A data processing method of distributed optical fiber temperature sensor

Abstract

The invention discloses a data processing method of a distributed optical fiber temperature sensor, which belongs to the technical field of sensors, and comprises: performing edge detection on the continuous temperature data before using a filter template to perform filtering processing on the continuous temperature data measured by the distributed temperature sensor; When the edge detection result is a sudden temperature change, the filter template skips this edge when smoothing. The invention detects the edges in the temperature data before smoothing and filtering the data measured by the distributed optical fiber temperature sensor, and then skips each edge during the smoothing and filtering. This avoids smoothing the edge position when using smoothing on the edge position, thereby reducing the spatial positioning accuracy and temperature measurement accuracy of the system.

Description

technical field [0001] The invention relates to the technical field of sensors, in particular to a data processing method of a distributed optical fiber temperature sensor. Background technique [0002] Distributed optical fiber temperature sensor is a technology for real-time continuous measurement of temperature field distribution along the direction of optical fiber. Most of this technology uses the sensitivity of Raman backscattered light intensity in optical fiber to temperature to realize temperature adjustment. However, due to the very weak Raman scattered light, at present, the cumulative average method is generally used to provide a high signal-to-noise ratio. Theoretically, it can be considered that as long as enough average times are performed, the noise level can always be controlled within an acceptable range. However, in practical applications, since the real-time performance of temperature measurement is also an important parameter for distributed optical fibe...

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Problems solved by technology

Although the smoothing filter in the spatial domain can effectively remove the influence of noise, it has serious defects such as reducing the spatial resolution contained in the distributed temperature data and increasing the measurement error of some temperature data.

[0004] However, long-term practice has shown that although the wavelet transform filtering method of digital signals in the frequency domain can effectively improve the signal-to-noise ratio of the system, this method also has some obvious defects: First, this method requires sensors with different performance For separate debugging, the implementation and operation process is relatively complicated

The second is that this method is difficult to implement in the relevant hardware for data processing in embedded systems, and its application is greatly limited

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