A method and system for reconstructing the shape of optical fibers

Abstract

This invention provides a method and system for optical fiber shape reconstruction, comprising: acquiring an optical fiber reference signal and an optical fiber measurement signal; processing the acquired optical fiber reference signal and optical fiber measurement signal respectively to obtain an optical fiber reference spectrum and an optical fiber measurement spectrum; based on the optical fiber reference spectrum and optical fiber measurement spectrum, using a preset deviation calculation algorithm to obtain a wavelength offset including noise; performing noise reduction processing on the wavelength offset using a wavelength offset denoising model to obtain denoised strain information; and reconstructing the optical fiber shape based on the strain information using a shape reconstruction algorithm. This invention provides a method and system for optical fiber shape reconstruction that can achieve good accuracy in reconstructing the three-dimensional shape of optical fibers while effectively improving the speed of three-dimensional shape reconstruction, thus helping to meet the real-time requirements of optical fiber three-dimensional shape reconstruction applications.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensing technology, and in particular to a method and system for reconstructing the shape of an optical fiber. Background Technology

[0002] Multi-core fiber shape sensing is a sensing method based on multi-core fiber technology. It is a technique that reconstructs the three-dimensional shape of an optical fiber by utilizing the strain distribution at different locations of multiple fiber cores. It is widely used in fields such as structural health monitoring and medical surgical robots.

[0003] In existing technologies, Optical Frequency Domain Reflectometry (OFDR) is a high-precision distributed fiber optic sensing technology commonly used in fiber optic 3D shape reconstruction. For fiber optic 3D shape reconstruction, both high accuracy and fast reconstruction speed are required. Existing OFDR-based fiber optic shape reconstruction methods cannot simultaneously meet these two requirements, especially under high-precision conditions, where the reconstruction speed is often slow, severely impacting the real-time performance and thus presenting numerous limitations in practical applications. Summary of the Invention

[0004] The purpose of this invention is to address the problem that existing fiber shape reconstruction methods based on OFDR systems cannot simultaneously achieve high shape reconstruction accuracy while maintaining real-time performance. This invention proposes a fiber shape reconstruction method and system that achieves fiber shape reconstruction with this method. The fiber shape reconstruction achieved by this method can not only reach the accuracy of traditional cross-correlation demodulation algorithms, but also greatly improve the demodulation speed, thereby realizing fast and accurate fiber shape reconstruction.

[0005] To achieve the above objectives, this invention proposes a fiber shape reconstruction method and system. The specific implementation method is as follows: first, the fiber shape information is collected using an OFDR shape sensing device; then, the strain data is processed using a deviation calculation and wavelength offset denoising model; and finally, the fiber shape is reconstructed using a shape reconstruction algorithm.

[0006] A method for reconstructing the shape of an optical fiber, the method comprising:

[0007] Acquire fiber reference signal when the fiber has no shape change, and acquire fiber measurement signal when the fiber has shape change;

[0008] The acquired fiber reference signal and the fiber measurement signal are processed separately to obtain the fiber reference spectrum and the fiber measurement spectrum;

[0009] Based on the fiber reference spectrum and the fiber measurement spectrum, a preset deviation calculation algorithm is used to obtain the wavelength offset containing noise information.

[0010] The wavelength offset is denoised using a pre-trained wavelength offset denoising model to obtain denoised strain information.

[0011] Based on the strain information, the shape of the optical fiber is reconstructed using a shape reconstruction algorithm.

[0012] In some embodiments, the acquired fiber reference signal and the fiber measurement signal are processed separately to obtain the fiber reference spectrum and the fiber measurement spectrum, specifically including:

[0013] The acquired fiber reference signal and the fiber measurement signal are processed by Fast Fourier Transform to obtain the reference time domain result and the measurement time domain result, respectively.

[0014] The reference time-domain result and the measurement time-domain result at the same location are selected by sliding a window;

[0015] The reference time-domain results and the measurement time-domain results at the same selected location are respectively converted to the wavelength domain by inverse Fourier transform to obtain the local reference spectrum and the local measurement spectrum.

[0016] Repeat the steps described above, selecting the reference time-domain results and the measurement time-domain results at the same location through a sliding window, to obtain the fiber reference spectrum by arranging all the obtained local reference spectra in chronological order, and to obtain the fiber measurement spectrum by arranging all the obtained local measurement spectra in chronological order.

[0017] In some embodiments, the formula for representing the fiber optic reference signal includes:

[0018]

[0019] The formula for representing the fiber optic measurement signal includes:

[0020]

[0021] Where σ represents the detector's responsivity, E0 represents the light intensity, and R... Z Let represent the backscattering Rayleigh coefficient at position Z on the multi-core optical fiber, γ represent the tuning rate of the light source, f0 represent the initial frequency of the light source, and τ represent the backscattering coefficient. z Φ represents transmission delay. z This represents the phase noise of the light source. This indicates the phase difference caused by strain.

[0022] In some embodiments, due to strain ε z The formulas for calculating the resulting phase difference include:

[0023] Δλ=λ c(1-P e )ε z ;

[0024] Among them, P e λ represents the photoelastic coefficient of the material in a multi-core optical fiber, c represents the speed of light in a vacuum, and λ represents the optical fiber material. c Indicates the center wavelength.

[0025] In some embodiments, the formula for representing the wavelength offset includes:

[0026] Δλ=λ c (1-P e )ε z ;

[0027] Where Δλ represents the wavelength offset, λ c P represents the center wavelength. e ε represents the photoelastic coefficient of the material in a multi-core optical fiber. z This indicates the strain experienced by the optical fiber.

[0028] In some embodiments, the establishment of the preset wavelength offset denoising model includes:

[0029] A sample set is obtained for pre-training of a neural network model. The sample set includes multiple training samples and validation samples. Both the training samples and the validation samples include noise data and strain data.

[0030] If the loss value obtained by the strain data output by the neural network model and the noise data tends to stabilize, then the wavelength offset denoising model is obtained.

[0031] The pre-trained wavelength offset denoising model is used to denoise the input data, which is strain data containing phase noise, and the output data is the strain data after noise removal.

[0032] The input data of the wavelength offset denoising model is represented by the following formula:

[0033]

[0034] Where α0 represents the strain amplitude, X1 represents a random signal below the fundamental frequency, α1 and α2 represent parameters used to adjust the variation of the strain signal, Γ0 represents the spin velocity of the helical fiber, and X2 represents Φ z The random frequency jitter exhibited after Fourier transform.

[0035] In some embodiments, the framework of the wavelength offset denoising model includes any one or more of DPDNet, MWCNN, RIDNet, Unet, and RFDNet;

[0036] The optical fiber corresponding to the optical fiber reference signal and the optical fiber measurement signal includes one or any combination of multi-core straight optical fiber, multi-core spiral optical fiber, multi-core weak grating array optical fiber, multi-core spiral weak grating array optical fiber, and optical fiber bundle.

[0037] The shape reconstruction algorithm includes any one of the following: arc length splicing method, Frenet frame, and minimum rotation frame.

[0038] In some embodiments, acquiring the fiber optic reference signal and the fiber optic measurement signal includes:

[0039] The main interferometer of the OFDR shape sensor is used to acquire signals when the optical fiber is in different shapes.

[0040] The absolute wavelength of the light output by the light source is obtained through the wavelength calibration module of the OFDR shape sensing device.

[0041] The phase change of the output light of the light source is obtained by the auxiliary interferometer of the OFDR shape sensing device;

[0042] The signal acquisition module of the OFDR shape sensing device acquires the signals of the optical fiber in different shapes, the absolute wavelength of the light output from the light source, and the phase change of the light output from the light source to obtain the optical fiber reference signal and the optical fiber measurement signal.

[0043] The light source of the OFDR shape sensing device is connected to the first fiber optic coupler through the first isolator. The first fiber optic coupler is connected to the main interferometer module and the auxiliary interferometer module respectively. The auxiliary interferometer is connected to the wavelength calibration module. The main interferometer module, the auxiliary interferometer module and the wavelength calibration module are respectively connected to the signal acquisition module.

[0044] In some embodiments, the auxiliary interferometer module includes: a second fiber coupler, a third fiber coupler, a second isolator, a first photodetector, a fiber delay line, a first Faraday rotator mirror, and a second Faraday rotator mirror, wherein the first photodetector is connected to the signal acquisition module;

[0045] The main interferometer module includes: a fourth fiber coupler, a circulator, an optical switch, a polarization beam splitter, a first balanced photodetector, a second balanced photodetector, and a sensing fiber. The first balanced photodetector and the second balanced photodetector are respectively connected to the signal acquisition module.

[0046] The wavelength calibration module includes: a hydrogen cyanide chamber and a second photodetector, wherein the hydrogen cyanide chamber is connected to the second photodetector, and the second photodetector is connected to the signal acquisition module;

[0047] The first fiber optic coupler is connected to the second fiber optic coupler and the fourth fiber optic coupler respectively; the second fiber optic coupler is connected to the hydrogen cyanide chamber and the second isolator respectively; the second isolator is connected to the third fiber optic coupler, the third fiber optic coupler is connected to the fiber delay line, the first Faraday rotator and the first photodetector respectively, and the fiber delay line is connected to the second Faraday rotator.

[0048] The fourth fiber coupler is connected to the circulator and the polarization beam splitter respectively. The circulator is connected to the first balanced photodetector and the optical switch respectively. The optical switch is connected to the sensing fiber. The polarization beam splitter is connected to the second photodetector.

[0049] An optical fiber shape reconstruction system for performing an optical fiber shape reconstruction method as described in any one of the first parts, comprising:

[0050] The signal acquisition section is used to acquire fiber optic reference signals and fiber optic measurement signals;

[0051] The spectrum generation section is used to process the acquired fiber reference signal and the fiber measurement signal respectively to obtain the fiber reference spectrum and the fiber measurement spectrum;

[0052] The deviation calculation section is used to obtain the wavelength offset including noise based on the optical fiber reference spectrum and the optical fiber measurement spectrum using a preset deviation calculation algorithm.

[0053] The denoising process is used to reduce the wavelength shift by using a pre-trained wavelength shift denoising model to obtain denoised strain information.

[0054] The shape reconstruction section is used to reconstruct the shape of the optical fiber based on the strain information using a shape reconstruction algorithm.

[0055] The beneficial effects of the present invention are as follows: Unlike the prior art, the fiber shape reconstruction method and system provided by the present invention can not only achieve good fiber three-dimensional shape reconstruction accuracy, but also effectively improve the fiber three-dimensional shape reconstruction speed, which helps to meet the real-time requirements of fiber three-dimensional shape reconstruction applications.

[0056] Specifically, a deviation calculation algorithm is used to obtain the wavelength offset caused by changes in fiber shape, significantly reducing the demodulation time and thus improving the speed of fiber 3D shape reconstruction. Based on the wavelength offset obtained through the deviation calculation algorithm, a preset wavelength offset denoising model is used to remove residual nonlinear tuning phase noise in the wavelength offset, reducing the impact of noise. This improves both demodulation speed and reconstruction accuracy, facilitating real-time fiber 3D shape reconstruction. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart of the fiber shape reconstruction method in this embodiment;

[0059] Figure 2 The flowchart for establishing the preset wavelength offset denoising model in this embodiment is shown.

[0060] Figure 3 A system diagram for fiber shape reconstruction in an embodiment;

[0061] Figure 4 This is a structural diagram of the OFDR shape sensing device in an embodiment;

[0062] Figure 5 This is a schematic diagram of the fiber shape reconstruction method steps in an embodiment;

[0063] Figure 6 The composition and denoising effect of the wavelength offset denoising model in the example;

[0064] Figure 7 A schematic diagram of the wavelength offset denoising model for this embodiment;

[0065] Figure 8 A schematic diagram of the fiber optic measurement spectrum and fiber optic reference spectrum for an embodiment;

[0066] Figure 9 This is a schematic diagram of the wavelength offset after the deviation calculation algorithm in the embodiment.

[0067] Figure 10 The image shows a comparison of the three-dimensional shape reconstruction effects of optical fibers in the example.

[0068] Figure label:

[0069] 1-Signal acquisition section; 2-Spectrum generation section; 3-Dispersion calculation section; 4-Noise reduction section; 5-Shape reconstruction section; 1a-Fiber optic cable; 11a-First fiber optic coupler; 2a-Auxiliary interferometer module; 21a-Second fiber optic coupler; 22a-Third fiber optic coupler; 23a-Second isolator; 24a-First photodetector; 25a-Fiber optic delay line; 26a-First Faraday rotator mirror; 27a-Second Faraday rotator mirror; 3a-Main interferometer module; 31a-Fourth fiber optic coupler; 32a-Circulator; 33a-Optical switch; 34a-Polarization beam splitter; 35a-First balanced photodetector; 36a-Second balanced photodetector; 37a-Sensing fiber optic cable; 4a-Wavelength calibration module; 41a-Hydrogen cyanide chamber; 42a-Second photodetector; 5a-Signal acquisition module; 6a-Light source; 61a-First isolator. Detailed Implementation

[0070] Various embodiments of this disclosure will be described more fully below. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0071] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0072] In various embodiments of this disclosure, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

[0073] The terms used in the various embodiments of this disclosure (such as "first," "second," etc.) may modify various components in the various embodiments, but do not limit the corresponding components. For example, the above terms do not limit the order and / or importance of the components. The above terms are only used for the purpose of distinguishing one component from others. For example, a first user device and a second user device refer to different user devices, although both are user devices. For example, a first component may be referred to as a second component without departing from the scope of the various embodiments of this disclosure, and similarly, a second component may also be referred to as a first component.

[0074] It should be noted that if a description is made of "connecting" one component to another, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component, it can be understood that there is no third component between the first and second components.

[0075] The term "user" as used in various embodiments of this disclosure may refer to a person using an electronic device or a device using an electronic device (e.g., an artificial intelligence electronic device).

[0076] The terminology used in the various embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the various embodiments of this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this disclosure pertain. Terms (such as those defined in a generally used dictionary) are to be interpreted as having the same meaning as in the context of the relevant technical field and are not to be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this disclosure.

[0077] Example 1

[0078] This application proposes a method for optical fiber shape reconstruction, such as... Figure 1 As shown, it includes:

[0079] S100, acquire fiber optic reference signal and fiber optic measurement signal;

[0080] Specifically, in some embodiments of this application, the reference signal is the optical signal generated when light passes through the multi-core optical fiber in a stress-free state, that is, when there is no shape change.

[0081] The fiber optic measurement signal is the optical signal generated when light passes through a multi-core fiber under stress.

[0082] S200 processes the acquired fiber reference signal and fiber measurement signal respectively to obtain the fiber reference spectrum and fiber measurement spectrum;

[0083] Specifically, in some embodiments of this application, such as Figure 8 As shown, the fiber reference spectrum is obtained after processing the fiber reference signal, and the fiber measurement spectrum is obtained after processing the fiber measurement signal. The spectrum refers to the optical signal related to the physical properties of the fiber, such as shape, stress, and temperature, by analyzing the propagation, scattering, or absorption of light in the fiber.

[0084] The S300, based on the fiber optic reference spectrum and the fiber optic measurement spectrum, uses a preset deviation calculation algorithm to obtain the wavelength offset containing noise information.

[0085] By using a deviation calculation algorithm, the difference between the fiber measurement signal at each time point and location in the fiber measurement spectrum and the corresponding fiber reference signal in the fiber reference spectrum can be measured. This allows for the acquisition of the difference value between the fiber measurement signal in the fiber measurement spectrum and the corresponding fiber reference signal in the fiber reference spectrum, thereby obtaining the wavelength offset and its changing trend. Specifically, this can be achieved as follows: Figure 9 As shown.

[0086] More specifically, in some embodiments of this application, the difference between the fiber measurement spectrum and the fiber reference spectrum can be obtained by a preset difference calculation algorithm. Of course, no specific difference calculation algorithm is limited here.

[0087] S400 uses a wavelength offset denoising model to denoise the wavelength offset and obtain the denoised strain information.

[0088] Specifically, in some embodiments of this application, strain information includes or relates to strain data output by a wavelength offset denoising model.

[0089] Understandably, the wavelength offset obtained by the deviation calculation algorithm still contains residual phase noise from the nonlinear tuning of the tunable laser. The presence of phase noise will affect the accuracy of the three-dimensional shape reconstruction of the optical fiber. Therefore, it is necessary to perform noise reduction processing on the wavelength offset to obtain the strain information of the optical fiber.

[0090] The pre-trained wavelength shift denoising model is a neural network model proposed to improve the accuracy and demodulation speed of shape sensing, taking into account the characteristics of fiber strain changes during fiber shape sensing.

[0091] The S500 uses a shape reconstruction algorithm to reconstruct the shape of optical fibers based on strain information.

[0092] Specifically, in some embodiments of this application, the shape of the optical fiber is reconstructed based on the demodulated strain information using a shape reconstruction algorithm.

[0093] Strain information specifically refers to the response or feedback of the propagation characteristics of light to the external stress on the optical fiber when light propagates in the optical fiber. These responses or feedback are sensed and collected by the optical fiber sensor to form a set of information.

[0094] Optionally, the propagation characteristics of light can be reflection, refraction, propagation speed or scattering, etc. Of course, there are no restrictions on the specific propagation characteristics of the light sensed by the fiber optic sensor.

[0095] Based on strain information, the location and degree of external stress on the optical fiber can be obtained, thereby enabling the reconstruction of the three-dimensional morphology of the optical fiber.

[0096] Furthermore, such as Figure 5 As shown, the acquired fiber optic reference signal and fiber optic measurement signal are processed separately to obtain the fiber optic reference spectrum and fiber optic measurement spectrum, specifically including:

[0097] The acquired fiber reference signal and fiber measurement signal are processed by Fast Fourier Transform to obtain the reference time domain result and the measurement time domain result, respectively.

[0098] Select the reference time-domain result and the measurement time-domain result at the same location using a sliding window;

[0099] The reference time-domain results and measurement time-domain results at the same selected location are subjected to inverse Fourier transform processing to obtain the local reference spectrum and the local measurement spectrum, respectively.

[0100] Repeat the steps described above, selecting the reference time-domain results and measurement time-domain results at the same location using a sliding window. Arrange all the obtained local reference spectra in chronological order to obtain the fiber reference spectrum, and arrange all the obtained local measurement spectra in chronological order to obtain the fiber measurement spectrum.

[0101] Specifically, in some embodiments of this application, the multi-core optical fiber includes multiple cores, and one or more cores for which strain needs to be solved are selected;

[0102] The entire signal of the optical fiber is divided into n segments by a sliding window. Understandably, the larger the value of n, the more detailed the division of the entire signal of the optical fiber, which helps to improve the accuracy of the three-dimensional morphology reconstruction of the optical fiber. The reference time domain results and measurement time domain results of each optical fiber signal segment are processed by inverse Fourier transform to obtain the local reference spectrum and the local measurement spectrum.

[0103] Furthermore, the formula for representing the fiber optic reference signal includes:

[0104]

[0105] The formulas for representing fiber optic measurement signals include:

[0106]

[0107] Where σ represents the detector's responsivity, E0 represents the light intensity, and R... Z Let γ represent the Rayleigh backscattering coefficient at position Z on the multi-core optical fiber, γ represent the tuning rate of the light source, f0 represent the initial frequency, and τ represent the frequency. z Indicates transmission delay, Φ z This represents the phase noise of the light source. This indicates the phase difference caused by strain.

[0108] The light source is a tunable light source (TLS);

[0109] The fiber reference signal and fiber measurement signal are calculated separately by means of detector responsivity, light propagation characteristics in fiber, and light source state. The light propagation characteristics in fiber mainly involve light intensity and scattering coefficient. By calculating the fiber reference signal and fiber measurement signal through these influencing factors, the strain information of the fiber can be more accurately reflected, thereby further improving the accuracy of fiber 3D morphology reconstruction.

[0110] Furthermore, due to strain ε z The formulas for calculating the resulting phase difference include:

[0111]

[0112] Among them, P e λ represents the photoelastic coefficient of the material in a multi-core optical fiber, c represents the speed of light in a vacuum, and λ represents the optical fiber material. c Indicates the center wavelength.

[0113] The phase difference caused by strain is affected by a variety of factors, including the center wavelength of the multi-core optical fiber, the photoelastic coefficient of the multi-core optical fiber material, and the speed of light propagation in a vacuum.

[0114] Among them, the phase difference is positively correlated with the refractive index of the multi-core optical fiber, and negatively correlated with the refractive index of the multi-core optical fiber and the wavelength of light in vacuum.

[0115] The phase difference calculation takes into full account a variety of related influencing factors, which helps to improve the accuracy of phase difference calculation and thus improve the accuracy of fiber optic three-dimensional morphology reconstruction.

[0116] Furthermore, the formula for expressing the wavelength offset includes:

[0117] Δλ=λ c (1-P e )ε z ;

[0118] Where Δλ represents the wavelength offset, λ c P represents the center wavelength. e ε represents the photoelastic coefficient of the material in a multi-core optical fiber. z This indicates the strain experienced by the optical fiber.

[0119] The wavelength offset between the fiber reference spectrum and the fiber measurement spectrum can be obtained through the deviation calculation algorithm, so as to compare the difference between the fiber reference spectrum and the fiber measurement spectrum. This can more clearly highlight the location of the fiber subjected to external stress and the trend of stress magnitude change, and improve the demodulation speed, which helps to improve the real-time performance of fiber 3D shape reconstruction.

[0120] Furthermore, such as Figure 2 As shown, the establishment of the preset wavelength offset denoising model includes:

[0121] S401, Obtain a sample set for pre-training of the neural network model. The sample set includes multiple training samples and validation samples. Both training samples and validation samples include noise data and strain data.

[0122] Specifically, in some embodiments of this application, the training samples are represented as simulated curves, and the data involved in the simulated curves include noise, strain data without phase noise, and strain data containing phase noise.

[0123] Among them, strain data refers to the signal generated when a multi-core optical fiber undergoes deformation, i.e., under external stress. In order to fully simulate the actual strain information in the experiment, the initial part of the optical fiber is not subjected to stress when it is shaping.

[0124] Specifically, strain data can also reflect the location and magnitude of stress on the optical fiber, and there are no restrictions on the specific strain data.

[0125] S402, if the loss value obtained by the preset target of the strain data and noise data output by the neural network model tends to stabilize, then the wavelength offset denoising model is obtained.

[0126] When the loss value calculated from the preset target of the strain data and noise data output by the neural network model tends to stabilize, it indicates that the wavelength offset denoising model training is complete.

[0127] S403, the pre-trained wavelength offset denoising model is used to denoise the input data, which is strain data containing phase noise, and the output data is the denoised strain data.

[0128] Furthermore, the formula for representing the input data is as follows:

[0129]

[0130] Where α0 represents the strain amplitude, X1 represents a random signal below the fundamental frequency, α1 and α2 represent parameters used to adjust the variation of the strain signal, Γ0 represents the spin velocity of the helical fiber, and X2 represents Φ z The random frequency jitter exhibited after Fourier transform; z represents the position coordinate.

[0131] Specifically, in some embodiments of this application, the mean square error between the actual output data and the preset output data in the training samples is defined as the loss. The wavelength offset denoising model is trained using a data sample set including 10,000 data samples, and the optimizer used is Adaptive Moment Estimation with a learning rate of 10⁻³. The Python version used for the wavelength offset denoising model is 3.8, the PyTorch version is 2.1.0, and the wavelength offset model is trained for 200 epochs using an NVIDIA RTX A5000 GPU.

[0132] Furthermore, the framework of the wavelength offset denoising model includes any one or more of DPDNet, MWCNN, RIDNet, Unet, and RFDNet;

[0133] The optical fibers corresponding to the optical fiber reference signal and the optical fiber measurement signal include one or any combination of multi-core straight optical fiber, multi-core spiral optical fiber, multi-core weak grating array optical fiber, multi-core spiral weak grating array optical fiber, and optical fiber bundle.

[0134] Shape reconstruction algorithms include any one of the following: arc length splicing method, Frenet frame, and minimum rotation frame.

[0135] Furthermore, no restrictions are placed on the architecture of the specific wavelength offset denoising model, the type of optical fiber, or the specific shape reconstruction algorithm.

[0136] Furthermore, in the wavelength offset denoising model, the encoder is responsible for extracting key features, while the decoder is used to recover data details and perform dimensionality reduction to remove redundant information and retain the core content of the data. In addition, the nonlinear transformation of the activation layer further enhances the network's ability to identify and suppress noise, enabling CNNs to exhibit superior performance and accuracy when processing complex spectral data. Combining OFDR spectral features with deep learning not only effectively removes noise and improves demodulation speed and accuracy, but also provides strong technical support for the automation and intelligent development of spectral analysis.

[0137] For example, such as Figure 6 and Figure 7 As shown, the wavelength offset denoising model, taking the Unet model architecture as an example, includes an encoder and a decoder;

[0138] The encoder's main task is feature extraction, capturing comprehensive spectral information from the deviation operation result (wavelength offset); the decoder's main role is feature fusion and dimension recovery; after upsampling, dimension concatenation, and convolution, the fused spectral features can be obtained, and the denoised wavelength offset result can be output.

[0139] More specifically, in some embodiments of this application, the wavelength offset denoising model includes an encoder and a decoder;

[0140] The encoder includes four encoder modules connected in sequence, each encoder module including a convolutional layer, a batch normalization layer and a pooling layer connected in sequence.

[0141] The decoder includes four decoder modules connected in sequence, each decoder module including a convolutional layer, a batch normalization layer and a sampling layer connected in sequence.

[0142] Furthermore, such as Figure 4 As shown, fiber optic reference signals and fiber optic measurement signals are acquired to improve the accuracy of acquiring fiber optic reference signals and fiber optic measurement signals, thereby improving the accuracy of fiber optic reference spectra and fiber optic measurement spectra. This facilitates the improvement of the accuracy of the wavelength offset calculated by the deviation operation in a fiber optic reconstruction method in this embodiment of the application, thereby improving the authenticity of fiber optic morphology reconstruction.

[0143] The OFDR shape sensing device includes: a light source 6a, an optical fiber 1a, a main interferometer module 3a, an auxiliary interferometer module 2a, a wavelength calibration module 4a, and a signal acquisition module 5a;

[0144] Acquiring fiber optic reference signals and fiber optic measurement signals, including:

[0145] The signals of optical fiber 1a in different shapes are obtained by the main interferometer 3a of the OFDR shape sensing device.

[0146] The absolute wavelength of the light output from the light source 6a is obtained through the wavelength calibration module 4a of the OFDR shape sensor.

[0147] The phase change of the output light of the light source 6a is obtained by the auxiliary interferometer 2a of the OFDR shape sensing device;

[0148] The signal acquisition module 5a of the OFDR shape sensing device acquires the signals of the optical fiber 1a in different shapes, the absolute wavelength of the output light of the light source 6a, and the phase change of the output light of the light source 6a to obtain the optical fiber reference signal and the optical fiber measurement signal.

[0149] The light source 6a is connected to the optical fiber 1a through the first isolator 61a. The optical fiber 1a is connected to the main interferometer module 3a and the auxiliary interferometer module 2a respectively. The auxiliary interferometer module 2a is connected to the wavelength calibration module 4a.

[0150] The main interferometer module 3a, the auxiliary interferometer module 2a, and the wavelength calibration module 4a are respectively connected to the signal acquisition module 5a.

[0151] Among them, light source 6a is a tunable laser source used to emit optical signals and provide stable optical signals for the optical fiber whose three-dimensional shape is to be reconstructed; optical fiber 1a is used to transmit optical signals from light source 6a; first isolator 61a connects light source 6a and first optical coupler 11a to reduce the interference of external environment on the signal stability of light source 6a, thereby improving measurement accuracy; main interferometer module 3a is used to acquire signals when the optical fiber is in different shapes, and auxiliary interferometer module 2a is used to acquire the phase change of the output light of the tunable laser; wavelength calibration module 4a is used to acquire the absolute wavelength of the output light of the light source, improving the accuracy and consistency of the wavelength of light source 6a.

[0152] Furthermore, such as Figure 4 As shown, optical fiber 1a includes: a first optical fiber coupler 11a, which is a 90:10 optical fiber coupler; the output end with a 90% optical signal output ratio is connected to the main interferometer module 3a; and the output end with a 10% optical signal output ratio is connected to the auxiliary interferometer module 2a.

[0153] The auxiliary interferometer module 2a includes: a second fiber coupler 21a, a third fiber coupler 22a, a second isolator 23a, a first photodetector 24a, a fiber delay line 25a, a first Faraday rotator mirror 26a, and a second Faraday rotator mirror 27a.

[0154] The second fiber coupler 21a and the third fiber coupler 22a are both 50:50 fiber couplers, and the fiber delay line 25a is 50 meters long.

[0155] The main interferometer module 3a includes: a fourth fiber coupler 31a, a circulator 32a, an optical switch 33a, a polarization beam splitter 34a, a first balanced photodetector 35a, a second balanced photodetector 36a, and a sensing fiber 37a.

[0156] The fourth fiber coupler 31a is a 90:10 fiber coupler. The output end with a 90% optical signal output ratio is connected to the circulator 32a, and the output end with a 10% optical signal output ratio is connected to the polarization beam splitter 34a.

[0157] The wavelength calibration module 4a includes: a hydrogen cyanide chamber 41a and a second photodetector 42a. The hydrogen cyanide chamber 41a is connected to the second photodetector 42a, and the second photodetector 42a is connected to the signal acquisition module 5a.

[0158] The first fiber optic coupler 11a is connected to the second fiber optic coupler 21a and the fourth fiber optic coupler 31a respectively; the second fiber optic coupler 21a is connected to the hydrogen cyanide chamber 41a and the second vibration isolator 23a respectively; the second vibration isolator 23a is connected to the third fiber optic coupler 22a; the third fiber optic coupler 22a is connected to the fiber optic delay line 25a, the first Faraday rotator mirror 26a and the first photodetector 24a respectively; the fiber optic delay line 25a is connected to the second Faraday rotator mirror 27a; and the first photodetector 24a is connected to the signal acquisition module 5a.

[0159] The beat frequency signal generated by the two beams of the third fiber coupler 22a is detected by the first photodetector 24a and used as an external clock for the data acquisition component to compensate for the effects of nonlinear tuning of the light source 6a.

[0160] The fourth fiber coupler 31a is connected to the circulator 32a and the polarization beam splitter 34a respectively. The circulator 32a is connected to the first balanced photodetector 35a and the optical switch 33a respectively. The optical switch 33a is connected to the sensing fiber 37a. The polarization beam splitter 34a is connected to the second photodetector 42a. The first balanced photodetector 35a and the second balanced photodetector 36a are connected to the signal acquisition module 5a respectively.

[0161] The main interferometer module 3a is divided into a reference arm and a measurement arm by a 10:90 fourth fiber coupler 31a. 90% of the light reaches the measurement arm and is transmitted to the sensing fiber 37a after passing through a circulator and an optical switch 33a. The backscattered Rayleigh signal from the sensing fiber 37a returns to the circulator 32a and the polarization beam splitter 34a. The polarization beam splitter 34a splits the light into s-beams and p-beams, which are collected by the first balanced photodetector 35a and the second balanced photodetector 36a, respectively. The four signals of the auxiliary interferometer 2a and the main interferometer 3a are all acquired by the four-channel signal acquisition component 5a.

[0162] No requirements are placed on the collection location or specific polarization direction of the s-ray and p-ray.

[0163] This invention proposes a fiber shape reconstruction method to reconstruct the three-dimensional shape of the fiber, and verifies the reconstruction effect. The experimental content and results are as follows:

[0164] A multi-core optical fiber with a length of 31.14 cm was wound around a cylinder with a radius of 5 cm. The spectral information of each fiber core was collected using an OFDR device. A standard group, a control group, and an experimental group were set up. The strain information was demodulated by cross-correlation calculation and the optical fiber shape reconstruction method of this application. Finally, based on the two sets of strain information obtained by processing, the shape of the optical fiber was reconstructed by the shape reconstruction algorithm.

[0165] The reconstruction result is as follows Figure 10 As shown, the dotted line is the curve of the cylindrical model, i.e., Figure 2; the dashed line is the reconstructed curve after cross-correlation operation, i.e., Figure 3; and the solid line is the reconstructed curve obtained after processing by the fiber shape reconstruction method of this application, i.e., Figure 1.

[0166] Depend on Figure 10 It can be seen that the overlap between dotted lines and solid lines is higher than that between dotted lines and dashed lines. Therefore, it can be seen that the preset difference calculation algorithm and wavelength offset denoising model in the fiber shape reconstruction method of this application are used to process and obtain strain information, which significantly improves the accuracy of fiber three-dimensional shape reconstruction.

[0167] In terms of demodulation speed, cross-correlation calculation takes 211.392605 seconds to process one strain data point, while fast deviation strain demodulation calculation takes 0.006045 seconds. The root mean square error of the Euclidean distance of the curve after cross-correlation calculation is 0.0935 cm, and the root mean square error of the Euclidean distance of the curve after fast deviation strain demodulation calculation is 0.0755 cm.

[0168] In summary, the fiber shape reconstruction method proposed in this invention utilizes a demodulation method based on deviation calculation and offset denoising model, which can effectively replace cross-correlation calculation, thereby reducing demodulation time and improving demodulation accuracy.

[0169] The embodiments of the present invention have at least the following beneficial effects:

[0170] This invention provides a fiber shape reconstruction method that can achieve good fiber 3D shape reconstruction accuracy and effectively improve fiber 3D shape reconstruction speed, thus helping to meet the real-time requirements of fiber 3D shape reconstruction applications.

[0171] Specifically, a pre-defined deviation calculation algorithm is used to obtain the wavelength offset caused by changes in fiber shape, significantly reducing the required demodulation time and thus improving the speed of fiber 3D shape reconstruction and enhancing real-time performance. Based on the wavelength offset obtained through the deviation calculation algorithm, a pre-defined wavelength offset denoising model is used to remove residual nonlinear tuning phase noise in the wavelength offset, reducing the impact of phase noise. This improves both demodulation speed and reconstruction accuracy, thereby facilitating real-time fiber 3D shape reconstruction.

[0172] Example 2

[0173] This invention provides an optical fiber shape reconstruction system for performing an optical fiber shape reconstruction method according to Embodiment 1, such as... Figure 3 As shown, it includes:

[0174] Signal acquisition section 1 is used to acquire fiber optic reference signals and fiber optic measurement signals;

[0175] The spectrum generation section 2 is used to process the acquired fiber reference signal and fiber measurement signal respectively to obtain the fiber reference spectrum and fiber measurement spectrum;

[0176] The deviation calculation section 3 is used to obtain the wavelength offset including noise based on the fiber reference spectrum and the fiber measurement spectrum using a preset deviation calculation algorithm.

[0177] The denoising processing section 4 is used to denoise the wavelength offset using a pre-trained wavelength offset denoising model to obtain denoised strain information.

[0178] The shape reconstruction section 5 is used to reconstruct the shape of the optical fiber based on strain information through a shape reconstruction algorithm.

[0179] The rest of the content is the same as or similar to that in Example 1, and will not be repeated here.

[0180] Those skilled in the art will understand that the modules in the apparatus of the implementation scenario can be distributed within the apparatus of the implementation scenario as described, or they can be located in one or more apparatuses different from this implementation scenario, with corresponding changes. The modules of the above-described implementation scenario can be combined into one module, or they can be further divided into multiple sub-modules.

[0181] The serial numbers of the present invention mentioned above are for descriptive purposes only and do not represent the superiority or inferiority of the implementation scenarios.

[0182] The above-disclosed examples are only a few specific implementation scenarios of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method of optical fiber shape reconfiguration, characterized by, The method comprises the following steps: acquiring a fiber reference signal and a fiber measurement signal; processing the acquired fiber reference signal and fiber measurement signal respectively to obtain a fiber reference spectrum and a fiber measurement spectrum; based on the fiber reference spectrum and the fiber measurement spectrum, using a preset dispersion operation algorithm to obtain a wavelength shift containing noise information; performing denoising processing on the wavelength shift through a wavelength shift denoising model to obtain denoised strain information; based on the strain information, reconstructing the fiber shape through a shape reconstruction algorithm; the establishment of the preset wavelength shift denoising model comprises: acquiring a sample set for neural network model pre-training, the sample set comprises a plurality of training samples and verification samples, and the training samples and the verification samples both comprise noise data and strain data; if the loss value of the strain data and the noise data output by the neural network model tends to be stable, the wavelength shift denoising model is obtained; the pre-trained wavelength shift denoising model is used for denoising of input data, the input data is strain data containing phase noise, and the output data is strain data after noise removal; the representation formula of the input data of the wavelength shift denoising model is as follows: ； wherein, represents the amplitude of the strain, X 1 represents a random signal below the base frequency, and represents a parameter for adjusting the variation of the strain signal, Γ 0 represents the spin rate of the helical fiber, z represents the position coordinate, represents the phase noise of the light source, X 2 represents Φ z the random frequency jitter embodied after the Fourier transform.

2. The method of claim 1, wherein, processing the acquired fiber reference signal and fiber measurement signal respectively to obtain a fiber reference spectrum and a fiber measurement spectrum, specifically comprising: performing fast Fourier transform processing on the acquired fiber reference signal and fiber measurement signal to obtain reference time domain results and measurement time domain results respectively; selecting the reference time domain results and the measurement time domain results at the same position through a sliding window; performing inverse Fourier transform conversion of the selected reference time domain results and measurement time domain results at the same position to the wavelength domain to obtain local reference spectrum and local measurement spectrum respectively; repeating the step of selecting the reference time domain results and the measurement time domain results at the same position through a sliding window to arrange all the local reference spectrum in order to obtain a fiber reference spectrum, and arrange all the local measurement spectrum in order to obtain a fiber measurement spectrum.

3. The method of claim 2, wherein, The representation formula of the fiber reference signal comprises: ； The representation formula of the fiber measurement signal comprises: ； wherein, denotes the responsivity of the probe, denotes the intensity of the light, denotes the backscattering Rayleigh coefficient at position Z on the multicore optical fiber, denotes the tuning rate of the light source, denotes the initial frequency of the light source, denotes the transmission delay, denotes the phase difference due to the strain.

4. The method of claim 3, wherein, Due to strain ε z The formula for calculating the phase difference including: ； wherein represents the photoelastic coefficient of the material of the multicore optical fiber, c represents the propagation speed of light in vacuum, λ c represents the center wavelength.

5. The method of claim 1, wherein, The representation formula of the wavelength shift comprises: ； where Δ λ represents a wavelength shift, λ c represents a center wavelength, represents a photoelastic coefficient of the material of the multicore optical fiber; ε z represents a strain to which the optical fiber is subjected.

6. The method of claim 1, wherein, The framework of the wavelength shift denoising model comprises any one or any multiple of DPDNet, MWCNN, RIDNet, Unet and RFDNet; The fiber corresponding to the fiber reference signal and the fiber measurement signal comprises one or any multiple of a multi-core straight fiber, a multi-core spiral fiber, a multi-core weak grating array fiber, a multi-core spiral weak grating array fiber, and a fiber bundle; The shape reconstruction algorithm comprises any one of arc length splicing method, Frenet frame and minimum rotation frame.

7. The fiber shape reconstruction method according to claim 1, wherein the acquisition of the fiber reference signal and the fiber measurement signal comprises: acquiring signals of the fiber in different shapes through a main interferometer of an OFDR shape sensing device; The absolute wavelength of the light source output light is obtained by a wavelength calibration module of the OFDR shape sensing device; The phase change of the light source output light is obtained by an auxiliary interferometer of the OFDR shape sensing device; The fiber reference signal and the fiber measurement signal are obtained by a signal acquisition module of the OFDR shape sensing device based on the signals of the fiber in different shapes, the absolute wavelength of the light source output light, and the phase change of the light source output light. The light source of the OFDR shape sensing device is connected to a first fiber coupler through a first isolator, and the first fiber coupler is connected to the main interferometer module and the auxiliary interferometer module respectively; the auxiliary interferometer is connected to the wavelength calibration module; and the main interferometer module, the auxiliary interferometer module, and the wavelength calibration module are connected to the signal acquisition module respectively.

8. The method of claim 7, wherein, The auxiliary interferometer module comprises a second fiber coupler, a third fiber coupler, a second isolator, a first photodetector, a fiber delay line, a first Faraday rotating mirror, and a second Faraday rotating mirror, and the first photodetector is connected to the signal acquisition module; The main interferometer module comprises a fourth fiber coupler, a circulator, an optical switch, a polarization beam splitter, a first balanced photodetector, a second balanced photodetector, and a sensing fiber, and the first balanced photodetector and the second balanced photodetector are connected to the signal acquisition module respectively; The wavelength calibration module comprises a hydrogen cyanide gas chamber and a second photodetector, the hydrogen cyanide gas chamber is connected to the second photodetector, and the second photodetector is connected to the signal acquisition module; The first fiber coupler is connected to the second fiber coupler and the fourth fiber coupler respectively; the second fiber coupler is connected to the hydrogen cyanide gas chamber and the second isolator respectively; the second isolator is connected to the third fiber coupler, and the third fiber coupler is connected to the fiber delay line, the first Faraday rotating mirror, and the first photodetector respectively, and the fiber delay line is connected to the second Faraday rotating mirror; The fourth fiber coupler is connected to the circulator and the polarization beam splitter respectively, the circulator is connected to the first balanced photodetector and the optical switch respectively, the optical switch is connected to the sensing fiber, and the polarization beam splitter is connected to the second photodetector.

9. A fiber shape reconfiguration system characterized by, A fiber shape reconstruction method for performing any one of claims 1 to 6, comprising: a signal acquisition part for acquiring a fiber reference signal and a fiber measurement signal; a spectrum generation part for processing the acquired fiber reference signal and fiber measurement signal respectively to obtain a fiber reference spectrum and a fiber measurement spectrum; a dispersion operation part for obtaining a wavelength offset containing noise based on the fiber reference spectrum and the fiber measurement spectrum by using a preset dispersion operation algorithm; a denoising processing part for denoising the wavelength offset by using a pre-trained wavelength offset denoising model to obtain denoised strain information; a shape reconstruction part for reconstructing the fiber shape based on the strain information by using a shape reconstruction algorithm. The preset wavelength offset denoising model comprises: obtaining a sample set for neural network model pre-training, wherein the sample set comprises a plurality of training samples and verification samples, and the training samples and the verification samples each comprise noise data and strain data; if a loss value obtained by a preset target of the strain data and the noise data output by the neural network model tends to be stable, the wavelength offset denoising model is obtained; the pre-trained wavelength offset denoising model is used for denoising input data, the input data is strain data containing phase noise, and output data is strain data after noise removal; a representation formula of the input data of the wavelength offset denoising model is as follows: ； wherein, represents the amplitude of the strain, X 1 represents a random signal below the base frequency, and represents a parameter for adjusting the variation of the strain signal, Γ 0 represents the spin rate of the helical fiber, z represents the position coordinate, represents the phase noise of the light source, X 2 represents Φ z the random frequency jitter embodied after the Fourier transform.

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