Method for diagnosing state of particulate matter filter cotton of vehicle tail gas detection equipment

The invention discloses a method for diagnosing the state of particulate matter filter cotton of vehicle tail gas detection equipment. The method comprises the following steps: classifying the states of a plurality of pieces of filter cotton which are known to be replaced or not; collecting data of particulate matter concentrations at the front end and the rear end of the two types of classified filter cotton and flow velocity of to-be-filtered gas, and dividing the collected data into training set samples and test set samples; training the established probabilistic neural network by using a training set sample; testing the trained filter cotton recognition probabilistic neural network by using a test set sample; and inputting the front and rear end particulate matter concentration and to-be-filtered gas flow rate data of the to-be-processed filter cotton collected in real time into the filter cotton recognition probabilistic neural network meeting the accuracy requirement, and judging whether the to-be-proce