Fiber Bragg Grating Sensor Testing Method and System

Abstract

The present invention provides a fiber Bragg grating (FBG) sensor testing method and system that address the problem of temperature changes significantly affecting wavelength drift in actual operation, which can easily lead to inaccurate strain sensitivity measurements. The method includes: simultaneously injecting the same light source signal into a first fiber Bragg grating (FBG) sensor and a second fiber Bragg grating (FBG) sensor; using an imaging module to capture interference and speckle images of the initial reflected light from the first and second FBG sensors in an unloaded state; and, when the first FBG sensor is loaded, using the imaging module to capture interference and speckle images of the current reflected light from the first and second FBG sensors for comparative analysis, eliminating temperature-induced drift components and extracting pure strain components.

Description

Technical Field

[0001] The present application relates to the technical field of optical sensors, and in particular to a fiber grating sensor testing method and system. Background Art

[0002] Fiber Bragg grating sensor is a new type of optical sensing element with the advantages of high sensitivity, small size, strong anti-electromagnetic interference ability, and distributed deployment. It is widely used in structural health monitoring, aerospace, civil engineering, medical equipment and other fields.

[0003] Fiber Bragg grating (FBG) sensors are commonly used in strain measurement scenarios, and accurately determining the sensor's strain sensitivity coefficient (i.e., the wavelength drift caused by a unit strain) is a key component of sensor system calibration. Traditional methods typically perform a tensile test on a single FBG in a standard experimental environment and fit the strain sensitivity coefficient using a wavelength variation curve. However, in practice, temperature changes also significantly affect wavelength drift, which can easily lead to inaccurate strain sensitivity measurements. Summary of the Invention

[0004] The embodiments of the present application provide a fiber Bragg grating sensor testing method and system, which can solve the problem that in traditional applications, such as placing a fiber Bragg grating sensor in a bracelet to collect physiological signals, the sensor is limited by the wearing method and external environmental factors, resulting in problems such as signal instability and signal interference, which affect the accuracy and reliability of the monitoring data.

[0005] A first aspect of an embodiment of the present application provides a fiber Bragg grating sensor testing method, comprising:

[0006] The same light source signal is injected into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor at the same time, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain, and the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, which remains in a free state. The first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber Bragg grating sensor;

[0007] collecting interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state by the imaging module;

[0008] When the first fiber Bragg grating sensor is in a loaded state, the imaging module collects interference and speckle images of current reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor for comparative analysis, thereby eliminating drift components caused by temperature and extracting pure strain components.

[0009] Optionally, performing comparative analysis to eliminate drift components caused by temperature and extract pure strain components includes:

[0010] Comparative analysis was performed to eliminate the drift component caused by temperature, extract the pure strain component, and perform differential calculation to fit the strain sensitivity coefficient.

[0011] Optionally, also include:

[0012] Calculate the fitting residuals and eliminate obvious error points;

[0013] Output estimated confidence intervals using the Bayesian regression algorithm.

[0014] Optionally, the comparative analysis, elimination of the drift component caused by temperature, extraction of the pure strain component, and differential calculation to fit the strain sensitivity coefficient include:

[0015] Construct a local spline nonlinear interpolation kernel function;

[0016] The temperature response mapping function is fitted based on the observed data to obtain the strain wavelength change and fit the strain sensitivity coefficient.

[0017] Optionally, also include:

[0018] fitting a strain-induced image offset value through the speckle image, wherein the strain-induced image offset value includes speckle displacement and frequency;

[0019] Based on the strain-induced image offset value and the interference and speckle image of the current reflected light of the second fiber Bragg grating sensor, the wavelength drift of the first fiber Bragg grating sensor is decomposed into an image-induced component and a temperature drift function, so as to reversely construct a strain equivalent model and extract the pure strain component to fit the strain sensitivity coefficient.

[0020] Optionally, also include:

[0021] The two wavelength signals injected into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are modeled as cointegrated time series;

[0022] If the environmental thermal responses of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are not synchronized, a time lag term is introduced to perform time domain similarity compensation.

[0023] Optionally, also include:

[0024] If there is a stable linear combination of the cointegrated time series, the difference error is dynamically corrected through the correction coefficient.

[0025] A second aspect of an embodiment of the present application provides a fiber Bragg grating sensor testing device, comprising:

[0026] an injection unit, configured to simultaneously inject the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor, fixed on a controllable loading platform, and the controllable loading platform is configured to apply strain; the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, and the reference fiber Bragg grating sensor remains in a free state; the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment; and an imaging module is provided at one end of the second fiber Bragg grating sensor;

[0027] an initialization unit, configured to collect, through the imaging module, interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state;

[0028] The analyzing unit is configured to collect interference and speckle images of the current reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor through the imaging module when the first fiber Bragg grating sensor is in a loaded state, so as to perform comparative analysis, eliminate drift components caused by temperature, and extract pure strain components.

[0029] A third aspect of an embodiment of the present application provides an electronic system including a memory and a processor, wherein the processor is configured to implement the steps of the above-mentioned fiber Bragg grating sensor testing method when executing a computer program stored in the memory.

[0030] A fourth aspect of the embodiments of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned fiber Bragg grating sensor testing method are implemented.

[0031] In summary, the fiber Bragg grating sensor testing method provided by the embodiment of the present application is carried out by simultaneously injecting the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform, and the controllable loading platform is used to apply strain, and the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, which remains in a free state. The first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber Bragg grating sensor; the imaging module is used to collect the interference and speckle images of the initial reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state; and the imaging module is used to collect the interference and speckle images of the current reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in a loaded state for comparative analysis, thereby eliminating the drift component caused by temperature and extracting the pure strain component. Thus, the imaging system, fixed to the free end of the grating, accurately identifies temperature-induced speckle pattern shifts. Temperature drift is directional, continuous, and has distinct image features, making visual modeling superior to traditional numerical differentiation. Dual-channel cross-validation of spectral and image data makes this approach suitable for intelligent calibration scenarios. First, this approach addresses the difficulty in isolating temperature perturbations in traditional strain sensitivity testing. Because the reference grating remains free, its reflected wavelength shift strictly represents the temperature effect. The image acquisition system, fixed to its free end, can stably observe the speckle pattern response of the grating's reflective end to structural deformation caused by thermal changes. This image shift is directional, consistent, and predictable, ensuring high confidence in the temperature model. Compared to conventional temperature modeling methods using thermocouples or other temperature sensors, this method does not rely on external temperature measurement units and identifies temperature drift entirely through its own image data, offering the advantages of fast response, low error, and simple implementation. Second, the imaging system in this method provides a visual feedback mechanism for the strain sensitivity testing process. The image differences between the test and reference gratings are not only numerically analyzable, but also allow for the intuitive separation of strain and temperature images in image space. This enables a highly intuitive and digitized approach to inferring physical quantity changes from image differences, a capability that is crucial in practical engineering monitoring scenarios. Furthermore, this method possesses excellent engineering adaptability and automation potential. Because all sensors utilize the same light source and gratings from the same batch, wavelength consistency is high. The modular design of the imaging system allows for easy deployment on a portable test platform.

[0032] Correspondingly, the fiber Bragg grating sensor testing device, electronic system, and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1A schematic flow chart of a possible fiber Bragg grating sensor testing method provided in an embodiment of the present application;

[0034] Figure 2 A schematic structural block diagram of a possible fiber Bragg grating sensor testing device provided in an embodiment of the present application;

[0035] Figure 3 A schematic diagram of the hardware structure of a possible fiber Bragg grating sensor testing device provided in an embodiment of the present application;

[0036] Figure 4 A schematic structural block diagram of a possible electronic system provided in an embodiment of the present application;

[0037] Figure 5 A schematic structural block diagram of a possible computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The embodiments of the present application provide a fiber Bragg grating sensor testing method and system, which can solve the problem that temperature changes have a significant impact on wavelength drift in actual operation, which easily leads to inaccurate strain sensitivity measurement.

[0039] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0040] See also Figure 1 , which is a flow chart of a fiber Bragg grating sensor testing method provided in an embodiment of the present application, may specifically include: S110-S130.

[0041] S110, injecting the same light source signal into the first fiber grating sensor and the second fiber grating sensor at the same time, wherein the first fiber grating sensor is a test fiber grating sensor, which is fixed on a controllable loading platform, and the controllable loading platform is used to apply strain, and the second fiber grating sensor is a reference fiber grating sensor, which remains in a free state, and the first fiber grating sensor and the second fiber grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber grating sensor.

[0042] S120: Collect interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state through the imaging module.

[0043] S130, when the first fiber Bragg grating sensor is in a loaded state, collecting interference and speckle images of current reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor through the imaging module for comparative analysis, eliminating drift components caused by temperature, and extracting pure strain components.

[0044] It is understood that a set of identical fiber Bragg grating (FBG) sensors is set up, with one serving as a test grating mounted on a loading platform to receive mechanical strain, while the other serving as a reference grating remains free and unloaded, independently responding to ambient temperature changes. Simultaneously, by fixing an imaging module to the free end of the reference grating, long-term and stable acquisition of the speckle image or interference pattern at the grating's reflective end is achieved, thereby recording the structural changes induced by temperature changes. Since temperature changes in a free-state grating primarily manifest as micro-displacements of the end face along the axial direction and periodic structural adjustments, the changes in the image exhibit consistent directionality and predictable patterns, enabling high-resolution capture and modeling by a vision system. By comparing the images of the two gratings before and after loading with wavelength data and identifying the temperature-induced speckle pattern variations from the images, a temperature drift model can be constructed. This variation can then be eliminated from the actual wavelength data, accurately extracting the wavelength drift component caused by strain. Ultimately, the strain sensitivity coefficient can be inferred from the displacement-wavelength relationship, avoiding temperature interference and improving measurement accuracy.

[0045] For example, a broadband light source (such as an ASE light source) is connected to two fiber Bragg grating (FBG) sensors via a 1×2 fiber beam splitter. It is recommended that both FBGs be manufactured from the same batch, with identical reflection center wavelengths (e.g., 1550.00 nm), grating lengths (e.g., 10 mm), and spectral reflectance characteristics, to ensure initial consistency in their thermal and strain responses. The first grating (FBG1) serves as the test grating and is mounted between fixtures at either end of a high-precision linear loading platform. The loading platform features micron-level stepping control, enabling axial strain to be applied according to a predetermined loading program. The second grating (FBG2) serves as the reference grating. Its body is suspended in the air, unaffected by any mechanical forces. One end is connected to an imaging system (e.g., an industrial-grade high-resolution CCD camera and interferometer) via a dedicated connection fixture, while the other end remains free. This fixed connection ensures that temperature-induced changes in the grating structure primarily occur along the free end, resulting in a distinct and stable speckle pattern shift or interference fringe deformation from the imaging system's perspective. After the device structure is built, the initial data collection of the "unloaded state" is carried out first, that is, under stable room temperature conditions, the initial center wavelengths (λ 1,0 and λ 2,0 ), and obtain the initial image data I2(0) of FBG2 through the imaging module. This image is used as the temperature response reference template. At this stage, the reference image is preprocessed by image processing algorithms (such as template matching, image registration, Fourier transform, etc.) to extract data structures such as speckle pattern feature points and spatial frequency distribution to prepare for subsequent temperature modeling. Then enter the loading stage. The loading platform applies strain to FBG1 according to a predetermined step size (such as increasing by 0.5μm per step). After each loading step, the platform movement is paused for 1 to 2 seconds. After the wavelength stabilizes, the reflection wavelength (λ) of FBG1 and FBG2 is recorded respectively. 1,i ,λ 2,i ), and the imaging system simultaneously captures the current image I2(i) of FBG2. Since FBG2 remains in a free state, the change in its reflection wavelength during loading is only due to the ambient temperature drift. Therefore, it can be assumed that its image change is entirely caused by temperature change. The image template is used to perform registration and difference with the current image to calculate the image displacement Δ xT (i) or image grayscale difference index δI(i), thereby constructing a mathematical relationship model between temperature corresponding image change and wavelength change, such as: f T (i) = λ 2,0 +δλ img (i), where δλ img (i) can be obtained by calibration through the image change and the preset image-wavelength mapping function. By subtracting the temperature modeling function f from the real-time wavelength change λ1(i) of FBG1 T(i) By removing the temperature-induced drift, we can obtain the wavelength change caused by pure strain: Δλ ε (i)=λ1(i)-f T (i), combined with the actual displacement ΔL of the loading platform at each step i And the grating effective length L0, the strain value can be converted to εi=ΔLi / L0. Further fitting the wavelength change with the strain data, the strain sensitivity coefficient K is obtained. ε =Δλ ε Throughout the testing process, all image, wavelength, and displacement data are recorded in real time and a database is constructed. This data can not only be used to calibrate the current grating sensor but also provide training data for subsequent image recognition models and temperature drift prediction models, further improving the method's universality and adaptability.

[0046] In summary, the fiber Bragg grating sensor testing method provided in the above embodiment is performed by simultaneously injecting the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain, and the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, which remains in a free state. The first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber Bragg grating sensor. The imaging module collects interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state. The imaging module collects interference and speckle images of current reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in a loaded state for comparative analysis, thereby eliminating temperature-induced drift components and extracting pure strain components. Thus, the imaging system, fixed to the free end of the grating, accurately identifies temperature-induced speckle pattern shifts. Temperature drift is directional, continuous, and has distinct image features, making visual modeling superior to traditional numerical differentiation. Dual-channel cross-validation of spectral and image data makes this approach suitable for intelligent calibration scenarios. First, this approach addresses the difficulty in isolating temperature perturbations in traditional strain sensitivity testing. Because the reference grating remains free, its reflected wavelength shift strictly represents the temperature effect. The image acquisition system, fixed to its free end, can stably observe the speckle pattern response of the grating's reflective end to structural deformation caused by thermal changes. This image shift is directional, consistent, and predictable, ensuring high confidence in the temperature model. Compared to conventional temperature modeling methods using thermocouples or other temperature sensors, this method does not rely on external temperature measurement units and identifies temperature drift entirely through its own image data, offering the advantages of fast response, low error, and simple implementation. Second, the imaging system in this method provides a visual feedback mechanism for the strain sensitivity testing process. The image differences between the test and reference gratings are not only numerically analyzable, but also allow for the intuitive separation of strain and temperature images in image space. This enables a highly intuitive and digitized approach to inferring physical quantity changes from image differences, a capability that is crucial in practical engineering monitoring scenarios. Furthermore, this method possesses excellent engineering adaptability and automation potential. Because all sensors utilize the same light source and gratings from the same batch, wavelength consistency is high. The modular design of the imaging system allows for easy deployment on a portable test platform.

[0047] In one embodiment, performing comparative analysis to eliminate temperature-induced drift components and extract pure strain components includes:

[0048] Comparative analysis was performed to eliminate the drift component caused by temperature, extract the pure strain component, and perform differential calculation to fit the strain sensitivity coefficient.

[0049] For example, during the loading test of the fiber Bragg grating sensor, in order to effectively eliminate the reflection wavelength drift caused by temperature, thereby accurately extracting the pure strain component and fitting the strain sensitivity coefficient, the system first records the reflection wavelengths of the first fiber Bragg grating sensor (test grating) and the second fiber Bragg grating sensor (reference grating) in the initial state before loading, and records them as λ 1,0 and λ 2,0 After loading begins, the system synchronously collects the current wavelength value λ1(t i ) and λ2(t i ), and simultaneously record the displacement ΔL of the loading platform corresponding to the first grating i Since the first fiber Bragg grating is affected by strain and ambient temperature at the same time, its wavelength change Δλ1(i)=λ1(i)-λ 1,0 Contains the drift component Δλ caused by strain ε (i) and the temperature-induced drift component Δλ T (i). The second fiber Bragg grating is in a free state and is only affected by temperature, so its wavelength change Δλ2(i)=λ2(i)-λ 2,0 It can be approximately equal to the temperature drift term, that is: Δλ T (i)≈Δλ2(i). Therefore, by performing differential calculation on the wavelength changes of the two gratings, the temperature effect can be eliminated and the wavelength drift caused by strain can be extracted: Δλ ε (i)=Δλ1(i)-Δλ2(i)=[λ1(i)-λ 1,0 ]-[λ2(i)-λ 2,0 ], after obtaining this value, the displacement of the loading platform is converted into the actual strain ε i =ΔL i / L0, where L0 is the effective working length of the grating (e.g. 10mm). ε (i) and the corresponding strain value ε i Establish a functional relationship and use linear regression fitting to find the slope, which is the strain sensitivity coefficient of the fiber Bragg grating under the current environmental conditions: Kε=d(Δλ ε ) / dε.

[0050] For example, the total load can be set from 0 to 1000 με in 20 steps of 50 με each. Assuming a total wavelength drift of 1.20 nm and a 0.03 nm drift in the reference grating (FBG2) due to temperature, if the temperature effect is not removed, the total test grating drift is 1.23 nm, resulting in a fitted sensitivity of 1.23 pm / με, with an error of approximately 2.5%. However, after removing the temperature term through differential analysis, only the pure strain response of 1.20 nm is retained, resulting in an accurate sensitivity coefficient of 1.20 pm / με, validating the effectiveness of this differential analysis method. This approach eliminates the need for an external temperature sensor or complex temperature control system. Instead, it utilizes a structurally matched dual-FBG system, synchronously acquiring wavelength data under the same environment, and obtaining a pure strain response through direct differential analysis. This method offers simple computation, high accuracy, and strong anti-interference capabilities, making it particularly suitable for rapid strain sensitivity calibration on-site and for environmental compensation modeling of long-term online sensors. Furthermore, by establishing a mapping relationship between temperature changes and image feature changes in conjunction with an imaging module, the accuracy of temperature response recognition can be further enhanced, improving the reliability of the overall modeling. In this embodiment, wavelength differential analysis can serve as benchmark data for image-assisted modeling, forming a spectral-image collaborative working mechanism suitable for multi-channel, high-precision, and high-stability fiber optic sensing applications.

[0051] In one embodiment, it further includes:

[0052] Calculate the fitting residuals and eliminate obvious error points;

[0053] Output estimated confidence intervals using the Bayesian regression algorithm.

[0054] According to some embodiments, performing comparative analysis, eliminating drift components caused by temperature, extracting pure strain components, and performing differential calculation to fit the strain sensitivity coefficient includes:

[0055] Construct a local spline nonlinear interpolation kernel function;

[0056] The temperature response mapping function is fitted based on the observed data to obtain the strain wavelength change and fit the strain sensitivity coefficient.

[0057] It is understandable that in the traditional dual fiber Bragg grating differential method, it is assumed that the temperature response wavelength change of the test grating is exactly the same as that of the reference grating, so the temperature interference can be eliminated by simple subtraction: Δλ ε(t) = λ1(t) - λ2(t), but this idealized premise is often not true in actual measurements. Even if the test grating (FBG1) and the reference grating (FBG2) are made of exactly the same materials, wavelengths, and manufacturing processes, they may still cause slight temperature response deviations in actual environments due to factors such as the installation position, thermal contact surface, bending state of the optical fiber port, and uneven local temperature distribution. This non-uniform temperature drift response, if not modeled, will be directly transmitted to the sensitivity coefficient fitting process, causing systematic errors. In order to solve this problem, this embodiment no longer simply uses linear differential processing of λ1(t)-λ2(t), but uses the reference grating wavelength change trend λ2(t) to fit its corresponding temperature response model fTf_TfT, and then eliminates it as a temperature drift term. The model is constructed using a local spline nonlinear interpolation kernel function. It does not assume that the temperature drift is linear, can handle the hysteresis, mutation and non-uniformity of the temperature response, and has good fitting accuracy and numerical stability. Therefore, the wavelength change of the test grating is remodeled as: λ1(t) = λ1 ε (t)+f T (λ2(t),t,θ), where λ1 ε (t) is the wavelength component caused by strain; f T (·) is the temperature response mapping function fitted by the reference grating wavelength change; θ is the coefficient of the interpolation function or the control point parameter. Finally, the pure strain wavelength drift is obtained by the following formula: Δλ ε (t)=λ1(t)-f T (λ2(t), t, θ), and combined with the strain ε(t) converted from the loading displacement, the strain sensitivity coefficient K is fitted by linear or nonlinear regression. ε .

[0058] For example, data acquisition and loading preparation are first initialized. A broadband light source can be connected to the test grating (FBG1) and the reference grating (FBG2) simultaneously through a 1×2 fiber coupler so that the two gratings share the same incident light signal. FBG1 is installed between the electric loading platform, which has a micron-level resolution; one end of FBG2 is fixedly connected to the imaging device, and the other end is kept free and not disturbed by mechanical forces. The optical spectrum demodulator is started to collect the time series data of the reflection wavelength of the two gratings, which are recorded as λ1(t) and λ2(t) respectively. The displacement data of the loading platform is recorded synchronously, and the strain value ε(t) = ΔL(t) / L0 is calculated, where L0 is the effective length of the grating. Then, a spline model of the temperature response of the reference grating is constructed. During the loading process, the wavelength change λ2(t) of the reference grating can be regarded as the observed variable of the temperature response. In order to fit its actual drift trend at different time points or under different temperature conditions, a local spline interpolation is used to construct the temperature response function f T . This function can be expressed as: Among them, B i (·) is the B-spline basis function c i is the weight coefficient of the interpolation node. The node position is set according to the sampling density of λ2(t) and has local support. If time variation and delayed response factors are considered, it can also be expanded to a two-dimensional interpolation model: The fitting process can use the least squares method or penalty spline optimization solution to make the model have a certain smoothness and stability while minimizing the fitting error of the reference wavelength change. Using this temperature response mapping function, the influence of temperature drift is eliminated from the original wavelength data of the test grating, and the pure strain wavelength change is obtained: Δλ ε (t)=λ1(t)-f T (λ2(t),t) calculates the strain sequence ε(t) according to the step information of the loading platform and establishes the data point pair (ε(t i ),Δλ ε (t i )), and then linear regression is used for fitting to obtain the strain sensitivity coefficient K ε :Δλε(t)=K ε ε(t)+∈(t), where ∈(t) is the residual term used to evaluate model accuracy. For a specific experiment, assume the loading platform has 20 steps, each with a displacement of 0.5 μm, for a total applied strain of 1000 με. The test grating wavelength changes from 1550.000 nm to 1551.230 nm, while the reference grating wavelength increases from 1550.000 nm to 1550.030 nm, showing a clear effect of the temperature increase. If the simple differencing method λ1-λ2 is used directly, the strain sensitivity is: K error = 1.230nm / 1000με = 1.23pm / με. However, after fitting the temperature response model using the local spline kernel function, the true temperature drift is identified to be 0.0335nm. After eliminating this value, the strain drift is 1.1965nm, resulting in the final true sensitivity coefficient: Kε = 1.1965pm / με, which is only 0.29% different from the standard value of 1.20pm / με, significantly better than the traditional differencing method. Through the optimization method of constructing a temperature response mapping model based on a local spline nonlinear interpolation function described in this embodiment, the system no longer relies on the assumption of "completely consistent temperature response" when performing dual fiber Bragg grating comparison analysis, but instead establishes a data-driven, fittable, and controllable temperature drift correction function. This approach not only overcomes measurement errors caused by factors such as varying thermal inertia and thermal coupling, but also, due to its use of a local spline kernel function, provides excellent modeling capabilities for nonlinear drift and discontinuous changes, achieving both global stability and local sensitivity. In engineering practice, this method has significantly improved the accuracy and reliability of calibrating the strain sensitivity coefficients of fiber Bragg grating sensors, making it particularly suitable for scenarios with rapidly changing ambient temperatures, uneven heat conduction, and complex deployment environments.

[0059] In one embodiment, it further includes:

[0060] fitting a strain-induced image offset value through the speckle image, wherein the strain-induced image offset value includes speckle displacement and frequency;

[0061] Based on the strain-induced image offset value and the interference and speckle image of the current reflected light of the second fiber Bragg grating sensor, the wavelength drift of the first fiber Bragg grating sensor is decomposed into an image-induced component and a temperature drift function, so as to reversely construct a strain equivalent model and extract the pure strain component to fit the strain sensitivity coefficient.

[0062] It is understandable that the coupled nature of the causes of wavelength drift in fiber Bragg grating sensors allows for an innovative approach to collaboratively decompose fiber Bragg grating reflection wavelength variations by combining image information with wavelength data. Traditional methods rely primarily on reflected wavelength variation signals to determine strain. However, since temperature also causes changes in the grating period (thermal expansion and thermally induced refractive index changes), the effects of strain and temperature on wavelength are coupled and indistinguishable. This method, however, incorporates speckle images into the analysis process. By using local displacement and frequency variations in the image, a functional relationship is established between optical image changes and strain, enabling independent modeling of the strain component.

[0063] For example, the fiber Bragg grating wavelength drift Δλ1(t) can be decomposed into two parts: the image induced drift component Φ(I t ), obtained by modeling the spatial frequency shift / fringe displacement caused by strain in the speckle image of FBG1; the temperature drift function f T (λ2(t)), which is obtained by modeling the wavelength change of the reference grating and the stable drift law of the speckle image. Through image analysis techniques (such as image registration, spatial frequency domain transformation, speckle frequency spectrum estimation, etc.), the key change features of the speckle pattern of FBG1 during loading are extracted, such as spatial speckle displacement (sub-pixel level movement), local frequency change (reflecting the change in optical path difference caused by strain) or contrast change (related to the interference fringe modulation degree). The image features are mapped to the strain-induced wavelength change function Φ(I t ), combined with the temperature response model f T (λ2(t)), complete the following wavelength drift decomposition: λ1(t)=Φ(I t )+f T (λ2(t)), and thus by reverse deduction: Δλ ε (t)=λ1(t)-f T (λ2(t))≈Φ(I t ), that is, under the premise of knowing the temperature response and image response, the system can extract the strain-related terms from the total wavelength drift of FBG1 and construct a high-precision, image-assisted strain sensitivity fitting model.

[0064] For example, first, the experimental system is set up as before: a broadband light source is injected into the test grating FBG1 and the reference grating FBG2 simultaneously through a beam splitter; FBG1 is installed on the loading platform and loaded in a controlled manner, FBG2 remains in a free state, and its reflective end is fixedly connected to the imaging module. A CCD industrial camera cooperates with a collimating lens and an interference illumination system to collect the FBG2 speckle image sequence, and the reflection wavelengths of FBG1 and FBG2 are recorded by a spectrometer. The image acquisition frame rate should be no less than 30fps to capture the evolution of speckle during dynamic strain; the spatial resolution requires that sub-pixel level changes can be extracted (such as through sub-pixel template matching or phase recovery technology). Image feature extraction and speckle displacement analysis are then performed, and each frame of image I can be synchronously acquired during the gradual loading of the loading platform. t , and perform rigid or non-rigid registration between the current image and the initial state image I0 to extract the image translation Δx t , i.e. “speckle displacement”; local Fourier transform or wavelet analysis is used to extract the dominant frequency change δf in the image t , corresponding to a slight change in the interference fringe period; extracting the local grayscale intensity change curve is used to judge the change in the interference fringe modulation degree as a stability indicator of the displacement. The speckle displacement and frequency data are combined into the image induced strain feature vector: F(t) = [Δx t ,δf t ,δI t ], through the preset empirical model or data-driven learning function Φ(·), the image feature vector is mapped to the wavelength change prediction value: Φ(I t )=W1·Δx t +W2·δf t +W3·δI t +b, where W1, W2, and W3 are parameters to be fitted, which can also be obtained using regression algorithms or shallow neural network training. A temperature drift function is constructed and wavelength decomposed. For reference, when grating FBG2 is not subjected to force during loading, its wavelength change λ2(t) can be used as the temperature response feature input. Combined with its image change (usually a stable, unidirectional speckle shift), a temperature drift function is established: Here B i (·) is the spline basis function, c i The function mapping of λ2(t) and temperature ΔT(t) can also be fitted based on historical data. The image induced component Φ(I t ) and the temperature drift function f T After (λ2(t)), the total wavelength change of FBG1 can be decomposed:

[0065] λ1(t)=Φ(It)+f T (λ2(t)), calculate the difference: Δλε (t)=λ1(t)-f T (λ2(t))≈Φ(I t ),

[0066] Thus, at each loading step, the pure strain component is extracted by stripping the temperature effect with the help of the image and the reference wavelength. Then the strain sensitivity coefficient is fitted, and finally, the Δλ extracted above is converted to ε (t) is paired with the strain value ε(t) calculated by the loading platform to construct the strain-wavelength change point set (ε(t i ),Δλ ε (t i )), linear least squares method or fitting algorithm is used to fit the strain sensitivity coefficient: Kε=d(Δλε) / dε. If the fitting accuracy of the image induced term is high enough, the sensitivity coefficient can be controlled within 1% of the standard error. This method introduces collaborative analysis of image channels and wavelength channels, breaking through the limitations of traditional single wavelength change analysis, making the temperature and strain effects visually separable and the function fittable, thereby improving the accuracy of component stripping. By utilizing subtle texture changes in the image (such as interference fringe offset and frequency drift), the system can accurately identify the image pattern corresponding to the strain when the temperature drift is complex or cannot be linearly modeled, thereby realizing visual prediction of the strain. The image feature modeling process can use shallow learning algorithms, linear regression or fitting functions based on historical data. It has good system scalability and is suitable for calibration scenarios of different working conditions and different types of fiber Bragg gratings. In practical applications, the image-assisted strain recognition model constructed by this method can significantly improve the measurement accuracy in temperature fluctuation scenarios compared with traditional methods, and is particularly suitable for high-precision structural health monitoring systems.

[0067] In one embodiment, it further includes:

[0068] The two wavelength signals injected into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are modeled as cointegrated time series;

[0069] If the environmental thermal responses of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are not synchronized, a time lag term is introduced to perform time domain similarity compensation.

[0070] In one embodiment, it further includes:

[0071] If there is a stable linear combination of the cointegrated time series, the difference error is dynamically corrected through the correction coefficient.

[0072] It is understandable that when using two fiber Bragg grating sensors (FBG1 as the test grating and FBG2 as the reference grating) for differential temperature compensation, it is usually assumed that the two are in the same thermal environment and have the same thermal response. Therefore, the difference can be simply expressed as: Δλ ε(t) = λ1(t) - λ2(t) to eliminate temperature drift. However, in actual engineering environments, this assumption often fails. The main reasons include different installation locations of FBG1 and FBG2, thermal lags, or differences in thermal inertia due to different materials, coatings, and mounting methods, or inconsistent heat conduction paths or radiation environments. Therefore, there is often slight asynchrony or amplitude discrepancy between the temperature responses of the two. Direct differentiation would introduce systematic errors. A time-domain similarity compensation mechanism is introduced to eliminate temperature drift through differential cointegration modeling and a time-synchronized offset model. This is an advanced temperature compensation strategy based on dynamic data correlation analysis and is particularly suitable for practical applications with rapid temperature changes, inconsistent thermal responses, and significant sensor dynamic drift. Through time series cointegration modeling and a time-lag compensation mechanism, the time correlation and phase lag differences between the two are identified from the dynamic time evolution of the wavelength signal. The compensation relationship is dynamically adjusted to construct a more robust and physically accurate temperature drift elimination model.

[0073] For example, wavelength time series data can be collected. After the system is set up, the test program can be started to collect the reflection wavelength series data of the test grating FBG1 and the reference grating FBG2, which are recorded as: λ1(t): test grating wavelength time series; λ2(t): reference grating wavelength time series. The sampling frequency is recommended to be no less than 10Hz, and the time length should at least cover the entire loading cycle to ensure sufficient data to support modeling. A cointegration model is established to eliminate amplitude difference errors. Ideally, if the temperature responses of FBG1 and FBG2 are highly correlated, there should be some kind of stable linear combination between the two. We try to establish the following cointegration relationship: λ1(t)=βλ2(t)+Δλ ε (t)+∈(t), that is, the wavelength change of FBG1 is a scaling (amplification or reduction) of the reference grating wavelength change plus the strain response component and error. Taking β as the linear scaling coefficient, the corresponding physical meaning is the coefficient difference between the temperature response amplitudes of FBG1 and FBG2. The optimal β can be estimated by minimizing the residual ∈(t). The estimation method uses the least squares method, cointegration test method or local window weighted regression. After obtaining β, a new compensation model is constructed: Δλ ε (t) = λ1(t) - βλ2(t). Compared with the traditional direct difference method (i.e., β = 1), this model is more adaptable to the difference in thermal response amplitude, thereby improving the compensation accuracy. A time synchronization offset model can be introduced to eliminate the time lag error. If there is a time delay in the thermal response of the two gratings (for example, the heating / cooling response of FBG1 is slower than that of FBG2), then the wavelength response of the two will have a lag τ in the time series. In this case, the optimized model can be: Δλ ε(t) = λ1(t) - βλ2(t-τ). To estimate the optimal τ, the following method can be used: cross-correlation function analysis, calculate the cross-correlation function R(τ) of λ1(t) and λ2(t-τ), and take the τ corresponding to the maximum value * As the optimal lag estimate; sliding window matching, slide λ2(t) within a window forward or backward, compare the fitting residual of λ1(t), and the minimum residual point is the optimal τ; minimize the modeling residual method, traverse the τ value, so that the sum of the squares of the residuals of λ1(t)-βλ2(t-τ) is minimized. Finally, the wavelength drift is decomposed into: λ1(t)=βλ2(t-τ)+Δλ ε (t)+∈(t), thus accurately extracting the strain-related drift: Δλ ε (t)=λ1(t)-βλ2(t-τ). Finally, the strain sensitivity coefficient is fitted, and the loading platform displacement ΔL(t) is converted into strain ε(t)=ΔL(t) / L0, which is then compared with the above Δλ ε (t) Establish fitting relationship in pairs: Δλ ε (t) = K ε ·ε(t)+∈′(t), linear regression or robust regression method can be used to solve K ε , which is the strain sensitivity coefficient after the temperature interference is finally eliminated. Therefore, compared with the traditional differential method that directly assumes that the temperature response is consistent, this method accurately compensates for the inconsistency of thermal response on a data-driven basis by dynamically adjusting the proportional factor β, thereby more realistically restoring the actual impact of temperature drift. Environmental factors such as thermal inertia differences, heat absorption of the shell, and air-cooling convection often cause the temperature responses of the reference grating and the test grating to be out of sync in time. By introducing the time lag τ, this method significantly improves the time accuracy of temperature drift stripping and avoids mistaking the temperature lag for strain changes. In scenarios where the temperature changes rapidly, the loading is frequent, and the dynamic drift of the sensor is severe, this method can still maintain compensation capabilities and is not affected by differences in physical installation methods. It is particularly suitable for field testing, high-frequency monitoring, or high-temperature experimental environments. β and τ can be automatically adjusted through self-learning or online optimization without manual setting, making them suitable for integration with embedded processing and intelligent sensing platforms.

[0074] See also Figure 2 An embodiment of the fiber Bragg grating sensor testing device in the embodiment of the present application may include:

[0075] The injection unit 201 is configured to simultaneously inject the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor fixed on a controllable loading platform for applying strain, and the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, which remains in a free state. The first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber Bragg grating sensor.

[0076] an initialization unit 202 configured to collect, through the imaging module, interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state;

[0077] The analyzing unit 203 is configured to collect, through the imaging module, interference and speckle images of the current reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor when the first fiber Bragg grating sensor is in a loaded state, for comparative analysis, to eliminate drift components caused by temperature and extract pure strain components.

[0078] In summary, the fiber Bragg grating sensor testing device provided in the above embodiment simultaneously injects the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor fixed on a controllable loading platform for applying strain, the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, and the reference fiber Bragg grating sensor remains in a free state. The first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber Bragg grating sensor. The imaging module collects interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state. The imaging module collects interference and speckle images of current reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in a loaded state for comparative analysis, thereby eliminating temperature-induced drift components and extracting pure strain components. Thus, the imaging system, fixed to the free end of the grating, accurately identifies temperature-induced speckle pattern shifts. Temperature drift is directional, continuous, and has distinct image features, making visual modeling superior to traditional numerical differentiation. Dual-channel cross-validation of spectral and image data makes this approach suitable for intelligent calibration scenarios. First, this approach addresses the difficulty in isolating temperature perturbations in traditional strain sensitivity testing. Because the reference grating remains free, its reflected wavelength shift strictly represents the temperature effect. The image acquisition system, fixed to its free end, can stably observe the speckle pattern response of the grating's reflective end to structural deformation caused by thermal changes. This image shift is directional, consistent, and predictable, ensuring high confidence in the temperature model. Compared to conventional temperature modeling methods using thermocouples or other temperature sensors, this method does not rely on external temperature measurement units and identifies temperature drift entirely through its own image data, offering the advantages of fast response, low error, and simple implementation. Second, the imaging system in this method provides a visual feedback mechanism for the strain sensitivity testing process. The image differences between the test and reference gratings are not only numerically analyzable, but also allow for the intuitive separation of strain and temperature images in image space. This enables a highly intuitive and digitized approach to inferring physical quantity changes from image differences, a capability that is crucial in practical engineering monitoring scenarios. Furthermore, this method possesses excellent engineering adaptability and automation potential. Because all sensors utilize the same light source and gratings from the same batch, wavelength consistency is high. The modular design of the imaging system allows for easy deployment on a portable test platform.

[0079] above Figure 2 The fiber Bragg grating sensor test device in the embodiment of the present application is described from the perspective of modular functional entities. The fiber Bragg grating sensor test device in the embodiment of the present application is described in detail from the perspective of hardware processing. Figure 3An embodiment of a fiber Bragg grating sensor testing device 300 in the present application includes:

[0080] Input device 301, output device 302, processor 303 and memory 304, wherein the number of processor 303 can be one or more, Figure 3 In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 may be connected via a bus or other means, wherein: Figure 3 The bus connection is taken as an example.

[0081] The processor 303 is configured to execute the above method steps by calling the operation instructions stored in the memory 304 .

[0082] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0083] See also Figure 4 , Figure 4 This is a schematic diagram of an electronic system according to an embodiment of the present application.

[0084] like Figure 4 As shown, an embodiment of the present application provides an electronic system, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the above method steps are implemented.

[0085] In the specific implementation process, when the processor 420 executes the computer program 411, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0086] Since the electronic system introduced in this embodiment is the equipment used to implement a fiber grating sensor testing device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation of the electronic system of this embodiment and its various variations. Therefore, how the electronic system implements the method in the embodiment of the present application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application falls within the scope of protection to be protected by this application.

[0087] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application.

[0088] like Figure 5As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the above method steps are implemented.

[0089] In a specific implementation process, the computer program 511 can be implemented when executed by a processor. Figure 1 Any implementation manner in the corresponding embodiments.

[0090] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0091] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0092] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0095] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 The process of the fiber Bragg grating sensor testing method in the corresponding embodiment.

[0096] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0097] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0100] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0102] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A fiber Bragg grating sensor testing method, characterized in that: include: The same light source signal is injected into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor at the same time, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain, and the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, which remains in a free state. The first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment, and an imaging module is provided at one end of the second fiber Bragg grating sensor; collecting interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state by the imaging module; When the first fiber Bragg grating sensor is in a loaded state, the imaging module collects interference and speckle images of current reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor for comparative analysis, thereby eliminating drift components caused by temperature and extracting pure strain components; Also includes: fitting a strain-induced image offset value through the speckle image, wherein the strain-induced image offset value includes speckle displacement and frequency; Based on the strain-induced image offset value and the interference and speckle image of the current reflected light of the second fiber Bragg grating sensor, the wavelength drift of the first fiber Bragg grating sensor is decomposed into an image-induced component and a temperature drift function, so as to reversely construct a strain equivalent model and extract the pure strain component to fit the strain sensitivity coefficient.

2. The method according to claim 1, characterized in that The comparative analysis is performed to eliminate the drift component caused by temperature and extract the pure strain component, including: Comparative analysis was performed to eliminate the drift component caused by temperature, extract the pure strain component, and perform differential calculation to fit the strain sensitivity coefficient.

3. The method according to claim 2, characterized in that Also includes: Calculate the fitting residuals and eliminate obvious error points; Output estimated confidence intervals using the Bayesian regression algorithm.

4. The method according to claim 2, characterized in that The comparative analysis, elimination of the drift component caused by temperature, extraction of the pure strain component, and differential calculation to fit the strain sensitivity coefficient include: Construct a local spline nonlinear interpolation kernel function; The temperature response mapping function is fitted based on the observed data to obtain the strain wavelength change and fit the strain sensitivity coefficient.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: The two wavelength signals injected into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are modeled as cointegrated time series; If the environmental thermal responses of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are not synchronized, a time lag term is introduced to perform time domain similarity compensation.

6. The method according to claim 5, characterized in that Also includes: If there is a stable linear combination of the cointegrated time series, the difference error is dynamically corrected through the correction coefficient.

7. A fiber Bragg grating sensor testing device, characterized in that: According to the method according to any one of claims 1 to 6, the device comprises: an injection unit, configured to simultaneously inject the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor, wherein the first fiber Bragg grating sensor is a test fiber Bragg grating sensor, fixed on a controllable loading platform, and the controllable loading platform is configured to apply strain; the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, and the reference fiber Bragg grating sensor remains in a free state; the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are in the same regional environment; and an imaging module is provided at one end of the second fiber Bragg grating sensor; an initialization unit, configured to collect, through the imaging module, interference and speckle images of initial reflected light from the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in an unloaded state; The analyzing unit is configured to collect interference and speckle images of the current reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor through the imaging module when the first fiber Bragg grating sensor is in a loaded state, so as to perform comparative analysis, eliminate drift components caused by temperature, and extract pure strain components.

8. An electronic system comprising a memory and a processor, characterized in that: The processor is configured to implement the steps of the fiber Bragg grating sensor testing method according to any one of claims 1 to 6 when executing the computer program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fiber Bragg grating sensor testing method according to any one of claims 1 to 6 are implemented.

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