured. Based on these pressure values, automatic zero-point calibration, CO2 multi-parameter collaborative calibration across a range, H2O cross-validation range calibration, and operational
verification are performed sequentially. This invention solves the technical problem of significant calibration errors caused by large differences in the
operating environment between traditional calibration processes and actual measurement processes.