Real-time clustering early warning method for oil and gas pipeline monitoring based on massive vibration interference events

Abstract

The invention provides a real-time clustering early warning method for oil and gas pipeline monitoring based on massive vibration interference events. In the method, a Rayleigh scattering-based coherent light time domain reflection vibration sensing system is included, and the method is characterized in that the Rayleigh scattering-based coherent light time domain reflection vibration sensing system comprises a laser, amplifiers, acousto-optic regulators, couplers, a digital signal processor, a driver, an isolator, a delay fiber, a piezoelectric ceramic driver, a photodetector, a central processing unit and a circulator. The A real-time clustering analysis method is adopted to perform cluster analysis on the vibration interference event data obtained through analysis in the time and spacedistribution dimensions at the same time, that is, the spatial distribution characteristics of abnormal interference events are considered, so as to better match the data characteristics caused by theinterference event behavior, which can significantly reduce the number of false positives, reduce the possibility of false negatives, and improve the accuracy and effectiveness of early warning.

Description

technical field [0001] The invention relates to the technical field of oil and gas pipeline detection, in particular to a real-time clustering and early warning method for oil and gas pipeline monitoring based on massive vibration interference events. Background technique [0002] Existing real-time early warning and monitoring systems for oil and gas pipelines generally classify and identify vibration source interference events based on the amplitude and phase characteristics of optical fiber time domain signals. If the amplitude and phase thresholds are set low, the number of identified vibration disturbance events will be large, and many weak surface normal activities will also be identified as abnormal disturbance events, thereby reducing the early warning accuracy of the monitoring system; on the contrary, if the amplitude If the threshold and phase threshold are set high, it is easy to miss the early warning because the threshold for triggering the early warning is set...

AI Technical Summary

Problems solved by technology

If the amplitude and phase thresholds are set low, the number of identified vibration disturbance events will be large, and many weak surface normal activities will also be identified as abnormal disturbance events, thereby reducing the early warning accuracy of the monitoring system; on the contrary, if the amplitude If the threshold and phase threshold are set high, it is easy to miss the early warning because the threshold setting for triggering the early warning is too high.

Therefore, this threshold early warning mechanism based on the characteristics of pulse data often has disadvantages such as poor early warning accuracy and high early warning false alarm rate, which is difficult to meet actual production needs.

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