Fiber grating sensor testing method and system

Abstract

The embodiment of the invention provides a fiber grating sensor testing method and system, and can solve the problem that in actual operation, temperature change also has significant influence on wavelength drift, and strain sensitivity measurement is easy to be inaccurate. The method comprises the following steps: injecting the same light source signal into a first fiber grating sensor and a second fiber grating sensor at the same time; acquiring interference and speckle images of initial reflected light of the first fiber grating sensor and the second fiber grating sensor in an unloaded state through the imaging module; under the loading state of the first fiber grating sensor, the imaging module collects interference and speckle images of current reflected light of the first fiber grating sensor and the second fiber grating sensor so as to carry out comparative analysis, eliminate drift components caused by temperature and extract pure strain components.

Description

Technical Field

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

[0002] Fiber Bragg grating sensors are a new type of optical sensing element, which have the advantages of high sensitivity, small size, strong anti-electromagnetic interference ability, and can be distributed in a layout, etc., and are widely used in the fields of structural health monitoring, aerospace, civil engineering, medical equipment, etc.

[0003] Fiber Bragg grating sensors are often used in strain measurement scenarios, and accurately obtaining the strain sensitivity coefficient of the sensor (that is, the wavelength drift caused by unit strain) is the core content of the calibration of the sensing system. Traditional methods usually conduct a tensile test on a single grating in a standard experimental environment and fit the strain sensitivity coefficient through a wavelength change curve. However, in actual operation, temperature changes also have a significant impact on wavelength drift, which easily leads to inaccurate measurement of the strain sensitivity. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for testing fiber Bragg grating sensors, which can solve the problems in traditional applications, such as setting fiber Bragg grating sensors in bracelets for physiological signal acquisition, being limited by the wearing method and external environmental factors, having problems such as unstable signals and signal interference, and affecting the accuracy and reliability of monitoring data.

[0005] The first aspect of the embodiments of the present application provides a method for testing a fiber Bragg grating sensor, including:

[0006] Inject the same light source signal into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor at the same time. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, the first fiber Bragg grating sensor is fixed on a controllable loading platform, the controllable loading platform is used to apply strain, the second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, the 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;

[0007] 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 the unloaded state through the imaging module;

[0008] In the loaded state of the first fiber Bragg grating sensor, 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 through the imaging module for comparison and analysis, eliminate the drift component caused by temperature, and extract the pure strain component.

[0009] Optionally, the comparison analysis is performed to eliminate the drift component caused by temperature and extract the pure strain component, including:

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

[0011] Optionally, it further includes:

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

[0013] Output the estimated confidence interval through the Bayesian regression algorithm.

[0014] Optionally, the comparison analysis is performed to eliminate the drift component caused by temperature, extract the pure strain component, and perform differential calculation to fit the strain sensitivity coefficient, including:

[0015] Construct a local spline non-linear interpolation kernel function;

[0016] Based on the observed data, fit the temperature response mapping function to obtain the change in strain wavelength and fit the strain sensitivity coefficient.

[0017] Optionally, it further includes:

[0018] Fit the strain-induced image offset value through the speckle image, and 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, it further includes:

[0021] Model the two-way wavelength signals injected into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor as a 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, introduce a time delay term to perform time domain similarity compensation.

[0023] Optionally, it further includes:

[0024] If there is a stable linear combination in the cointegrated time series, dynamically correct the differential error through a correction coefficient.

[0025] The second aspect of the embodiments of the present application provides a fiber Bragg grating sensor test device, including:

[0026] An injection unit for injecting the same light source signal into a first fiber Bragg grating sensor and a second fiber Bragg grating sensor simultaneously. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain. 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.

[0027] An initialization unit for collecting interference and speckle images of the initial reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in the unloaded state through the imaging module.

[0028] An analysis unit for collecting 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 under the loaded state of the first fiber Bragg grating sensor, for comparison and analysis, removing the drift component caused by temperature, and extracting the pure strain component.

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

[0030] A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored. The computer program is used to implement the steps of the above-mentioned fiber Bragg grating sensor testing method when executed by a processor.

[0031] In summary, the fiber Bragg grating sensor testing method provided by the embodiments of the present application injects the same light source signal into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor at the same time. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, and the first fiber Bragg grating sensor is fixed on a controllable loading platform for applying strain. The second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, and the fiber Bragg grating sensor is kept 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 arranged at one end of the second fiber Bragg grating sensor. 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 the unloaded state are collected through the imaging module. In the loaded state of the first fiber Bragg grating sensor, the interference and speckle images of the current reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are collected through the imaging module for comparison and analysis, so as to eliminate the drift component caused by temperature and extract the pure strain component. Thus, the imaging system is fixed at the free grating end to accurately identify the offset of the speckle pattern caused by temperature. The temperature drift has directionality, continuity, and obvious image features, and the visualization modeling is superior to the traditional numerical difference. The spectral data and the image data are cross-validated in a dual-channel manner, which is suitable for the intelligent calibration scenario. First, the problem that it is difficult to separate the temperature disturbance in the traditional strain sensitivity test is solved. Since the reference grating is kept in a free state, the drift of its reflection wavelength strictly represents the temperature influence, and the image acquisition system fixed at its free end can stably observe the speckle pattern response at the reflection end of the grating caused by the structural deformation due to thermal change. This image change has directionality, consistency, and pattern predictability, making the temperature model highly reliable. Compared with the conventional temperature modeling method using a thermocouple or other temperature sensors, this method does not rely on an external temperature measurement unit and completely realizes the temperature drift identification through its own image data, having the advantages of fast response, small error, and simple implementation. Second, the image system in this method provides a visual feedback mechanism for the strain sensitivity test process. The image difference between the test grating and the reference grating can not only be analyzed numerically, but also the strain image and the temperature image can be intuitively separated in the image space, realizing the high visualization and dataization of inferring the change of physical quantities from the image difference, and this ability is of great significance in the actual engineering monitoring scenario. Third, this method has good engineering adaptability and automation potential. Since all sensors come from the same light source and the same batch of gratings, the wavelength consistency is high; the imaging system adopts a modular design and can be easily deployed on a portable test platform.

[0032] Correspondingly, the fiber Bragg grating sensor testing device, electronic system, and computer-readable storage medium provided by the embodiments of the present invention also have the above technical effects. Description of the Drawings

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

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

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

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

[0037] Figure 5 Schematic structural block diagram of a possible computer-readable storage medium provided by an embodiment of the present application. Detailed implementation manners

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

[0039] Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or 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 with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

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

[0041] S110. Simultaneously inject the same light source signal into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain. 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 arranged at one end of the second fiber Bragg grating sensor.

[0042] S120. Through the imaging module, 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 the unloaded state.

[0043] S130. In the loaded state of the first fiber Bragg grating sensor, through the imaging module, 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 for comparison and analysis, eliminate the drift component caused by temperature, and extract the pure strain component.

[0044] It can be understood that a set of fiber Bragg grating sensors with exactly the same structure is set. One of them is installed on the loading platform as a test grating to receive mechanical strain, and the other is used as a reference grating to remain in a free state without force and is used to respond to environmental temperature changes alone. At the same time, by fixedly installing the image imaging module at the free end position of the reference grating, long-term and stable collection of the speckle image or interference image at the reflection end of the grating is realized, so as to record the characteristics of the grating structure change induced by temperature change. Since the temperature change in the grating in the free state is mainly manifested as the end face micro-displacement and periodic structure adjustment in the axial direction, its change in the image has direction consistency and pattern predictability, and can be captured and modeled by the vision system with high resolution. By comparing the images and wavelength data of the two gratings before and after loading, and identifying the speckle change law induced by temperature from the images, a temperature drift model can be constructed, so as to eliminate this part of the change in the actual wavelength data and accurately extract the wavelength drift component caused by strain. Finally, the strain sensitivity coefficient can be deduced by the displacement-wavelength relationship, avoiding temperature interference and improving the measurement accuracy.

[0045] Exemplarily, a set of broadband light sources (such as ASE light sources) is connected to two fiber Bragg grating sensors through a 1×2 fiber optic splitter. It is recommended that the two gratings use FBG components prepared in the same batch, with exactly the same reflection center wavelength (for example, 1550.00 nm), grating length (such as 10 mm), and spectral reflection characteristics, ensuring the initial consistency of their thermal response and strain response. Among them, the first grating (FBG1) is used as the test grating and is installed between the fixtures at both ends of a high-precision linear loading platform. The loading platform has a micron-level step control ability and can apply axial strain according to a predetermined loading program; the second grating (FBG2) is used as the reference grating, and its body is suspended without being affected by any mechanical force. One end is connected to an imaging system (such as an industrial-grade high-resolution CCD camera and an interferometer group) through a special connection fixture, and the other end remains free. This fixed connection structure enables the grating structure changes caused by temperature changes to mainly unfold along the free end direction, thus presenting obvious and stable speckle pattern offsets or interference fringe deformations from the perspective of the imaging system. After the device structure is built, first, the data acquisition in the "unloaded state" is initialized, that is, under stable room temperature conditions, the initial center wavelengths (λ 1,0 and λ 2,0 ) of FBG1 and FBG2 are respectively recorded, and the initial image data I2(0) of FBG2 is obtained through the imaging module. This image serves as the temperature response reference template. At this stage, the reference image is preprocessed through 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 distributions, preparing for subsequent temperature modeling. Subsequently, the loading stage begins. The loading platform applies strain to FBG1 at a predetermined step size (such as increasing 0.5 μm per step). After each step of loading, the platform movement is paused for 1 to 2 seconds. After the wavelength stabilizes, the reflection wavelengths (λ 1,i , λ 2,i ) of FBG1 and FBG2 at this time are respectively recorded, and the current image I 2 (i) of FBG2 is synchronously collected by the imaging system. Since FBG2 remains in a free state, the change in its reflection wavelength during the loading process only comes from the ambient temperature drift. Therefore, it can be considered that the change in its image is completely caused by temperature changes. The image displacement Δ xT (i) or the image gray difference index δI(i) is calculated by registering and differentiating the aforementioned image template and the current image, thereby constructing a mathematical relationship model between the temperature corresponding image change and the wavelength change, such as: f T (i) = λ 2,0 + δλ img (i), where δλ img (i) can be obtained through calibration by the image change amount and the preset image-wavelength mapping function. By comparing the real-time wavelength change λ 1(i) Subtract the temperature modeling function f T (i), and the drift caused by temperature can be eliminated to obtain the wavelength change caused by pure strain: Δλ ε (i) = λ 1 (i) - f T (i). Combining with the actual displacement ΔL of each step of the loading platform i and the effective length L of the grating 0 , the strain value can be converted to εi = ΔLi / L0. Further fitting the wavelength change amount with the strain data, the strain sensitivity coefficient K ε = Δλ ε / ε. During the whole test process, all image, wavelength, and displacement data are recorded in real time and a database is constructed, which 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, thus further improving the universality and adaptive ability of this method.

[0046] In summary, the fiber Bragg grating sensor testing method provided by the above embodiments injects the same light source signal into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor at the same time. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, and the first fiber Bragg grating sensor is fixed on a controllable loading platform. The controllable loading platform is used to apply strain. The second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, and the 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. 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 the unloaded state; in the loaded state of the first fiber Bragg grating sensor, 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 for comparison and analysis, eliminate the drift component caused by temperature, and extract the pure strain component. Thus, the imaging system is fixed at the free grating end to accurately identify the offset of the speckle pattern caused by temperature; the temperature drift has directionality, continuity, and obvious image features, and the visualization modeling is superior to the traditional numerical difference; the spectral data and the image data are cross-validated in a dual-channel manner, which is suitable for the intelligent calibration scenario. First, the problem that it is difficult to separate the temperature disturbance in the traditional strain sensitivity test is solved. Since the reference grating remains in a free state, the drift of its reflection wavelength strictly represents the temperature influence, and the image acquisition system fixed at its free end can stably observe the speckle pattern response of the structural deformation caused by the thermal change at the reflection end of the grating. This kind of image change has directionality, consistency, and pattern predictability, making the temperature model highly reliable. Compared with the conventional temperature modeling method using thermocouples or other temperature sensors, this method does not rely on an external temperature measurement unit and completely realizes the temperature drift identification through its own image data, having the advantages of fast response, small error, and simple implementation. Second, the image system in this method provides a visual feedback mechanism for the strain sensitivity test process. The image difference between the test grating and the reference grating can not only be analyzed numerically, but also the strain image and the temperature image can be intuitively separated in the image space, realizing the high visualization and dataization of inferring the change of physical quantities from the image difference. This ability is of great significance in the actual engineering monitoring scenario. Third, this method has good engineering adaptability and automation potential. Since all sensors come from the same light source and the same batch of gratings, the wavelength consistency is high; the imaging system adopts a modular design and can be easily deployed on a portable test platform.

[0047] In one embodiment, the comparison and analysis, eliminating the drift component caused by temperature, and extracting the pure strain component include:

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

[0049] Exemplarily, during the loading test of the fiber Bragg grating sensor, in order to effectively eliminate the reflection wavelength drift caused by temperature, so as to accurately extract the pure strain component and fit 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, denoted as λ 1,0 and λ 2,0 . After the loading starts, for each loading step, the system synchronously acquires the current wavelength values λ 1 (t i ) and λ 2 (t i ), and simultaneously records the displacement ΔL i of the loading platform corresponding to the first grating. Since the first fiber Bragg grating is affected by both strain and environmental temperature, its wavelength change amount Δλ 1 (i) = λ 1 (i) - λ 1,0 includes the drift component Δλ ε (i) caused by strain and the drift component Δλ T (i) caused by temperature. While the second fiber Bragg grating is in a free state and is only affected by temperature, so its wavelength change amount Δλ 2 (i) = λ 2 (i) - λ 2,0 can be approximately equal to the temperature drift term, that is: Δλ T (i) ≈ Δλ 2 (i). Therefore, by performing differential calculation on the wavelength changes of the two gratings, the temperature influence can be eliminated, and the wavelength drift caused by strain can be extracted: Δλ ε (i) = Δλ 1 (i) - Δλ 2 (i) = [λ 1 (i) - λ 1,0 - [λ 2 (i) - λ 2,0 . After obtaining this value, the displacement of the loading platform is converted into the actual strain ε i = ΔL i / L 0 , where L 0 is the effective working length of the grating (for example, 10 mm). The differential wavelength change value Δλ ε (i) and the corresponding strain value ε i are used to establish a functional relationship, and the slope is obtained by using linear regression fitting, which is the strain sensitivity coefficient of the fiber Bragg grating under the current environmental conditions: Kε = d(Δλ ε ) / dε.

[0050] Exemplarily, the total loading amount can be set to 0 - 1000 με, loaded in 20 steps, with each step being 50 με; assuming the total wavelength drift is 1.20 nm, and the reference grating (FBG2) drifts by 0.03 nm due to temperature change, then if the temperature influence is not removed, the total drift amount of the test grating is 1.23 nm, and the fitting sensitivity is 1.23 pm / με, with an error of approximately 2.5%; after removing the temperature term through differentiation, only the pure strain response of 1.20 nm is retained, and the accurate sensitivity coefficient of 1.20 pm / με is obtained, verifying the effectiveness of this differential method. There is no need to additionally install an external temperature sensor, nor rely on a complex temperature control system. Instead, through a combination of two fiber Bragg gratings with structural matching, wavelength data is synchronously collected in the same environment, and a pure strain response can be obtained through direct differential operation. This method has simple operation, high precision, and strong anti-interference ability, and is especially suitable for rapid calibration of strain sensitivity at the engineering site and environmental compensation modeling of long-term online sensors. In addition, if combined with an image imaging module to establish a mapping relationship between temperature change and image feature change, the recognition accuracy of temperature response can be further enhanced, and the reliability of overall modeling can be improved. In this embodiment, wavelength differential analysis can be used as the reference data for image-assisted modeling, forming a spectral-image collaborative working mechanism, which is applicable to multi-channel, high-precision, and high-stability fiber optic sensing application scenarios.

[0051] In one embodiment, it further includes:

[0052] Calculate the fitting residual and remove obvious error points;

[0053] Output the estimated confidence interval through the Bayesian regression algorithm.

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

[0055] Construct a local spline non-linear interpolation kernel function;

[0056] Based on the observed data, fit the temperature response mapping function to obtain the strain wavelength change and fit the strain sensitivity coefficient.

[0057] It can be understood that in the traditional two-fiber Bragg grating differential method, it is considered 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 removed by simple subtraction: Δλ ε (t) = λ 1 (t) - λ 2(t), but this idealized premise often does not hold in actual measurements. Even if the test grating (FBG1) and the reference grating (FBG2) are made of the same material, have the same wavelength, and are fabricated using the same process, there may still be a slight temperature response deviation in the actual environment due to factors such as the installation position, the thermal contact surface, the bending state of the fiber optic port, and the uneven local temperature distribution. This non-uniform temperature drift response, if not modeled, will directly propagate to the sensitivity coefficient fitting process, causing systematic errors. To solve this problem, in this embodiment, the linear difference processing of λ 1 (t) - λ 2 (t) is no longer simply adopted. Instead, the temperature response model fTf_TfT corresponding to the wavelength change trend λ 2 (t) of the reference grating is fitted and used as the temperature drift term to be eliminated. This model is constructed using a local spline non-linear interpolation kernel function and has the characteristics of not forcibly assuming that the temperature drift is linear, being able to handle the hysteresis, sudden changes, and non-uniformity of the temperature response, and having good fitting accuracy and numerical stability. Therefore, the wavelength change of the test grating is re-modeled as: λ1(t) = λ 1 ε (t) + f T (λ 2 (t), t, θ), where λ 1 ε (t) is the wavelength component caused by strain; f T (·) is the temperature response mapping function fitted through the wavelength change of the reference grating; θ is the coefficient or control point parameter of the interpolation function. Finally, the pure strain wavelength drift amount is obtained through the following formula: Δλ ε (t) = λ1(t) - f T (λ 2 (t), t, θ), and combined with the strain ε(t) calculated from the loaded displacement, the strain sensitivity coefficient K ε is fitted through linear or non-linear regression.

[0058] Exemplarily, first, initialize the data acquisition and loading preparation. The broadband light source can be simultaneously connected to the test grating (FBG1) and the reference grating (FBG2) through a 1×2 fiber optic coupler, so that the two gratings share the same incident optical signal. FBG1 is installed between the electric loading platforms, and the loading platforms have a micron-level resolution; one end of FBG2 is fixedly connected to the imaging device, and the other end is kept free from mechanical force interference. Start the spectral demodulator to collect the reflection wavelength time series data of the two gratings, denoted as λ 1 (t) and λ 2 (t) respectively. Synchronously record the displacement data of the loading platform and calculate the strain value ε(t) = ΔL(t) / L 0 , where L 0is 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 λ 2 (t) of the reference grating is regarded as the observed variable of the temperature response. To fit its true drift trend at different time points or under different temperature conditions, local spline interpolation is used to construct the temperature response function f T . This function can be expressed as: where B i (·) is the B-spline basis function, c i is the weight coefficient of the interpolation nodes, and the node positions are set according to the sampling density of λ 2 (t), with local support. If time variation and lag response factors are considered, it can also be extended to a two-dimensional interpolation model: The least squares method or penalized spline optimization solution can be used in the fitting process 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 removed from the original wavelength data of the test grating to obtain the pure strain wavelength change: Δλ ε (t) = λ 1 (t) - f T (λ 2 (t), t). According to the step information of the loading platform, the strain sequence ε(t) is calculated, and the data point pairs (ε(t i ), Δλ ε (t i )) are established. 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 the model accuracy. Taking a specific experiment as an example, assume that the loading platform has 20 steps, each step has a displacement of 0.5 μm, and a total strain of 1000 με is applied. The wavelength of the test grating changes from 1550.000 nm to 1551.230 nm, and the wavelength of the reference grating rises from 1550.000 nm to 1550.030 nm, which is obviously affected by the temperature rise. If the simple difference method λ1 - λ2 is directly used, the strain sensitivity is: K 误差= 1.230 nm / 1000 με = 1.23 pm / με. After fitting the temperature response model through the local spline kernel function, the true temperature drift is identified as 0.0335 nm. After removing this value, the strain drift is obtained as 1.1965 nm. Then the true sensitivity coefficient is finally fitted: Kε = 1.1965 pm / με, with an error of only 0.29% from the standard value of 1.20 pm / με, which is significantly better than the traditional differential method. Through the optimization method for constructing the temperature response mapping model based on the local spline non-linear interpolation function described in this embodiment, when the system performs dual fiber Bragg grating comparison analysis, it no longer relies on the assumption of "completely consistent temperature response", but instead establishes a data-driven, fittable, and adjustable temperature drift correction function. This method not only overcomes the measurement errors caused by different thermal inertia, thermal coupling, etc., but also, due to the use of the local spline kernel function, the system has good modeling capabilities for non-linear drift and discontinuous changes, and has both global stability and local sensitivity. In engineering practice, this method greatly improves the accuracy and reliability of the calibration of the strain sensitivity coefficient of fiber Bragg grating sensors, and is particularly suitable for actual scenarios with rapid environmental temperature changes, uneven heat conduction, and complex installation environments.

[0059] In one embodiment, it further includes:

[0060] Fitting the strain-induced image offset value from the speckle image, where 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 inversely construct a strain equivalent model and extract the pure strain component to fit the strain sensitivity coefficient.

[0062] It can be understood that based on the coupling of the causes of wavelength drift of fiber Bragg grating sensors, an innovative idea of jointly using image information and wavelength data to synergistically decompose the change of the reflected wavelength of fiber Bragg grating can be proposed. Traditional methods mainly rely on the change signal of the reflected wavelength to judge strain. However, since temperature also causes changes in the grating period (thermal expansion and thermo-optic refractive index change), the effects of strain and temperature on wavelength are coupled and indistinguishable. And this method introduces the speckle image into the analysis process, and through the local displacement and frequency change in the image, a functional relationship between the change of the optical image and strain is established to achieve independent modeling of the strain component.

[0063] Exemplarily, the wavelength drift Δλ 1 (t) of the fiber Bragg grating can be decomposed into two parts: the image-induced drift component Φ(I t ), which is obtained by modeling the spatial frequency shift / stripe displacement caused by strain of FBG1 in the speckle image; the temperature drift function fT (λ 2 (t)) 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, spatio-frequency domain transformation, speckle frequency spectrum estimation, etc.), key change features of the speckle pattern of FBG1 during loading are extracted, such as spatial speckle displacement (sub-pixel movement), local frequency change (reflecting the change in optical path difference caused by strain), or contrast change (related to the modulation degree of interference fringes), etc. Map the image features to the strain-induced wavelength change function Φ(I t ), combined with the temperature response model f T (λ 2 (t)), to complete the following wavelength drift decomposition: λ 1 (t)=Φ(I t )+f T (λ 2 (t)), and then through reverse derivation: Δλ ε (t)=λ 1 (t)-f T (λ 2 (t))≈Φ(I t ), that is, on the premise of knowing the temperature response and the image response, the system can extract the strain-related term from the total wavelength drift of FBG1 and construct a high-precision, image-assisted strain sensitivity fitting model.

[0064] Exemplarily, first, the experimental system is set up as before: the broadband light source is injected into the test grating FBG1 and the reference grating FBG2 simultaneously through the beam splitter; FBG1 is installed on the loading platform and is controlled to be loaded, FBG2 remains in a free state, and its reflection end is fixedly connected to the imaging module. The CCD industrial camera cooperates with the collimating lens and the interference illumination system to collect the speckle image sequence of FBG2, and at the same time, the reflection wavelengths of FBG1 and FBG2 are recorded by the spectrometer. The image acquisition frame rate should be no less than 30fps to capture the speckle evolution during the dynamic strain process; the spatial resolution requires that sub-pixel level changes can be extracted (such as through sub-pixel template matching or phase retrieval technology). Then, image feature extraction and speckle displacement analysis are carried out. During the process of gradually loading the loading platform, each frame of image I t can be obtained synchronously, and rigid or non-rigid registration is performed on the current image and the initial state image I 0 to extract the image translation amount Δx t , that is, the "speckle displacement"; local Fourier transform or wavelet analysis is used to extract the main frequency change δf t in the image, corresponding to the micro-change of the interference fringe period; the local gray intensity change curve is extracted to judge the change in the modulation degree of the interference fringes and is used as a stability index of the displacement amount. The speckle displacement and frequency data are combined into the image-induced strain feature vector: F(t)=[Δx t ,δft , δI t , through a preset empirical model or data-driven learning function Φ(·), map the image feature vector to a predicted value of wavelength change: Φ(I t ) = W 1 ·Δx t + W 2 ·δf t + W 3 ·δI t + b, where W 1 , W 2 , W 3 are parameters to be fitted, and can also be obtained by training with regression algorithms or shallow neural networks. And construct a temperature drift function and decompose the wavelength. It can be referred that the grating FBG2 is not stressed during loading, and its wavelength change λ 2 (t) can be used as an input of temperature response feature quantity, and combined with its image change (usually stable, unidirectional speckle shift), establish a temperature drift function: Here B i (·) is a spline basis function, and c i is a coefficient. It is also possible to fit the function mapping between λ 2 (t) and temperature ΔT(t) based on historical data. After completing the image-induced component Φ(I t ) and the temperature drift function f T (λ2(t)), the total wavelength change of FBG1 can be disassembled:

[0065] λ 1 (t) = Φ(It) + f T (λ 2 (t)), calculate the difference: Δλ ε (t) = λ 1 (t) - f T (λ 2 (t)) ≈ Φ

[0066] (I t ), so that at each loading step, the temperature effect can be assisted to be peeled off through the image and the reference wavelength, and the pure strain component can be extracted. Then, perform strain sensitivity coefficient fitting. Finally, pair the above-extracted Δλ ε (t) with the strain value ε(t) calculated by the loading platform to construct a strain-wavelength change point set (ε(t i ), Δλ ε (t i), the strain sensitivity coefficient is fitted using the linear least squares method or the fitting algorithm: 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 the collaborative analysis of the image channel and the wavelength channel, breaking through the limitation of the traditional single-wavelength change analysis, making the temperature and strain effects visually separable and the functions fit, and improving the accuracy of component stripping. Using the tiny texture changes in the image (such as interference fringe shift, frequency drift), the system can still accurately identify the image pattern corresponding to the strain under complex temperature drift or non-linear modeling conditions, realizing the visual prediction of the strain. The image feature modeling process can adopt shallow learning algorithms, linear regression or fitting functions based on historical data, with good system expansion ability, and is applicable to the calibration scenarios of different working conditions and different types of fiber Bragg gratings. In practical applications, by constructing an image-assisted strain recognition model through this method, the measurement accuracy can be significantly improved compared with the traditional method in the temperature fluctuation scenario, especially suitable for high-precision structural health monitoring systems.

[0067] In one embodiment, it further includes:

[0068] Model the two-way wavelength signals injected into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor as a 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-delay term is introduced for time-domain similarity compensation.

[0070] In one embodiment, it further includes:

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

[0072] It can be understood that when using two fiber Bragg grating sensors (FBG1 is the test grating and FBG2 is the reference grating) for differential temperature compensation, the general assumption is that they are in the same thermal environment and have the same thermal response. Therefore, the difference can be simply used: Δλ ε (t) = λ 1 (t) - λ 2(t) to eliminate temperature drift. However, in the actual engineering environment, this assumption often does not hold. The main reasons include that the installation positions of FBG1 and FBG2 are different, and the thermal inertia differences or the thermal conduction paths or radiation environments are inconsistent due to different heat lags or materials, coatings, and fixing methods. Therefore, there is often a weak asynchrony or amplitude difference between the temperature responses of the two. If direct differencing is used, systematic errors will be introduced. A time-domain similarity compensation mechanism is introduced to eliminate temperature drift through cointegration modeling and time synchronization offset model. This part belongs to an advanced temperature compensation strategy based on dynamic data correlation analysis, especially suitable for practical application scenarios with rapid temperature changes, inconsistent thermal responses, and significant dynamic drifts of sensors. Through time series cointegration modeling and time delay compensation mechanism, the time correlation and phase lag difference between the two are identified from the dynamic time evolution process of the wavelength signal to dynamically adjust the compensation relationship and construct a more robust temperature drift elimination model closer to physical laws.

[0073] Exemplarily, wavelength time series data can be collected. After the system is set up, the test program can be started to collect the reflected wavelength sequence data of the test grating FBG1 and the reference grating FBG2, denoted as: λ 1 (t): The wavelength time series of the test grating; λ 2 (t): The wavelength time series of the reference grating. The sampling frequency is recommended to be not less than 10 Hz, and the time length should cover at least the full loading cycle to ensure sufficient data to support the modeling. A cointegration model is established to eliminate the amplitude difference error. In an ideal situation, if the temperature responses of FBG1 and FBG2 are highly correlated, there should be a certain stable linear combination between them. We try to establish the following cointegration relationship: λ 1 (t) = βλ 2 (t) + Δλ ε (t) + ∈(t), that is, the wavelength change of FBG1 is a scaling (amplification or reduction) of the wavelength change of the reference grating 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, etc. After obtaining β, a new compensation model is constructed: Δλ ε (t) = λ 1 (t) - βλ 2 (t). Compared with the traditional direct differencing method (i.e., β = 1), this model can better adapt to the amplitude difference of the thermal response, thus improving the compensation accuracy. A time synchronization offset model can be introduced to eliminate the time delay error. If there is a time delay in the thermal responses of the two gratings (for example, the heating / cooling response of FBG1 is slower than that of FBG2), there is a lag τ in the wavelength responses of the two in the time series. At this time, the model can be optimized as: Δλ ε (t) = λ1 (t)-βλ 2 (t-τ), to estimate the optimal τ, the following methods can be used: cross-correlation function analysis, calculation of λ 1 (t) and λ 2 The cross-correlation function R(τ) of (t-τ) is taken as the τ corresponding to the maximum value point. * As the optimal lag estimate; sliding window matching, the λ within a window 2 (t) Slide forward or backward, compare λ 1 (t) The minimum residual point is the optimal τ; minimize the modeling residual method, traverse the τ value, so that λ 1 (t)-βλ 2 The residual square sum of (t-τ) is the smallest. Finally, the wavelength drift is decomposed into: λ 1 (t) = βλ 2 (t-τ)+Δλ ε (t)+∈(t), thus accurately extracting the strain-related drift: Δλ ε (t) = λ 1 (t)-βλ 2 (t-τ). Finally, the strain sensitivity coefficient is fitted to convert the loading platform displacement ΔL(t) into strain ε(t) = ΔL(t) / L 0 , and compare it 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 dynamically adjusts the proportional factor β to accurately compensate for the inconsistency of thermal response on a data-driven basis, thereby more realistically restoring the actual impact of temperature drift. Environmental factors such as thermal inertia differences, shell heat absorption, and air-cooled 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 part 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 the compensation capability and is not affected by differences in physical installation methods. It is especially 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, which is 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] An 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. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain. 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 is configured to collect 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 through the imaging module.

[0077] An analysis unit 203 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 under the loaded state of the first fiber Bragg grating sensor for comparison and analysis, eliminate the drift component caused by temperature, and extract the pure strain component.

[0078] In summary, for the fiber Bragg grating sensor testing device provided in the above embodiments, the same light source signal is simultaneously injected into the first fiber Bragg grating sensor and the second fiber Bragg grating sensor. The first fiber Bragg grating sensor is a test fiber Bragg grating sensor, which is fixed on a controllable loading platform for applying strain. The second fiber Bragg grating sensor is a reference fiber Bragg grating sensor, and the 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 interference and speckle images of the initial reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor in the unloaded state are collected through the imaging module. In the loaded state of the first fiber Bragg grating sensor, the interference and speckle images of the current reflected light of the first fiber Bragg grating sensor and the second fiber Bragg grating sensor are collected through the imaging module for comparison and analysis, so as to eliminate the drift component caused by temperature and extract the pure strain component. Thus, the imaging system is fixed at the free grating end to accurately identify the offset of the speckle pattern caused by temperature. The temperature drift has directionality, continuity, and obvious image features, and the visual modeling is superior to the traditional numerical difference. The spectral data and the image data are cross-validated in a dual-channel manner, which is suitable for the intelligent calibration scenario. First, the problem that it is difficult to separate the temperature disturbance in the traditional strain sensitivity test is solved. Since the reference grating remains in a free state, the drift of its reflection wavelength strictly represents the temperature influence, and the image acquisition system fixed at its free end can stably observe the speckle pattern response of the structural deformation caused by the thermal change at the reflection end of the grating. This image change has directionality, consistency, and pattern predictability, making the temperature model highly reliable. Compared with the conventional temperature modeling method using a thermocouple or other temperature sensors, this method does not rely on an external temperature measurement unit and completely realizes the temperature drift identification through its own image data, having the advantages of fast response, small error, and simple implementation. Second, the image system in this method provides a visual feedback mechanism for the strain sensitivity test process. The image difference between the test grating and the reference grating can not only be analyzed numerically, but also the strain image and the temperature image can be intuitively separated in the image space, realizing the high visualization and dataization of inferring the change of physical quantities from the image difference. This ability is of great significance in the actual engineering monitoring scenario. Third, this method has good engineering adaptability and automation potential. Since all sensors come from the same light source and the same batch of gratings, the wavelength consistency is high; the imaging system adopts a modular design and can be easily deployed on a portable test platform.

[0079] Above Figure 2 The fiber Bragg grating sensor testing device in the embodiments of the present application has been described from the perspective of modular functional entities. Next, the fiber Bragg grating sensor testing device in the embodiments of the present application will be described in detail from the perspective of hardware processing. Please refer to Figure 3, An embodiment of the fiber Bragg grating sensor testing device 300 in the embodiments of the present application includes:

[0080] An input device 301, an output device 302, a processor 303, and a memory 304. Among them, the number of processors 303 can be one or more. Figure 3 Here, one processor 303 is taken as an example. In some embodiments of the present application, the input device 301, the output device 302, the processor 303, and the memory 304 can be connected through a bus or other means. Among them, Figure 3 Here, connection through a bus is taken as an example.

[0081] Among them, by invoking the operation instructions stored in the memory 304, the processor 303 is used to execute the above method steps.

[0082] By invoking the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 any one of the corresponding embodiments.

[0083] Please refer to Figure 4 , Figure 4 , which is a schematic diagram of an embodiment of the electronic system provided by the embodiments of the present application.

[0084] As Figure 4 shown, the embodiments of the present application provide an electronic system, including a memory 410, a processor 420, and a computer program 411 stored on the memory 420 and operable 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 implement Figure 1 any one of the corresponding embodiments.

[0086] Since the electronic system introduced in this embodiment is the device adopted for implementing a fiber Bragg grating sensor testing device in the embodiments of the present application, based on the method introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic system in this embodiment. Therefore, the specific implementation of how this electronic system implements the method in the embodiments of the present application will not be described in detail here. As long as the device adopted by those skilled in the art to implement the method in the embodiments of the present application belongs to the scope protected by the present application.

[0087] Please refer to Figure 5 , Figure 5 , which is a schematic diagram of an embodiment of a computer-readable storage medium provided by the embodiments of the present application.

[0088] As Figure 5As shown in the figure, 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 the specific implementation process, when the computer program 511 is executed by a processor, it can implement Figure 1 any implementation manner in the corresponding embodiment.

[0090] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0091] Those skilled in the art should understand 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 combining software and hardware aspects. 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

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

[0093] These computer program instructions can 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 generate a manufactured product including an instruction device, and the instruction device implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide for implementing in the processFigure 1 one or more processes and / or blocks Figure 1 steps of the functions specified in one or more blocks.

[0095] The embodiments of the present application also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the processes in the fiber Bragg grating sensor testing method in the corresponding embodiments as Figure 1 described.

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

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

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

[0099] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0101] If the above-mentioned 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 such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable 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 methods described in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0102] As described above, the above embodiments are only used to illustrate the technical solution of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present application.

Claims

1. A fiber grating sensor testing method, characterized in that: include: The same light source signal is injected into a first fiber grating sensor and a second fiber grating sensor at the same time, wherein the first fiber grating sensor is a test fiber grating sensor, the first fiber grating sensor is fixed on a controllable loading platform, the controllable loading platform is used to apply strain, the second fiber grating sensor is a reference fiber grating sensor, the fiber grating sensor is kept in a free state, the first fiber grating sensor and the second fiber grating sensor are in the same regional environment, and an imaging module is arranged at one end of the second fiber grating sensor; Collecting interference and speckle images of initial reflected light of 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 grating sensor is loaded, the imaging module collects interference and speckle images of current reflected light of the first fiber grating sensor and the second fiber grating sensor for comparative analysis, eliminating drift components caused by temperature and extracting pure strain components.

2. The method according to claim 1, characterized in that The comparative analysis is performed to remove 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 from the Bayesian regression algorithm.

4. The method according to claim 2, characterized in that: The comparative analysis is performed to remove the drift component caused by temperature, extract the pure strain component, and perform differential calculation to fit the strain sensitivity coefficient, including: Construct 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: Fitting a strain-induced image offset value through a speckle image, wherein the strain-induced image offset value includes a speckle displacement and a 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 to reversely construct a strain equivalent model and extract a pure strain component to fit the strain sensitivity coefficient.

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

7. The method according to claim 6, 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.

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

9. An electronic system, comprising a memory and a processor, characterized in that: The processor is used to implement the steps of the fiber Bragg grating sensor testing method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. 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 7 are implemented.

Images