Urban governance model based on data closed-loop feedback continues self-optimization method and system

By using data closed-loop feedback and machine learning models to perform time-frequency transformation and component decomposition on vibration signals of urban lifeline facilities, the system identifies and compensates for sensor coupling state drift, thus solving the problem of monitoring data distortion caused by sensor coupling state drift and improving the accuracy and reliability of the monitoring system.

CN122173910APending Publication Date: 2026-06-09ANHUI UNIV OF FINANCE & ECONOMICS
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ANHUI UNIV OF FINANCE & ECONOMICS
Filing Date
2026-05-08
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In existing vibration monitoring systems for urban lifeline facilities, the mechanical coupling state between sensors and the measured object can drift unpredictably during service, leading to distorted monitoring data. Existing systems cannot compensate for this in real time, affecting the accuracy of fault diagnosis.

Method used

By using a data-based closed-loop feedback method, machine learning models are employed to perform time-frequency transformation and component decomposition on vibration signals, identify and compensate for the effects of changes in coupling state, generate frequency-related compensation curves, and achieve continuous self-optimization of the monitoring system.

Benefits of technology

It effectively eliminates the interference of coupling drift on monitoring data, significantly improves the accuracy of vibration characteristic indicators and the reliability of long-term online monitoring of urban lifeline facilities.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122173910A_ABST
    Figure CN122173910A_ABST
Patent Text Reader

Abstract

This invention relates to the fields of smart city and machine learning technology, specifically a method for continuous self-optimization of urban governance models based on data closed-loop feedback. The scheme involves: acquiring raw waveform data and operating condition reference signals from vibration sensors of urban lifeline facilities; generating a time-frequency energy spectrum matrix through time-frequency transformation; decomposing this matrix into slow-changing and fast-changing components using a machine learning model; removing slow-changing components correlated with long-term operating conditions; labeling the remaining slow-changing components as the first component and the fast-changing components as the second component; generating a frequency-related compensation curve based on the difference between the first component and the historical energy spectrum; performing multiplicative correction; and simultaneously calculating a coupling health index, triggering a calibration work order and updating the historical energy spectrum when the index falls below a threshold. This invention achieves closed-loop optimization of sensor coupling state self-sensing, self-compensation, and self-calibration, significantly improving the long-term data stability and accuracy of urban lifeline monitoring systems.
Need to check novelty before this filing date? Find Prior Art