Loom abnormal data processing method based on probability distribution and XGBoost decision algorithm

The invention discloses a loom abnormal data processing method based on probability distribution and an XGBoost decision algorithm, and belongs to the field of intelligent manufacturing of weaving workshops in the textile industry. Calculating a change difference value between adjacent data points; calculating adaptive regression reference thresholds under different time windows; according to the adaptive regression reference threshold, credible intervals at different moments are updated; determining the data points of which the data values are out of the credible interval range as abnormal data; constructing a Bayesian network abnormal data identification model based on probability distribution; determining loom parameters which cause the abnormal data points to generate data abnormity through a probability distribution-based Bayesian network abnormal data identification model; constructing a loom missing data restoration model based on an XGBoost decision method; training a loom missing data restoration mod