Intelligent elevator operation abnormity monitoring method and system based on data analysis

By adopting a data analysis-based monitoring method in smart elevators, using image recognition and feature mining technology combined with two-level abnormality analysis of operation log data, the problem of low reliability of elevator abnormality monitoring in the existing technology is solved, and more accurate and reliable abnormality recognition is achieved.

CN119693876APending Publication Date: 2025-03-25TENGYI ELEVATOR (JIANGSU) CO LTD
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Patent Information

Application Number
CN202411844957.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-16
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

In the prior art, the reliability of elevator operation abnormality monitoring is not high, and it is difficult to effectively analyze and identify abnormal situations in elevator operation.

Method used

The intelligent elevator operation abnormality monitoring method based on data analysis is adopted. By extracting the monitoring images to be analyzed of the target intelligent elevator, image recognition and feature mining are carried out, and two-level abnormality analysis is carried out in combination with the operation log data to improve the reliability of monitoring.

Benefits of technology

Through image recognition and feature mining technology, the status of the smart elevator can be accurately reflected, and the specific operating abnormal types can be identified through the analysis of the operation log data, which significantly improves the reliability of elevator abnormality monitoring.

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Abstract

The invention provides an intelligent elevator operation abnormity monitoring method and system based on data analysis, and relates to the technical field of artificial intelligence. According to the method, a to-be-analyzed monitoring image of a target intelligent elevator is extracted, and the to-be-analyzed monitoring image is obtained by conducting image collection on the elevator internal space of the target intelligent elevator according to the image recognition result; image recognition operation is conducted on the to-be-analyzed monitoring image, so that an image recognition result corresponding to the to-be-analyzed monitoring image is output, and the image recognition result is used for reflecting the state of the target intelligent elevator; and under the condition that the image recognition result reflects that the state of the target intelligent elevator with the image recognition result is not matched with the preset normal state, operation log data of the target intelligent elevator with the image recognition result is analyzed, and an abnormal operation monitoring result of the target intelligent elevator is output. Based on the above content, the reliability of elevator abnormity monitoring can be improved to a certain extent.
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