A maskless Transform framework-based intelligent identification method for operation events of a small
nuclear power system comprises the following steps of: 1, acquiring multi-source heterogeneous
time sequence data related to an operation state by using a
sensor system or a
simulation platform carried by the small
nuclear power system to form an
original data set; 2, preprocessing the
original data set; 3, creating a linear embedded layer network, and mapping a multi-dimensional parameter vector in the
sample sequence to a high-dimensional feature space by using the network to form an
event data embedded vector; 4, performing position coding on the
event data embedding vector, and adding
time sequence information in the
sample sequence into the
event data embedding vector; (5) a maskless Transform
encoder network formed by stacking N identical
encoder layers is built, and N is a positive integer greater than or equal to 1, and N is a positive integer greater than or equal to 1; 6, the event data embedding vector is input into a maskless Transform
encoder network, and a feature sequence is output; and 7, calculating the probability that the
sample sequence belongs to each operation event category through a
Softmax function, and judging the category with the maximum probability as a final recognition result. The method is high in recognition accuracy and strong in
time sequence characteristic capturing capability.