The invention discloses a reactor core
neutron field ex-situ explicit inversion method based on a graph structure, and belongs to the technical field of reactor core
safety monitoring and control. According to the method, reactor core space coordinates,
energy spectrum grouping, material physical properties and
prior information are fused through an explicit graph structure, a lightweight graph convolutional
network architecture is adopted, and accurate inversion of a high-dimensional reactor core
neutron field is achieved. And meanwhile, the inversion precision under the high-dimensional resolution can be remarkably improved, and reliable
technical support can be provided for
safe operation and intelligent monitoring of an advanced reactor type. Compared with an existing
deep learning black box model, the method has the advantages that the
occurrence probability of local high deviation can be remarkably reduced at different reactor core
change positions, and more uniform and stable prediction performance can be realized in the whole space. And under the condition that the reactor core has multi-region change, better inversion performance is still shown. When the middle and
low complexity changes, the fine multi-region features of the reactor core can be effectively and stably captured, and the method has better overall error suppression capability.