Local spectrum matching method, device and storage medium based on optical frequency reflection technology

Abstract

The present invention provides a local spectrum matching method, device and storage medium based on optical frequency reflection technology. The method comprises: inputting S-path and P-path spectra of a reference spectrum and a test spectrum, performing fast Fourier transform (FFT); intercepting the transformed data using a sliding window, and performing zero padding and inverse fast Fourier transform on the intercepted data; selecting a reference spectrum of a predetermined length within the sliding window as a local reference spectrum; successively selecting local test spectra of a predetermined length with a step frequency of N, and calculating the distance between the local test spectrum and the local reference spectrum; selecting local measurement spectra corresponding to M groups of distance data with the smallest distance, and determining corresponding M starting data points; selecting local measurement spectra corresponding to each starting data point within a neighborhood range of the M starting data points, and obtaining the starting point of the most matching local measurement spectrum through distance calculation; determining the number of spectrum drift points of the test spectrum based on the starting point of the most matching local measurement spectrum, and calculating the change of the corresponding physical quantity.

Description

Technical Field

[0001] The present invention relates to the field of optical technology, and in particular to a local spectrum matching method, device and storage medium based on optical frequency reflection technology. Background Art

[0002] Optical frequency domain reflectometry (OFDR) is a distributed fiber-optic sensing technology with advantages such as high spatial resolution, long measurement distance, and resistance to electromagnetic interference. It is primarily used to monitor physical quantities such as temperature and stress in various materials and structures, and is particularly well-suited for monitoring temperature and stress in aircraft and their microstructures operating in complex environments. OFDR distributed sensing systems are designed and constructed based on the inherent Rayleigh backscattering phenomenon of optical fibers, using the principle of optical heterodyne detection. Changes in physical quantities such as the temperature and stress of the optical fiber cause changes in the Rayleigh scattering spectrum. The basic principle is: in the detection system, there are two optical signals, one is the backscattered Rayleigh scattered signal light returning along the sensing optical fiber, and the other is the local oscillator light used for beat frequency. The signal light and the local oscillator light will become mixed signals after passing through the optical coupler. The mixed signal reaches the photodetector. Since the two beams of light can be coherent, a beat frequency will occur on the photosensitive surface, and the mixed light signal will be converted into an electrical signal. After filtering and amplification by an amplifier, the beat frequency signal can be obtained. The frequency of the beat frequency signal represents the difference between the frequencies of the signal light and the local oscillator light.

[0003] Figure 1This is a diagram of the basic structure of an OFDR system. The OFDR system primarily consists of a linearly tunable laser, a main interferometer, an auxiliary interferometer (which provides an external sampling clock signal to eliminate the nonlinear effects of the light source), a polarization beam splitter (PBS), a photodetector (PD), and high-speed data acquisition and processing equipment. Both the main and auxiliary interferometers are based on Mach-Zehnder interferometers. The linearly swept laser light emitted by the tunable laser is split into two paths by a fiber coupler: one enters the main interferometer, and the other enters the auxiliary interferometer. The laser light entering the main interferometer is further split into two paths by a fiber coupler: one enters the fiber under test as the signal arm, and the other as the reference arm. The optical signal in the signal arm passes through a circulator and enters the sensing fiber under test, where it returns a backscattered Rayleigh light signal carrying physical information such as strain and temperature. The optical signal in the reference arm is primarily used for beat frequency. Because the laser emits linearly swept light and there is an optical path difference between the signal and reference arms, the frequencies of the two output beams differ. The two optical beams will beat each other and eventually be converted into electrical signals on the photodetector, which are then collected and processed. Based on the wavelength offset of the backscattered Rayleigh scattering, the magnitude of physical quantities such as strain and temperature can be calculated. The sensing fiber used in the OFDR system is a single-mode fiber, and the system will experience polarization fading. In order to eliminate polarization fading, a polarization diversity receiver is designed. Figure 1 As shown, a polarization diversity receiver may include a polarization controller (PC), a 50:50 coupler, a PBS, and two PDs. After passing through the polarization beam splitter, the mixed signal is split into two orthogonal signals: P light and S light. The polarization controller is used to change the polarization state of the reference light, ensuring that the reference light has the same intensity in the P and S directions. These two optical signals are converted into electrical signals by photodetectors.

[0004] In traditional OFDR strain demodulation algorithms, cross-correlation is usually used to calculate the relative drift of the Rayleigh scattering spectra of the test signal and the reference signal. In practical applications, when the microstrain applied to the test fiber is greater than 1000, the spectrum will drift significantly, and a new spectrum with no similarity to the original spectrum will be left at the original spectrum position. The introduction of this new spectral component reduces the similarity between the entire reference and test spectra, causing multiple peaks and pseudo-peaks in the cross-correlation results, which in turn causes the drift point number to be misjudged, seriously affecting the strain demodulation capability. The principle is as follows Figure 2 shown. Figure 2In the example, when tensile strain is applied to the test fiber, the measured Rayleigh spectrum shifts to the left, while when compressive strain is applied, the measured Rayleigh spectrum shifts to the right. Relative to the reference Rayleigh spectrum, both spectra drift significantly, reducing the overall similarity between the reference and test spectra. The cross-correlation algorithm compares the drift of two spectrum segments by comparing the overall similarity of the two signals, which is inevitably affected by the new spectrum introduced under high stress. The cross-correlation algorithm's processing flow is as follows:

[0005] (1) Data acquisition: The OFDR system needs to perform two data acquisition processes. The first is when the fiber under test is in a natural state, and the beat frequency interference signal is collected as reference data S1(t), which represents the physical quantity information contained in the fiber under test in the current environment, and t is the time variable. The second is when the fiber under test is in a state where the external physical quantity changes, and the test data S2(t) is obtained by collecting.

[0006] (2) Convert the time domain data S1(t) into frequency domain data: Convert the reference data from time domain data into frequency domain data S1(l) through fast Fourier transform (FFT) and save it, where l is the frequency variable.

[0007] (3) Sliding window selection: Select a suitable sliding window according to the set spatial resolution, use the sliding window to intercept the corresponding frequency domain data at both ends of the positive and negative frequency domains, and perform data selection on S1(l).

[0008] (4) Data zero padding: The data taken out by the sliding window is zero-filled to offset the effect of reduced spectral resolution caused by the sliding window interception of data in the previous step.

[0009] (5) Restore frequency domain data to time domain data: Restore (reconstruct) the zero-inserted signal to time domain data S′1(t) through inverse Fourier transform.

[0010] (6) Detection and interpolation: Take the signal envelope of the reconstructed time domain data S′1(t) to reduce the noise increase caused by data zero filling. Then, the two sets of signal horizontal coordinate points are filled in by the spline interpolation algorithm, recorded as the time domain integrated array Z′1(t), and the data is saved. The subsequent collected time domain data is processed and cross-correlated with Z′1(t) to demodulate the physical quantity information of the test fiber. The test data is processed in the same way as the reference data (2) to (6) to obtain the test path time domain integrated data Z′2(t).

[0011] (7) Cross-correlation operation: Perform cross-correlation operation on Z′1(t) and Z′2(t) to obtain the cross-correlation result R(z).

[0012] (8) Cross-correlation normalization: In order to accurately find the cross-correlation peak, the cross-correlation result is normalized to find the position where the highest point is 1, and this position is compared with the cross-correlation center position to obtain the cross-correlation offset.

[0013] When microstrain exceeds 1000, the demodulation results obtained using the cross-correlation algorithm exhibit multiple peaks and spurious peaks, making it impossible to accurately demodulate the strain value. As strain increases, the similarity between the measured and reference spectra decreases significantly, and the cross-correlation calculation results become inaccurate or even ineffective under high loads. Clearly, the cross-correlation algorithm cannot meet the requirements of OFDR systems for measuring large strain ranges. Using a local spectrum matching algorithm based on the least squares method can avoid this problem.

[0014] In this local spectrum matching algorithm, a certain length of information of the reference spectrum is selected as the local reference spectrum. Taking the example of padding each sliding window to 10,000 points, for each sliding window, 60% of the 10,000 points of the reference data sliding window, i.e., 6,000 points, are selected as the local reference spectrum. This local reference spectrum is used to perform residual calculations one by one with a local test spectrum of equal length (6,000 points) from beginning to end with a step frequency of 1, i.e., the residual sum of squares of 6,000 points is calculated 4,000 times each time, and the result of each residual sum of squares calculation is saved as a similarity index. Among all (4,000) residual sum of squares calculation results of a sliding window, the smallest value represents the highest similarity between the local reference and local test spectra. By subtracting the starting value of the horizontal coordinate of the reference spectrum from the starting position value of the horizontal coordinate of the matched local measurement spectrum, the number of spectrum drift points can be directly calculated, and then converted into strain or temperature change.

[0015] Traditional local spectrum matching algorithms need to compare the distance between each local test spectrum and the local reference spectrum point by point, and select the minimum point as the number of spectrum drift points, which results in slow calculation speed and low efficiency.

[0016] How to improve the matching speed of the test spectrum and the reference spectrum and ensure the matching accuracy is a problem to be solved. Summary of the Invention

[0017] In view of this, the present invention provides a local spectrum matching method and device based on optical frequency reflection technology, which improves the efficiency of matching the test spectrum and the reference spectrum and ensures the matching accuracy by combining coarse matching with fine matching.

[0018] One aspect of the present invention provides a local spectrum matching method based on optical frequency reflection technology, the method comprising the following steps:

[0019] Input the S-path and P-path spectra of the reference spectrum and the test spectrum, and perform fast Fourier transform to obtain the frequency spectrum of the two spectra;

[0020] The data is intercepted by using a sliding window, and the spectrum data in the sliding window is zero-filled and inverse fast Fourier transformed to obtain the transformed reference spectrum and test spectrum;

[0021] The transformed reference spectrum and test spectrum are normalized, and a reference spectrum with a predetermined length within the sliding window is selected as a local reference spectrum;

[0022] Successively selecting the transformed test spectrum of the predetermined length within the sliding window as a local test spectrum at a step frequency of N, and calculating the distance between the spectral points of the local test spectrum and the local reference spectrum, or multiple sets of distance data, where N is an integer greater than 1;

[0023] Sorting the obtained multiple sets of distance data, and selecting a predetermined number of spectral points with the smallest distance in each set of distance data;

[0024] Selecting points within a predetermined length neighborhood of each spectral point, and calculating the distance between the local test spectrum and the local reference spectrum point by point within the predetermined length neighborhood, or obtaining the best matching point;

[0025] The number of spectrum drift points of the test spectrum is determined based on the obtained best matching point, and the change of the corresponding physical quantity is calculated based on the determined number of spectrum drift points.

[0026] In some embodiments of the present invention, the reference spectrum of the predetermined length within the sliding window is a spectrum within the sliding window that occupies 70% of the length of the center of the reference spectrum.

[0027] In some embodiments of the present invention, N is 10-50; the predetermined number of spectral points is 3-10 spectral points; and the neighborhood range of the predetermined length is 1 / 2N to 1 / 3N.

[0028] In some embodiments of the present invention, the corresponding physical quantity is microstrain or temperature.

[0029] In some embodiments of the present invention, the method further includes: the reference spectrum includes a P-path reference spectrum and an S-path reference spectrum, the test spectrum includes a P-path test spectrum and an S-path test spectrum; the local reference spectrum includes a local P-path reference spectrum and a local S-path reference spectrum, and the local test spectrum includes a local P-path test spectrum and a local S-path test spectrum;

[0030] The respectively calculating the distances between the local test spectrum and the local reference spectrum comprises: respectively calculating the distances between the P-path local test spectrum and the P-path local reference spectrum and respectively calculating the distances between the S-path local test spectrum and the S-path local reference spectrum;

[0031] The starting point of obtaining the best-matched local measurement spectrum includes: obtaining the starting point of the best-matched P-path local measurement spectrum and obtaining the starting point of the best-matched S-path local measurement spectrum;

[0032] The method of determining the number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum and the starting point of the local reference spectrum, and calculating the change of the corresponding physical quantity based on the determined number of spectrum drift points, includes: determining a first number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum of the P path and the starting point of the local reference spectrum of the P path, determining a second number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum of the S path and the starting point of the local reference spectrum of the S path, and averaging the first number of spectrum drift points and the second number of spectrum drift points to obtain a final result of the change of the physical quantity.

[0033] In some embodiments of the present invention, the method further comprises: determining the neighborhood range of the predetermined length by utilizing a changing trend of data distribution.

[0034] In some embodiments of the present invention, the distance between the spectral points of the local test spectrum and the local reference spectrum satisfies the following formula:

[0035]

[0036] Where d(j) represents the residual sum of squares of the spectrum, y LRS (i) represents the local reference spectrum, y LMS (i+j) represents the local measurement spectrum, i represents the local reference spectrum index, j represents the local measurement spectrum frequency shift, and n represents the local reference spectrum length.

[0037] In some embodiments of the present invention, the amount of microstrain is calculated based on the following formula:

[0038]

[0039] Among them, 16000 and 1.2 are system-related conversion coefficients, and the number of sliding window difference points is a value set through specific experiments.

[0040] Another aspect of the present invention provides a local spectrum matching device based on optical frequency reflection technology, which includes a computer device, the computer device including a processor and a memory, the memory storing computer instructions, and the processor being used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the method for constructing a tripartite strongly non-local non-scalable orthogonal basis applied to quantum cryptography protocols as described above.

[0041] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above-mentioned method when executed by a processor.

[0042] The local spectrum matching method and device based on optical frequency reflection technology of the present invention selects comparison points at intervals, selects a local spectrum for each interval point and calculates the distance, so as to roughly match the local spectrum of the test spectrum and the local spectrum of the reference spectrum based on the distance; then, a point-by-point comparison is performed near some points with the smallest distance difference, and more matching points are further screened out, thereby improving the spectrum matching efficiency while ensuring the matching accuracy.

[0043] Additional advantages, objects, and features of the present invention will be set forth in part in the following description and will become apparent to those skilled in the art upon examination of the following or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the structures particularly pointed out in the description and drawings.

[0044] Those skilled in the art will understand that the purposes and advantages that can be achieved by the present invention are not limited to the above specific descriptions, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention. In the drawings:

[0046] Figure 1 A schematic block diagram of an existing OFDR system.

[0047] Figure 2 Schematic illustration of the spectral drift in the presence of large strain in the optical fiber.

[0048] Figure 3 Schematic diagram of the flow of a local spectrum matching method based on optical frequency reflection technology in one embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0050] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, the accompanying drawings only show structures and / or processing steps closely related to the solutions according to the present invention, while other details that are not closely related to the present invention are omitted.

[0051] It should be emphasized that the term "include / comprises" when used herein refers to the existence of features, elements, steps or components, but does not exclude the existence or addition of one or more other features, elements, steps or components.

[0052] To improve operational efficiency, the present invention improves existing local spectrum matching strategies and proposes a local spectrum matching method based on OFDR technology. Conventionally, a sliding window is typically selected point by point from the test spectrum and compared with a reference spectrum. Considering that sliding windows at adjacent points often overlap, resulting in a large amount of redundant calculations, long runtimes, and low efficiency, the present OFDR-based local spectrum matching method utilizes the local continuity of the spectrum—that is, the fact that the local spectrum does not undergo large abrupt changes at adjacent points—to search for the optimal matching point between the test and reference local spectra using a combination of coarse and fine matching. Based on the optimal matching point, microstrain or temperature is determined, thereby improving algorithm efficiency. The present local spectrum matching method based on optical frequency reflectometry is an improved search-based local spectrum matching method. First, comparison points (or interval points) are selected at intervals, preferably with the interval between the comparison points not exceeding 10% of the sliding window length. Then, for each interval point, a local reference spectrum and a local test spectrum are selected, and the distance between the spectral points of the local test spectrum and the local reference spectrum is calculated. Next, point-by-point comparisons are performed near the points with the smallest distance differences to further select the most closely matching points.

[0053] like Figure 3 FIG. 1 is a flow chart of a local spectrum matching method based on search improvement in one embodiment of the present invention. Figure 3 As shown, the method includes the following steps:

[0054] The main steps of the search-based improved local spectrum matching algorithm are as follows:

[0055] In step S110 , the S-path and P-path spectra of the reference spectrum and the test spectrum are input, and a fast Fourier transform (FFT) is performed to obtain the frequency spectra of the two-path spectra.

[0056] In the embodiment of the present invention, data acquisition is performed twice. The first time is when the optical fiber to be tested is in a natural state, and the S-path and P-path spectra are collected using the existing OFDR system, which serve as the S-path and P-path spectra of the reference spectrum; the second time is when the optical fiber to be tested is in an environment where external physical quantities change, and the S-path and P-path spectra are collected using the existing OFDR system, which serve as the S-path and P-path spectra of the test spectrum.

[0057] In this step, the collected reference spectrum and the S-path and P-path spectra of the test spectrum are imported into the local spectrum matching method execution system, which performs a fast Fourier transform (FFT) to convert the time domain data into frequency domain data to obtain the frequency spectra of the two spectra.

[0058] In step S120 , for each spectrum data in the reference spectrum and the test spectrum, a predetermined length of spectrum data is intercepted using a sliding window, and the spectrum data in the sliding window is zero-filled and inverse fast Fourier transformed to obtain transformed reference spectrum and test spectrum data.

[0059] In this step, an appropriate sliding window is selected based on the set spatial resolution. The corresponding frequency domain data is captured using the sliding window. This data is then padded with zeros to offset the reduced spectral resolution caused by the sliding window. The resulting zero-padded data is then subjected to an inverse fast Fourier transform (IFFT) to obtain the Rayleigh backscattering spectrum.

[0060] As an example, for each channel of spectral data in the reference spectrum, a first sliding window can be used to intercept a predetermined length of spectral data from the reference spectrum (for example, 1000-5000 points of spectral data can be intercepted), and the intercepted data can be padded with zeros. The length of the first sliding window matches the length of the intercepted reference spectrum. For example, if the intercepted data is 2000 points of spectral data, the 2000 points of spectral data can be padded with zeros to 10,000 points. The zero-padded data can then be subjected to an inverse fast Fourier transform (IFFT) to obtain the backscattered Rayleigh spectrum.

[0061] For each channel of spectrum data in the test spectrum, a sliding window is used to intercept data of a predetermined length, and then zero-padded (eg, to 10,000 points) is performed. The zero-padded data is then subjected to an inverse fast Fourier transform (IFFT).

[0062] Here, 2000 points and 10000 points are merely examples, and the present invention is not limited thereto.

[0063] In step S130 , the transformed reference spectrum and the test spectrum are normalized, and a reference spectrum of a predetermined length within the sliding window is selected as a local reference spectrum.

[0064] As an example, for the transformed reference spectrum and test spectrum, the sum of the squares of each data point of the S-path and P-path data after inverse fast Fourier transform can be calculated and the square root can be taken respectively. The calculated value can be used as the denominator and the data of each data point can be used as the numerator to normalize the S-path and P-path data of the reference spectrum and the test spectrum.

[0065] After normalization, a local reference spectrum is selected. The longer the local spectrum, the more information it contains, the more accurate the matching results, and the fewer errors in the calculation results. As an example, 70% of the length of the center of the reference spectrum within the second sliding window of 10,000 data points can be selected as the local reference spectrum, that is, the predetermined length is 7,000 data points. The 70% here is only an example and can also be larger or smaller values, such as 60% or 80%.

[0066] In step S140 , the transformed test spectra of the predetermined length in the sliding window are successively selected as local test spectra at a step frequency of N, and the distances between the local test spectra and the local reference spectra are respectively calculated to obtain multiple sets of distance data, where N is an integer greater than 1.

[0067] In this embodiment of the present invention, local test spectra are selected at intervals of N, and the distances between the corresponding data points (or spectral points) of the local test spectra and the local reference spectrum are calculated. By observing the fluctuation patterns of spectral amplitude, the fluctuation of the local spectral distance between adjacent data points generally does not exceed 0.2, and the difference between the maximum and minimum is generally around 100. In this case, the step frequency N used to select the local test spectra at intervals can be set between 10 and 50. The range of 10-50 is only an example and the present invention is not limited thereto.

[0068] For example, for each sliding window, the 7000 data points in the center of the sliding window of 10,000 data points are used as the local reference spectrum. This section of the local reference spectrum is used to perform residual calculation from the beginning to the end with the local test spectrum of equal length (7000 data points) with a step frequency of N. The distance between the local test spectrum and the local reference spectrum is calculated respectively, that is, the residual sum of squares of 7000 data points is calculated 3000 / N times each time. The result of each residual sum of squares calculation is saved as a similarity index, thereby obtaining multiple groups (3000 / N groups) of distance data characterizing the similarity between the local test spectrum and the local reference spectrum.

[0069] The measure of similarity between the local test spectrum and the local reference spectrum can be performed by calculating the sum of squares between the normalized local test spectrum and local reference spectrum data points as the distance between the local test spectrum and local reference spectrum data points:

[0070]

[0071] Where d(j) represents the residual sum of squares of the spectrum, that is, the distance between the local measured spectrum and the local reference spectrum data point, y LRS (i) represents the local reference spectrum, y LMS (i+j) represents the local measurement spectrum, i represents the local reference spectrum index, j represents the local measurement spectrum frequency shift, and n represents the local reference spectrum length.

[0072] Among all 3000 / N residual sum of squares calculations within a sliding window, the smallest residual sum of squares represents the smallest distance. This smallest distance value indicates the highest similarity between the local reference spectrum and the local test spectrum corresponding to that distance. At this point, if the starting point of the horizontal coordinate of the reference spectrum is subtracted from the starting point of the horizontal coordinate of the matched local measured spectrum, the number of spectral drift points can be directly calculated, which can then be converted into strain or temperature change. However, due to the large step frequency N, the number of spectral drift points calculated in this way is not accurate enough. In other words, the present invention only performs a coarse spectral match. To apply a more precise local spectral match, the present invention proceeds to perform the following steps.

[0073] Step S150 : selecting M segments of local measurement spectra corresponding to M segments of distance data with the smallest distances based on the obtained multiple groups of distance data, and determining M starting data points corresponding to the M segments of local measurement spectra.

[0074] The selected multiple sets (3000 / N sets) of local measurement spectra are sorted from smallest to largest by distance from the local reference spectra. The M sets of local measurement spectra with the smallest distances are selected. Thus, the M starting data points of these M sets of local measurement spectra are determined. M is an integer greater than 1. To balance efficiency and accuracy, M is preferably selected between 3 and 10. This value is for illustrative purposes only and is not intended to limit the present invention.

[0075] Step S160: Select local measurement spectra corresponding to starting data points within a predetermined neighborhood of M starting data points, calculate the distances between the local test spectra corresponding to the starting data points within the predetermined neighborhood and the local reference spectra one by one, and obtain the starting point of the local measurement spectrum that best matches the starting point based on the calculated distances.

[0076] More specifically, in this step, for M starting data points (starting spectrum points), the neighborhood range is Q data points long, and each data point in the neighborhood range is used as the starting data point to select the corresponding local test spectrum and calculate the distance between the selected local test spectrum and the local reference spectrum.

[0077] Preferably, the length of Q can be arrive Select between, that is, the neighborhood range is to The value between to The neighborhood range values ​​between are only examples, and the present invention is not limited thereto.

[0078] Step S170 , determining the number of spectrum drift points of the test spectrum based on the starting point of the best-matched local measurement spectrum and the starting point of the local reference spectrum, and calculating the change of the corresponding physical quantity based on the determined number of spectrum drift points.

[0079] In one embodiment of the present invention, when the variation is microstrain, the value of the microstrain can be calculated using the following formula based on the existing relationship between the number of drift points and the microstrain.

[0080]

[0081] Among them, 16000 and 1.2 are system-related conversion coefficients, and the number of sliding window difference points is a value set through specific experiments.

[0082] Similarly, when the variation is temperature, the temperature variation can also be obtained based on the existing relationship between the number of drift points and temperature.

[0083] In some embodiments of the present invention, the reference spectrum includes a P-path reference spectrum and an S-path reference spectrum, the test spectrum includes a P-path test spectrum and an S-path test spectrum; the local reference spectrum includes a local P-path reference spectrum and a local S-path reference spectrum, and the local test spectrum includes a local P-path test spectrum and a local S-path test spectrum; and the respectively calculating the distances between the local test spectrums and the local reference spectrums includes: respectively calculating the distances between the P-path local test spectrum and the P-path local reference spectrum and respectively calculating the distances between the S-path local test spectrum and the S-path local reference spectrum;

[0084] The starting point of obtaining the best-matched local measurement spectrum includes: obtaining the starting point of the best-matched P-path local measurement spectrum and obtaining the starting point of the best-matched S-path local measurement spectrum;

[0085] The method of determining the number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum and the starting point of the local reference spectrum, and calculating the change of the corresponding physical quantity based on the determined number of spectrum drift points, includes: determining a first number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum of the P path and the starting point of the local reference spectrum of the P path, determining a second number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum of the S path and the starting point of the local reference spectrum of the S path, and averaging the first number of spectrum drift points and the second number of spectrum drift points to obtain a final result of the change of the physical quantity.

[0086] That is, in the aforementioned steps, local reference spectra are selected based on the P-path and S-path data of the reference spectrum, and local test spectra are selected based on the P-path and S-path data of the test spectrum. The selected local test spectra of the P-path are matched with the local reference spectrum of the P-path one by one, and the selected local test spectra of the S-path are coarsely and finely matched with the local reference spectrum of the S-path one by one, and changes in corresponding physical quantities such as microstrain and temperature are calculated based on the matching results. In one embodiment of the present invention, the changes in physical quantities such as microstrain calculated based on the matching results of the local test spectra of the P-path and the changes in physical quantities such as microstrain calculated based on the matching results of the local test spectra of the S-path are averaged to obtain the final change result of the physical quantity.

[0087] Experiments have shown that when the average change in fluctuation caused by the interval N value is less than 1% of the difference between the maximum and minimum values, the error caused by the interval N is within the tolerance range. Therefore, the value of N can be determined based on the tolerance range.

[0088] The improved local spectrum matching algorithm based on search of the present invention adopts the local spectrum of the data point to calculate the interval, which can greatly reduce the computational redundancy. Without considering the subsequent precise matching, the computational complexity can be reduced to the traditional local spectrum matching algorithm. Precise matching is to further search near the M sampling starting points with the smallest distance, and find the best matching local measurement spectrum or the best matching starting point by comparing the spectrum of each starting point in each neighborhood. In order to avoid the influence of abnormal fluctuation points on the results and to prevent excessive additional calculations, after repeated verification, M can be selected from 3 to 10. If the neighborhood range Q is selected, There will be too much overlap with the local spectrum of the adjacent points; if it is less than It will miss points, so the neighborhood range Q is preferably selected arrive The value between .

[0089] The improved search-based local spectrum matching algorithm significantly improves operational efficiency compared to traditional local spectrum matching methods. The comparison results are shown in the table below. The data is collected from 2M sampling points along a 50m optical fiber. The laptop CPU is an Intel i5 processor. The improved search-based local spectrum matching algorithm is significantly faster than the traditional algorithm, improving computational efficiency by 87.8%.

[0090] Table 1 Comparison of running time of local spectrum matching algorithms

[0091]

[0092] In some embodiments of the present invention, coarse matching and fine matching can be improved accordingly based on the pre-estimated data distribution, including using the changing trend of the existing data distribution to predict the possible area or value range of the matching point distribution. Once the search exceeds the corresponding area or value, the calculation can be stopped, thereby further improving the matching efficiency.

[0093] Corresponding to the above method, the present invention also provides a local spectrum matching device based on optical frequency reflection technology, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps applied to the local spectrum matching method as described above.

[0094] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the art.

[0095] It should be understood by those skilled in the art that the various exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is specifically performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0096] It should be understood that the present invention is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method of the present invention is not limited to the specific steps described and illustrated. Those skilled in the art may make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present invention.

[0097] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or replace features of other embodiments.

[0098] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations to the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A local spectrum matching method based on optical frequency reflection technology, characterized in that: The method comprises the following steps: Input the S-path and P-path spectra of the reference spectrum and the test spectrum, and perform fast Fourier transform to obtain the frequency spectrum of the two spectra; The two spectrum data are intercepted respectively by using a sliding window, and the spectrum data in the sliding window are zero-filled and inverse fast Fourier transformed to obtain the transformed reference spectrum and test spectrum; The transformed reference spectrum and test spectrum are normalized, and a reference spectrum with a predetermined length within the sliding window is selected as a local reference spectrum; Selecting the transformed test spectra of the predetermined length within the sliding window as local test spectra one by one with a step frequency of N, and calculating the distances between the local test spectra and the local reference spectra to obtain multiple sets of distance data, wherein N is an integer greater than 1; Selecting M segments of local measurement spectra corresponding to M segments of distance data with the smallest distances based on the obtained multiple groups of distance data, and determining M starting data points corresponding to the M segments of local measurement spectra, where M is an integer greater than 1; Selecting local measurement spectra corresponding to starting data points within a predetermined neighborhood of the M starting data points, calculating the distances between the local test spectra corresponding to the starting data points within the predetermined neighborhood and the local reference spectrum one by one, and obtaining the starting point of the local measurement spectrum that best matches the starting point based on the calculated distances; The number of spectrum drift points of the test spectrum is determined based on the starting point of the best-matched local measurement spectrum and the starting point of the local reference spectrum, and the change of the corresponding physical quantity is calculated based on the determined number of spectrum drift points.

2. The method according to claim 1, characterized in that The reference spectrum of the predetermined length in the sliding window is a spectrum that occupies 70% of the length of the center of the reference spectrum in the sliding window.

3. The method according to claim 1, characterized in that N is 10-50; The predetermined number of spectral points is 3-10 spectral points; The neighborhood of the predetermined length ranges from 1 / 2N to 1 / 3N.

4. The method according to claim 3, characterized in that The corresponding physical quantity is microstrain or temperature.

5. The method according to claim 1, wherein The reference spectrum includes a P-path reference spectrum and an S-path reference spectrum, and the test spectrum includes a P-path test spectrum and an S-path test spectrum; the local reference spectrum includes a local P-path reference spectrum and a local S-path reference spectrum, and the local test spectrum includes a local P-path test spectrum and a local S-path test spectrum; The respectively calculating the distances between the local test spectrum and the local reference spectrum comprises: respectively calculating the distances between the P-path local test spectrum and the P-path local reference spectrum and respectively calculating the distances between the S-path local test spectrum and the S-path local reference spectrum; The obtaining of the starting point of the best-matched local measurement spectrum includes: obtaining the starting point of the best-matched P-path local measurement spectrum and obtaining the starting point of the best-matched S-path local measurement spectrum; The method of determining the number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum and the starting point of the local reference spectrum, and calculating the change of the corresponding physical quantity based on the determined number of spectrum drift points, includes: determining a first number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum of the P path and the starting point of the local reference spectrum of the P path, determining a second number of spectrum drift points of the test spectrum based on the starting point of the obtained best-matched local measurement spectrum of the S path and the starting point of the local reference spectrum of the S path, and averaging the first number of spectrum drift points and the second number of spectrum drift points to obtain a final result of the change of the physical quantity.

6. The method according to claim 3, characterized in that The method further comprises: The neighborhood range of the predetermined length is determined by utilizing the changing trend of data distribution.

7. The method according to claim 1, characterized in that The distance between the spectral points of the local test spectrum and the local reference spectrum satisfies the following formula: Where d(j) represents the residual sum of squares of the spectrum, y LRS (i) represents the local reference spectrum, y LMS (i+j) represents the local measurement spectrum, i represents the local reference spectrum index, j represents the local measurement spectrum frequency shift, and n represents the local reference spectrum length.

8. The method according to claim 4, characterized in that The amount of microstrain is calculated based on the following formula:

9. A local spectrum matching device based on optical frequency reflection technology, comprising a processor and a memory, characterized in that: The memory stores computer instructions, and the processor is configured to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the method according to any one of claims 1 to 8.

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

Images