A fiber optic shape sensing method based on core combination optimization adaptive algorithm

Abstract

The present invention belongs to the technical field of optical fiber shape sensors, and discloses a shape sensing method based on a core combination optimization adaptive algorithm, which is used for shape sensing of a multi-core optical fiber frequency domain reflectometer. The shape sensing method of the present invention comprises: calculating the signal-to-noise ratio of the peripheral cores of the multi-core optical fiber, eliminating the cores whose signal-to-noise ratio is lower than a set threshold, and using the remaining cores as candidate cores; demodulating the strain of each candidate core, and then normalizing, differentiating, and square-accumulating the demodulated core strain, thereby obtaining the strain jump degree of each core whose signal-to-noise ratio is higher than the set threshold; forming the candidate cores into different triangular combinations, calculating the total jump degree of the core strain of each combination, and then selecting the core combination with the smallest total strain jump degree to reconstruct the optical fiber shape. The present invention greatly improves the measurement accuracy of the optical fiber shape sensor and is worthy of promotion and application.

Description

Technical Field

[0001] The invention belongs to the technical field of optical fiber shape sensors, and in particular relates to a high-precision shape sensing method for measuring the shape of an optical fiber. Background Art

[0002] Fiber-optic shape sensing measures the strain of different cores in a multi-core optical fiber or fiber bundle, establishing a relationship between the strain of each core and the fiber's bending angle and curvature to reconstruct the fiber's shape. Distributed fiber-optic shape sensing technology based on optical frequency domain reflectometry offers advantages such as lack of visual assistance, immunity to electromagnetic interference, compact size, and high spatial resolution. Therefore, it has shown broad application value in fields such as civil engineering, aerospace, and in vivo navigation of medical devices. However, for this technology to become practical, improving shape sensing accuracy remains a key issue.

[0003] First, traditional fiber shape reconstruction methods utilize the three cores in a multi-core fiber to form an equilateral triangle. For example, for a seven-core fiber, there are only two possible combinations of cores forming an equilateral triangle. If one of these cores exhibits a low signal-to-noise ratio and a large bend curvature, resulting in significant strain measurement errors, the accuracy of fiber shape sensing will be severely affected. While some researchers have proposed using other core combinations within a seven-core fiber for fiber shape reconstruction, they have not yet analyzed how to select the optimal core combination for shape reconstruction. Seven-core fibers offer 20 possible triangular core combinations for shape sensing, significantly improving the error tolerance of shape sensing compared to using only the two equilateral triangle combinations. By analyzing the strain measurement accuracy of each core combination and selecting the optimal combination, the accuracy of shape sensing in multi-core fibers can be significantly improved under extreme conditions such as large bend curvature, low core signal-to-noise ratio, and even core fracture.

[0004] Secondly, when a multi-core optical fiber exhibits significant bend curvature and a low core signal-to-noise ratio, measuring the frequency drift of the multi-core optical fiber core using an optical frequency domain reflectometer based on cross-correlation demodulation can result in large jumps in the demodulated frequency drift. These frequency drift jumps reduce the accuracy of strain measurement and, consequently, the accuracy of fiber shape reconstruction. To address this issue, most methods utilize denoising algorithms, such as smoothing denoising, to remove these frequency drift jumps. However, this method fails to correctly demodulate the frequency drift caused by actual strain, thus affecting the accuracy of fiber shape reconstruction. Therefore, a method to accurately remove frequency drift jumps is needed to address this issue.

[0005] In addition, compared with cross-correlation demodulation, phase demodulation is more susceptible to coherent fading, polarization fading, large bending curvature of multi-core optical fiber, and low core signal-to-noise ratio, resulting in more phase jump points in the demodulated phase. Therefore, it is also necessary to develop a method to remove phase jumps to overcome this defect of the existing technology. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to propose a shape sensing method based on a fiber core combination optimization adaptive algorithm, aiming to improve the shape sensing accuracy.

[0007] To solve the above technical problems, the present invention proposes a shape sensing method based on a core combination optimization adaptive algorithm. First, the signal-to-noise ratio of the measurement signal of the peripheral cores in a multi-core optical fiber is calculated; then the signal-to-noise ratios of the peripheral cores are sorted from small to large, and the cores with signal-to-noise ratios lower than the set threshold are eliminated. The remaining peripheral cores are used as candidate cores for optical fiber shape reconstruction.

[0008] Preferably, the signal-to-noise ratio of the measurement signal of the peripheral core in the multi-core optical fiber is calculated in the following manner:

[0009] Performing Fourier transform on the reference signal and the measurement signal of the multi-core fiber in the optical fiber shape sensor to obtain a distance domain signal;

[0010] The signal-to-noise ratio of the measurement signal of each peripheral fiber core is calculated using the following formula: SNR i , i Number each outer fiber core:

[0011]

[0012] In the formula P si For the i The signal power of the signal in the root outer core distance domain, P ni For the i The noise power of the signal in the distance domain of the outer fiber core.

[0013] Furthermore, the shape sensing method based on the core combination optimization adaptive algorithm includes the following steps:

[0014] After obtaining the candidate fiber cores, the strain of each candidate fiber core is demodulated;

[0015] Normalize, differentiate, and square the strain of the demodulated fiber core to obtain the strain jump degree of each candidate fiber core;

[0016] The candidate fiber cores are organized into different triangle combinations, and the total jump degree of the fiber core strain in each combination is calculated;

[0017] The fiber core combination with the smallest total strain jump is selected to reconstruct the fiber shape.

[0018] Optionally, the shape sensing method based on the core combination optimization adaptive algorithm, wherein the strain demodulation of each candidate core is cross-correlation demodulation, specifically comprises the steps of:

[0019] Step A-1: ​​Measure the signal of a candidate fiber core in its natural state as a reference signal I ri ; The signal of the same fiber core after the shape is applied is measured as the measurement signal I mi ,right I ri 、 I mi Perform Fourier transform to obtain the distance domain signal 、 ;

[0020] Step A-2: convert the range domain signal 、 Divided into n segment, i.e. and ;right and Perform inverse Fourier transform and get I R1 、 I M1 ,right I R1 、 I M1 Perform cross-correlation to obtain the frequency drift curve of the measured signal relative to the reference signal C 1. Locate the peak of the frequency drift curve and obtain the frequency corresponding to the peak as the frequency drift magnitude of the first section of the fiber core. fs 1;

[0021] Step A-3, and Perform inverse Fourier transform and get I R2 、 I M2 , and I R2 、 I M2 Perform cross-correlation to obtain the frequency drift curve of the measured signal relative to the reference signal C 2. Frequency drift corresponding to the previous peak fs Positioning is performed within a range near 1, which is , w is a set positive integer,f Frequency drift curve C 2 horizontal axis frequency interval, the frequency corresponding to the peak value located within this range is used as the frequency drift magnitude of the second segment of the fiber core fs 2.

[0022] Step A-4, repeat the method of step A-3 to process the subsequent core segments in turn, and obtain the frequency drift size of each core segment, and finally obtain FS ={ fs 1, fs 2… fs n}, that is, the frequency drift of each position along the distance domain of the fiber core;

[0023] Step A-5: Repeat steps A-1 to A-4 to calculate the frequency drift of each candidate fiber core along the distance domain, that is, FS i , i is the number of the candidate fiber core;

[0024] Step A-6, FS i Divide by a constant , P e is the elastic-optical coefficient of the optical fiber, which is a constant of 0.78. v 0 is the center frequency of the tuned laser, and the strain magnitude of each candidate fiber core along the distance domain is obtained .

[0025] Optionally, the shape sensing method based on the core combination optimization adaptive algorithm, wherein the strain demodulation of each candidate fiber core is phase demodulation, specifically comprises the steps of:

[0026] Step B-1, calculate the phase of the distance domain signal after Fourier transform of the candidate core reference signal, calculate the phase of the distance domain signal after Fourier transform of the candidate core measurement signal, then make the difference between the two and unwrap them to obtain the unwrap differential phase , i is the number of the candidate fiber core;

[0027] Step B-2: Assumptions There are a total of N data, find the q Data before W indivual The mean of the data M 1, and q After the data W indivual The mean of the data M 2. If Greater than or equal to the set threshold , then the q to N indivual Data subtraction ;

[0028] Step B-3, loop through q= 1 to q=N , thus compensating All discontinuous segments in the Perform SG filtering;

[0029] Step B-4, obtain the following for each candidate fiber core obtained after step B-3: Perform the difference to get The strain magnitude of each candidate fiber core along the distance domain is obtained by the following formula: :

[0030]

[0031] in is the theoretical spatial resolution, is the initial wavelength of the tuned laser, is the effective refractive index of the fiber core.

[0032] Preferably, the normalizing, differencing, and square accumulation of the strain of the demodulated fiber core to obtain the strain jump degree of each candidate fiber core includes:

[0033] right Use the following formula to perform normalization:

[0034]

[0035] The normalized strain is treated by the following differential equation:

[0036]

[0037] right Square each value in and add them up to get S i , S i Used to measure the degree of jump in fiber core strain.

[0038] Further preferably, the step of forming the candidate cores into different triangular combinations and calculating the total jump degree of the core strain of each combination includes:

[0039] The candidate cores are combined into different triangle core combinations. Assuming there are g different triangle core combinations, calculate the total strain jump degree of each combination core. GSm ( m =1, 2 …g), that is:

[0040]

[0041] S m1 、 S m2 、 S m3 is the magnitude of the strain jump of the three fiber cores that make up the triangular fiber core combination, that is, the set { S m1 、 S m2 、 S m3} is the set { S i}.

[0042] The present invention has the following beneficial effects:

[0043] 1. The present invention proposes an adaptive algorithm for shape reconstruction of a multi-core optical fiber. The algorithm calculates the signal-to-noise ratio of each outer core of the multi-core optical fiber, eliminates the cores with low signal-to-noise ratio, and does not use them for shape reconstruction. The strain of the candidate core is then demodulated, and the demodulated core strain is normalized, differentiated, and squared to obtain the degree of strain jump of each candidate core. Finally, the candidate cores are grouped into different triangular combinations, and a combination with the smallest degree of strain jump of the core among all combinations is selected for shape reconstruction. Compared with the use of only traditional equilateral triangle core combinations for shape reconstruction, this algorithm has a high fault tolerance rate, and greatly improves the accuracy of shape reconstruction when the multi-core optical fiber has a large curvature, a low core signal-to-noise ratio, or even a core break.

[0044] 2. This invention improves the cross-correlation peak-finding algorithm to address the problem of numerous frequency shifts when using traditional cross-correlation algorithms to demodulate frequency drift in multi-core fiber shape sensors when the multi-core fiber has large curvature and a low core signal-to-noise ratio. Compared to using denoising algorithms such as smoothing to address frequency shifts, the improved cross-correlation peak-finding algorithm can accurately demodulate frequency drift caused by actual strain, thereby improving shape reconstruction accuracy.

[0045] 3. The present invention proposes an algorithm for removing phase jumps in phase demodulation, removing phase jump points caused by coherent fading, polarization fading, large bending curvature of multi-core optical fiber, and low fiber core signal-to-noise ratio, thereby improving the demodulation accuracy and shape reconstruction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0047] Figure 1 is a diagram of an optical frequency domain reflectometer shape sensing device;

[0048] Figure 2 This is a schematic diagram of 20 triangular core combinations in a seven-core optical fiber;

[0049] Figure 3 It is a flow chart of the adaptive algorithm of the present invention;

[0050] Figure 4 This is a comparison chart of the frequency drift curve demodulated using the traditional cross-correlation algorithm and the frequency drift curve demodulated using the improved cross-correlation peak-finding algorithm;

[0051] Figure 5 This is a comparison of the differential phase diagram before and after removing the phase jump point;

[0052] Figure 6 It is a schematic diagram of the positional relationship between two adjacent sensing points in the optical fiber. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention and to make the above-mentioned purposes, features and advantages of the embodiments of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0054] Without loss of generality, the optical frequency domain reflectometer system used in the present invention adopts a seven-core optical fiber, see Figure 1 The measurement device includes: a tunable laser 1; a 1:99 fiber coupler 2; an auxiliary interferometer part, including: a 1:1 fiber coupler 3, a 60-meter delay fiber 4, a 1:1 fiber coupler 5, and a balanced detector 6; and a main interferometer part, including: a 1:99 fiber coupler 7, a polarization controller 8, a fiber circulator 9, a 1:1 polarization-maintaining fiber coupler 10, a polarization diversity receiver 11, a data acquisition card 12, an optical switch 13, a fan-in and fan-out 14, and a seven-core fiber 15.

[0055] During signal acquisition, the output light from the tunable laser 1 is split into two optical paths by the fiber coupler 2. Light with a 1% energy content enters the auxiliary interferometer. The auxiliary interferometer's beat signal is detected by the balanced detector 6 and then collected by the data acquisition card 12. The other path of light from the fiber coupler 2, with a 99% energy content, enters the main interferometer. The fiber coupler 7 splits the input light into two paths. The 1% light enters the polarization controller 8 and then the polarization-maintaining fiber coupler 10. The polarization controller is used to control the intensities of the P-polarized and S-polarized light to be equal. The 99% light passes through the fiber circulator 9, then through the optical switch 13 and the fan-in and fan-out 14 to enter the core of the seven-core optical fiber 15. The backscattered light from the core then returns to the circulator 9 along the original path and enters the polarization-maintaining fiber coupler 10. The polarization diversity receiver 11 is used to detect the beat signals of the P-polarized and S-polarized light. The beat signals of the P-polarized and S-polarized light are collected by the data acquisition card 12.

[0056] The seven-core optical fiber 15 consists of a middle core 7 and outer cores 1 to 6. The six outer cores are equidistant from the center, and the angle between adjacent cores is 60°. Figure 2 In the experiment, the adaptive algorithm will be used to select Figure 2 The shape of the fiber cores is reconstructed using one of the 20 possible triangular-shaped fiber core combinations shown in the figure. The measurement and reference signals in the experiment include the auxiliary interferometer data and the main interferometer data from the seven fiber cores.

[0057] The shape sensing method based on the core combination optimization adaptive algorithm proposed in the present invention has the following overall technical scheme: first, the signal-to-noise ratio of the measurement signal of the peripheral cores in the multi-core optical fiber is calculated; the signal-to-noise ratio of the peripheral cores is sorted from small to large, and the cores with a signal-to-noise ratio lower than a set threshold are eliminated, and the remaining peripheral cores are used as candidate cores for optical fiber shape reconstruction; the strain of each candidate core is demodulated; the strain of the demodulated core is normalized, differentiated, and squared and accumulated to obtain the strain jump degree of each candidate core; the candidate cores are combined into different triangular combinations, and the total jump degree of the core strain of each combination is calculated; and the core combination with the smallest total strain jump degree is selected for optical fiber shape reconstruction.

[0058] Next, the technical solution of the present invention is described in more detail.

[0059] Process 1: Two signal acquisition experiments were conducted. In the first experiment, the optical fiber was in a naturally extended state, and the signal from each fiber core was collected as a reference signal. In the second experiment, the optical fiber was bent into a specific shape, and the signal from each fiber core was collected as a measurement signal. First, the auxiliary interferometer beat frequency signal of the reference signal and the measurement signal of each fiber core was used to obtain the instantaneous optical frequency information of the tunable laser in both experiments. Then, the main interferometer beat frequency signal of the reference signal and the measurement signal of each fiber core was synchronously resampled with equal frequency to compensate for the nonlinear tuning of the tunable laser and the inconsistent frequency scanning range of the tunable laser in the two experiments.

[0060] Process 2: Adaptive algorithm is used to reconstruct the fiber shape, combined with Figure 3 As shown, it includes sub-processes (I) to (IV):

[0061] (1) The main interferometer signal in the seven fiber core measurement signals after compensating for the nonlinear tuning of the tuned laser and the inconsistent frequency scanning range I mi Perform Fourier transform to obtain the distance domain signal , the signal-to-noise ratio of the measurement signal of the six outer cores of the seven-core optical fiber is calculated by the following expression: SNR i ( i is the number of each peripheral fiber core, that is i =1, 2…6):

[0062]

[0063] In the formula P si For the i The signal power of the signal in the root outer core distance domain, P ni For the i The noise power of the signal in the distance domain of the outer fiber core. Sort the signal-to-noise ratio of the six fiber cores from small to large and calculate the 10th percentile of this set of data. T , and T As the set threshold, remove the signal-to-noise ratio below the threshold T The corresponding fiber core.

[0064] (2) Existing fiber optic sensors perform signal demodulation in the measurement process in two main modes: cross-correlation demodulation and phase demodulation. The adaptive demodulation algorithms for these two demodulation modes are described below.

[0065] A. Cross-correlation demodulation. Combined Figure 3 As shown in the dotted box on the left, the specific steps are as follows:

[0066] Step A-1: ​​Select a candidate fiber core iThe main interferometer signal in the reference signal I ri ; The signal of the same fiber core after the shape is applied is measured as the measurement signal I mi ,right I ri 、 I mi Perform Fourier transform to obtain the distance domain signal 、 .

[0067] Step A-2, and Divided into n segment, i.e. and .right and Perform inverse Fourier transform and get I R1 、 I M1 , and I R1 、 I M1 Perform cross-correlation to obtain the frequency drift curve of the measured signal relative to the reference signal C 1. Locate the peak of the frequency drift curve and obtain the frequency corresponding to the peak as the frequency drift magnitude of the first section of the fiber core fs 1.

[0068] Step A-3, and Perform inverse Fourier transform and get I R2 、 I M2 , and I R2 、 I M2 Perform cross-correlation to obtain the frequency drift curve of the measured signal relative to the reference signal C 2. Since the strain change on the fiber core should be continuous, there will be no sudden jump in the strain of two adjacent fiber cores, so the frequency drift curve C 2 peak to locate the frequency drift corresponding to the previous peak. fs Positioning is performed within a range near 1, which is , w is a set positive integer, f Frequency drift curve C 2 horizontal axis frequency interval. The frequency corresponding to the peak value located within this range is used as the frequency drift magnitude of the second segment of the fiber core. fs 2.

[0069] Step A-4, repeat step A-3 to process the subsequent core segments in turn, and obtain the frequency drift size of each core segment, and finally obtain fs 1, fs 2… fs n , that is, the frequency drift of each position of the fiber core along the distance domain FS .

[0070] Figure 4 The frequency drift curve of a fiber core demodulated using the traditional cross-correlation algorithm and the frequency drift curve of the fiber core demodulated using the improved cross-correlation peak-finding algorithm described in steps 1 to 4 of the present invention are shown. The bending radius of the seven-core optical fiber is 2.5 cm, the demodulation length is 0.21 m, and the fiber core is divided into n =101 segments, w =50, f =0.54 GHz. It can be seen that when the bending curvature is large, the improved cross-correlation peak-finding algorithm of the present invention can effectively suppress the frequency drift jump, thereby correctly reflecting the frequency drift caused by strain.

[0071] Step A-5: Repeat steps A-2 to A-4 to find the frequency drift of each candidate fiber core at each position along the distance domain. FS i , i The number of the candidate fiber core.

[0072] Step A-6, FS i Divide by a constant , P e is the elastic-optical coefficient of the optical fiber, which is a constant of 0.78. v 0 is the center frequency of the tuned laser, and the signal-to-noise ratio of each signal is higher than the threshold T Corresponding to the strain magnitude of the fiber core along the distance domain .

[0073] B. Phase demodulation. Combined Figure 3 As shown in the dotted box on the right, the specific steps are as follows:

[0074] Step B-1, calculate the candidate core distance domain signal Phase, calculate the candidate core distance domain signal Phase, then the difference between the two is taken and unwrapped to obtain the unwrapped differential phase , i is the number of the candidate fiber core;

[0075] Step B-2: Since the phase jump point will cause is divided into many discontinuous segments and needs to be The discontinuous part in the equation can be compensated. There are a total of N data, find the q The first 4 data The mean of the data M 1, and q 4 after the data The mean of the data M 2. If Greater than or equal to the set threshold , then the q to N indivual Data subtraction ;

[0076] Step B-3, loop through q= 1 to q=N ; thus compensating All discontinuous segments in the final compensation Perform SG filtering;

[0077] Figure 5 The differential phase result diagram of a certain fiber core before and after demodulation using the algorithm of the present invention is shown. It can be seen that the algorithm of the present invention can effectively remove phase jumps.

[0078] Step B-4, obtain the value of each candidate fiber core through the above steps. , and differentiate it to get , the signal-to-noise ratio of each root is higher than the threshold value by the following formula T Corresponding to the strain magnitude of the fiber core along the distance domain :

[0079]

[0080] in is the theoretical spatial resolution, is the initial wavelength of the tuned laser, is the effective refractive index of the fiber core.

[0081] (3) Corresponding strain size Perform normalization:

[0082]

[0083] Perform differential processing on the normalized strain of each candidate fiber core, that is:

[0084]

[0085] right Square each value in and add them up to get S i , that is, the fiber core i The magnitude of the strain jump.

[0086] (IV) The remaining fiber cores are formed as follows Figure 2 Different triangular fiber core combinations are shown. Assume that there are g different triangular fiber core combinations. , calculate the total jump degree of strain of each combined fiber core GS m ( m =1, 2 …g), that is:

[0087]

[0088] S m1 、 S m2 、 S m3 is the magnitude of the strain jump of the three fiber cores that make up the triangular fiber core combination, that is, the set { S m1 、 S m2 、 S m3} is the set { S i}, take the smallest subset GS m The shape of the seven-core optical fiber is reconstructed by the triangular fiber core combination corresponding to the value.

[0089] Process 3: Fiber shape reconstruction, including the following steps:

[0090] Step 3-1, assuming that the strain magnitudes of the three cores of the optimal triangular core combination along the distance domain are a, b, and c represent the three cores in the optimal triangular core combination, where the angle between the straight line passing through core a and the middle core 7 and the straight line passing through core b and the middle core 7 is α 1, the angle between the straight line passing through the fiber core b and the middle fiber core 7 and the straight line passing through the fiber core c and the middle fiber core 7 is α 2. That is, Figure 6 shown.

[0091] Step 3-2, solve the following equations to obtain the bending direction angle, bending curvature and torsion of the optical fiber:

[0092]

[0093] in is the bending curvature, is the bending direction angle, is the torsional strain, r is the distance between the outer core and the middle core, when 、 The solution of the above equations is:

[0094]

[0095] when 、 The solution of the above equations is:

[0096]

[0097] when 、 The solution of the above equations is:

[0098]

[0099] The arc length of the outer core rotation on the optical fiber cross section caused by torsion l , that is, Figure 6 As shown, according to the modulus constant between the shear strain and the torsion angle of the optical fiber G , the torsion angle can be obtained according to the following expression :

[0100]

[0101] Step 3-3, reconstruct the fiber shape based on the second transformation matrix. Figure 6 As shown, assuming P j and P j+1 are the coordinates of two adjacent sensing points on the optical fiber, j Indicates the j sensing points, i.e. j =1, 2, 3…, the distance between two adjacent sensing points on the middle axis of the optical fiber is the sensing resolution of the system z . No. j +1 sensor point coordinates P j+1 You can j The coordinates of the sensing points P j Substitute the following transformation matrix framework to obtain:

[0102]

[0103] in To compensate for the bending direction angle of each sensing point after the torsion angle is compensated, , To solve the bending curvature of each sensing point, The bending radius at each sensing point is . Therefore, the coordinates of each sensing point on the optical fiber can be obtained in sequence according to the above homogeneous transformation matrix framework, thereby reconstructing the shape of the entire optical fiber.

[0104] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0105] It will be appreciated that the present invention is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and variations can be made without departing from its scope, which is governed solely by the appended claims.

[0106] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A shape sensing method based on a core combination optimization adaptive algorithm, characterized in that: First, the signal-to-noise ratio of the measurement signal of the peripheral cores in the multi-core optical fiber is calculated. Then, the signal-to-noise ratio of the peripheral cores is sorted from small to large, and the cores with signal-to-noise ratios below a set threshold are eliminated. The remaining peripheral cores are used as candidate cores for optical fiber shape reconstruction. After obtaining the candidate fiber cores, the strain of each candidate fiber core is demodulated; Normalize, differentiate, and square the strain of the demodulated fiber core to obtain the strain jump degree of each candidate fiber core; The candidate fiber cores are organized into different triangle combinations, and the total jump degree of the fiber core strain in each combination is calculated; The fiber core combination with the smallest total strain jump is selected to reconstruct the fiber shape.

2. The shape sensing method based on the core combination optimization adaptive algorithm according to claim 1 is characterized in that: The specific steps of calculating the signal-to-noise ratio of the peripheral core measurement signal in the multi-core optical fiber include: Performing Fourier transform on the reference signal and the measurement signal of the multi-core fiber in the optical fiber shape sensor to obtain a distance domain signal; The signal-to-noise ratio of the measurement signal of each peripheral fiber core is calculated using the following formula: SNR i , i Number each outer fiber core: In the formula P si For the i The signal power of the signal in the root outer core distance domain, P ni For the i The noise power of the signal in the distance domain of the outer fiber core.

3. The shape sensing method based on the core combination optimization adaptive algorithm according to claim 2, characterized in that: The demodulation of the strain of each candidate fiber core is cross-correlation demodulation, which specifically includes the steps of: Step A-1: ​​Measure the signal of a candidate fiber core in its natural state as a reference signal I ri ; The signal of the same fiber core after the shape is applied is measured as the measurement signal I mi ,right I ri 、 I mi Perform Fourier transform to obtain the distance domain signal 、 ; Step A-2: convert the range domain signal 、 Divided into n segment, i.e. and ;right and Perform inverse Fourier transform and get I R1 、 I M1 ,right I R1 、 I M1 Perform cross-correlation to obtain the frequency drift curve of the measured signal relative to the reference signal C 1. Locate the peak of the frequency drift curve and obtain the frequency corresponding to the peak as the frequency drift magnitude of the first section of the fiber core. fs 1; Step A-3, and Perform inverse Fourier transform and get I R2 、 I M2 , and I R2 、 I M2 Perform cross-correlation to obtain the frequency drift curve of the measured signal relative to the reference signal C 2. Frequency drift corresponding to the previous peak fs Positioning is performed within a range near 1, which is , w is a set positive integer, f Frequency drift curve C 2 horizontal axis frequency interval, the frequency corresponding to the peak value located within this range is used as the frequency drift magnitude of the second segment of the fiber core fs 2; Step A-4, repeat the method of step A-3 to process the subsequent core segments in turn, and obtain the frequency drift size of each core segment, and finally obtain FS ={ fs 1, fs 2… fs n }, that is, the frequency drift of each position along the distance domain of the fiber core; Step A-5: Repeat steps A-1 to A-4 to calculate the frequency drift of each candidate fiber core along the distance domain, that is, FS i , i is the number of the candidate fiber core; Step A-6, FS i Divide by a constant , P e is the elastic-optical coefficient of the optical fiber, which is a constant of 0.

78. v 0 is the center frequency of the tuned laser, and the strain magnitude of each candidate fiber core along the distance domain is obtained .

4. The shape sensing method based on the core combination optimization adaptive algorithm according to claim 2, characterized in that: The strain demodulation of each candidate fiber core is phase demodulation, which specifically includes the steps of: Step B-1, calculate the phase of the distance domain signal after Fourier transform of the candidate core reference signal, calculate the phase of the distance domain signal after Fourier transform of the candidate core measurement signal, then make the difference between the two and unwrap them to obtain the unwrap differential phase , i is the number of the candidate fiber core; Step B-2: Assumptions There are a total of N data, find the q Data before W indivual The mean of the data M 1, and q After the data W indivual The mean of the data M 2. If Greater than or equal to the set threshold , then the q to N indivual Data subtraction ; Step B-3, loop through q= 1 to q=N , thus compensating All discontinuous segments in the Perform SG filtering; Step B-4, obtain the following for each candidate fiber core obtained after step B-3: Perform the difference to get The strain magnitude of each candidate fiber core along the distance domain is obtained by the following formula: : in is the theoretical spatial resolution, is the initial wavelength of the tuned laser, is the effective refractive index of the fiber core.

5. The shape sensing method based on the core combination optimization adaptive algorithm according to claim 3 or 4, characterized in that: Normalizing, differentiating, and accumulating squares of the strain of the demodulated fiber core to obtain the strain jump degree of each candidate fiber core includes: right Use the following formula to perform normalization: The normalized strain is treated by the following differential equation: right Square each value in and add them up to get S i , S i Used to measure the degree of jump in fiber core strain.

6. The shape sensing method based on the core combination optimization adaptive algorithm according to claim 5, characterized in that: The step of forming the candidate fiber cores into different triangle combinations and calculating the total jump degree of the fiber core strain in each combination includes: The candidate cores are combined into different triangle core combinations. Assuming there are g different triangle core combinations, calculate the total strain jump degree of each combination core. GS m ( m =1, 2 …g), that is: S m1 、 S m2 、 S m3 is the magnitude of the strain jump of the three fiber cores that make up the triangular fiber core combination, that is, the set { S m1 、 S m2 、 S m3 } is the set { S i }.

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