A strain and temperature synchronous calibration method and device based on distributed optical fiber sensing

Abstract

The application provides a strain and temperature synchronous calibration method and device based on distributed optical fiber sensing, and relates to the technical field of optical fiber sensing calibration.The initial measurement node spacing and distribution density are obtained, the reflected signal is captured according to the simulated optical fiber layout, the time delay, intensity and phase change are recorded, the strain value and temperature value of each measurement node are calculated, the calibration model of the strain and temperature relationship is established, the polynomial fitting method is used for simulation, the real-time data are obtained and corrected, the adaptive measurement node adjustment mechanism is established, the calibration deviation coefficient is generated by analyzing the deviation between the current monitoring data and the correction value, the change rate of the strain and temperature is calculated when the deviation exceeds the preset threshold interval, the abnormal area is identified and the number of measurement nodes is dynamically adjusted.The application realizes high-precision calibration of the strain and temperature of the key area of the structure through the dynamic layout of the distributed optical fiber sensor and the adaptive measurement node adjustment mechanism.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensing calibration technology, specifically to a method and apparatus for synchronous calibration of strain and temperature based on distributed fiber optic sensing. Background Technology

[0002] Currently, the method for simultaneous strain and temperature calibration is based on fiber optic sensing technology. This technology utilizes scattering phenomena in optical fibers, such as Rayleigh scattering and Raman scattering, to achieve high-precision measurements of temperature and strain. In recent years, with advancements in fiber optic manufacturing processes and sensor signal processing technologies, the application scope of distributed fiber optic sensors has gradually expanded, providing long-range, real-time monitoring capabilities, making them particularly suitable for structural health monitoring in complex environments. This technology not only possesses high sensitivity and spatial resolution but also advantages such as resistance to electromagnetic interference and corrosion, making it suitable for extreme environments and long-distance monitoring.

[0003] With the development of IoT, big data, and AI technologies, strain and temperature synchronous calibration methods based on distributed fiber optic sensing have broad application prospects in multiple industries. Particularly in civil engineering, energy, transportation, and smart building sectors, the increasing demand for structural health monitoring and automated management is driving continuous innovation and improvement in related technologies. Furthermore, with enhanced data analysis capabilities and the widespread adoption of cloud computing, the integration of real-time data processing and intelligent early warning systems will further enhance the application effects of distributed fiber optic sensing, improving the safety and reliability of infrastructure. Therefore, this field will develop towards higher accuracy, wider application scope, and greater intelligence in the future.

[0004] In existing technologies, traditional monitoring methods often rely on discrete sensor arrangements, resulting in insufficient spatial resolution and an inability to fully cover stress concentration and temperature variation zones of the structure. The calibration processes used depend on fixed experimental conditions; typically, strain and temperature calibration steps performed in a calibration chamber fix the optical fiber in a specific position, lacking adaptability to dynamic changes. Therefore, this method cannot fully reflect the true strain and temperature characteristics of the structure under test under different environmental conditions and loads in practical applications.

[0005] Secondly, existing technologies mostly employ simple winding methods for fiber optic cable arrangement, resulting in insufficient monitoring of local strain and temperature distribution. This fails to fully utilize the advantages of distributed optical fibers, making it impossible to achieve high spatial resolution monitoring. Furthermore, the limitations of existing technologies in data processing and model building lead to a weak correlation between strain and temperature, thus affecting the correction effect.

[0006] Therefore, it is necessary to provide a method and apparatus for synchronous calibration of strain and temperature based on distributed optical fiber sensing to solve the aforementioned problem.

[0007] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to provide a method and apparatus for synchronous calibration of strain and temperature based on distributed optical fiber sensing, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method and apparatus for synchronous calibration of strain and temperature based on distributed optical fiber sensing, comprising the following steps:

[0011] Step 1: Determine the key area of ​​the structure to be tested, divide the key area into multiple equidistant grid cells, set a measurement node at the center of each grid cell, and deploy distributed fiber optic sensors at each measurement node to obtain strain and temperature signals of the key area of ​​the structure to be tested.

[0012] Step 2: Use a laser to emit pulsed light signals to the distributed optical fiber sensor, collect the echo signals as the light signals propagate along the optical fiber, and analyze and preprocess the collected echo signals. Extract the strain values ​​of each measurement node in the optical fiber based on Rayleigh scattering information, and analyze the temperature values ​​of each measurement node in the optical fiber based on Raman scattering information.

[0013] Step 3: Perform data matching processing on the strain and temperature values ​​obtained from each measurement node, establish a mathematical model of the strain-temperature coupling relationship, construct a calibration compensation model using the polynomial curve fitting method, perform temperature compensation processing on the raw strain data acquired in real time, and output the strain measurement value corrected by the calibration compensation model.

[0014] Step 4: Set the calibration deviation threshold range. When the system detects that the difference between the real-time strain value and the corrected strain value of any measurement node exceeds the preset calibration deviation threshold range, the abnormal characteristics of the grid cell where the node is located are evaluated based on the spatial distribution and the rate of change over time. The abnormal area is determined and the configuration density of the measurement nodes is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

[0015] Furthermore, the key areas of the structure under test and the measurement spacing of the distributed fiber optic sensors were determined using the following method:

[0016] By using engineering analysis methods to identify the critical areas of the structure under test, and by performing finite element analysis on the structure under test, a physical model is first established based on the actual size of the structure under test, and corresponding boundary conditions and loads are applied. Then, the stress, strain distribution and deformation of the structure under test under different working conditions are simulated to identify areas of stress concentration or obvious deformation. These areas are the weak links of the structure under test and are defined as critical areas.

[0017] The critical area is divided into equidistant grid cells of 10cm*10cm. A measurement node is set at the center of each grid cell. The distance d0 between the measurement nodes is 10cm. Distributed fiber optic sensors are deployed at the measurement nodes to obtain strain and temperature signals in the critical area of ​​the structure under test.

[0018] Furthermore, the acquired echo signals are analyzed and preprocessed. Based on the Rayleigh and Raman scattering information in the echo signals, the strain and temperature values ​​of each measurement node are obtained, and a mathematical model of the strain-temperature coupling relationship is established. The method used is as follows:

[0019] The echo signal received by the distributed fiber optic sensor is analyzed in the time and frequency domains using a high-sensitivity photodetector to identify and extract useful Rayleigh and Raman scattering information components. A denoising algorithm is applied to the echo signal to filter out the influence of environmental noise. The time-domain signal is then converted to the frequency domain using fast Fourier transform technology to separate different frequency components and extract the Rayleigh scattering signal with specific wavelength changes. The amplitude change is then analyzed to obtain the strain value of each measurement node. The temperature value of each measurement node is determined by monitoring the frequency shift of the Raman scattering signal.

[0020] Based on Rayleigh scattering and Raman scattering information, strain and temperature values ​​are matched to establish a mathematical model of the strain-temperature coupling relationship. The fitting formula is set as follows:

[0021] Δ∈=a0+a1T+a2T 2 +……+a n T n

[0022] Where T is the measured temperature value, and a0, a1, a2…a n The fitting coefficients are n, the order of the polynomial is n, and Δ represents the strain value caused by temperature change.

[0023] The least squares method is used to solve for the polynomial fitting coefficients, minimizing the error between the fitting result and the experimental data. An error function is constructed, and the fitting coefficients are solved by minimizing the error function. The expression of the error function is as follows:

[0024]

[0025] Where μ represents the error value between the fitting result and the experimental data, j is the index of the sample point, and j∈[1,K], K is the specific number of sample points in the experiment, and ∈′ represents the strain prediction value of the model, ∈ j Let be the strain value of the j-th sample point.

[0026] Furthermore, a calibration compensation model is constructed to perform temperature compensation processing on the real-time acquired raw strain data, and the strain measurement value corrected by the calibration compensation model is output. The method used is as follows:

[0027] Based on a mathematical model of strain-temperature coupling, combined with the calculated fitting coefficients a0, a1, a2…a n The formula used to construct the calibration compensation model is as follows:

[0028]

[0029] in, Let represent the strain value at the i-th measurement node caused by temperature change, and use it as the strain correction value. This represents the temperature value measured at the i-th measurement node;

[0030] The actual strain values ​​monitored at each measurement node are used as input, and the corrected strain values ​​are output based on the constructed calibration compensation model. The formula used is as follows:

[0031]

[0032] in, This represents the strain value corrected for the i-th measurement node. This represents the strain value actually monitored at the i-th measurement node in the optical fiber.

[0033] Furthermore, a calibration deviation coefficient is generated based on the degree of deviation between the strain values ​​at all measurement nodes of the optical fiber and the corrected strain values. The formula used is as follows:

[0034]

[0035] Among them, BD i This represents the calibration deviation coefficient of the i-th measurement node.

[0036] Furthermore, based on the spatial distribution and temporal rate of change, the anomalous characteristics of the grid cell where the node is located are evaluated, the anomalous region is determined, and the configuration density of the measurement nodes is dynamically adjusted. The method used is as follows:

[0037] Establish the deviation threshold interval [y] min ,y max When BD i <y minOr BD i >y max At that time, a strain change threshold τ is established. ∈ With temperature change threshold τ T And calculate the rates of change of strain and temperature in space and time, based on the following formula:

[0038]

[0039] Among them, G ∈ (x,t), G T (x,t) represents the rate of change of strain and temperature in space, V ∈ (x,t), V T (x,t) represents the rate of change of strain and temperature over time; x is the spatial location of the measurement node, and t is the moment when the strain and temperature values ​​of the measurement node are acquired.

[0040] The criteria for determining abnormal regions are set as follows:

[0041] G ∈ (x,t)>τ ∈ or V ∈ (x,t)>τ ∈

[0042] G T (x,t)>τ T or V T (x,t)>τ T

[0043] When the judgment condition is met, the equidistant grid cell where the measurement node is located is marked as an abnormal grid, and the abnormal region is composed of adjacent abnormal grids connected together.

[0044] Based on the degree of change in the abnormal grid, adjust the spacing of the measurement nodes, reducing the spacing to d between adjacent abnormal grids. new The number of measurement nodes is increased in the abnormal mesh, based on the following formula:

[0045]

[0046] f = 1 + α*max[G] ∈ (x,t),V ∈ (x,t),G T (x,t),V T (x,t)]

[0047] Where, d new This represents the adjusted distance between measurement nodes, f is the resolution factor, and α is the sensitivity coefficient, used to control the resolution adjustment range.

[0048] The present invention also provides a strain and temperature synchronous calibration device based on distributed optical fiber sensing, the calibration device being used to perform the above-described strain and temperature synchronous calibration method based on distributed optical fiber sensing, comprising:

[0049] An initial measurement node design module is used to determine the key area of ​​the structure to be measured, divide the key area into multiple equidistant grid cells, set a measurement node at the center of each grid cell, and deploy distributed optical fiber sensors at each measurement node to obtain strain and temperature signals of the key area of ​​the structure to be measured.

[0050] The signal acquisition and data analysis module is used to emit pulsed light signals to the distributed optical fiber sensor using a laser, acquire the echo signals during the propagation of the light signals along the optical fiber, and analyze and preprocess the acquired echo signals. Based on Rayleigh scattering information, the strain values ​​of each measurement node in the optical fiber are extracted, and based on Raman scattering information, the temperature values ​​of each measurement node in the optical fiber are analyzed.

[0051] The calibration model and data correction module is used to perform data matching processing on the strain and temperature values ​​acquired by each measurement node, establish a mathematical model of strain-temperature coupling relationship, construct a calibration compensation model using a polynomial curve fitting method, perform temperature compensation processing on the raw strain data acquired in real time, and output the strain measurement value corrected by the calibration compensation model.

[0052] The adaptive adjustment and deviation analysis module is used to set a calibration deviation threshold range. When the system detects that the difference between the real-time strain value and the corrected strain value of any measurement node exceeds the preset calibration deviation threshold range, it evaluates the abnormal characteristics of the grid cell where the node is located based on the spatial distribution and the rate of change over time, determines the abnormal area, and dynamically adjusts the configuration density of the measurement nodes to achieve adaptive optimization of the distributed measurement network system.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] This invention achieves higher resolution in critical areas by optimizing the fiber optic layout and dynamically adjusting the number of measurement nodes, ensuring comprehensive monitoring of stress concentration and temperature difference changes, improving data accuracy. The introduction of a real-time monitoring mechanism and adaptive measurement node adjustment allows for dynamic adjustment of the number and position of measurement nodes based on environmental changes and deviations in monitoring data. The calibration model established using polynomial fitting enables precise and synchronous recording of strain and temperature data under different temperature and load conditions, avoiding systematic errors caused by separate calibration in traditional methods.

[0055] This invention optimizes the fiber optic layout and combines it with the principle of multi-distribution to ensure comprehensive coverage in areas with stress concentration and large temperature differences, thereby improving the sensitivity and accuracy of monitoring. By introducing a dynamic adjustment mechanism, it allows for flexible adjustment of measurement nodes based on real-time monitoring data, thus improving the system's adaptability to environmental changes and monitoring accuracy. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the overall method flow of the present invention.

[0057] Figure 2 This is a schematic diagram of the calibration deviation coefficient analysis of the present invention.

[0058] Figure 3 This is a schematic diagram of the system module flow of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0060] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0061] Example:

[0062] Please see Figure 1 A method for synchronous calibration of strain and temperature based on distributed optical fiber sensing, comprising the following steps:

[0063] Step 1: Determine the key area of ​​the structure to be tested, divide the key area into multiple equidistant grid cells, set a measurement node at the center of each grid cell, and deploy distributed fiber optic sensors at each measurement node to obtain strain and temperature signals of the key area of ​​the structure to be tested.

[0064] Step 2: Use a laser to emit pulsed light signals to the distributed optical fiber sensor, collect the echo signals as the light signals propagate along the optical fiber, and analyze and preprocess the collected echo signals. Extract the strain values ​​of each measurement node in the optical fiber based on Rayleigh scattering information, and analyze the temperature values ​​of each measurement node in the optical fiber based on Raman scattering information.

[0065] Step 3: Perform data matching processing on the strain and temperature values ​​obtained from each measurement node, establish a mathematical model of the strain-temperature coupling relationship, construct a calibration compensation model using the polynomial curve fitting method, perform temperature compensation processing on the raw strain data acquired in real time, and output the strain measurement value corrected by the calibration compensation model.

[0066] Step 4: Set the calibration deviation threshold range. When the system detects that the difference between the real-time strain value and the corrected strain value of any measurement node exceeds the preset calibration deviation threshold range, the abnormal characteristics of the grid cell where the node is located are evaluated based on the spatial distribution and the rate of change over time. The abnormal area is determined and the configuration density of the measurement nodes is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

[0067] It should be noted that the critical areas of the structure under test in step 1 refer to areas where strain and temperature change significantly, such as connection points and joints, load application points, areas with large temperature gradients, bending corners, and support points. Stress concentration areas usually appear where the geometry changes drastically, such as abrupt changes, holes, and acute angles. The stress values ​​in these areas are significantly higher than in other areas. Therefore, stress concentration areas can be identified by calculating the stress intensity in different areas. A common method to quantify the degree of stress concentration is to use the stress concentration factor. The stress concentration factor is the ratio of the maximum stress in a specific area to the stress in the unaffected part of that area. The larger the stress concentration factor value, the more severe the stress concentration. Areas with significant deformation can have their displacement at each node obtained through finite element analysis. The deformation can be quantified by calculating the displacement of the structure in a specific direction. If the displacement in some areas is significantly greater than in other parts, these areas can be considered as areas with significant deformation.

[0068] It should be noted that by accurately calculating the initial spacing and distribution density of the measurement nodes, comprehensive coverage of the sensors in the critical area is ensured. This not only optimizes the fiber optic layout and effectively captures minute changes caused by stress concentration and temperature variations, but also allows for flexible adjustment of the number and spacing of measurement nodes based on the physical characteristics of the structure under test and the expected strain and temperature variation range. This improves the accuracy and reliability of the monitoring data, ensures efficient real-time monitoring in complex environments, and provides a basis for subsequent adjustments to the spacing and number of measurement nodes.

[0069] Therefore, it is necessary to determine the key areas of the structure under test and the measurement spacing of the distributed fiber optic sensors. The method used is as follows:

[0070] By using engineering analysis methods to identify the critical areas of the structure under test, and by performing finite element analysis on the structure under test, a physical model is first established based on the actual size of the structure under test, and corresponding boundary conditions and loads are applied. Then, the stress, strain distribution and deformation of the structure under test under different working conditions are simulated to identify areas of stress concentration or obvious deformation. These areas are the weak links of the structure under test and are defined as critical areas.

[0071] The critical area is divided into equidistant grid cells of 10cm*10cm. A measurement node is set at the center of each grid cell. The distance d0 between the measurement nodes is 10cm. Distributed fiber optic sensors are deployed at the measurement nodes to obtain strain and temperature signals in the critical area of ​​the structure under test.

[0072] It should be noted that Rayleigh scattering and Raman scattering provide highly accurate real-time monitoring capabilities. Rayleigh scattering directly reflects the strain state of a material through phase changes, making strain monitoring extremely sensitive and accurate. Raman scattering, on the other hand, reflects temperature changes through frequency changes in light signals, enabling precise measurements over a wide temperature range. By calculating the strain and temperature values ​​at the measurement nodes using these two principles, comprehensive monitoring of the structure under test can be achieved, thereby ensuring its safety and reliability during use.

[0073] The application of Rayleigh scattering principles enables distributed fiber optic sensors to capture minute deformations of materials at the microscopic level. These changes manifest as sensitive signals in phase, reflecting real-time changes in strain state. This highly sensitive strain monitoring capability is crucial for the early identification of potential problems such as microcracks and fatigue damage in structures, thus providing precise data support for structural maintenance. Raman scattering sources, by analyzing the frequency changes of scattered light, can accurately measure temperature changes, maintaining good measurement accuracy even over large ranges. Combining the advantages of these two scattering principles, distributed fiber optic sensing technology can achieve synchronous monitoring of strain and temperature within the same system, generating real-time data. The real-time temperature and strain data from these measurement nodes not only reflect the structural state under different operating conditions but also, by establishing a model of the relationship between strain and temperature, further improve the accuracy and reliability of monitoring.

[0074] It should be noted that by collecting strain and temperature values ​​at each measurement node and using polynomial fitting for simulation, the correlation between the two can be effectively revealed, thus forming a systematic and quantifiable model. This not only helps improve the resolution of monitoring data and reduce measurement errors caused by environmental changes, but also provides a stronger scientific basis for structural health monitoring, optimizes maintenance decisions, and extends the service life of the structure.

[0075] Therefore, it is necessary to analyze and preprocess the acquired echo signals, obtain the strain and temperature values ​​of each measurement node based on the Rayleigh and Raman scattering information in the echo signals, and establish a mathematical model of the strain-temperature coupling relationship. The method used is as follows:

[0076] The echo signal received by the distributed fiber optic sensor is analyzed in the time and frequency domains using a high-sensitivity photodetector to identify and extract useful Rayleigh and Raman scattering information components. A denoising algorithm is applied to the echo signal to filter out the influence of environmental noise. The time-domain signal is then converted to the frequency domain using fast Fourier transform technology to separate different frequency components and extract the Rayleigh scattering signal with specific wavelength changes. The amplitude change is then analyzed to obtain the strain value of each measurement node. The temperature value of each measurement node is determined by monitoring the frequency shift of the Raman scattering signal.

[0077] Based on Rayleigh scattering and Raman scattering information, strain and temperature values ​​are matched to establish a mathematical model of the strain-temperature coupling relationship. The fitting formula is set as follows:

[0078] Δ∈=a0+a1T+a2T 2 +……+a n T n

[0079] Where T is the measured temperature value, and a0, a1, a2…a n The fitting coefficients are n, the order of the polynomial is n, and Δ represents the strain value caused by temperature change.

[0080] The least squares method is used to solve for the polynomial fitting coefficients, minimizing the error between the fitting result and the experimental data. An error function is constructed, and the fitting coefficients are solved by minimizing the error function. The expression of the error function is as follows:

[0081]

[0082] Where μ represents the error value between the fitting result and the experimental data, j is the index of the sample point, and j∈[1,K], K is the specific number of sample points in the experiment, and ∈′ represents the strain prediction value of the model, ∈ jLet be the strain value of the j-th sample point. In the above formula, the least squares method is used because, for polynomial fitting, the quadratic form of the error function ensures that its optimal solution in the parameter space is unique. This makes the solution process more stable and easier to calculate. Furthermore, squaring the error ensures that all error terms are positive, thus amplifying any deviation, especially larger deviations. It should be noted that the sample points in the above experiment refer to ideal measurement nodes where the optical fiber is placed without the influence of external stress.

[0083] It should be noted that by monitoring the strain and temperature values ​​in the optical fiber in real time and correcting the data using the established strain-temperature calibration model, the accuracy of the measurement and the reliability of the system can be significantly improved, eliminating the coupling effect between strain and temperature. This process enhances the efficiency of data analysis, reduces human error, supports the scientific basis of engineering decisions, and adapts to applications under different environmental conditions. Furthermore, with the accumulation of data and the establishment of a feedback mechanism, the model can be continuously optimized, ensuring continuous reliability in structural health monitoring, thereby improving engineering safety and durability.

[0084] Therefore, it is necessary to construct a calibration compensation model to perform temperature compensation processing on the raw strain data acquired in real time, and output the strain measurement value corrected by the calibration compensation model. The method used is as follows:

[0085] Based on a mathematical model of strain-temperature coupling, combined with the calculated fitting coefficients a0, a1, a2…a n The formula used to construct the calibration compensation model is as follows:

[0086]

[0087] in, Let represent the strain value at the i-th measurement node caused by temperature change, and use it as the strain correction value. This represents the temperature value measured at the i-th measurement node;

[0088] The actual strain values ​​monitored at each measurement node are used as input, and the corrected strain values ​​are output based on the constructed calibration compensation model. The formula used is as follows:

[0089]

[0090] in, This represents the strain value corrected for the i-th measurement node. This represents the strain value actually monitored at the i-th measurement node in the optical fiber.

[0091] It should be noted that the reason for calculating the fitted strain correction value and temperature correction value is that in distributed fiber optic sensors, changes in strain and temperature simultaneously affect the signal of the optical fiber. Strain causes mechanical deformation of the fiber material, thereby affecting the phase or frequency of the optical signal, while temperature changes alter the thermal properties of the fiber material, thus affecting the time delay, intensity, and scattering characteristics of the optical signal. Since the effects of strain and temperature are coupled, the signal captured by the distributed fiber optic sensor is often a superposition of the two effects. If these two effects are not decoupled and the original sensor data is used directly, the measurement results may have large errors. For example, when the temperature change is small, the signal may mainly reflect strain information, but when the temperature change is large, the signal may be dominated by the temperature effect, masking the true strain change. Therefore, in order to obtain a single physical quantity, that is, the corrected true strain value, the original data must be corrected to eliminate the coupling effect.

[0092] In the above formula, the strain correction value is obtained by fitting the formula, and the final accurate calibration value is obtained by correcting the strain value monitored in real time. The fitting formula adopts a polynomial structure because in the temperature-strain relationship of many materials and structures, the influence of temperature on strain is not linear. The polynomial model can effectively capture this nonlinear relationship, and the higher-order terms of the polynomial make the model more flexible and can be adjusted according to the characteristics of the actual measurement data. By selecting an appropriate polynomial order, a good fitting effect can be achieved in different temperature ranges or strain ranges, thereby improving the adaptability of the model.

[0093] It should be noted that by calculating the calibration deviation coefficient, the degree of deviation between the real-time monitored value and the corrected value is quantified, the effect of the correction is evaluated, the monitoring system is optimized, anomalies are identified, and engineers are supported in making maintenance decisions based on reliable data. Furthermore, periodic calculation of the calibration deviation coefficient enhances the reliability of the monitoring system, promotes industry standardization, improves the uniformity and compatibility of technologies, and ultimately improves the overall performance and reliability of the system.

[0094] Therefore, a calibration deviation coefficient needs to be generated based on the deviation between the strain values ​​at all measurement nodes of the optical fiber and the corrected strain values. The formula used is as follows:

[0095]

[0096] Among them, BD i This represents the calibration deviation coefficient of the i-th measurement node. In the above formula, the degree of error elimination is determined by a quantitative index that measures the deviation between the corrected strain value and the original strain value. When the calibration deviation coefficient is close to 1, it indicates that the correction process is effective and the deviation between the corrected strain value and the original strain value is small. If the deviation coefficient is far from 1, it indicates that the correction effect is poor and the model needs to be adjusted.

[0097] The reason for the need to correct the strain is that the strain data acquired by the distributed fiber optic sensor is affected by temperature changes. It must be corrected by the strain-temperature calibration model to eliminate the temperature effect and ensure the accuracy of the strain data. By correcting the coupling effect between strain and temperature, the accuracy of the measurement results can be significantly improved, enabling the system to monitor the state of the structure in real time under dynamic conditions. Furthermore, by continuously optimizing the calibration model and deviation coefficient, the system can adaptively adjust the number and spacing of measurement nodes, maintaining high reliability under different environmental conditions.

[0098] Table 1 - Calculation Table of Calibration Deviation Coefficient

[0099]

[0100] As shown in Table 1, this invention selected 20 measurement nodes for verification of the calibrated experimental results. A distributed fiber optic sensor for measuring grain and oil distribution was used in the measurement experiment, with a micro-strain range of 0 to 140. The BD value was calculated for each set of data to determine the accuracy of the strain values ​​after model calibration. From the specific data in the table, the difference between the original strain and the corrected strain at each measurement node in each group indicates the degree of strain correction. The corrected strain value is usually closer to the true value than the original strain value. The calculation of the BD value reflects the calibration deviation of the strain; the closer the BD value is to 1, the better the correction effect and the higher the measurement accuracy. As the stress value increases, the BD value generally shows an upward trend, indicating that the correction effect gradually strengthens and the deviation between the original strain value and the corrected strain value gradually decreases. In the first 5 sets of data, the BD value increases from 0.8000 to 0.9444, indicating that the model's fitting correction effect is weak in a small strain range. However, as the actual measured strain value gradually increases, the BD value stabilizes above 0.95, indicating that the correction effect is good in this range. From the 16th to the 20th set, the BD value further approaches 1, indicating that the model has a very small calibration deviation in this stress measurement range and can effectively eliminate the influence of temperature effect on strain data.

[0101] It should be noted that when the BD value is not within the set threshold range, monitoring the rate of change of strain and temperature in space and time can promptly identify abnormal areas, ensuring timely feedback on the status of critical areas and enabling appropriate measures to be taken. By adjusting the number and density of measurement nodes, monitoring accuracy can be increased in abnormal areas, while maintaining the initial measurement distance in areas where no changes have occurred, thus effectively utilizing resources and avoiding unnecessary waste. This dynamic adjustment mechanism not only improves the responsiveness of the monitoring system but also provides important protection for the safety of the maintenance structure.

[0102] Therefore, it is necessary to assess the anomaly characteristics of the grid cell containing the node based on its spatial distribution and rate of change over time, determine the anomaly region, and dynamically adjust the configuration density of the measurement nodes. The method used is as follows:

[0103] Establish the deviation threshold interval [y] min ,y max When BD i <y min Or BD i >y max At that time, a strain change threshold τ is established. ∈ With temperature change threshold τ T And calculate the rates of change of strain and temperature in space and time, based on the following formula:

[0104]

[0105] Among them, G ∈ (x,t), G T (x,t) represents the rate of change of strain and temperature in space, V ∈ (x,t), V T (x,t) represents the rate of change of strain and temperature over time; x is the spatial location of the measurement node, and t is the moment when the strain and temperature values ​​of the measurement node are acquired.

[0106] It should be noted that, based on the calibration deviation coefficient calculation table above and the measurement accuracy range of the grain and oil distributed sensor, the deviation threshold range is set to [0.95, 1.05]. This range is set because grain and oil distributed fiber optic sensors are typically used to monitor various physical quantities in agricultural production in real time, such as temperature, humidity, strain, and gas concentration. Their accuracy directly affects the reliability of the data and the responsiveness of the control system. An error accuracy within 5% provides sufficient precision for decision-making; otherwise, it may lead to problems such as grain mold and spoilage, resulting in quality loss. By setting this threshold, it is ensured that the measurement errors of these important parameters will not adversely affect the monitoring of the grain and oil storage environment.

[0107] The criteria for determining abnormal regions are set as follows:

[0108] G ∈ (x,t)>τ ∈ or v ∈ (x,t)>τ ∈

[0109] G T (x,t)>τ T or V T (x,t)>τ T

[0110] When the judgment condition is met, the equidistant grid cell where the measurement node is located is marked as an abnormal grid, and the abnormal region is composed of adjacent abnormal grids connected together.

[0111] Based on the degree of change in the abnormal grid, adjust the spacing of the measurement nodes, reducing the spacing to d between adjacent abnormal grids. new The number of measurement nodes is increased in the abnormal mesh, based on the following formula:

[0112]

[0113] f = 1 + α*max[G] ∈ (x,t),V ∈ (x,t),G T (x,t),V T (x,t)]

[0114] Where, d new This represents the adjusted distance between measurement nodes, where f is the resolution factor and α is the sensitivity coefficient used to control the resolution adjustment range; in the formula for adjusting the distance between measurement nodes above, max[G ∈ (x,t),V ∈ (x,t),G T (x,t),V T [x,t] The method of using the maximum value ensures that the measurement nodes can be adjusted more flexibly when there are significant changes in strain and temperature. The addition of 1 is because if no change is detected, the distance between the measurement nodes is not changed to prevent the result from being 0. By introducing the addition of 1, each adjustment is relative to a reference value, which can effectively control the magnitude of the change in the distance, thus making the adjustment process smoother and more controllable.

[0115] Please see Figure 2As shown in the schematic diagram of calibration deviation coefficient analysis, the black squares represent the calibration deviation coefficients corresponding to different corrected strain values, and the red curve represents the fitting trend of the data points, showing the trend of the calibration deviation coefficients changing with the strain value. It can be seen from the figure that the calibration deviation coefficients are relatively low when the strain value is low, indicating that the correction effect is not obvious. As the strain value increases, the BD value begins to rise and tends to stabilize, showing that the correction effect of the data gradually strengthens with the increase of strain. When the strain value exceeds 100, the BD value approaches 1.00, indicating that the accuracy and reliability of the measurement data are significantly improved at this time. The reduced Chi-Sqr value is 0.8724, indicating a good fitting effect and a small difference between the data points and the fitted curve. The R-squared value is 0.7601, indicating that about 76% of the data variation can be explained by the fitted curve, and the correlation of the data is strong. The adjusted R-squared value is 0.8854, reflecting the explanatory power of the model; a higher value indicates a better fit of the model to the data.

[0116] Please see Figure 3 The present invention also provides a strain and temperature synchronous calibration device based on distributed optical fiber sensing, the calibration device being used to perform the above-described strain and temperature synchronous calibration method based on distributed optical fiber sensors, comprising:

[0117] An initial measurement node design module is used to obtain the initial measurement node spacing of the distributed optical fiber sensor based on the multi-distribution principle, and set the distribution density of the measurement nodes based on the measurement node algorithm. The optical fiber type and the obtained initial measurement node spacing and distribution density of the measurement nodes are selected according to the monitoring requirements. Computer-aided design software is used to simulate the layout of the distributed optical fiber sensor and install the distributed optical fiber sensor in the key area of ​​the structure to be measured.

[0118] The signal acquisition and data analysis module is used to transmit pulsed light signals to the distributed optical fiber sensor using a laser, capture echo signals during propagation in the optical fiber, record the time delay, intensity change, and phase change of the echo signals acquired by the measurement nodes, analyze the received echo signals, calculate the strain value of the measurement nodes in the optical fiber based on the Rayleigh scattering principle, and calculate the temperature value of the measurement nodes in the optical fiber using the Raman scattering principle.

[0119] The calibration model and data correction module is used to collect the strain and temperature values ​​corresponding to each measurement node, establish a calibration model of the relationship between strain and temperature, and use a polynomial fitting method to simulate the model to obtain the strain and temperature values ​​of the measurement nodes in the real-time monitoring fiber of the system. The established calibration model is used to correct the real-time data to obtain the strain correction value and temperature correction value, and the corrected strain and temperature values ​​are calculated.

[0120] The adaptive adjustment and deviation analysis module is used to establish an adaptive measurement node adjustment mechanism. It generates a calibration deviation coefficient based on the deviation between the real-time monitoring value of strain and the corrected strain value in all measurement nodes of the optical fiber. When the calibration deviation coefficient exceeds the preset deviation threshold range, it calculates the rate of change of strain and temperature in space and time, judges abnormal areas, and dynamically adjusts the number of measurement nodes.

[0121] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0122] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0124] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for synchronous calibration of strain and temperature based on distributed optical fiber sensing, characterized in that, The specific steps include: Step 1: Determine the key area of ​​the structure to be tested, divide the key area into multiple equidistant grid cells, set a measurement node at the center of each grid cell, and deploy distributed fiber optic sensors at each measurement node to obtain strain and temperature signals of the key area of ​​the structure to be tested. Step 2: Use a laser to emit pulsed light signals to the distributed optical fiber sensor, collect the echo signals as the light signals propagate along the optical fiber, and analyze and preprocess the collected echo signals. Extract the strain values ​​of each measurement node in the optical fiber based on Rayleigh scattering information, and analyze the temperature values ​​of each measurement node in the optical fiber based on Raman scattering information. Step 3: Perform data matching processing on the strain and temperature values ​​obtained from each measurement node, establish a mathematical model of the strain-temperature coupling relationship, construct a calibration compensation model using the polynomial curve fitting method, perform temperature compensation processing on the raw strain data acquired in real time, and output the strain measurement value corrected by the calibration compensation model. Step 4: Set the calibration deviation threshold range. When the system detects that the difference between the real-time strain value and the corrected strain value of any measurement node exceeds the preset calibration deviation threshold range, the abnormal characteristics of the grid cell where the node is located are evaluated based on the spatial distribution and the rate of change over time. The abnormal area is determined and the configuration density of the measurement nodes is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system. The acquired echo signals are analyzed and preprocessed. Based on the Rayleigh and Raman scattering information in the echo signals, the strain and temperature values ​​of each measurement node are obtained, and a mathematical model of the strain-temperature coupling relationship is established. The method used is as follows: The echo signal received by the distributed fiber optic sensor is analyzed in the time and frequency domains using a high-sensitivity photodetector to identify and extract useful Rayleigh and Raman scattering information components. A denoising algorithm is applied to the echo signal to filter out the influence of environmental noise. The time-domain signal is then converted to the frequency domain using fast Fourier transform technology to separate different frequency components and extract the Rayleigh scattering signal with specific wavelength changes. The amplitude change is then analyzed to obtain the strain value of each measurement node. The temperature value of each measurement node is determined by monitoring the frequency shift of the Raman scattering signal. Based on Rayleigh scattering and Raman scattering information, strain and temperature values ​​are matched to establish a mathematical model of the strain-temperature coupling relationship. The fitting formula is set as follows: Δ∈=a0+a1T+a2T 2 +……+a n T n Where T is the measured temperature value, and a0, a1, a2…a n The fitting coefficients are n, the order of the polynomial is n, and Δ represents the strain value caused by temperature change. The least squares method is used to solve for the polynomial fitting coefficients, minimizing the error between the fitting result and the experimental data. An error function is constructed, and the fitting coefficients are solved by minimizing the error function. The expression of the error function is as follows: Where μ represents the error value between the fitting result and the experimental data, j is the index of the sample point, and j∈[1,K], K is the specific number of sample points in the experiment, and ∈′ represents the strain prediction value of the model, ∈ j Let j be the strain value of the j-th sample point; A calibration compensation model is constructed to perform temperature compensation processing on the raw strain data acquired in real time, and the strain measurement value corrected by the calibration compensation model is output. The method used is as follows: Based on a mathematical model of strain-temperature coupling, combined with the calculated fitting coefficients a0, a1, a2…a n The formula used to construct the calibration compensation model is as follows: in, Let represent the strain value at the i-th measurement node caused by temperature change, and use it as the strain correction value. This represents the temperature value measured at the i-th measurement node; The actual strain values ​​monitored at each measurement node are used as input, and the corrected strain values ​​are output based on the constructed calibration compensation model. The formula used is as follows: in, This represents the strain value corrected for the i-th measurement node. This represents the strain value actually monitored at the i-th measurement node in the optical fiber.

2. The method for synchronous calibration of strain and temperature based on distributed optical fiber sensing according to claim 1, characterized in that... The method used to determine the key areas of the structure under test and the measurement spacing of the distributed fiber optic sensors is as follows: By using engineering analysis methods to identify the critical areas of the structure under test, and by performing finite element analysis on the structure under test, a physical model is first established based on the actual size of the structure under test, and corresponding boundary conditions and loads are applied. Then, the stress, strain distribution and deformation of the structure under test under different working conditions are simulated to identify areas of stress concentration or obvious deformation. These areas are the weak links of the structure under test and are defined as critical areas. The critical area is divided into equidistant grid cells of 10cm*10cm. A measurement node is set at the center of each grid cell. The distance d0 between the measurement nodes is 10cm. Distributed fiber optic sensors are deployed at the measurement nodes to obtain strain and temperature signals in the critical area of ​​the structure under test.

3. The method for synchronous calibration of strain and temperature based on distributed optical fiber sensing according to claim 2, characterized in that, The calibration deviation coefficient is generated based on the degree of deviation between the strain values ​​at all measurement nodes of the optical fiber and the corrected strain values. The formula used is as follows: Among them, BD i This represents the calibration deviation coefficient of the i-th measurement node.

4. The method for synchronous calibration of strain and temperature based on distributed optical fiber sensing according to claim 3, characterized in that, The method used to assess the anomalous characteristics of the grid cell containing the node based on its spatial distribution and rate of change over time, identify anomalous regions, and dynamically adjust the configuration density of measurement nodes is as follows: Establish the deviation threshold interval [y] min ,y max When BD i <y min Or BD i >y max At that time, a strain change threshold τ is established. ∈ With temperature change threshold τ T And calculate the rates of change of strain and temperature in space and time, based on the following formula: Among them, G ∈ (x,t), G T (x,t) represents the rate of change of strain and temperature in space, V ∈ (x,t), V T (x,t) represents the rate of change of strain and temperature over time; x is the spatial location of the measurement node, and t is the moment when the strain and temperature values ​​of the measurement node are acquired. The criteria for determining abnormal regions are set as follows: G ∈ (x,t)>τ ∈ or V ∈ (x,t)>τ ∈ G T (x,t)>τ T or V T (x,t)>τ T When the judgment condition is met, the equidistant grid cell where the measurement node is located is marked as an abnormal grid, and the abnormal region is composed of adjacent abnormal grids connected together. Based on the degree of change in the abnormal grid, adjust the spacing of the measurement nodes, reducing the spacing to d between adjacent abnormal grids. new The number of measurement nodes is increased in the abnormal mesh, based on the following formula: f=1+α*max[G ∈ (x,t),V ∈ (x,t),G T (x,t),V T (x,t)] Where, d new This represents the adjusted distance between measurement nodes, f is the resolution factor, and α is the sensitivity coefficient, used to control the resolution adjustment range.

5. A strain and temperature synchronous calibration device based on distributed optical fiber sensing, characterized in that, The calibration device is used to perform the strain and temperature synchronous calibration method based on distributed optical fiber sensing as described in any one of claims 1-4, including: An initial measurement node design module is used to determine the key area of ​​the structure to be measured, divide the key area into multiple equidistant grid cells, set a measurement node at the center of each grid cell, and deploy distributed optical fiber sensors at each measurement node to obtain strain and temperature signals of the key area of ​​the structure to be measured. The signal acquisition and data analysis module is used to emit pulsed light signals to the distributed optical fiber sensor using a laser, acquire the echo signals during the propagation of the light signals along the optical fiber, and analyze and preprocess the acquired echo signals. Based on Rayleigh scattering information, the strain values ​​of each measurement node in the optical fiber are extracted, and based on Raman scattering information, the temperature values ​​of each measurement node in the optical fiber are analyzed. The calibration model and data correction module is used to perform data matching processing on the strain and temperature values ​​acquired by each measurement node, establish a mathematical model of strain-temperature coupling relationship, construct a calibration compensation model using a polynomial curve fitting method, perform temperature compensation processing on the raw strain data acquired in real time, and output the strain measurement value corrected by the calibration compensation model. The adaptive adjustment and deviation analysis module is used to set a calibration deviation threshold range. When the system detects that the difference between the real-time strain value and the corrected strain value of any measurement node exceeds the preset calibration deviation threshold range, it evaluates the abnormal characteristics of the grid cell where the node is located based on the spatial distribution and the rate of change over time, determines the abnormal area, and dynamically adjusts the configuration density of the measurement nodes to achieve adaptive optimization of the distributed measurement network system.

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