Intelligent reasoning to-be-tested object safety range data model extraction method

The invention belongs to the technical field of data processing, and particularly relates to an intelligent reasoning to-be-tested object safety range data model extraction method. The method comprises the steps of 1, collecting time sequence signal data of a to-be-tested object, and performing Haar wavelet transform on the time sequence signal data to obtain an intermediate signal; 2, extracting a feature vector of the intermediate signal by using a pre-trained growth neural network; 3, performing logic simplification on the feature vector of the intermediate signal by using a Boolean function; 4, the states of the to-be-tested object are set, each state is represented by the feature vector of the intermediate signal corresponding to the time sequence signal data, and all the states form a state space; and 5, obtaining safety range data corresponding to the target numerical value of the to-be-tested object. Intelligent reasoning and efficient monitoring of a complex system safety range are realized. 本发明属于数据处