The invention relates to the technical field of intelligent
environmental protection, in particular to an
animal cage pollutant identification and early warning method based on
machine learning, which comprises the following steps: continuously acquiring
pollutant data and cleaning data of an IVC cage through a high-precision sensor, extracting
pollutant average concentration and concentration change rate characteristics from a pre-
processing data set, and calculating the concentration change rate characteristic of the pollutant; the method comprises the following steps: training a selected model by adopting a preprocessed
data set, adjusting parameters through
cross validation to optimize performance, placing a
verification model in an actual environment, monitoring IVC cage pollutant data in real time, automatically triggering early warning when a predicted value exceeds a standard, continuously optimizing the performance of the model, and periodically evaluating the model to obtain an
animal cage pollutant identification
early warning model. The problems that in traditional
animal cage pollutant monitoring, the precision of a sensor is low, data preprocessing is rough,
model selection lacks comparison optimization, an early warning mechanism is not intelligent, and dynamic adjustment can not be conducted according to actual feedback to accurately recognize pollutant types, accurately predict concentration and adapt to new pollutants are solved.