Distributed optical fiber temperature sensor data processing method

Abstract

The invention discloses a distributed optical fiber temperature sensor data processing method, and belongs to the technical field of sensors. The method comprises the following steps: before continuous temperature data measured by a distributed temperature sensor is filtered using a filter template, the edge of the continuous temperature data is detected; and when the edge detection result shows that the edge is a temperature jump point, the filter template skips the edge during smooth filtering. According to the invention, before data measured by a distributed optical fiber temperature sensor is smoothly filtered, the edge in the temperature data is detected first, and then, the edge is skipped during smooth filtering. Thus, the problem that the edge point becomes smooth after edge point smoothing and the spatial positioning accuracy and temperature measurement accuracy of the system are reduced is avoided.

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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