Butterfly spectrum and multi-perception constraint-based optical fiber sensing data processing method and system

Abstract

This application provides a method and system for processing fiber optic sensing data based on butterfly spectroscopy and multi-sensor constraints, addressing the problems of physical mismatch in single-peak models, limited single-channel information dimensions, and lack of coordinate mapping references in existing methods. The method steps are as follows: acquiring Brillouin spectrum data from at least two independent sensing units; constraining the main peak frequencies of each independent sensing unit to be consistent and performing joint fitting; separating the main peak and at least one secondary peak in the spectrum of each distance point; calculating the force ratio at each distance point based on the amplitudes of the main and secondary peaks; determining the front and rear boundary positions of the stressed section and the bare fiber fusion splice section based on the force ratio curve, achieving sub-pixel-level boundary positioning; correcting the distance coordinates based on the front and rear boundaries of the stressed section or the bare fiber fusion splice section, and outputting the corrected strain and / or temperature curves. This application improves the boundary positioning accuracy from the hardware resolution level to the sub-pixel level, reducing the measurement error of the stressed section length.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring and signal processing technology, and in particular to a fiber optic sensing data processing method and system based on butterfly spectrum and multi-sensor constraints. Background Technology

[0002] Distributed fiber optic sensing technology, especially BOTDA technology, has been widely used for strain and temperature monitoring in large-scale infrastructure. Its basic principle is to calculate physical quantities by measuring the Brillouin frequency shift along the path of independent sensing units; the accuracy of the frequency shift extraction directly determines the measurement accuracy.

[0003] Existing spectrum processing methods have the following technical limitations: 1. Physical mismatch of the single-peak model: Traditional methods assume that the Brillouin spectrum of each sampling point is an ideal single-peak Lorentz shape and use nonlinear least squares fitting. However, in practical engineering, due to the physical limitations of spatial resolution, a single sampling point (length is...) The area may contain multiple different physical states. Taking the smart reinforcement anchorage zone as an example, a 0.4-meter-long sampling point may simultaneously contain the stress section (strain) and the strain section (stress). ) and free paragraph ( The spectrum of the single-peak model exhibits a double-peak superposition. Under these conditions, the single-peak model produces a systematic bias, specifically manifested as: the frequency shift near the boundary lies between the true strain and zero, forming a false transition zone, leading to blurred boundary positions and measurement errors in the length of the stressed section. 2. Limitations of single-channel information dimensions: Existing methods often use a single independent sensing unit (e.g., optical fiber) for independent measurement, failing to utilize the inherent redundancy information of multiple independent sensing units in the same physical field. When a single sensing unit experiences local damage or bending, resulting in a decrease in the signal-to-noise ratio, the reliability of the single-channel measurement results significantly decreases. This single-dimensionality of information makes the system lack self-checking and fault tolerance capabilities. 3. Lack of a reference for coordinate mapping: Traditional methods directly multiply the sampling point index by the resolution to obtain the distance coordinate, i.e. This mapping implicitly assumes a one-to-one correspondence between sampling points and physical segments, but fails to consider the ambiguity effect of non-stressed areas such as the leading-out section and transition section on the physical boundary. Furthermore, due to the lack of an absolute spatial reference, when the sampling clock drifts or the speed of light becomes uncertain, the coordinates of the entire curve may undergo systematic stretching or compression, causing the measured length of the stressed segment to deviate from reality.

[0004] In summary, there is an urgent need for a spectrum data processing method that can overcome resolution limitations, utilize multi-channel redundant information, and achieve sub-pixel-level boundary positioning. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for processing fiber optic sensing data based on butterfly spectroscopy and multi-sensor constraints. This addresses the technical problems of existing spectral processing methods, such as physical mismatch of single-peak models, limitations in single-channel information dimensionality, and lack of coordinate mapping references.

[0006] First, this application provides a fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints, the specific steps of which are as follows:

[0007] S1: Acquire Brillouin spectrum data of at least two independent sensing units located in the same structure. The at least two independent sensing units are fused together at the far end to form a loop optical path. The acquired forward and reverse Brillouin spectrum curves constitute a mirror-symmetric butterfly spectrum.

[0008] S2: Constrain the main peak frequency of each independent sensing unit to meet the consistency constraint, and perform joint fitting on the Brillouin spectrum data of multiple independent sensing units;

[0009] S3: Based on the joint fitting results, separate the main peak and at least one secondary peak in the Brillouin spectrum of each distance point, and calculate the amplitude of the main peak and the amplitude of the secondary peak for each independent sensing unit.

[0010] S4: Calculate the force ratio at each distance point based on the main peak amplitude and secondary peak amplitude of each independent sensing unit;

[0011] S5: Based on the distribution curve of the force ratio along the distance, determine the front and rear boundary positions of the force-bearing section and the front and rear boundary positions of the bare fiber fusion splice section to achieve sub-pixel level boundary positioning.

[0012] S6: Correct the distance coordinates based on the front and rear boundaries of the stressed section or the front and rear boundaries of the bare fiber welded section, and output the corrected strain and / or temperature curves.

[0013] Optionally, step S1 also includes using the mirror symmetry of the forward and reverse Brillouin spectrum curves to perform a quality self-check on the data. When the frequency shift deviation of the forward and reverse curves at the corresponding distance point exceeds a preset threshold, the data at that point is marked as abnormal, and a resampling or alarm signal is triggered.

[0014] Optionally, in step S2, the main peak frequency of each independent sensing unit is forcibly constrained to the same variable. The Brillouin spectral data of multiple independent sensing units are jointly fitted using least squares, with the objective function being:

[0015] ;

[0016] in, The number of independent sensing units. For the first The unit is at the distance point The measured spectrum, For the first The unit is at the distance point The fitted spectrum, For the set of fitting parameters; The K-peak Lorentz model was used to fit the result as follows:

[0017] ;

[0018] in, The main peak frequency of the independent sensing unit. For the first Independent sensing unit The center frequency of the peak For the first The first independent sensing unit The amplitude of the peak For the first The half-height and full width of the peak The number of peaks Used as the baseline.

[0019] Optionally, in step S4, the force percentage at each distance point is the ratio of the sum of the amplitudes of the main peaks of all independent sensing units to the sum of the amplitudes of all peaks:

[0020] ;

[0021] in, Distance point First The amplitude of the main peak of each independent sensing unit. Distance point First The first independent sensing unit Peak amplitude.

[0022] Optionally, the force ratio The physical meaning is the proportion of the length occupied by the force-bearing segment within the sampling point: ,in: The length of the sampling point. The length of the stressed section is [length], and the length of the free section is [length]. .

[0023] Optionally, the specific method in step S5 is as follows:

[0024] The force distribution is determined by interpolation. The two intersections of the curve and the 0.5 horizontal line determine the front boundary of the stress-bearing segment. and backend boundary ;

[0025] In terms of force ratio In the curve, find a continuous region that satisfies Furthermore, the signal strength is below a preset threshold in the range where the front and rear boundaries of this range correspond to the front boundary of the bare fiber fusion splice section. and backend boundary The lie in after, and The area between them is the backend working segment, which is a free segment.

[0026] Optionally, in step S6, when using the front and rear boundaries of the force-bearing segment, the distance coordinates are corrected as follows:

[0027] ;

[0028] in, and These are the sampled and corrected coordinate data, respectively. This is the nominal length of the stressed section;

[0029] In step S6, when using the front and rear boundaries of the bare fiber fusion splice segment, the distance coordinates are corrected as follows:

[0030] ;

[0031] in, and These are the sampled and corrected coordinate data, respectively. The prior length of the bare fiber fusion splice segment. Accurate measurements are taken and entered into the system during the construction phase.

[0032] Optionally, step S7 is also included: verifying the accuracy of coordinate correction using the prior length of the bare fiber fusion splice or the nominal length of the stress-bearing section, and issuing an abnormal warning when the deviation exceeds a preset threshold.

[0033] When the distance coordinates are corrected using the front and rear boundaries of the stressed section, the accuracy of the coordinate correction is verified by using the prior length of the bare fiber fusion splice section; when the distance coordinates are corrected using the front and rear boundaries of the bare fiber fusion splice section, the accuracy of the coordinate correction is verified by using the nominal length of the stressed section.

[0034] Optionally, in step S1, the combination of independent sensing units participating in joint fitting is dynamically selected based on the signal-to-noise ratio, fitting residual, or consistency deviation of each independent sensing unit.

[0035] When the fitting residual of an independent sensing unit exceeds the threshold or the signal-to-noise ratio is lower than the preset lower limit, it is automatically removed, and only the remaining independent sensing units are used for joint fitting. Redundancy verification and anomaly diagnosis are achieved through combination switching. In step S2, fitting weights can be dynamically allocated according to the signal-to-noise ratio of each independent sensing unit.

[0036] Secondly, this application provides a fiber optic sensing data processing system based on butterfly spectrum and multi-sensor constraints to implement the above-mentioned fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints, including a data acquisition unit, a data processing unit and an output unit.

[0037] The data acquisition unit is used to acquire Brillouin spectrum data of at least two independent sensing units within the smart rib; the data processing unit is used to process the acquired Brillouin spectrum data and correct the distance coordinates; and the output unit is used to output the corrected strain and / or temperature curves.

[0038] Because of the adoption of the above technical solution, the present invention has the following advantages:

[0039] 1. This application establishes a quantitative mapping between spectral characteristics and physical boundaries by using the force ratio R. It explains the cause of the bimodal spectrum in terms of physical essence—the length ratio of the force segment to the free segment within the sampling point. This quantitative mapping relationship provides a theoretical basis for sub-pixel positioning and elevates spectral processing from "numerical fitting" to the level of "physical inversion".

[0040] 2. This application introduces the main frequency consistency constraint of multiple independent sensing units into joint fitting, transforming the traditional single-channel independent optimization problem into multi-channel joint optimization. This framework uses physical laws (the same strain of the same structure) as constraints, which fundamentally improves the stability and accuracy of parameter estimation.

[0041] 3. The redundant information of the multiple independent sensing units in this application enables the algorithm to have inherent fault tolerance. When a certain independent sensing unit is locally damaged, the system performance is basically unaffected and the effective measurement range remains unchanged.

[0042] 4. This application improves the boundary positioning accuracy from the hardware resolution level (0.4 meters) to the sub-pixel level (<0.1 meters), and reduces the measurement error of the force-bearing segment length from more than 5% to less than 1%.

[0043] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0044] The accompanying drawings of this invention are described below.

[0045] Figure 1 This is a flowchart of the fiber optic sensing data processing method of the present invention.

[0046] Figure 2 This is a bimodal spectrum obtained from a certain distance point in this invention.

[0047] Figure 3 This is a graph showing the results of the traditional single-peak fitting of this invention.

[0048] Figure 4 This is a graph showing the result of the dual independent sensing units combined with bimodal fitting in this invention.

[0049] Figure 5 The force ratio of the present invention A schematic diagram of the principle of boundary positioning.

[0050] Figure 6 This is a structural diagram of two independent sensing units of the present invention being fused together at the far end of the smart rib to form a loop optical path. Detailed Implementation

[0051] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise explicitly specified and limited, the terms "connected" or "linked" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, an integral connection, an electrical connection, or a signal connection; it can be a direct connection or an indirect connection through an intermediate medium.

[0052] Example 1:

[0053] like Figure 1 The illustrated fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints includes the following steps:

[0054] S1: Acquire Brillouin spectrum data of at least two independent sensing units located within the same structure. ,in: , Sampling frequency, For distance sampling point index, The number of independent sensing units; such as Figure 2 The image shows a bimodal spectrum obtained from a certain distance point;

[0055] In this embodiment, at least two independent sensing units are fused together at the far end to form a loopback optical path, so that the two Brillouin spectrum curves obtained in a single measurement form a butterfly spectrum with mirror symmetry. The mirror symmetry of the two Brillouin spectrum curves is used to perform data quality self-checking. When the frequency shift deviation of the forward and reverse curves at the corresponding distance point exceeds a preset threshold, the data at that point is marked as abnormal, and a resampling or alarm signal is triggered.

[0056] In this embodiment, the combination of independent sensing units participating in joint fitting is dynamically selected based on the signal-to-noise ratio, fitting residual, or consistency deviation of each independent sensing unit. When the fitting residual of an independent sensing unit exceeds a threshold or the signal-to-noise ratio is lower than a preset lower limit, it is automatically removed, and only the remaining independent sensing units are used for joint fitting. Redundancy verification and anomaly diagnosis are achieved through combination switching.

[0057] In this embodiment, the natural symmetry of the butterfly spectrum is utilized to automatically identify the following characteristic regions in the physical structure of the smart rib: Front transition zone: the region where the strain of the positive curve begins to rise, corresponding to the anchoring start point; Stress-bearing main body region: the strain-stable region, corresponding to the main body of the smart rib; Rear transition zone: the region where the strain of the positive curve begins to decrease and the strain of the reverse curve begins to rise; Bare fiber fusion splice zone: the "gap" region where the signal strength drops sharply. Its physical length can be obtained by precise construction measurements and can serve as a natural marker of the rear boundary and an absolute reference for coordinate calibration. In this application, "stressed segment" and "free segment" are defined from the perspective of strain state. "Stressed segment" refers to the segment where the optical fiber is subjected to anchoring tension and generates strain, corresponding to the stress-bearing main body region and part of the front and rear transition zones in the smart rib physical structure; "free segment" includes the front lead-out segment, part of the front and rear transition zones, and the bare fiber fusion splice segment; the front transition zone in the physical structure simultaneously contains the starting parts of both the free segment and the stressed segment, thus exhibiting a smooth transition from 0 to 1 on the stress ratio R curve.

[0058] S2: Constrain the main peak frequency of each independent sensing unit to meet the consistency constraint, and perform joint fitting of the Brillouin spectrum data of multiple independent sensing units; the specific steps are as follows:

[0059] The main peak frequency of each independent sensing unit is forcibly constrained to the same variable. The Brillouin spectral data of multiple independent sensing units are jointly fitted using least squares, with the objective function being:

[0060] ;

[0061] in, The number of independent sensing units. For the first The unit is at the distance point The measured spectrum, For the first The unit is at the distance point The fitted spectrum, For the set of fitting parameters; The K-peak Lorentz model was used to fit the result as follows:

[0062] ;

[0063] in, This represents the dominant peak frequency for all independent sensing units. For the first Independent sensing unit The center frequency of the peak For the first The first independent sensing unit The amplitude of the peak For the first The half-height and full width of the peak The number of peaks Used as the baseline.

[0064] In this embodiment, since all independent sensing units are located within the same structure and experience the same strain / temperature field, the dominant peak frequency of each unit is... Consistency constraints should be satisfied, transforming multiple single-channel fitting problems into a multi-channel joint optimization problem; as one embodiment of this application, fitting weights can be dynamically allocated based on the signal-to-noise ratio of each unit. Figure 3 The image shown is the result of traditional single-peak fitting, which exhibits significant deviations in its fitting results. Figure 4 The figure shown is the result of bimodal fitting of two independent sensing units.

[0065] S3: Based on the joint fitting results, separate the main peak and at least one secondary peak in the Brillouin spectrum of each distance point, and calculate the amplitude of the main peak and the amplitude of the secondary peak for each independent sensing unit.

[0066] S4: Calculate the force ratio at each distance point based on the main peak amplitude and secondary peak amplitude of each independent sensing unit; the force ratio at each distance point is the ratio of the sum of the main peak amplitudes of all independent sensing units to the sum of the amplitudes of all peaks.

[0067] ;

[0068] in, Distance point First The amplitude of the main peak of each independent sensing unit. Distance point First The first independent sensing unit Peak amplitude.

[0069] In this embodiment, the force ratio The physical meaning is the proportion of the length of the optical fiber in the stressed section within the sampling point: ,in: The length of the sampling point. The length of the stressed section is [length], and the length of the free section is [length]. Force ratio The value in The continuous changes between them reflect the gradual transition from the free segment to the stressed segment.

[0070] S5: Based on the distribution curve of the stress ratio along the distance, determine the positions of the front and rear boundaries of the stress-bearing section and the front and rear boundaries of the bare fiber fusion splice section to achieve sub-pixel-level boundary positioning; the specific method is as follows:

[0071] The force distribution is determined by interpolation. The two intersections of the curve and the 0.5 horizontal line determine the front boundary of the stress-bearing segment. and backend boundary :

[0072] ;

[0073] in, and For front end Crossing two adjacent sampling points of 0.5, and For backend Crossing two adjacent sampling points of 0.5, The sampling interval;

[0074] In terms of force ratio In the curve, find a continuous region that satisfies Furthermore, the signal strength is below a preset threshold in the range where the front and rear boundaries of this range correspond to the front boundary of the bare fiber fusion splice section. and backend boundary The lie in after, and The area between them is the backend working segment, which is a free segment.

[0075] In this embodiment, this method overcomes the limitations of hardware resolution. The limitations. Theoretically, if The curve has a sufficiently high signal-to-noise ratio, achieving a positioning accuracy of 0.01. On the order of magnitude. In practical systems, due to noise, the positioning accuracy is typically as low as 0.1. Within. For example Figure 5 As shown, the force distribution of this application is... The principle of boundary positioning.

[0076] S6: Correct the distance coordinates based on the front and rear boundaries of the stressed section or the front and rear boundaries of the bare fiber welded section, and output the corrected strain and / or temperature curves. The specific method is as follows:

[0077] When using the front and rear boundaries of the stressed segment, the distance coordinates are corrected as follows:

[0078] ;

[0079] in, and These are the sampled and corrected coordinate data, respectively. This is the nominal length of the stressed section;

[0080] In step S6, when using the front and rear boundaries of the bare fiber fusion splice segment, the distance coordinates are corrected as follows:

[0081] ;

[0082] in, and These are the sampled and corrected coordinate data, respectively. The prior length of the bare fiber fusion splice segment. Accurate measurements are taken and entered into the system during the construction phase.

[0083] In this embodiment, the length of the bare fiber fusion splice section As the absolute spatial reference, the length of the bare fiber fusion splice is preferred. Perform calibration; if no bare fiber splice data is available, use the nominal length of the stressed section. The final output is the corrected strain and / or temperature curve, with the horizontal axis representing the absolute calibration distance and the vertical axis representing the strain / temperature value.

[0084] S7: Verify the accuracy of coordinate correction using the prior length of the bare fiber fusion splice or the nominal length of the stressed section, and issue an abnormal warning when the deviation exceeds a preset threshold; the specific method is as follows:

[0085] When using the front and rear boundaries of the stressed section to correct the distance coordinates, the accuracy of the coordinate correction is verified by using the prior length of the bare fiber fusion splice section.

[0086] Using the prior length of the bare fiber splice (Obtained through precise construction measurements) as the absolute spatial reference. The measured length of the bare fiber fusion splice section is... ,calculate and If the deviation exceeds the threshold, it indicates that there may be construction errors or fiber optic abnormalities.

[0087] When using the front and rear boundaries of the bare fiber fusion splice section to correct the distance coordinates, the nominal length of the stressed section is used to verify the accuracy of the coordinate correction.

[0088] The measured length of the stressed section is ,calculate and If the deviation exceeds the threshold, it indicates that there may be construction errors or fiber optic abnormalities.

[0089] S8: Experimental Verification and Simulation Analysis

[0090] S8.1: Armored smart rib for the loopback structure of dual independent sensing units: The smart rib includes a front lead-out section (approximately 1 meter jumper), a front transition section (0.3 meters, freely placed inside the steel strand), a load-bearing main body section (length L meters), a rear transition section (0.3 meters), a bare fiber fusion splice section (0.5~0.8 meters, bent and coiled), and a fusion point. The two independent sensing units are fused at the far end to form a loopback optical path. Port 1 of the demodulator emits pump light, and port 2 receives probe light. A single measurement yields two Brillouin frequency shift curves, one positive and one negative, in a symmetrical "butterfly wing" shape, with a noticeable gap at the bare fiber fusion splice section.

[0091] By using the method described in this application to force the main peak frequencies of the two channels to be equal, and calculating the force ratio R based on the amplitudes of the main and secondary peaks obtained from the fitting, the positioning accuracy of the front-end boundary reaches 0.08 meters (better than 0.4 meters resolution) and the positioning accuracy of the rear-end boundary reaches 0.09 meters by finding the interpolation position where R=0.5. The notch length of the bare fiber fusion splice section matches the nominal value (0.6 meters), verifying the accuracy of the coordinate correction.

[0092] S8.2: For an array of smart ribs with five independent sensing units (e.g., optical fibers), the robustness of joint fitting by multiple independent sensing units was verified. Experimental setup: four independent sensing units were used for strain measurement, and one independent sensing unit was used for temperature compensation. A simulated damage point was set in the middle of the smart rib (the signal-to-noise ratio of a certain independent sensing unit decreased by 20dB locally), and the data were processed using single-channel fitting and the method of this application, respectively.

[0093] Simulation setup: All 5 independent sensing units are located within the same smart rib; Damage simulation: In the 20-25 meter section, the signal-to-noise ratio of independent sensing unit 1 decreases additionally (damage level), while the other independent sensing units remain normal. Evaluation metrics: front-end boundary error (actual front-end at 10 meters), rear-end boundary error (actual rear-end at 40 meters), root mean square error (RMSE) of the entire strain curve, and effective measurement range (based on strain error <10με). Comparison scheme: Single-channel fitting uses only data from the damaged independent sensing unit 1; this application uses data from all five independent sensing units for multi-channel joint fitting, forcing a consistent main frequency. Results are shown in Table 1.

[0094] Table 1 Performance comparison under 20dB fixed injury

[0095]

[0096] Table 2 Comparison of boundary errors under different damage levels

[0097]

[0098] As shown in Tables 1 and 2, the joint constraint of multiple independent sensing units in this application plays a crucial role: 1. Redundancy information utilization: Even if a certain independent sensing unit is locally damaged, the frequency consistency constraint provided by the remaining normal independent sensing units can still dominate the joint optimization and suppress the abnormal contribution of the damaged independent sensing unit. 2. Combination switching mechanism: In subsequent processing, the algorithm can automatically identify and reduce the weight of the damaged independent sensing unit, or even temporarily remove it, further ensuring stability. 3. System-level robustness: The effective measurement range is not affected by local damage, while the effective range of the single-channel method is significantly shortened after damage (from 65km to 42km).

[0099] S8.3: Gain analysis of switching combinations of different independent sensing units (e.g., optical fibers): In a multi-independent sensing unit array, any two independent sensing units can form a joint fitting pair; this embodiment compares the performance of different combination methods.

[0100] Table 3. Signal-to-noise ratio improvement and positioning error of different combinations

[0101]

[0102] The results show that as the number of independent sensing units participating in the joint fitting increases, the signal-to-noise ratio and positioning accuracy continue to improve; when an independent sensing unit is damaged, the system can still maintain high performance by eliminating abnormal channels through combination switching; this combination switching mechanism can be dynamically adjusted according to monitoring needs, achieving a balance between computational efficiency and measurement accuracy.

[0103] S8.4: Joint fitting and combination switching of arrays of multiple independent sensing units (e.g., optical fibers): Using an array of 5 independent sensing units, the joint fitting effect of single independent sensing unit, dual independent sensing unit, triple independent sensing unit, and quadruple independent sensing unit was tested under the condition that the local signal-to-noise ratio of independent sensing unit 1 decreased by 20dB.

[0104] Experimental results: Fitting with a single independent sensing unit (using damaged independent sensing unit 1): boundary error 0.35m, strain RMSE 35.2με; Combining two independent sensing units (independent sensing unit 1 + 2): boundary error 0.12m, strain RMSE 12.1με; Combining three independent sensing units (independent sensing units 2 + 3 + 4): boundary error 0.06m, strain RMSE 5.8με; Combining four independent sensing units (independent sensing units 2 + 3 + 4 + 5): boundary error 0.05m, strain RMSE 4.9με.

[0105] The results show that even when damaged independent sensing units participate in joint fitting, the overall performance can still be maintained as long as other independent sensing units provide sufficient consistency constraints. If the damaged independent sensing units are removed and only normal independent sensing units are used in conjunction, the performance is further improved. This demonstrates the adaptive combination and switching capability of the method of this invention.

[0106] S8.5: Simulation Analysis:

[0107] To systematically evaluate the performance of the method of this invention, Monte Carlo simulation analysis was performed to compare the boundary localization errors of this application with those of the traditional single-peak fitting method under different signal-to-noise ratios.

[0108] Simulation settings: Hardware resolution: 0.4 meters (sampling point interval 0.4 meters); Smart rib length: 10 meters (front boundary at 1.0 meter, rear boundary at 11.0 meters, considering a 0.3-meter transition section); Spectrum model: The spectrum of each distance point is composed of the superposition of the main peak (stressed section) and the secondary peak (free section). The main peak frequency corresponds to a strain of 1000 με, and the secondary peak frequency corresponds to 0 με. The interval between the two peaks is approximately 50 MHz; Noise model: Gaussian white noise is added to the ideal spectrum. The signal-to-noise ratio (SNR) is defined as the ratio of the peak power of the signal to the variance of the noise, ranging from 5 to 30 dB; 1000 Monte Carlo simulations are performed for each analysis, and the root mean square value of the boundary positioning error is taken. The simulation results are shown in Table 4.

[0109] Table 4 Comparison of Boundary Positioning Errors under Different Signal-to-Noise Ratios

[0110]

[0111] As shown in Table 4, the method of this application can still control the error within 0.15 meters even under the harsh condition of SNR=5dB, while the traditional method has reached 0.45 meters. With the improvement of SNR, the error of the method of this invention is reduced to 0.04 meters, realizing true sub-pixel positioning (theoretically, the limit can reach the order of 0.01 meters, limited by algorithm and noise). This is due to: 1. The joint constraint of dual independent sensing units effectively suppresses the interference of noise on the main frequency estimation; 2. The force ratio curve provides continuous boundary transition information, and the interpolation accuracy is not limited by the sampling interval.

[0112] Example 2:

[0113] A fiber optic sensing data processing system based on butterfly spectrum and multi-sensor constraints is provided to implement the fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints described in Example 1. The system includes a data acquisition unit, a data processing unit, and an output unit.

[0114] The data acquisition unit is used to acquire Brillouin spectrum data of at least two independent sensing units within the smart rib; the data processing unit is used to process the acquired Brillouin spectrum data and correct the distance coordinates; and the output unit is used to output the corrected strain and / or temperature curves.

[0115] In this embodiment, the smart rib includes a fiber optic mother-daughter rod containing multiple optical fibers, and the independent sensing units are selected from the optical fibers of the smart rib. The number of independent sensing units participating in the joint fitting is dynamically adjusted according to measurement accuracy requirements, signal-to-noise ratio, fitting residual, or consistency deviation. Figure 6 As shown, at least two independent sensing units are fused together at the far end of the smart rib to form a loop optical path.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints, characterized in that, The specific steps are as follows: S1: Acquire Brillouin spectrum data of at least two independent sensing units located in the same structure. The at least two independent sensing units are fused together at the far end to form a loop optical path. The acquired forward and reverse Brillouin spectrum curves constitute a mirror-symmetric butterfly spectrum. S2: Constrain the main peak frequency of each independent sensing unit to meet the consistency constraint, and perform joint fitting on the Brillouin spectrum data of multiple independent sensing units; S3: Based on the joint fitting results, separate the main peak and at least one secondary peak in the Brillouin spectrum of each distance point, and calculate the amplitude of the main peak and the amplitude of the secondary peak for each independent sensing unit. S4: Calculate the force ratio at each distance point based on the main peak amplitude and secondary peak amplitude of each independent sensing unit; S5: Based on the distribution curve of the force ratio along the distance, determine the front and rear boundary positions of the force-bearing section and the front and rear boundary positions of the bare fiber fusion splice section to achieve sub-pixel level boundary positioning. S6: Correct the distance coordinates based on the front and rear boundaries of the stressed section or the front and rear boundaries of the bare fiber welded section, and output the corrected strain and / or temperature curves.

2. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1, characterized in that, Step S1 also includes using the mirror symmetry of the forward and reverse Brillouin spectrum curves to perform a quality self-check on the data. When the frequency shift deviation of the forward and reverse curves at the corresponding distance point exceeds a preset threshold, the data at that point is marked as abnormal, and a resampling or alarm signal is triggered.

3. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1, characterized in that, In step S2, the main peak frequency of each independent sensing unit is forcibly constrained to the same variable. The Brillouin spectral data of multiple independent sensing units are jointly fitted using least squares, with the objective function being: ； in, The number of independent sensing units. For the first The unit is at the distance point The measured spectrum, For the first The unit is at the distance point The fitted spectrum, For the set of fitting parameters; The K-peak Lorentz model was used to fit the result as follows: ； in, The main peak frequency of the independent sensing unit. For the first Independent sensing unit The center frequency of the peak For the first The first independent sensing unit The amplitude of the peak For the first The half-height and full width of the peak The number of peaks Used as the baseline.

4. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1, characterized in that, In step S4, the force distribution at each distance point is the ratio of the sum of the amplitudes of the main peaks of all independent sensing units to the sum of the amplitudes of all peaks: ； in, Distance point First The amplitude of the main peak of each independent sensing unit. Distance point First The first independent sensing unit Peak amplitude.

5. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1 or 4, characterized in that, The proportion of force received The physical meaning is the proportion of the length occupied by the force-bearing segment within the sampling point: ,in: The length of the sampling point. The length of the stressed section is [length], and the length of the free section is [length]. .

6. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1 or 3, characterized in that, The specific method in step S5 is as follows: The force distribution is determined by interpolation. The two intersections of the curve and the 0.5 horizontal line determine the front boundary of the stress-bearing segment. and backend boundary ; In terms of force ratio In the curve, find a continuous region that satisfies Furthermore, the signal strength is below a preset threshold in the range where the front and rear boundaries of this range correspond to the front boundary of the bare fiber fusion splice section. and backend boundary The lie in after, and The area between them is the backend working segment, which is a free segment.

7. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 4, characterized in that, In step S6, when using the front and rear boundaries of the force-bearing segment, the distance coordinates are corrected as follows: ； in, and These are the sampled and corrected coordinate data, respectively. This is the nominal length of the stressed section; In step S6, when using the front and rear boundaries of the bare fiber fusion splice segment, the distance coordinates are corrected as follows: ； in, and These are the sampled and corrected coordinate data, respectively. The prior length of the bare fiber fusion splice segment. Accurate measurements are taken and entered into the system during the construction phase.

8. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1 or 7, characterized in that, It also includes step S7: verifying the accuracy of coordinate correction using the prior length of the bare fiber fusion splice section or the nominal length of the stress section, and issuing an abnormal warning when the deviation exceeds a preset threshold; When the distance coordinates are corrected using the front and rear boundaries of the stressed section, the accuracy of the coordinate correction is verified by using the prior length of the bare fiber fusion splice section; when the distance coordinates are corrected using the front and rear boundaries of the bare fiber fusion splice section, the accuracy of the coordinate correction is verified by using the nominal length of the stressed section.

9. The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to claim 1, characterized in that, In step S1, the combination of independent sensing units participating in joint fitting is dynamically selected based on the signal-to-noise ratio, fitting residual, or consistency deviation of each independent sensing unit. When the fitting residual of an independent sensing unit exceeds the threshold or the signal-to-noise ratio is lower than the preset lower limit, it is automatically removed, and only the remaining independent sensing units are used for joint fitting. Redundancy verification and anomaly diagnosis are achieved through combination switching. In step S2, fitting weights can be dynamically allocated according to the signal-to-noise ratio of each independent sensing unit.

10. A fiber optic sensing data processing system based on butterfly spectrum and multi-sensor constraints, characterized in that, The fiber optic sensing data processing method based on butterfly spectrum and multi-sensor constraints according to any one of claims 1-9 includes a data acquisition unit, a data processing unit, and an output unit. The data acquisition unit is used to acquire Brillouin spectrum data of at least two independent sensing units within the smart rib; the data processing unit is used to process the acquired Brillouin spectrum data and correct the distance coordinates; and the output unit is used to output the corrected strain and / or temperature curves.

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