Fiber bragg grating multi-peak detection method, device, equipment, medium and product

Abstract

This application discloses a method, apparatus, device, medium, and product for multi-peak detection of fiber Bragg gratings. The method includes: acquiring the reflection spectrum sequence of the fiber Bragg grating; determining multiple reflection peak centers and corresponding local window spectral data for each reflection peak center; determining the second derivative corresponding to the local window spectral data, negating the second derivative to obtain a corrected second derivative, normalizing the corrected second derivative to obtain an adaptive wavelet template; performing Hilbert transform on the local window spectral data and the adaptive wavelet template respectively to determine the first zero-crossing point wavelength and the second zero-crossing point wavelength; determining the reflection peak wavelength offset based on the first and second zero-crossing point wavelengths; and superimposing the reflection peak wavelength offset with the reflection peak center to obtain the target wavelength of the reflection peak. This application enables high-precision multi-peak detection of fiber Bragg gratings.

Description

Technical Field

[0001] This application relates to the field of fiber optic sensing technology, and in particular to a method, apparatus, equipment, medium, and product for detecting multiple peaks of fiber Bragg gratings. Background Technology

[0002] In fiber Bragg grating (FBG) sensing systems, it is typically necessary to accurately detect the center wavelengths of multiple reflection peaks from the acquired reflection spectrum, and then demodulate the measured physical quantities such as temperature, stress, and strain based on this. Therefore, achieving high-precision and robust multi-peak detection in complex spectral environments has always been one of the core technical requirements in this field.

[0003] Commonly used methods in related technologies include direct peak finding algorithms, polynomial fitting algorithms, centroid methods, and Gaussian fitting algorithms. However, in actual reflectance spectra, each reflectance peak is affected by various factors such as grating fabrication process, operating environment, and sensor network crosstalk, resulting in significant differences in peak width, symmetry, and background noise levels. The aforementioned conventional peak finding methods are difficult to universally adapt to the dynamic changes in the morphology of each reflectance peak, especially in complex spectral environments where detection accuracy decreases significantly and is prone to false identification or missed detection. Summary of the Invention

[0004] In view of this, this application provides a method, apparatus, device, medium and product for multi-peak detection of fiber Bragg gratings, so as to achieve high-precision multi-peak detection of fiber Bragg gratings.

[0005] This application provides the following solution: In a first aspect, a method for detecting multiple peaks in a fiber Bragg grating is provided, the method comprising: Acquire the reflectance spectral sequence of fiber Bragg gratings; The reflection peak center of the fiber Bragg grating is detected by performing reflection peak center detection to obtain multiple reflection peak centers. For any reflection peak center, the reflection spectral sequence of the fiber Bragg grating is truncated with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center. The second derivative corresponding to the local window spectral data is determined using the five-point central difference method. The second derivative corresponding to the local window spectral data is then negative to obtain the corrected second derivative. The corrected second derivative is then normalized to obtain the adaptive wavelet template. Hilbert transforms are performed on the local window spectral data and the adaptive wavelet template respectively to obtain the first Hilbert transform sequence corresponding to the local window spectral data and the second Hilbert transform sequence corresponding to the adaptive wavelet template; The first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence are determined. The reflection peak wavelength offset is determined based on the first zero-crossing wavelength and the second zero-crossing wavelength. The reflection peak wavelength offset is superimposed with the center of the reflection peak to obtain the target wavelength of the reflection peak.

[0006] Optionally, after acquiring the reflection spectrum sequence of the fiber Bragg grating, the method further includes: The reflection spectrum sequence of the fiber Bragg grating is subjected to sliding window filtering based on local polynomial least squares fitting to obtain the filtered reflection spectrum sequence. The reflection peak center of the fiber Bragg grating is detected by performing reflection peak center detection to obtain multiple reflection peak centers. For any reflection peak center, the reflection spectral sequence of the fiber Bragg grating is truncated with the reflection peak center as the center to obtain local window spectral data corresponding to the reflection peak center, including: The filtered reflectance spectrum sequence is subjected to reflectance peak center detection to obtain multiple reflectance peak centers. For any reflectance peak center, the filtered reflectance spectrum sequence is truncated with the reflectance peak center as the center to obtain the local window spectral data corresponding to the reflectance peak center.

[0007] Optionally, the reflection peak center detection of the reflection spectrum sequence of the fiber Bragg grating yields multiple reflection peak centers, including: The reflection spectrum sequence of the fiber Bragg grating is subjected to first-order difference processing to obtain spectral difference data; Traverse any adjacent first and second sampling points in the spectral difference data. If the spectral difference value corresponding to the first sampling point is greater than zero and the spectral difference value corresponding to the second sampling point is less than zero, then determine the second sampling point as the center of the reflection peak, so as to obtain multiple reflection peak centers.

[0008] Optionally, the reflection spectrum sequence of the fiber Bragg grating is truncated with the center of the reflection peak as the center to obtain local window spectral data corresponding to the center of the reflection peak, including: Obtain the estimated peak width and pixel size of the fiber Bragg grating; The width of the local window is determined based on the estimated peak width of the fiber Bragg grating and the pixel size; Centered on the center of the reflection peak, the reflection spectrum sequence of the fiber Bragg grating is truncated based on the width of the local window to obtain the local window spectral data corresponding to the center of the reflection peak.

[0009] Optionally, determining the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and determining the second zero-crossing wavelength corresponding to the second Hilbert transform sequence includes: Traverse any two adjacent sampling points in the target Hilbert transform sequence; wherein, the target Hilbert transform sequence is either the first Hilbert transform sequence or the second Hilbert transform sequence; If the product of the Hilbert transform values ​​corresponding to the two sampling points is less than zero, the wavelength corresponding to the two sampling points is obtained. Linear fitting is performed on the Hilbert transform values ​​and wavelengths corresponding to the two sampling points to obtain the zero-crossing wavelengths corresponding to the target Hilbert transform sequence.

[0010] Optionally, the linear fitting formula for determining the zero-crossing wavelength corresponding to the target Hilbert transform sequence includes: ; in, This represents the wavelength corresponding to the j-th sampling point. This represents the wavelength corresponding to the (j+1)th sampling point. This represents the Hilbert transform value corresponding to the j-th sampling point. This represents the Hilbert transform value corresponding to the (j+1)th sampling point. This represents the zero-crossing wavelength of the target Hilbert transform sequence corresponding to the center of the k-th reflection peak.

[0011] Secondly, a fiber Bragg grating multi-peak detection device is provided, the device comprising: The reflectance spectral sequence acquisition unit is configured to acquire the reflectance spectral sequence of a fiber Bragg grating; The local window spectral data truncation unit is configured to detect the reflection peak center of the reflection spectral sequence of the fiber Bragg grating, obtain multiple reflection peak centers, and for any reflection peak center, truncate the reflection spectral sequence of the fiber Bragg grating with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center. The adaptive wavelet template construction unit is configured to determine the second derivative corresponding to the local window spectral data using the five-point central difference method, take the negative of the second derivative corresponding to the local window spectral data to obtain the corrected second derivative, and normalize the corrected second derivative to obtain the adaptive wavelet template. The Hilbert transform unit is configured to perform Hilbert transform on the local window spectral data and the adaptive wavelet template respectively, to obtain a first Hilbert transform sequence corresponding to the local window spectral data and a second Hilbert transform sequence corresponding to the adaptive wavelet template; The reflection peak target wavelength determination unit is configured to determine the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence, determine the reflection peak wavelength offset based on the first zero-crossing wavelength and the second zero-crossing wavelength, and superimpose the reflection peak wavelength offset with the center of the reflection peak to obtain the reflection peak target wavelength.

[0012] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0013] Fourthly, an electronic device is provided, comprising: One or more processors; and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any one of the first aspects above.

[0014] Fifthly, a computer program product comprising a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: 1) This application constructs an adaptive wavelet template by negating and normalizing the second derivative of the local window spectral data, so that the template dynamically matches the actual shape of the current reflection peak without the need to preset wavelet scale parameters; then, Hilbert transform is performed on the local window spectral data and the adaptive wavelet template respectively, and the stable correspondence between the zero cross points of the Hilbert transform and the center of the signal structure is used to accurately locate the wavelength of the reflection peak by the offset between the wavelength of the first zero cross point and the wavelength of the second zero cross point, breaking through the limitation of sampling interval and realizing sub-pixel level multi-peak high-precision detection.

[0016] 2) This application achieves effective suppression of high-frequency random noise while preserving spectral peak characteristics and avoiding peak position shift by using sliding window filtering based on local polynomial least squares fitting. This prevents the amplification effect of noise on subsequent second derivative calculations, thereby improving the robustness of the constructed template and the final peak wavelength in noisy environments.

[0017] 3) This application utilizes the property of the first-order difference zero-crossing point to directly reflect the location of the local maximum value of the spectrum. Without iteration or fitting, it can quickly lock the coarse center of all reflection peaks with extremely low computational complexity, creating conditions for subsequent fine processing of only local regions and significantly reducing the overall computational cost of the algorithm.

[0018] 4) By utilizing the estimated peak width and pixel size, this application can calculate the window width that can completely cover a single reflection peak, thereby ensuring that each local window contains complete peak shape information for template construction, while isolating crosstalk between adjacent spectral peaks, thus improving the representativeness of the template and the accuracy of the final positioning.

[0019] 5) This application directly calculates the sub-pixel-level wavelength position of the local window spectral data and the zero-crossing point of the adaptive wavelet template by utilizing the linear relationship between adjacent positive and negative points, thus breaking through the limitation of the original sampling interval on the positioning resolution.

[0020] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a system architecture diagram applicable to the embodiments of this application.

[0023] Figure 2 A flowchart of the fiber Bragg grating multi-peak detection method provided in the embodiments of this application.

[0024] Figure 3 This is a schematic diagram of the multi-peak peak finding result of the fiber Bragg grating provided in the embodiments of this application.

[0025] Figure 4 A schematic block diagram of a fiber Bragg grating multi-peak detection device provided in an embodiment of this application.

[0026] Figure 5 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0028] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0030] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0031] Currently, commonly used methods in related technologies include direct peak finding algorithms, polynomial fitting algorithms, centroid methods, and Gaussian fitting algorithms. However, in actual reflectance spectra, each reflectance peak is affected by various factors such as grating fabrication process, operating environment, and sensor network crosstalk, resulting in significant differences in peak width, symmetry, and background noise levels. The aforementioned conventional peak finding methods are difficult to universally adapt to the dynamic changes in the morphology of each reflectance peak, especially in complex spectral environments where detection accuracy decreases significantly and is prone to misidentification or missed detection.

[0032] In view of this, this application provides a new approach. To facilitate understanding of this application, the system architecture on which this application is based will first be described. Figure 1 An exemplary system architecture that can be applied to embodiments of this application is shown, such as Figure 1 As shown, the fiber Bragg grating sensing system may include a spectral acquisition unit and a signal processing unit.

[0033] The spectral acquisition unit is used to acquire the reflection spectral sequence of fiber Bragg gratings, and it can be an acquisition device such as a spectrometer.

[0034] The signal processing unit is used to receive the reflection spectrum sequence and execute the fiber Bragg grating multi-peak detection method to determine the target wavelength of multiple reflection peaks, thereby achieving high-precision multi-peak detection.

[0035] Fiber Bragg grating sensing systems can be installed in devices such as spectral demodulators and distributed fiber optic sensor analyzers; for example, they can be installed in fire alarm devices.

[0036] It should be understood that Figure 1 The number of spectral acquisition units and signal processing units shown is merely illustrative. Depending on implementation requirements, any number of spectral acquisition units and signal processing units can be included.

[0037] Figure 2 This application provides a flowchart of a fiber Bragg grating multi-peak detection method, which can be implemented by... Figure 1 The fiber Bragg grating sensing system shown is applicable to distributed and quasi-distributed sensing scenarios based on spectral analysis. Figure 2 As shown, the method may include the following steps: Step 201: Acquire the reflection spectrum sequence of the fiber Bragg grating.

[0038] Step 202: Detect the center of reflection peaks in the reflection spectrum sequence of the fiber Bragg grating to obtain multiple reflection peak centers. For any reflection peak center, truncate the reflection spectrum sequence of the fiber Bragg grating with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center.

[0039] Step 203: Use the five-point central difference method to determine the second derivative corresponding to the local window spectral data, take the negative of the second derivative corresponding to the local window spectral data to obtain the corrected second derivative, and normalize the corrected second derivative to obtain the adaptive wavelet template.

[0040] Step 204: Perform Hilbert transform on the local window spectral data and the adaptive wavelet template respectively to obtain the first Hilbert transform sequence corresponding to the local window spectral data and the second Hilbert transform sequence corresponding to the adaptive wavelet template.

[0041] Step 205: Determine the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence. Determine the reflection peak wavelength offset based on the first and second zero-crossing wavelengths. Superimpose the reflection peak wavelength offset with the center of the reflection peak to obtain the target wavelength of the reflection peak.

[0042] As can be seen from the above process, this application constructs an adaptive wavelet template by negating and normalizing the second derivative of the local window spectral data, so that the template dynamically matches the actual shape of the current reflection peak without the need to preset wavelet scale parameters; then, Hilbert transform is performed on the local window spectral data and the adaptive wavelet template respectively, and the stable correspondence between the zero cross points of the Hilbert transform and the center of the signal structure is used to accurately locate the wavelength of the reflection peak by the offset between the wavelength of the first zero cross point and the wavelength of the second zero cross point, breaking through the limitation of sampling interval and realizing sub-pixel level multi-peak high-precision detection.

[0043] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments. It should be noted that the terms "first" and "second" used in this disclosure do not have limitations in terms of size, order, or quantity, but are only used to distinguish them in name.

[0044] First, the above step 201, namely "acquiring the reflection spectrum sequence of the fiber Bragg grating", will be described in detail with reference to the embodiments.

[0045] Among them, the reflectance spectrum sequence refers to a discrete data sequence containing wavelength information and corresponding reflected light intensity information.

[0046] For example, a reflectance spectral sequence can be acquired using a spectrometer or other spectral acquisition unit. The reflectance spectral sequence can be represented as follows: ,in, Represents the reflectance spectrum sequence, This represents the (N-1)th sampling wavelength, where N is the total number of sampling points. This represents the reflected light intensity corresponding to the (N-1)th sampling wavelength.

[0047] The following describes in detail step 202, namely, "detecting the reflection peak center of the reflection spectrum sequence of the fiber Bragg grating to obtain multiple reflection peak centers, and for any reflection peak center, truncating the reflection spectrum sequence of the fiber Bragg grating with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center", with reference to the embodiments.

[0048] In this embodiment, the reflection peak center is used to indicate the approximate location of the reflection peak in the reflection spectrum sequence at a wavelength point. It can be called a coarse peak center, and there can be multiple such centers. For example, the set corresponding to multiple reflection peak centers can be represented as... ,in, This represents the set corresponding to the centers of multiple reflection peaks. This indicates the center of the Mth reflection peak.

[0049] Local window spectral data refers to a segment of spectral data extracted from the reflectance spectral sequence with the center of the reflectance peak as the reference. It can be used for fine analysis of the reflectance peak.

[0050] Understandably, by truncating the reflection spectrum sequence of the fiber Bragg grating with the center of the reflection peak as the center, it is possible to independently truncate the local window of each coarse peak, isolate the interference of adjacent peaks, thereby improving the peak detection accuracy and reducing the amount of subsequent calculation data.

[0051] As one possible approach, after acquiring the reflection spectrum sequence of the fiber Bragg grating, the method further includes performing a sliding window filtering process on the reflection spectrum sequence of the fiber Bragg grating based on local polynomial least squares fitting to obtain a filtered reflection spectrum sequence; detecting the reflection peak center of the reflection spectrum sequence of the fiber Bragg grating to obtain multiple reflection peak centers; and for any reflection peak center, truncating the reflection spectrum sequence of the fiber Bragg grating with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center. This includes: detecting the reflection peak center of the filtered reflection spectrum sequence to obtain multiple reflection peak centers; and for any reflection peak center, truncating the filtered reflection spectrum sequence with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center.

[0052] The local polynomial least squares fitting sliding window filter can be the Savitzky-Golay filter or other filtering methods that can achieve the same function. It should be noted that the Savitzky-Golay filter can effectively preserve the peak characteristics of the spectrum and avoid peak shifts while suppressing high-frequency random noise.

[0053] For example, the Savitzky-Golay filter is used to filter the reflection spectrum sequence of a fiber Bragg grating to obtain the filtered reflection spectrum sequence. The formula for Savitzky-Golay filtering can be as follows: ; in, The filter coefficients are determined by the polynomial order and the window size. The width of the window is -m ≤ L ≤ m; This represents the filtered reflected light intensity corresponding to the i-th sampling wavelength. Furthermore, the filtered reflectance spectrum sequence undergoes reflectance peak center detection and local window truncation to improve the reliability of these methods.

[0054] It should be noted that this application achieves effective suppression of high-frequency random noise while preserving spectral peak characteristics and avoiding peak position shift by using sliding window filtering based on local polynomial least squares fitting. This prevents the amplification effect of noise on subsequent second derivative calculations, thereby improving the robustness of the constructed template and the final peak wavelength in noisy environments.

[0055] Furthermore, as one possible approach, the reflection peak center of the fiber Bragg grating's reflection spectrum sequence is detected to obtain multiple reflection peak centers. This includes: performing first-order difference processing on the fiber Bragg grating's reflection spectrum sequence to obtain spectral difference data; traversing any adjacent first and second sampling points in the spectral difference data, if the spectral difference value corresponding to the first sampling point is greater than zero and the spectral difference value corresponding to the second sampling point is less than zero, then the second sampling point is determined as the reflection peak center, thus obtaining multiple reflection peak centers.

[0056] Among them, spectral difference data can reflect the changing trend of reflectance spectrum, which includes spectral difference values ​​of multiple sampling points.

[0057] For example, the filtered reflectance spectrum sequence is subjected to first-order difference processing to obtain spectral difference data, which can be expressed by the following formula: ; in, Indicates the first The spectral difference values ​​at each sampling point, when and At that time, it can be determined This refers to the center of the reflection peak, i.e., the center of the coarse peak. It can be understood that the above criteria can quickly identify the approximate positions of all peaks in the reflection spectrum sequence, providing a central reference for subsequent local window truncation and reducing global computational complexity.

[0058] Furthermore, as one possible approach, the reflection spectrum sequence of the fiber Bragg grating is truncated with the center of the reflection peak as the center to obtain the local window spectral data corresponding to the center of the reflection peak. This includes: obtaining the estimated peak width and pixel size of the fiber Bragg grating; determining the width of the local window based on the estimated peak width and pixel size of the fiber Bragg grating; and truncating the reflection spectrum sequence of the fiber Bragg grating with the center of the reflection peak as the center and based on the width of the local window to obtain the local window spectral data corresponding to the center of the reflection peak.

[0059] The estimated peak width refers to the estimated peak width of the fiber Bragg grating, which can be determined based on the grating parameters or empirical values ​​of the fiber Bragg grating. For example, the estimated peak width can be in the range of 0.2-0.5 nm.

[0060] Pixel size refers to the wavelength interval between adjacent pixels, i.e., the wavelength sampling interval. For example, the wavelength range of the reflectance spectrum sequence is 285nm-1100nm, and the reflectance spectrum sequence includes 256 values. Therefore, the pixel size is (1100-285) / 256.

[0061] The width of a local window can be the full width of the local window or half of its width; no specific limitation is made here.

[0062] For example, the formula for calculating the half-width of a partial window can be: ; in, This represents the expansion factor, which can be a value in the range of 1.5-2 to ensure that the window contains the complete peak shape. Indicates rounding down; Indicates the estimated peak width; Indicates pixel size, This represents the half-width of a local window. Further, using the... The center of each reflection peak Centered on, cut off a length of Local window spectral data : ; in, The local window of spectral data contains a 3dB bandwidth region with the peak intact.

[0063] The following describes in detail step 203, namely, "using the five-point central difference method to determine the second derivative corresponding to the local window spectral data, taking the negative of the second derivative corresponding to the local window spectral data to obtain the corrected second derivative, and normalizing the corrected second derivative to obtain the adaptive wavelet template," with reference to the embodiments.

[0064] The five-point center difference method is a numerical method that approximates the second derivative of a point by using the current sampling point and its two adjacent sampling points before and after it. Normalization methods can include Min-Max normalization or Z-score normalization, etc., without specific limitations here. The adaptive wavelet template refers to a template signal that matches the actual shape of the reflection peak corresponding to the center of the current reflection peak.

[0065] It should be noted that the five-point center difference method has high accuracy and can effectively reflect the "concave-convex" curvature change of the reflection peak. The second derivative calculated by it is consistent with the shape of the Mexican cap wavelet, with positive curvature at the center of the peak and negative curvature on both sides.

[0066] For example, for local window spectral data The formula for calculating its second derivative is as follows: ; in, Indicates pixel size, and Indicates the last two adjacent sampling points, and Indicates the first two adjacent sampling points, express The corresponding second derivative.

[0067] Furthermore, since the reflection peak is convex and the second derivative is concave, to make the direction of the second derivative consistent with the direction of the reflection peak, the direction needs to be reversed, that is, the second derivative needs to be negative: ; in, This represents the corrected second derivative, which aligns with the direction of the reflection peak, laying the foundation for subsequent template matching.

[0068] Furthermore, it can be Normalization to Within this range, to eliminate the influence of amplitude differences on template matching, and to ensure that the adaptive wavelet template retains only the peak morphological features, the normalized calculation formula can be: ; in, Represents the normalized result This refers to an adaptive wavelet template, which inherits the local structure of the current reflection peak (such as peak width and symmetry) and achieves complete adaptability without the need to preset wavelet scale or convolution parameters.

[0069] The following describes in detail step 204, namely, "performing Hilbert transforms on the local window spectral data and the adaptive wavelet template respectively to obtain the first Hilbert transform sequence corresponding to the local window spectral data and the second Hilbert transform sequence corresponding to the adaptive wavelet template," with reference to the embodiments.

[0070] The Hilbert transform is a mathematical transformation that converts a real spectral signal into an analytic signal containing phase information. Its zero-crossing points correspond to the structural centers of the signal and are unaffected by amplitude variations. A Hilbert transform sequence can include Hilbert transform values ​​from multiple sampling points.

[0071] Specifically, a Fast Fourier Transform (FFT) is performed on the local window spectral data to obtain the corresponding frequency domain sequence. A Hilbert filter that retains only the positive frequency components is constructed. The frequency domain sequence of the local window spectral data is multiplied by the Hilbert filter to obtain the filtered frequency domain sequence of the local window spectral data. An Inverse Fourier Transform (IFT) is then performed on the filtered frequency domain sequence of the local window spectral data to obtain the first Hilbert transform sequence of the local window spectral data. Similarly, a FFT is performed on the adaptive wavelet template to obtain the corresponding frequency domain sequence of the adaptive wavelet template. A Hilbert filter that retains only the positive frequency components is constructed. The frequency domain sequence of the adaptive wavelet template is multiplied by the Hilbert filter to obtain the filtered frequency domain sequence of the adaptive wavelet template. An Inverse Fourier Transform (IFT) is then performed on the filtered frequency domain sequence of the adaptive wavelet template to obtain the second Hilbert transform sequence of the adaptive wavelet template.

[0072] For example, for discrete signals ( or Perform a Fast Fourier Transform to obtain the frequency domain sequence: ; Furthermore, a Hilbert filter is constructed. : ; in, Indicates signal length. This indicates the frequency domain index. This Hilbert filter retains only the positive frequency components and can double the amplitude to match the effect of a continuous Hilbert transform.

[0073] Furthermore, the formula for calculating the filtered frequency domain sequence is as follows: ; Wherein, "·" represents point-by-point multiplication in the frequency domain; further, for Performing the inverse Fourier transform yields the Hilbert transform sequence: ; Specifically, local window spectral data The corresponding first Hilbert transform sequence can be represented as The second Hilbert transform sequence corresponding to the adaptive wavelet template can be expressed as:

[0074] The following describes in detail step 205, namely, "determining the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence, determining the reflection peak wavelength offset based on the first and second zero-crossing wavelengths, and superimposing the reflection peak wavelength offset with the center of the reflection peak to obtain the target wavelength of the reflection peak", with reference to the embodiments.

[0075] The zero-crossing wavelength refers to the wavelength corresponding to the zero-crossing point. The zero-crossing point is the essential center of the signal, and it can be determined by the following condition: when the product of the Hilbert transform values ​​of adjacent sampling points in the Hilbert transform sequence is negative, it indicates that there is a zero-crossing point between the adjacent sampling points.

[0076] The reflection peak wavelength offset refers to the difference between the wavelength of the first zero-crossing point and the wavelength of the second zero-crossing point, which quantifies the phase shift of the true center of the reflection peak relative to the center of the coarse peak.

[0077] The target wavelength of the reflection peak refers to the center wavelength of the reflection peak obtained by superimposing the wavelength offset of the coarse peak center and the reflection peak, which represents the high-precision peak wavelength result output by the algorithm.

[0078] Furthermore, as one possible approach, determining the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence includes: traversing any two adjacent sampling points in the target Hilbert transform sequence; wherein the target Hilbert transform sequence is either the first Hilbert transform sequence or the second Hilbert transform sequence; obtaining the wavelengths corresponding to the two sampling points when the product of the Hilbert transform values ​​corresponding to the two sampling points is less than zero; and performing linear fitting on the Hilbert transform values ​​and wavelengths corresponding to the two sampling points to obtain the zero-crossing wavelengths corresponding to the target Hilbert transform sequence.

[0079] Specifically, the linear fitting formula for determining the zero-crossing wavelength corresponding to the target Hilbert transform sequence can include: ; in, This represents the wavelength corresponding to the j-th sampling point. This represents the wavelength corresponding to the (j+1)th sampling point. This represents the Hilbert transform value corresponding to the j-th sampling point. This represents the Hilbert transform value corresponding to the (j+1)th sampling point. This represents the zero-crossing wavelength corresponding to the target Hilbert transform sequence at the center of the k-th reflection peak. Furthermore, as one possible approach, determining the first zero-crossing wavelength corresponding to the first Hilbert transform sequence includes: traversing any adjacent third and fourth sampling points in the first Hilbert transform sequence; obtaining the wavelengths corresponding to the third and fourth sampling points if the product of the Hilbert transform value corresponding to the third sampling point and the Hilbert transform value corresponding to the fourth sampling point is less than zero; and performing linear fitting on the Hilbert transform value corresponding to the third sampling point, the wavelength corresponding to the third sampling point, the Hilbert transform value corresponding to the fourth sampling point, and the wavelength corresponding to the fourth sampling point to obtain the first zero-crossing wavelength corresponding to the first Hilbert transform sequence.

[0080] In this embodiment, the third and fourth sampling points are any adjacent sampling point pairs in the first Hilbert transform sequence. For example, the sampling point pair can be (j, j+1), and the Hilbert transform value corresponding to sampling point j can be... The Hilbert transform value corresponding to sampling point j+1 can be ,like This indicates that there is a zero-crossing point between adjacent sampling points (j, j+1). The wavelength of the zero-crossing point can be solved by linear fitting. The linear fitting formula is: ; Among them, here In the first Hilbert transform sequence, the th The wavelength of each sampling point In the first Hilbert transform sequence, the th +1 sampling point wavelength, and It can be a known quantity; This represents the wavelength of the first zero-crossing point.

[0081] As another feasible approach, determining the wavelength of the second zero-crossing point corresponding to the second Hilbert transform sequence includes: traversing any adjacent fifth and sixth sampling points in the second Hilbert transform sequence; obtaining the wavelengths corresponding to the fifth and sixth sampling points when the product of the Hilbert transform value corresponding to the fifth sampling point and the Hilbert transform value corresponding to the sixth sampling point is less than zero; and performing linear fitting on the Hilbert transform value corresponding to the fifth sampling point, the wavelength corresponding to the fifth sampling point, the Hilbert transform value corresponding to the sixth sampling point, and the wavelength corresponding to the sixth sampling point to obtain the wavelength of the second zero-crossing point corresponding to the second Hilbert transform sequence.

[0082] In this embodiment, the fifth and sixth sampling points are any adjacent sampling point pairs in the second Hilbert transform sequence. For example, the sampling point pair can be (j, j+1), and the Hilbert transform value corresponding to sampling point j can be... The Hilbert transform value corresponding to sampling point j+1 can be ,like This indicates that there is a zero-crossing point between adjacent sampling points (j, j+1). The wavelength of the zero-crossing point can be solved by linear fitting. The linear fitting formula is: ; Among them, here In the second Hilbert transform sequence, the first... The wavelength of each sampling point In the second Hilbert transform sequence, the first... +1 sampling point wavelength, and It can be a known quantity; This indicates the wavelength of the second zero-crossing point.

[0083] For example, the formula for calculating the wavelength shift of the reflection peak can be as follows: ; By superimposing the wavelength shift of the reflection peak onto the center of the coarse peak, the true peak wavelength is obtained, which is the target wavelength of the reflection peak. ; in, This represents the center of the kth coarse peak (i.e., the center of the reflection peak). This represents the wavelength shift of the reflection peak corresponding to the center of the kth coarse peak. Indicates the target wavelength of the reflection peak.

[0084] Repeat the above steps for all coarse peak centers in the reflectance spectrum sequence to collect the true peak wavelengths corresponding to all coarse peak centers. These wavelengths can be sorted in ascending order and output to obtain the accurate true peak wavelengths. M represents the number of reflection peaks.

[0085] Figure 3 This is a schematic diagram illustrating the multi-peak peak finding results of a fiber Bragg grating provided in an embodiment of this application. Figure 3 As shown, the horizontal axis represents the wavelength range, the vertical axis represents the reflected light intensity, the red dots represent the peak positions detected by the fiber Bragg grating multi-peak detection method in this embodiment (i.e., the positions corresponding to the target wavelength of the reflection peak), and the blue curve represents the reflection spectrum sequence of the fiber Bragg grating.

[0086] To facilitate understanding of the above methods, processes, and technical effects, application examples are provided below: S1: Initial data preprocessing. Specifically, the reflection spectrum sequence of the fiber Bragg grating is acquired, and the reflection spectrum sequence of the fiber Bragg grating is subjected to sliding window filtering based on local polynomial least squares fitting to obtain the filtered reflection spectrum sequence.

[0087] S2: Coarse Peak Detection and Local Window Truncation. Specifically, the filtered reflectance spectrum sequence is subjected to first-order difference processing to obtain spectral difference data. Iterating through any adjacent first and second sampling points in the spectral difference data, if the spectral difference value corresponding to the first sampling point is greater than zero and the spectral difference value corresponding to the second sampling point is less than zero, the second sampling point is determined as the center of the reflectance peak, thus obtaining multiple reflectance peak centers. For any reflectance peak center, the estimated peak width and pixel size of the fiber Bragg grating are obtained. Based on the estimated peak width and pixel size of the fiber Bragg grating, the width of the local window is determined. Using the reflectance peak center as the center, the filtered reflectance spectrum sequence is truncated based on the width of the local window to obtain the local window spectral data corresponding to the reflectance peak center.

[0088] S3: Adaptive wavelet template construction. Specifically, the second derivative corresponding to the local window spectral data is determined using the five-point central difference method. The second derivative corresponding to the local window spectral data is then negative to obtain the corrected second derivative. The corrected second derivative is then normalized to obtain the adaptive wavelet template.

[0089] S4: Hilbert Transform Calculation. Specifically, Hilbert transforms are performed on the local window spectral data and the adaptive wavelet template respectively to obtain the first Hilbert transform sequence corresponding to the local window spectral data and the second Hilbert transform sequence corresponding to the adaptive wavelet template.

[0090] S5: Peak Localization. Specifically, iterate through any two adjacent sampling points in the target Hilbert transform sequence; where the target Hilbert transform sequence is either the first Hilbert transform sequence or the second Hilbert transform sequence; if the product of the Hilbert transform values ​​corresponding to the two sampling points is less than zero, obtain the wavelengths corresponding to the two sampling points; perform linear fitting on the Hilbert transform values ​​and wavelengths corresponding to the two sampling points to obtain the zero-crossing wavelengths corresponding to the target Hilbert transform sequence. Subtract the second zero-crossing wavelength from the first zero-crossing wavelength to obtain the reflection peak wavelength offset; superimpose the reflection peak wavelength offset with the center of the reflection peak to obtain the target wavelength of the reflection peak.

[0091] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0092] According to another embodiment, a fiber Bragg grating multi-peak detection device is provided. Figure 4 A schematic block diagram of a fiber Bragg grating multi-peak detection apparatus according to one embodiment is shown, the apparatus being disposed in a fiber Bragg grating sensing system. Figure 4 As shown, the device 400 includes: a reflectance spectrum sequence acquisition unit 401, a local window spectral data extraction unit 402, an adaptive wavelet template construction unit 403, a Hilbert transform unit 404, and a reflectance peak target wavelength determination unit 405. The main functions of each component are as follows: The reflectance spectral sequence acquisition unit 401 is configured to acquire the reflectance spectral sequence of a fiber Bragg grating.

[0093] The local window spectral data truncation unit 402 is configured to detect the reflection peak center of the reflection spectral sequence of the fiber Bragg grating to obtain multiple reflection peak centers. For any reflection peak center, the reflection spectral sequence of the fiber Bragg grating is truncated with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center.

[0094] The adaptive wavelet template construction unit 403 is configured to determine the second derivative corresponding to the local window spectral data using the five-point central difference method, take the negative of the second derivative corresponding to the local window spectral data to obtain the corrected second derivative, and normalize the corrected second derivative to obtain the adaptive wavelet template.

[0095] Hilbert transform unit 404 is configured to perform Hilbert transform on the local window spectral data and the adaptive wavelet template respectively, to obtain a first Hilbert transform sequence corresponding to the local window spectral data and a second Hilbert transform sequence corresponding to the adaptive wavelet template.

[0096] The reflection peak target wavelength determination unit 405 is configured to determine the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence, determine the reflection peak wavelength offset based on the first zero-crossing wavelength and the second zero-crossing wavelength, and superimpose the reflection peak wavelength offset with the center of the reflection peak to obtain the reflection peak target wavelength.

[0097] As one possible implementation, the device 400 further includes: a filtering processing determination unit, configured to perform sliding window filtering processing on the reflection spectrum sequence of the fiber Bragg grating based on local polynomial least squares fitting to obtain a filtered reflection spectrum sequence; Correspondingly, the local window spectral data truncation unit 402 can be specifically configured to detect the reflection peak center of the filtered reflection spectral sequence to obtain multiple reflection peak centers. For any reflection peak center, the filtered reflection spectral sequence is truncated with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center.

[0098] As one possible implementation method, the local window spectral data extraction unit 402 can be specifically configured to: perform first-order difference processing on the reflection spectrum sequence of the fiber Bragg grating to obtain spectral difference data; traverse any adjacent first sampling point and second sampling point in the spectral difference data, if the spectral difference value corresponding to the first sampling point is greater than zero and the spectral difference value corresponding to the second sampling point is less than zero, then determine the second sampling point as the center of the reflection peak, so as to obtain multiple reflection peak centers.

[0099] As one possible implementation method, the local window spectral data truncation unit 402 can be specifically configured to: obtain the estimated peak width and pixel size of the fiber Bragg grating; determine the width of the local window based on the estimated peak width and pixel size of the fiber Bragg grating; and truncate the reflection spectrum sequence of the fiber Bragg grating based on the width of the local window, with the center of the reflection peak as the center, to obtain the local window spectral data corresponding to the center of the reflection peak.

[0100] As one possible implementation method, the reflection peak target wavelength determination unit 405 can be specifically configured as follows: Traverse any adjacent third and fourth sampling points in the first Hilbert transform sequence; If the product of the Hilbert transform value corresponding to the third sampling point and the Hilbert transform value corresponding to the fourth sampling point is less than zero, the wavelength corresponding to the third sampling point and the wavelength corresponding to the fourth sampling point are obtained. Linear fitting is performed on the Hilbert transform value corresponding to the third sampling point, the wavelength corresponding to the third sampling point, the Hilbert transform value corresponding to the fourth sampling point, and the wavelength corresponding to the fourth sampling point to obtain the first zero-crossing wavelength corresponding to the first Hilbert transform sequence.

[0101] As one possible implementation method, the reflection peak target wavelength determination unit 405 can also be configured as follows: Traverse any two adjacent sampling points in the target Hilbert transform sequence; wherein, the target Hilbert transform sequence is either the first Hilbert transform sequence or the second Hilbert transform sequence; If the product of the Hilbert transform values ​​corresponding to the two sampling points is less than zero, the wavelength corresponding to the two sampling points is obtained. Linear fitting is performed on the Hilbert transform values ​​and wavelengths corresponding to the two sampling points to obtain the zero-crossing wavelengths corresponding to the target Hilbert transform sequence.

[0102] As one possible approach, the linear fitting formula for determining the zero-crossing wavelength corresponding to the target Hilbert transform sequence includes: ; in, This represents the wavelength corresponding to the j-th sampling point. This represents the wavelength corresponding to the (j+1)th sampling point. This represents the Hilbert transform value corresponding to the j-th sampling point. This represents the Hilbert transform value corresponding to the (j+1)th sampling point. This represents the zero-crossing wavelength of the target Hilbert transform sequence corresponding to the center of the k-th reflection peak.

[0103] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. The system and device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0105] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0106] And an electronic device, comprising: One or more processors; and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0107] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0108] in, Figure 5 An exemplary architecture of an electronic device is shown, which may include a processor 510, a video display adapter 511, a disk drive 512, an input / output interface 513, a network interface 514, and a memory 520. The processor 510, video display adapter 511, disk drive 512, input / output interface 513, network interface 514, and memory 520 can communicate with each other via a communication bus 530.

[0109] The processor 510 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs and implement the technical solution provided in this application.

[0110] The memory 520 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 520 can store the operating system 521 for controlling the operation of the electronic device 500, and the basic input / output system (BIOS) 522 for controlling the low-level operations of the electronic device 500. Additionally, it can store a web browser 523, a data storage management system 524, and a fiber Bragg grating multi-peak detection device 400, etc. The aforementioned fiber Bragg grating multi-peak detection device 400 can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 520 and is called and executed by the processor 510.

[0111] Input / output interface 513 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0112] Network interface 514 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0113] Bus 530 includes a pathway for transmitting information between various components of the device, such as processor 510, video display adapter 511, disk drive 512, input / output interface 513, network interface 514, and memory 520.

[0114] It should be noted that although the above-described device only shows the processor 510, video display adapter 511, disk drive 512, input / output interface 513, network interface 514, memory 520, bus 530, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0115] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0116] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting multiple peaks in a fiber Bragg grating, characterized in that, The method includes: Acquire the reflectance spectral sequence of fiber Bragg gratings; The reflection peak center of the fiber Bragg grating is detected by performing reflection peak center detection to obtain multiple reflection peak centers. For any reflection peak center, the reflection spectral sequence of the fiber Bragg grating is truncated with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center. The second derivative corresponding to the local window spectral data is determined using the five-point central difference method. The second derivative corresponding to the local window spectral data is then negative to obtain the corrected second derivative. The corrected second derivative is then normalized to obtain the adaptive wavelet template. Hilbert transforms are performed on the local window spectral data and the adaptive wavelet template respectively to obtain the first Hilbert transform sequence corresponding to the local window spectral data and the second Hilbert transform sequence corresponding to the adaptive wavelet template; The first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence are determined. The reflection peak wavelength offset is determined based on the first zero-crossing wavelength and the second zero-crossing wavelength. The reflection peak wavelength offset is superimposed with the center of the reflection peak to obtain the target wavelength of the reflection peak.

2. The method according to claim 1, characterized in that, After acquiring the reflection spectrum sequence of the fiber Bragg grating, the method further includes: The reflection spectrum sequence of the fiber Bragg grating is subjected to sliding window filtering based on local polynomial least squares fitting to obtain the filtered reflection spectrum sequence. The reflection peak center of the fiber Bragg grating is detected by performing reflection peak center detection to obtain multiple reflection peak centers. For any reflection peak center, the reflection spectral sequence of the fiber Bragg grating is truncated with the reflection peak center as the center to obtain local window spectral data corresponding to the reflection peak center, including: The filtered reflectance spectrum sequence is subjected to reflectance peak center detection to obtain multiple reflectance peak centers. For any reflectance peak center, the filtered reflectance spectrum sequence is truncated with the reflectance peak center as the center to obtain the local window spectral data corresponding to the reflectance peak center.

3. The method according to claim 1, characterized in that, The reflection peak center detection of the reflection spectrum sequence of the fiber Bragg grating yields multiple reflection peak centers, including: The reflection spectrum sequence of the fiber Bragg grating is subjected to first-order difference processing to obtain spectral difference data; Traverse any adjacent first and second sampling points in the spectral difference data. If the spectral difference value corresponding to the first sampling point is greater than zero and the spectral difference value corresponding to the second sampling point is less than zero, then determine the second sampling point as the center of the reflection peak, so as to obtain multiple reflection peak centers.

4. The method according to claim 1, characterized in that, The reflection spectrum sequence of the fiber Bragg grating is truncated with the center of the reflection peak as the center to obtain the local window spectral data corresponding to the center of the reflection peak, including: Obtain the estimated peak width and pixel size of the fiber Bragg grating; The width of the local window is determined based on the estimated peak width of the fiber Bragg grating and the pixel size; Centered on the center of the reflection peak, the reflection spectrum sequence of the fiber Bragg grating is truncated based on the width of the local window to obtain the local window spectral data corresponding to the center of the reflection peak.

5. The method according to claim 1, characterized in that, Determining the wavelength of the first zero-crossing point corresponding to the first Hilbert transform sequence and determining the wavelength of the second zero-crossing point corresponding to the second Hilbert transform sequence includes: Traverse any two adjacent sampling points in the target Hilbert transform sequence; wherein, the target Hilbert transform sequence is either the first Hilbert transform sequence or the second Hilbert transform sequence; If the product of the Hilbert transform values ​​corresponding to the two sampling points is less than zero, the wavelength corresponding to the two sampling points is obtained. Linear fitting is performed on the Hilbert transform values ​​and wavelengths corresponding to the two sampling points to obtain the zero-crossing wavelengths corresponding to the target Hilbert transform sequence.

6. The method according to claim 5, characterized in that, The linear fitting formula for determining the zero-crossing wavelength corresponding to the target Hilbert transform sequence includes: ； in, This represents the wavelength corresponding to the j-th sampling point. This represents the wavelength corresponding to the (j+1)th sampling point. This represents the Hilbert transform value corresponding to the j-th sampling point. This represents the Hilbert transform value corresponding to the (j+1)th sampling point. This represents the zero-crossing wavelength of the target Hilbert transform sequence corresponding to the center of the k-th reflection peak.

7. A fiber Bragg grating multi-peak detection device, characterized in that, The device includes: The reflectance spectral sequence acquisition unit is configured to acquire the reflectance spectral sequence of a fiber Bragg grating; The local window spectral data truncation unit is configured to detect the reflection peak center of the reflection spectral sequence of the fiber Bragg grating, obtain multiple reflection peak centers, and for any reflection peak center, truncate the reflection spectral sequence of the fiber Bragg grating with the reflection peak center as the center to obtain the local window spectral data corresponding to the reflection peak center. The adaptive wavelet template construction unit is configured to determine the second derivative corresponding to the local window spectral data using the five-point central difference method, take the negative of the second derivative corresponding to the local window spectral data to obtain the corrected second derivative, and normalize the corrected second derivative to obtain the adaptive wavelet template. The Hilbert transform unit is configured to perform Hilbert transform on the local window spectral data and the adaptive wavelet template respectively, to obtain a first Hilbert transform sequence corresponding to the local window spectral data and a second Hilbert transform sequence corresponding to the adaptive wavelet template; The reflection peak target wavelength determination unit is configured to determine the first zero-crossing wavelength corresponding to the first Hilbert transform sequence and the second zero-crossing wavelength corresponding to the second Hilbert transform sequence, determine the reflection peak wavelength offset based on the first zero-crossing wavelength and the second zero-crossing wavelength, and superimpose the reflection peak wavelength offset with the center of the reflection peak to obtain the reflection peak target wavelength.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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