Intelligent temperature measurement method and system for mining optical fiber

Abstract

The invention relates to the technical field of temperature measurement, in particular to an intelligent temperature measurement method and system for a mining optical fiber, and the method comprises the steps: obtaining a reflection spectrum for any FBG in the mining optical fiber, and constructing a reflection spectrum curve; obtaining initial sub-curves according to data distribution characteristics in the reflection spectrum curve, and screening potential sub-curves according to data differences between each initial sub-curve and the reflection spectrum curve; for any potential sub-curve, obtaining the credibility of any potential sub-curve according to the data distribution characteristics in any potential sub-curve, obtaining the credibility of each potential sub-curve, and screening a target sub-curve from all the potential sub-curves; and obtaining an optimal Bragg peak value according to the data fluctuation characteristics in the target sub-curve, obtaining the temperature corresponding to any FBG according to the optimal Bragg peak value, obtaining the temperature corresponding to each FBG in the mining optical fiber, obtaining the temperatures of different positions in the mining optical fiber, and enhancing the temperature measurement accuracy of the mining optical fiber.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature measurement, and particularly to an intelligent temperature measurement method and system for mine optical fibers. Background Art

[0002] A mine optical fiber is an optical fiber communication device specifically designed and manufactured for the mine environment, which can provide reliable communication and data transmission functions under harsh conditions. To ensure the safety of the mine operation environment, especially in a complex and potentially dangerous environment such as a mine shaft, it is necessary to perform large-scale and high-precision multi-point temperature measurement through optical fibers to ensure the safety and stability of the mine operation environment.

[0003] Fiber Bragg Grating (FBG) is the most common optical fiber sensing technology. Temperature changes will cause the refractive index of FBG to change, thereby changing the reflection wavelength, so that there is a clear linear relationship between the reflection wavelength and temperature, which can accurately reflect the tiny changes in temperature. At the same time, FBG transmits signals through optical fibers and can be free from electromagnetic interference in places like a mine shaft with a complex and poor electromagnetic environment. Moreover, its excellent high-temperature resistance and strong corrosion prevention make it have higher reliability in such a harsh mine environment, and it is a very common and reliable temperature sensing technology underground.

[0004] In the traditional method, FBG is embedded in an optical fiber, and the light source signal is transmitted to FBG through a broadband light source (such as an ASE light source). FBG reflects a specific wavelength (i.e., the Bragg wavelength), and the remaining wavelengths are transmitted. Then, the reflected spectrum is collected again through a spectral adjuster to obtain a spectral signal. The noise influence in the spectral signal is effectively reduced through smoothing filtering (usually using Gaussian filtering). Then, the wavelength corresponding to the maximum value is selected as the Bragg peak, and the temperature corresponding to the current position of FBG is calculated through the Bragg peak for temperature monitoring. However, in the temperature measurement of mine optical fibers, to achieve multi-point temperature measurement, multiple FBG sensors are arranged along the same optical fiber. The reflected spectra of different FBGs may overlap with each other, resulting in multiple Bragg peaks appearing in the reflected spectrum of the same FBG. And due to the uneven stress and temperature gradient in the mine shaft affected by the bending and local stretching during the installation process of the optical fiber, the Bragg peak may be a distribution rather than a single point. Affected by this factor, in the actual scenario, the wavelength corresponding to the maximum value in the spectral signal no longer represents the actual Bragg peak, resulting in a large deviation between the calculated temperature and the actual temperature.

[0005] Therefore, how to obtain an accurate Bragg peak for temperature calculation, and thus enhance the accuracy of temperature measurement of mine optical fibers has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, an embodiment of the present invention provides a method and system for intelligent temperature measurement of mine optical fibers to solve the problem of how to obtain accurate Bragg peaks for temperature calculation, thereby enhancing the accuracy of temperature measurement of mine optical fibers.

[0007] In a first aspect, an embodiment of the present invention provides a method for intelligent temperature measurement of mine optical fibers, the method comprising the following steps:

[0008] For any FBG in the mine optical fiber, obtain the reflection spectrum of the any FBG to obtain a reflection spectrum signal, construct a reflection spectrum curve of the reflection spectrum signal, the abscissa of the reflection spectrum curve is wavelength, and the ordinate is reflectivity;

[0009] According to the data distribution characteristics in the reflection spectrum curve, obtain at least two initial sub-curves in the reflection spectrum curve, and screen at least one potential sub-curve from all the initial sub-curves according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve;

[0010] For any potential sub-curve, according to the data distribution characteristics in the any potential sub-curve, obtain the credibility of the existence of the best Bragg peak in the any potential sub-curve, obtain the credibility of the existence of the best Bragg peak in each potential sub-curve, and screen a target sub-curve from all the potential sub-curves according to the credibility of the existence of the best Bragg peak in each potential sub-curve;

[0011] According to the data fluctuation characteristics in the target sub-curve, obtain the best Bragg peak, obtain the temperature corresponding to the any FBG according to the best Bragg peak, obtain the temperature corresponding to each FBG in the mine optical fiber, and obtain the temperatures at different positions in the mine optical fiber.

[0012] Preferably, the step of obtaining at least two initial sub-curves in the reflection spectrum curve according to the data distribution characteristics in the reflection spectrum curve includes:

[0013] Obtain the average reflectivity in the reflection spectrum curve, and in the reflection spectrum curve, connect the data points with the same reflectivity as the average reflectivity to obtain a straight line, denoted as the reference line;

[0014] Use the reference line to divide the curve in the reflection spectrum curve to obtain at least two sub-curves, and in the reflection spectrum curve, use the sub-curve located above the reference line as the initial sub-curve.

[0015] Preferably, the step of screening at least one potential sub-curve from all the initial sub-curves according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve includes:

[0016] For any initial sub - curve, obtain the degree of difference of the any initial sub - curve according to the data difference between the data in the any initial sub - curve and the data in the reflection spectrum curve;

[0017] Obtain the degree of difference of each initial sub - curve, and mark the initial sub - curve with a degree of difference greater than the preset degree - of - difference threshold as a potential sub - curve.

[0018] Preferably, the obtaining the degree of difference of the any initial sub - curve according to the data difference between the data in the any initial sub - curve and the data in the reflection spectrum curve includes:

[0019] Obtain the maximum value in the reflection spectrum curve, correspondingly obtain the mean value of the maximum values, calculate the difference between the maximum value in the any initial sub - curve and the mean value of the maximum values to obtain the reflectivity difference, and perform hyperbolic tangent processing on the reflectivity difference to obtain the first degree of difference;

[0020] Obtain the full width at half maximum (FWHM) of each initial sub - curve in the reflection spectrum curve, correspondingly obtain the mean value of the FWHM, calculate the difference between the FWHM of the any initial sub - curve and the mean value of the FWHM to obtain the FWHM difference, and perform hyperbolic tangent processing on the FWHM difference to obtain the second degree of difference;

[0021] Obtain the degree of difference of the any initial sub - curve according to the mean value between the first degree of difference and the second degree of difference.

[0022] Preferably, the obtaining the credibility of the existence of the best Bragg peak in the any potential sub - curve according to the data distribution characteristics in the any potential sub - curve includes:

[0023] Obtain the approximate Gaussian distribution degree of the any potential sub - curve according to the data distribution characteristics in the any potential sub - curve;

[0024] Obtain the reliability of the any potential sub - curve according to the difference between the data in the any potential sub - curve and the reference line;

[0025] Obtain the credibility of the existence of the best Bragg peak in the any potential sub - curve according to the mean value between the approximate Gaussian distribution degree and the reliability.

[0026] Preferably, the obtaining the approximate Gaussian distribution degree of the any potential sub - curve according to the data distribution characteristics in the any potential sub - curve includes:

[0027] Denote the wavelength corresponding to the maximum reflectivity in the any potential sub - curve as the middle wavelength, and respectively obtain the minimum wavelength and the maximum wavelength corresponding to the reflectivity in the any potential sub - curve;

[0028] Obtain the difference between the maximum wavelength and the minimum wavelength, and obtain a preset multiple of the difference, which is denoted as the intermediate wavelength value. Perform inverse proportional normalization on the absolute value of the difference between the intermediate wavelength and the intermediate wavelength value to obtain the first approximation degree;

[0029] In the reflection spectrum curve, perform integral processing on the reflectivity between the minimum wavelength and the intermediate wavelength to obtain the left integral value, perform integral processing on the reflectivity between the intermediate wavelength and the maximum wavelength to obtain the right integral value, and perform inverse proportional normalization on the absolute value of the difference between the left integral value and the right integral value to obtain the second approximation degree;

[0030] According to the mean value between the first approximation degree and the second approximation degree, obtain the approximate Gaussian distribution degree of any potential sub-curve.

[0031] Preferably, the obtaining of the reliability degree of any potential sub-curve according to the difference between the data in any potential sub-curve and the reference line includes:

[0032] Respectively obtain the difference between each maximum value in any potential sub-curve and the mean value of the reflectivity, and correspondingly obtain the cumulative difference value. Perform hyperbolic tangent processing on the cumulative difference value to obtain the prominence degree of any potential sub-curve;

[0033] Respectively obtain the curvature of each maximum value in any potential sub-curve, and correspondingly obtain the cumulative curvature value. Perform hyperbolic tangent processing on the cumulative curvature value to obtain the sharpness degree of any potential sub-curve;

[0034] According to the mean value between the prominence degree and the sharpness degree, obtain the reliability degree of any potential sub-curve.

[0035] Preferably, the screening of a target sub-curve from all potential sub-curves according to the credibility degree of the existence of the best Bragg peak in each potential sub-curve includes:

[0036] Take the potential sub-curve corresponding to the maximum credibility degree as the target sub-curve.

[0037] Preferably, the obtaining of the best Bragg peak according to the data fluctuation characteristics in the target sub-curve includes:

[0038] Obtain the cumulative reflectivity value corresponding to all extreme points in the target sub-curve. For any extreme point in the target sub-curve, calculate the ratio of the reflectivity corresponding to the any extreme point to the cumulative reflectivity value to obtain the wavelength weight coefficient corresponding to the any extreme point;

[0039] Obtain the wavelength weight coefficients corresponding to each extreme point in the target sub-curve respectively, and perform weighted summation processing on the wavelengths corresponding to all extreme points in the target sub-curve to obtain the optimal Bragg peak.

[0040] In a second aspect, an intelligent temperature measurement system for a mine optical fiber provided by an embodiment of the present invention includes a memory and a processor. The processor executes a computer program stored in the memory to implement an intelligent temperature measurement method for a mine optical fiber as described in the first aspect.

[0041] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0042] In the present invention, for any FBG in a mine optical fiber, the reflection spectrum of the any FBG is obtained to obtain a reflection spectrum signal, and a reflection spectrum curve of the reflection spectrum signal is constructed. The abscissa of the reflection spectrum curve is wavelength, and the ordinate is reflectivity; according to the data distribution characteristics in the reflection spectrum curve, at least two initial sub-curves are obtained in the reflection spectrum curve, and at least one potential sub-curve is screened from all the initial sub-curves according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve; for any potential sub-curve, according to the data distribution characteristics in the any potential sub-curve, the credibility of the existence of the optimal Bragg peak in the any potential sub-curve is obtained, the credibility of the existence of the optimal Bragg peak in each potential sub-curve is obtained, and according to the credibility of the existence of the optimal Bragg peak in each potential sub-curve, one target sub-curve is screened from all the potential sub-curves; according to the data fluctuation characteristics in the target sub-curve, the optimal Bragg peak is obtained, and according to the optimal Bragg peak, the temperature corresponding to the any FBG is obtained, the temperature corresponding to each FBG in the mine optical fiber is obtained, and the temperatures at different positions in the mine optical fiber are obtained. Among them, according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve, potential sub-curves that may have the optimal Bragg peak are initially obtained from all the initial sub-curves; then, by calculating the credibility of the existence of the optimal Bragg peak in each potential sub-curve, one target sub-curve with the existence of the optimal Bragg peak is screened from all the potential sub-curves; finally, the optimal Bragg peak is obtained through the data fluctuation characteristics in the target sub-curve, and thus the temperatures corresponding to the FBGs at different positions in the mine optical fiber are obtained, enhancing the accuracy of temperature measurement of the mine optical fiber. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 It is a flowchart of a smart temperature measurement method for a mine - used optical fiber provided in the first embodiment of the present invention. Specific implementation manners

[0045] The following details the embodiments of the present disclosure, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0046] It should be noted that terms such as "first" and "second" in the description of the present disclosure and the above - mentioned accompanying drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described here can be implemented in an order different from those illustrated or described here. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0047] To illustrate the technical solution of the present invention, the following will be described through specific examples.

[0048] Refer to Figure 1 , which is a flowchart of a smart temperature measurement method for a mine - used optical fiber provided in the first embodiment of the present invention. As Figure 1 shown, the method includes:

[0049] Step S101, for any FBG in the mine - used optical fiber, obtain the reflection spectrum of the any FBG to get a reflection spectrum signal, and construct a reflection spectrum curve of the reflection spectrum signal. The abscissa of the reflection spectrum curve is wavelength, and the ordinate is reflectivity.

[0050] A mine - used optical fiber is an optical fiber communication device specifically designed and manufactured for the mine environment, which can provide reliable communication and data transmission functions under harsh conditions. Fiber Bragg grating (FBG) is the most common optical fiber sensing technology. Temperature change will cause the refractive index of the FBG to change, thereby changing the reflection wavelength, so that there is a clear linear relationship between the reflection wavelength and temperature, which can accurately reflect the minute changes in temperature. At the same time, the FBG transmits signals through the optical fiber and can be free from electromagnetic interference in places like mines with complex and poor electromagnetic environments. Moreover, its excellent high - temperature resistance and strong corrosion resistance enable it to have higher reliability in such harsh mine environments, and it is a very common and reliable temperature sensing technology underground.

[0051] To achieve multi-point temperature measurement, multiple FBGs are embedded in the mine-used optical fiber. The broadband light source ASE transmits the light source signal to the FBG. The FBG reflects the Bragg wavelength and transmits the remaining wavelengths. In this embodiment, for any FBG in the mine-used optical fiber, the spectral adjuster collects the reflection spectrum of any FBG to obtain a data sequence of a group of wavelengths and reflectivities. The Gaussian filter is used to smooth the data sequence of any FBG to reduce the noise influence in the spectral signal. Then, a fitting function is used to fit the smoothed data, and the reflection spectrum curve is drawn through a plotting software (such as MATLAB). The abscissa of the reflection spectrum curve is the wavelength, and the ordinate is the reflectivity. Among them, the spectral adjuster collects the reflection spectrum, the Gaussian filter performs smoothing processing, and the fitting function performs fitting, which belong to the prior art and will not be elaborated here.

[0052] In the traditional method, the wavelength corresponding to the maximum value in the reflection spectrum curve is selected as the Bragg peak, and the temperature corresponding to the position where the current FBG is located is calculated through the Bragg peak to complete the temperature monitoring. However, in order to achieve multi-point temperature measurement in the mine-used optical fiber temperature measurement, multiple FBG sensors are arranged along the same optical fiber. The reflection spectra of different FBGs may overlap with each other, resulting in multiple Bragg peaks in the reflection spectrum of the same FBG. And due to the bending and local stretching during the installation process of the optical fiber, it is affected by non-uniform stress and the temperature gradient in the mine, so that the Bragg peak may be a distribution rather than a single point. Affected by this factor, in the actual scenario, the wavelength corresponding to the maximum value in the spectral signal no longer represents the actual Bragg peak, resulting in a large deviation between the calculated temperature and the actual temperature.

[0053] Therefore, in this embodiment, in order to obtain the optimal Bragg peak, according to the data distribution characteristics in the reflection spectrum curve, the potential sub-curves are obtained, and then the credibility of the potential sub-curves is calculated to obtain the target sub-curves. Finally, according to the data fluctuation characteristics in the target sub-curves, the optimal Bragg peak is obtained, and then the temperatures corresponding to the FBGs at different positions in the mine-used optical fiber are obtained, enhancing the accuracy of the mine-used optical fiber temperature measurement.

[0054] Step S102, according to the data distribution characteristics in the reflection spectrum curve, at least two initial sub-curves are obtained in the reflection spectrum curve, and at least one potential sub-curve is screened from all the initial sub-curves according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve.

[0055] Since the FBG only strongly reflects within a specific wavelength range and almost all other wavelengths are transmitted, and system or background noise will also generate a relatively low reflectivity, resulting in most of the reflectivities being relatively low in the obtained reflection spectrum curve. To exclude the influence of system or background noise and obtain the average reflectivity in the reflection spectrum curve that does not depend on the subjective threshold setting, in the said reflection spectrum curve, the data points with the same reflectivity as the average reflectivity are connected to obtain a straight line, denoted as the reference line. The curve in the said reflection spectrum curve is divided by using the reference line to obtain at least two sub-curves. In the said reflection spectrum curve, the sub-curve located below the reference line is confirmed to be caused by system or background noise and does not need to be concerned about, and the sub-curve located above the reference line is used as the initial sub-curve, which is confirmed to be reflected by any FBG within a specific wavelength range.

[0056] As the Bragg wavelength is the wavelength with the strongest reflection, even if the reflection spectra of multiple FBGs in one optical fiber may overlap with each other, resulting in multiple Bragg peaks appearing in the reflection spectrum of the same FBG, the reflectivity corresponding to the optimal Bragg peak should be greater than the average maximum value in the entire reflection spectrum curve. Since noise will also cause local mutations in the reflectivity, which are also represented as sharp protrusions in the reflection spectrum curve, but the reflectivity mutations caused by noise are relatively sharp and the full width at half maximum is very small. Therefore, for any initial sub-curve, the degree of difference of any initial sub-curve can be obtained according to the data difference between the data in any initial sub-curve and the data in the reflection spectrum curve. Furthermore, according to the degree of difference of each initial sub-curve, the potential sub-curve that may correspond to the optimal Bragg peak can be obtained, excluding the influence of local mutations in the reflectivity caused by noise.

[0057] Among them, the method for obtaining the degree of difference of any initial sub-curve according to the data difference between the data in any initial sub-curve and the data in the reflection spectrum curve is as follows:

[0058] Obtain the maximum value in the said reflection spectrum curve, corresponding to obtain the average maximum value, calculate the difference between the maximum value in any initial sub-curve and the average maximum value to obtain the reflectivity difference, and perform hyperbolic tangent processing on the reflectivity difference to obtain the first degree of difference;

[0059] Obtain the full width at half maximum of each initial sub-curve in the said reflection spectrum curve, corresponding to obtain the average full width at half maximum, calculate the difference between the full width at half maximum of any initial sub-curve and the average full width at half maximum to obtain the full width at half maximum difference, and perform hyperbolic tangent processing on the full width at half maximum difference to obtain the second degree of difference;

[0060] According to the average value between the first degree of difference and the second degree of difference, obtain the degree of difference of any initial sub-curve.

[0061] In one embodiment, taking the $i$-th initial sub-curve in the reflection spectral curve as an example, the formula for calculating the degree of difference of the $i$-th initial sub-curve is:

[0062]

[0063] where $\alpha$ i is the degree of difference of the $i$-th initial sub-curve; $R$ i,max is the maximum value in the $i$-th initial sub-curve of the reflection spectral curve; $n$ is the number of initial sub-curves in the reflection spectral curve; $H$ i is the full width at half maximum of the $i$-th initial sub-curve in the reflection spectral curve; $\tanh()$ is the hyperbolic tangent function, which is used to limit the output result to $(-1, 1)$.

[0064] It should be noted that is the first degree of difference, the larger it is, the greater the difference between the maximum value (i.e., the maximum reflectance) in the $i$-th initial sub-curve and the mean value of the maxima in the entire reflection spectral curve, the greater the degree of difference of the $i$-th initial sub-curve, and the more likely the $i$-th initial sub-curve is the sub-curve corresponding to the optimal Bragg peak; is the second degree of difference, the larger it is, the greater the difference between the full width at half maximum of the $i$-th initial sub-curve and the mean value of the full widths at half maximum of all initial sub-curves in the reflection spectral curve, the greater the degree of difference of the $i$-th initial sub-curve, the less likely the $i$-th initial sub-curve is a reflectance mutation caused by noise, and the more likely the $i$-th initial sub-curve is the sub-curve corresponding to the optimal Bragg peak.

[0065] Furthermore, according to the above method for obtaining the degree of difference of the $i$-th initial sub-curve, the degree of difference of each initial sub-curve is obtained. Since the reflectance corresponding to the optimal Bragg peak is greater than the mean value of the maxima in the entire reflection spectral curve, and the reflectance mutation caused by noise is relatively sharp and the full width at half maximum is very small, the degree of difference threshold is set to 0. There is no limitation here and it can be set according to the specific implementation scenario. The initial sub-curves with a degree of difference greater than 0 are recorded as potential sub-curves.

[0066] Thus, at least one potential sub-curve is obtained among all the initial sub-curves.

[0067] Step S103, for any one of the potential sub-curves, according to the data distribution characteristics in the any one of the potential sub-curves, obtain the credibility that there is an optimal Bragg peak in the any one of the potential sub-curves, obtain the credibility that there is an optimal Bragg peak in each potential sub-curve, and according to the credibility that there is an optimal Bragg peak in each potential sub-curve, screen a target sub-curve among all the potential sub-curves.

[0068] In the ideal case, the shape of the reflection spectrum obtained by FBG is close to the Gaussian distribution. Therefore, in the reflection spectrum curve, the potential sub-curve that is closer to the Gaussian distribution usually means that the influencing factors are more uniform and is more likely to be the sub-curve corresponding to the optimal Bragg peak. Moreover, the reflection mechanism of FBG determines that its reflection wavelength has strong selectivity. The true Bragg peak usually appears as a sharp peak with good symmetry, high intensity, and moderate full width at half maximum. Therefore, in the reflection spectrum curve, the potential sub-curve with clear, sharp, and prominent extreme points is usually more likely to correspond to the true Bragg peak.

[0069] Therefore, for any potential sub-curve, the credibility of the existence of the optimal Bragg peak in the any potential sub-curve can be obtained according to the data distribution characteristics in the any potential sub-curve. Furthermore, according to the credibility of the existence of the optimal Bragg peak in each potential sub-curve, the target sub-curve corresponding to the optimal Bragg peak can be screened out among all potential sub-curves.

[0070] Among them, the method for obtaining the credibility of the existence of the optimal Bragg peak in the any potential sub-curve according to the data distribution characteristics in the any potential sub-curve is as follows:

[0071] (1) According to the data distribution characteristics in the any potential sub-curve, obtain the approximate Gaussian distribution degree of the any potential sub-curve.

[0072] Specifically, denote the wavelength corresponding to the maximum reflectivity in the any potential sub-curve as the middle wavelength, and respectively obtain the minimum wavelength and the maximum wavelength corresponding to the reflectivity in the any potential sub-curve;

[0073] Obtain the difference between the maximum wavelength and the minimum wavelength, obtain the difference multiplied by a preset multiple as the wavelength intermediate value, and perform inverse proportional normalization on the absolute value of the difference between the middle wavelength and the wavelength intermediate value to obtain the first approximation degree;

[0074] In the reflection spectrum curve, perform integral processing on the reflectivity between the minimum wavelength and the middle wavelength to obtain the left integral value, perform integral processing on the reflectivity between the middle wavelength and the maximum wavelength to obtain the right integral value, and perform inverse proportional normalization on the absolute value of the difference between the left integral value and the right integral value to obtain the second approximation degree;

[0075] According to the mean value between the first approximation degree and the second approximation degree, obtain the approximate Gaussian distribution degree of the any potential sub-curve.

[0076] In an embodiment, taking the j-th potential sub-curve as an example, the formula for calculating the approximate Gaussian distribution degree of the j-th potential sub-curve is:

[0077]

[0078] wherein, σ j is the degree of approximate Gaussian distribution of the j-th potential sub-curve; λ is the intermediate wavelength; λ max is the maximum wavelength corresponding to the reflectivity in the j-th potential sub-curve; λ min is the minimum wavelength corresponding to the reflectivity in the j-th potential sub-curve; f j is the j-th potential sub-curve; || is the absolute value symbol; exp(-) is the exponential function with the natural constant as the base, used for inverse proportional normalization; is a preset multiple.

[0079] It should be noted that is the first approximation degree, the larger it is, the closer the intermediate wavelength corresponding to the j-th potential sub-curve is to the wavelength median value, the more the j-th potential sub-curve conforms to the Gaussian distribution degree, and the greater the approximate Gaussian distribution degree of the j-th potential sub-curve; is the second approximation degree, the larger it is, the more similar the integral areas on both sides of the intermediate wavelength corresponding to the j-th potential sub-curve are, the more the j-th potential sub-curve conforms to the Gaussian distribution degree, and the greater the approximate Gaussian distribution degree of the j-th potential sub-curve.

[0080] (2) Obtain the reliability of any one of the potential sub-curves according to the difference between the data in any one of the potential sub-curves and the reference line.

[0081] Specifically, respectively obtain the difference between each maximum value in any one of the potential sub-curves and the mean value of the reflectivity, correspondingly obtain the cumulative value of the differences, and perform hyperbolic tangent processing on the cumulative value of the differences to obtain the prominence degree of any one of the potential sub-curves;

[0082] Respectively obtain the curvature of each maximum value in any one of the potential sub-curves, correspondingly obtain the cumulative value of the curvatures, and perform hyperbolic tangent processing on the cumulative value of the curvatures to obtain the sharpness degree of any one of the potential sub-curves;

[0083] Obtain the reliability of any one of the potential sub-curves according to the mean value between the prominence degree and the sharpness degree.

[0084] In one embodiment, taking the j-th potential sub-curve as an example, the formula for calculating the reliability of the j-th potential sub-curve is:

[0085]

[0086] wherein, τ j is the reliability of the j-th potential sub-curve; R' k is the k-th maximum value in the j-th potential sub-curve; is the average reflectivity; C k is the curvature of the k-th maximum in the j-th potential sub-curve; m is the number of maxima in the j-th potential sub-curve; tanh() is the hyperbolic tangent function.

[0087] It should be noted that is the prominence of the j-th potential sub-curve. The greater the difference between the maxima in the j-th potential sub-curve and the average reflectivity, the greater it is, indicating that the prominence of the extreme points in the j-th potential sub-curve is more obvious, and the reliability of the j-th potential sub-curve is greater; is the sharpness of the j-th potential sub-curve. The greater the curvature of the maxima in the j-th potential sub-curve, the greater it is, indicating that the sharpness of the extreme points in the j-th potential sub-curve is more obvious, and the reliability of the j-th potential sub-curve is greater.

[0088] (3) According to the mean value between the approximate Gaussian distribution degree and the reliability, obtain the credibility that there is an optimal Bragg peak in any potential sub-curve.

[0089] In one embodiment, taking the j-th potential sub-curve as an example, the formula for calculating the credibility of the j-th potential sub-curve is:

[0090]

[0091] where γ j is the credibility of the j-th potential sub-curve; σ j is the approximate Gaussian distribution degree of the j-th potential sub-curve; τ j is the reliability of the j-th potential sub-curve.

[0092] It should be noted that the greater the approximate Gaussian distribution degree of the j-th potential sub-curve, the more likely the j-th potential sub-curve is the sub-curve corresponding to the optimal Bragg peak, and the greater the credibility of the j-th potential sub-curve; the greater the reliability of the j-th potential sub-curve, the greater the possibility of clear, sharp and prominent extreme points appearing in the j-th potential sub-curve, the more likely the j-th potential sub-curve is the sub-curve corresponding to the optimal Bragg peak, and the greater the credibility of the j-th potential sub-curve.

[0093] Furthermore, according to the method for obtaining the credibility of the j-th potential sub-curve, obtain the credibility of each potential sub-curve, and take the potential sub-curve corresponding to the maximum credibility as the target sub-curve.

[0094] Thus, a target sub-curve is obtained among all potential sub-curves.

[0095] Step S104: Obtain the optimal Bragg peak according to the data fluctuation characteristics in the target sub-curve. Based on the optimal Bragg peak, obtain the temperature corresponding to any FBG, and obtain the temperatures corresponding to each FBG in the mining optical fiber, so as to obtain the temperatures at different positions in the mining optical fiber.

[0096] Due to uneven stress and temperature gradient in the mine caused by bending and local stretching during the installation of the optical fiber, the Bragg peak is expressed as a distribution rather than a single point. The maximum points in the reflection spectrum curve can reflect partial information in the FBG reflection spectrum and contribute to the optimal Bragg peak to a certain extent. Therefore, the contribution of each maximum point to the optimal Bragg peak can be comprehensively considered, and the optimal Bragg peak can be obtained by weighting each maximum value in the target sub-curve, avoiding the deviation caused by a single maximum value and overcoming the problem of peak distribution.

[0097] Among them, the method of weighting each maximum value in the target sub-curve to obtain the optimal Bragg peak is as follows:

[0098] Obtain the cumulative value of the reflectivities corresponding to all extreme points in the target sub-curve. For any extreme point in the target sub-curve, calculate the ratio of the reflectivity corresponding to the any extreme point to the cumulative value of the reflectivities, and obtain the wavelength weight coefficient corresponding to the any extreme point.

[0099] Respectively obtain the wavelength weight coefficients corresponding to each extreme point in the target sub-curve, and perform weighted summation processing on the wavelengths corresponding to all extreme points in the target sub-curve to obtain the optimal Bragg peak.

[0100] In an embodiment, the calculation formula for the optimal Bragg peak is:

[0101]

[0102] Among them, λ' is the optimal Bragg peak; R' s is the reflectivity corresponding to the s-th extreme point in the target sub-curve; Y is the number of extreme points in the target sub-curve; λ s is the wavelength corresponding to the s-th extreme point in the target sub-curve.

[0103] It should be noted that is the wavelength weight coefficient corresponding to the s-th extreme point in the target curve. The larger the reflectivity corresponding to the s-th extreme point in the target sub-curve, the larger the wavelength weight coefficient corresponding to the s-th extreme point in the target curve. The larger the wavelength corresponding to the s-th extreme point in the target curve, the larger the optimal Bragg peak.

[0104] After obtaining the optimal Bragg peak, the offset between the optimal Bragg peak and the reference wavelength is obtained by means of the approximate linear relationship between Bragg compensation and temperature. The current temperature is calculated by the offset, and the temperature corresponding to any FBG is obtained. Calculating the temperature according to the optimal Bragg peak belongs to the prior art and will not be elaborated here.

[0105] Furthermore, according to the method for obtaining the temperature corresponding to any FBG above, the temperatures corresponding to each FBG in the mining optical fiber at different positions are obtained, realizing multi-precision and multi-point temperature measurement of the mining optical fiber.

[0106] In summary, in the embodiment of the present invention, for any FBG in the mining optical fiber, the reflection spectrum of the any FBG is obtained to obtain a reflection spectrum signal, and a reflection spectrum curve of the reflection spectrum signal is constructed. The abscissa of the reflection spectrum curve is the wavelength, and the ordinate is the reflectivity; according to the data distribution characteristics in the reflection spectrum curve, at least two initial sub-curves are obtained in the reflection spectrum curve, and at least one potential sub-curve is screened from all the initial sub-curves according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve; for any potential sub-curve, according to the data distribution characteristics in the any potential sub-curve, the credibility of the existence of the optimal Bragg peak in the any potential sub-curve is obtained, the credibility of the existence of the optimal Bragg peak in each potential sub-curve is obtained, and a target sub-curve is screened from all the potential sub-curves according to the credibility of the existence of the optimal Bragg peak in each potential sub-curve; according to the data fluctuation characteristics in the target sub-curve, the optimal Bragg peak is obtained, and according to the optimal Bragg peak, the temperature corresponding to the any FBG is obtained, and the temperatures corresponding to each FBG in the mining optical fiber are obtained to obtain the temperatures at different positions in the mining optical fiber. Among them, according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve, potential sub-curves that may have the optimal Bragg peak are initially obtained from all the initial sub-curves; then, by calculating the credibility of the existence of the optimal Bragg peak in each potential sub-curve, a target sub-curve with the existence of the optimal Bragg peak is screened from all the potential sub-curves; finally, the optimal Bragg peak is obtained through the data fluctuation characteristics of the target sub-curve, and then the temperatures corresponding to the FBGs at different positions in the mining optical fiber are obtained, enhancing the accuracy of temperature measurement of the mining optical fiber.

[0107] Based on the same inventive concept as the above method, the embodiment of the present invention also provides an intelligent temperature measurement system for a mining optical fiber, including a memory and a processor, and the processor executes the computer program stored in the memory to implement the above intelligent temperature measurement method for a mining optical fiber.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A smart temperature measurement method for optical fiber used in mining, characterized in that: The method comprises: For any FBG in the mining optical fiber, obtain the reflection spectrum of any FBG, obtain the reflection spectrum signal, and construct a reflection spectrum curve of the reflection spectrum signal, wherein the abscissa of the reflection spectrum curve is the wavelength and the ordinate is the reflectivity; According to the data distribution characteristics in the reflection spectrum curve, at least two initial sub-curves are obtained in the reflection spectrum curve, and according to the data difference between the data in each initial sub-curve and the data in the reflection spectrum curve, at least one potential sub-curve is screened in all the initial sub-curves; For any potential sub-curve, according to the data distribution characteristics in the any potential sub-curve, obtain the credibility of the best Bragg peak in the any potential sub-curve, obtain the credibility of the best Bragg peak in each potential sub-curve, and select a target sub-curve from all potential sub-curves according to the credibility of the best Bragg peak in each potential sub-curve; According to the data fluctuation characteristics in the target sub-curve, the best Bragg peak is obtained, and according to the best Bragg peak, the temperature corresponding to any FBG is obtained, the temperature corresponding to each FBG in the mining optical fiber is obtained, and the temperature at different positions in the mining optical fiber is obtained.

2. The intelligent temperature measurement method of a mining optical fiber according to claim 1 is characterized in that: The step of acquiring at least two initial sub-curves from the reflectance spectrum curve according to the data distribution characteristics in the reflectance spectrum curve comprises: Obtaining a mean reflectivity value in the reflectivity spectrum curve, and connecting data points in the reflectivity spectrum curve with the same reflectivity as the mean reflectivity value to obtain a straight line, which is recorded as a reference line; The curve in the reflection spectrum curve is divided by using the reference line to obtain at least two sub-curves, and in the reflection spectrum curve, the sub-curve located above the reference line is used as an initial sub-curve.

3. The intelligent temperature measurement method of a mining optical fiber according to claim 1 is characterized in that: The step of screening at least one potential sub-curve from all the initial sub-curves according to the data difference between the data in each initial sub-curve and the data in the reflectance spectrum curve comprises: For any initial sub-curve, according to the data difference between the data in the any initial sub-curve and the data in the reflection spectrum curve, obtaining the difference degree of the any initial sub-curve; The difference degree of each initial sub-curve is obtained, and the initial sub-curve whose difference degree is greater than a preset difference degree threshold is recorded as a potential sub-curve.

4. The intelligent temperature measurement method of a mining optical fiber according to claim 3 is characterized in that: The obtaining the difference degree of any initial sub-curve according to the data difference between the data in any initial sub-curve and the data in the reflection spectrum curve includes: Obtaining a maximum value in the reflection spectrum curve, and correspondingly obtaining a maximum value mean, calculating a difference between the maximum value in any initial sub-curve and the maximum value mean, obtaining a reflectivity difference, and performing a hyperbolic tangent process on the reflectivity difference to obtain a first difference degree; Obtaining the half-width of each initial sub-curve in the reflection spectrum curve, and correspondingly obtaining the half-width mean, calculating the difference between the half-width of any initial sub-curve and the half-width mean, obtaining the half-width difference, and performing hyperbolic tangent processing on the half-width difference to obtain a second difference degree; The difference degree of any one of the initial sub-curves is obtained according to the average value between the first difference degree and the second difference degree.

5. The intelligent temperature measurement method of a mining optical fiber according to claim 2 is characterized in that: The obtaining, according to the data distribution characteristics in any potential sub-curve, the degree of confidence that the best Bragg peak exists in any potential sub-curve comprises: According to the data distribution characteristics in any potential sub-curve, obtaining the approximate Gaussian distribution degree of any potential sub-curve; Obtaining the reliability of any potential sub-curve according to the difference between the data in any potential sub-curve and the baseline; The confidence level of the existence of the best Bragg peak in any potential sub-curve is obtained according to the mean value between the approximate Gaussian distribution level and the reliability level.

6. The intelligent temperature measurement method of a mining optical fiber according to claim 5, characterized in that: The step of obtaining the approximate Gaussian distribution degree of any potential sub-curve according to the data distribution characteristics in any potential sub-curve includes: Record the wavelength corresponding to the maximum reflectivity in any potential sub-curve as the intermediate wavelength, and respectively obtain the minimum wavelength and the maximum wavelength corresponding to the reflectivity in any potential sub-curve; Obtaining a difference between the maximum wavelength and the minimum wavelength, obtaining a preset multiple of the difference as a wavelength intermediate value, performing inverse proportional normalization processing on the absolute value of the difference between the intermediate wavelength and the wavelength intermediate value, and obtaining a first approximation degree; In the reflection spectrum curve, the reflectivity between the minimum wavelength and the middle wavelength is integrated to obtain a left integral value, the reflectivity between the middle wavelength and the maximum wavelength is integrated to obtain a right integral value, and the absolute value of the difference between the left integral value and the right integral value is inversely normalized to obtain a second degree of approximation; The approximate Gaussian distribution degree of any potential sub-curve is obtained according to the average value between the first approximation degree and the second approximation degree.

7. The intelligent temperature measurement method of a mining optical fiber according to claim 5, characterized in that: The obtaining the reliability of any potential sub-curve according to the difference between the data in any potential sub-curve and the baseline includes: Respectively obtaining the difference between each maximum value and the reflectivity mean value in any potential sub-curve, obtaining a corresponding difference cumulative value, performing hyperbolic tangent processing on the difference cumulative value, and obtaining the prominence degree of any potential sub-curve; Respectively obtain the curvature of each maximum value in any potential sub-curve, obtain a corresponding curvature cumulative value, perform hyperbolic tangent processing on the curvature cumulative value, and obtain the sharpness of any potential sub-curve; The reliability of any potential sub-curve is obtained according to the average value between the protrusion degree and the sharpness degree.

8. The intelligent temperature measurement method of a mining optical fiber according to claim 1, characterized in that: According to the credibility of the best Bragg peak in each potential sub-curve, a target sub-curve is selected from all potential sub-curves, including: The potential sub-curve corresponding to the maximum credibility is taken as the target sub-curve.

9. The intelligent temperature measurement method of a mining optical fiber according to claim 1, characterized in that: The step of obtaining an optimal Bragg peak value according to the data fluctuation characteristics in the target sub-curve includes: Obtaining the reflectivity cumulative values ​​corresponding to all extreme points in the target sub-curve, and for any extreme point in the target sub-curve, calculating the ratio of the reflectivity corresponding to any extreme point to the reflectivity cumulative value, and obtaining the wavelength weight coefficient corresponding to any extreme point; The wavelength weight coefficient corresponding to each extreme point in the target sub-curve is obtained respectively, and the wavelengths corresponding to all extreme points in the target sub-curve are weighted and summed to obtain the optimal Bragg peak value.

10. An intelligent temperature measurement system for optical fiber used in mining, comprising a memory and a processor, characterized in that: The processor executes the computer program stored in the memory to implement the intelligent temperature measurement method for mining optical fiber as described in any one of claims 1-9.

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