Optimal arraying system based on shadow magnification

The utility model discloses an optimal arraying system based on shadow magnification, and the system comprises an obtaining module which is used for obtaining an arraying data sample; the matrix module is used for forming a data matrix according to the data samples; the preprocessing module is used for performing data preprocessing on the data matrix; the extraction and extension module is used for performing feature quantity extraction and implicit rule extension on the preprocessed data matrix; and the neural network is trained by using the expanded data matrix and is used for outputting the optimal arrangement characteristic parameter. Through the connection work of a plurality of modules, on the basis of environment data, a certain number of event analog simulation is carried out, data characteristic quantity mining and expansion are carried out for discretized distribution event data, and nonlinear fitting is carried out through a trained and tested Kohonen neural network clustering model to output optim