This study aims to explore a generalized and transferable method for predicting condenser vacuum based on Neural Module Network.Firstly,the neural module algorithm is employed to predict the condenser vacuum.Secondly,divide the original data,where the Gaussian noise fluctuates between 80％and 130％,to adequately simulate low-load and high-load data in the real world.Finally,utilizing the model trained with the neural module algorithm on the normal data set to predict low-load and high-load data under variable operating conditions.The experiments demonstrate that the model trained by the neural module algorithm exhibits generalization and can effectively predict condenser vacuum under different load conditions.
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