Intelligent testing method for flush valve based on multi-source sensing data

By using spatiotemporal alignment and fusion processing of multi-source sensor data streams and improved Hidden Markov Model decoding, the problem of lack of multi-dimensional data in flushing valve performance testing was solved, enabling a comprehensive characterization of the flushing valve's operating status and accurate determination of the root cause of the fault.

CN122282309BActive Publication Date: 2026-07-21ZHEJIANG KEXIN IND
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG KEXIN IND
Filing Date
2026-05-28
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing flushing valve performance testing relies solely on single sensor data acquisition, lacking the synchronous acquisition and integration of multi-dimensional sensor information. This makes it impossible to construct a unified form of state representation, resulting in an inability to accurately identify performance anomalies and troubleshoot faults.

Method used

The spatiotemporal alignment and fusion processing of multi-source sensor data streams is adopted, and an improved hidden Markov model is used to decode the internal working state sequence of the flushing valve, locate the performance abnormal events and spatiotemporal coordinates, and generate a set of abnormal cause hypotheses through reverse tracing to optimize the determination of the root cause of the fault.

Benefits of technology

It achieves a comprehensive and complete representation of the operating status of the flushing valve, accurately identifies performance abnormalities, promotes fault diagnosis through data-driven logic, improves the automated testing and fault identification process, and breaks away from the dominance of human experience.

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Abstract

The present application relates to the technical field of bathroom device detection, in particular to a flushing valve intelligent testing method based on multi-source sensing data, comprising: collecting the multi-source sensing data stream composed of the water pressure dynamic waveform, flow pulse sequence, valve body vibration spectrum and sealing surface image frame of the flushing valve in the test period, implementing space-time alignment and fusion processing on the multi-source sensing data stream, and constructing a flushing valve comprehensive state field. An improved hidden Markov model is used to constrain the state transition probability according to the flushing valve mechanical action stage characteristics, to decode the flushing valve internal working state sequence, accurately locate the performance abnormal event and its space-time coordinates. An abnormal reason hypothesis set is constructed through reverse tracing, and the fault root cause is determined through verification testing. The method realizes multi-dimensional running data integration and analysis of the flushing valve, accurately identifies running abnormalities, standardizes the fault tracing process, and improves the refinement degree of intelligent testing and fault identification of the flushing valve.
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