Flue gas oxygen content load prediction method and device based on sample migration

Abstract

The invention discloses a flue gas oxygen content load prediction method and device based on sample migration. The method comprises the following steps: acquiring equipment data of a first participant and a second participant under a joint learning architecture; wherein the first participant is a participant who puts forward a prediction demand, and the second participant is other participants except the first participant; training a prediction classifier by using the equipment data of the first participant and the equipment data of the second participant; according to the prediction classifier, determining weight data of the equipment data of the first participant about the equipment data of the second participant; based on the equipment data and the weight data of the second participant, training a predictive gradient lifting model; and utilizing the predictive gradient lifting model to predict the flue gas oxygen content load of the first participant equipment. According to the method, the problem of inaccurate flue gas oxygen content load prediction caused by data distribution difference of energy equipment generated under different processes is solved, and the cost of an energy equipment sensor is saved.

Description

technical field [0001] The present disclosure relates to the field of energy technology, and in particular to a method, device, computer equipment, and computer-readable storage medium for predicting the load of oxygen content in flue gas based on sample migration. Background technique [0002] At present, the application of comprehensive energy is an indispensable application in today's society. With a wide range of applications, the application of energy equipment is becoming more and more demanding. However, in industrial applications, it is impossible to update many large energy equipment at any time, or it is not easy to troubleshoot if the heat load exceeds the standard in the application. [0003] For example, in industrial energy applications, the distribution of relevant equipment data generated by boiler equipment under different processes may vary greatly, which often leads to low prediction accuracy of oxygen content in flue gas, which is not conducive to early w...

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Problems solved by technology

[0004] In view of this, the embodiment of the present disclosure provides a method, device, computer equipment, and computer-readable storage medium for predicting the load of flue gas oxygen content based on sample migration, so as to solve the problems in the prior art caused by different processes Differences in data distribution of energy equipment lead to inaccurate prediction of flue gas oxygen content load

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