A signal-to-text generation type pre-training large model

Abstract

The present application relates to the technical field of mechanical equipment predictive maintenance, and in particular to a signal-to-text generation type pre-training large model, comprising the following steps: constructing a signal unit, constructing a signal operator, constructing a random parameter, constructing a signal function, outputting a text description of mathematical features of the signal function, outputting signal data of the signal function, outputting a text description of physical features of the signal function, establishing a "signal data-text description" question and answer pair with a thinking chain, making a pre-training data set with the thinking chain, model training, model fine-tuning, model testing, model deployment, and using the model. The present application can convert signal data into text information, help equipment operation and maintenance managers make operation and maintenance decisions, avoid equipment failure, and improve production efficiency.

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