An elevator operation abnormality prediction and diagnosis method and system based on an internet of things

Abstract

The application provides an elevator operation abnormality prediction and diagnosis method and system based on the Internet of Things, and relates to the technical field of elevator fault diagnosis. The method comprises the following steps: acquiring historical audio data and performing time-frequency conversion to generate a plurality of first time-frequency graphs; clustering the historical audio data to obtain a plurality of elevator operation state clusters; generating a plurality of second time-frequency graphs for each elevator operation state cluster; performing event stream detection on each second time-frequency graph to obtain event stream feature data of each second time-frequency graph, performing time sequence abnormality diagnosis on the second time-frequency graph to generate an abnormality diagnosis result, and performing feature optimization on the second time-frequency graph to generate a third time-frequency graph corresponding to each second time-frequency graph; constructing a training set and training an elevator operation analysis model; and analyzing elevator operation audio data by using the trained elevator operation analysis model to generate an elevator operation abnormality detection result. The application realizes efficient prediction and fault diagnosis of elevator operation abnormality.

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