Prediction-model-building method, state prediction method and devices thereof

A prediction-model-building method, comprising:

• receiving a plurality of pieces of training data, with each of the plurality of pieces of training data comprising a plurality of training feature items and with each of the plurality of training feature items configured to record a training eigenvalue;
• determining whether each of the plurality of training feature items of each of the plurality of pieces of training data records the training eigenvalue or not;
• determining a training feature item without the training eigenvalue as a training missing item, determining a piece of training data comprising the training missing item as a defective piece of training data, generating a plurality of training augmented values corresponding to the training missing item of the defective piece of training data and, based on the plurality of training augmented values, generating a plurality of pieces of augmented training data corresponding to the defective piece of training data;
• constituting a training dataset at least comprising the plurality of pieces of augmented training data and pieces of training data without the training missing item;
• and
• training a prediction model by a machine-learning algorithm based on the training dataset, and storing or outputting the prediction model,
• wherein the plurality of pieces of augmented training data is a plurality of pieces of first augmented training data, and, before constituting the training dataset, the building method of prediction model further comprises:
• duplicating the pieces of training data without the training missing item by a ratio corresponding to a number of the plurality of training augmented values to additionally generate a plurality of pieces of duplicated training data, wherein the plurality of pieces of duplicated training data serves as a plurality of pieces of second augmented training data;
• wherein constituting the training dataset comprises:
• constituting the training dataset with the plurality of pieces of first augmented training data, the plurality of pieces of second augmented training data and the pieces of training data without the training missing item.