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

Abstract

The invention 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. Initial measurement node spacing and distribution density are obtained, reflected signals are captured according to simulated optical fiber layout, and time delay, intensity and phase change of the reflected signals are recorded; calculating a strain value and a temperature value of each measurement node, establishing a calibration model of a strain and temperature relationship, performing simulation by adopting a polynomial fitting method, obtaining and correcting real-time data, establishing a self-adaptive measurement node adjustment mechanism, and generating a calibration deviation coefficient by analyzing the deviation between the current monitoring data and a correction value. According to the invention, through the dynamic layout of the distributed optical fiber sensors and the self-adaptive measurement node adjustment mechanism, the high-precision calibration of the strain and the temperature of the key area of the structure is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber sensing calibration, and in particular to a strain and temperature synchronous calibration method and device based on distributed optical fiber sensing. Background Art

[0002] Current methods for simultaneous strain and temperature calibration are based on fiber optic sensing technology, which exploits scattering phenomena in optical fibers, such as Rayleigh and Raman scattering, to achieve high-precision temperature and strain measurements. In recent years, advancements in optical fiber manufacturing and sensor signal processing have expanded the application of distributed fiber optic sensors, enabling long-span, real-time monitoring capabilities and making them particularly suitable for structural health monitoring in complex environments. This technology not only offers high sensitivity and spatial resolution, but also exhibits resistance to electromagnetic interference and corrosion, making it suitable for monitoring in extreme environments and over long distances.

[0003] With the development of the Internet of Things, big data, and artificial intelligence technologies, distributed fiber optic sensing-based simultaneous strain and temperature calibration methods have broad application prospects across multiple industries. In particular, the growing demand for structural health monitoring and automated management in civil engineering, energy, transportation, and intelligent buildings is driving continuous innovation and improvement in related technologies. Furthermore, with improved 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 of distributed fiber optic sensing and improve the safety and reliability of infrastructure. Therefore, this field is poised to develop towards higher accuracy, wider application scope, and greater intelligence.

[0004] In existing technologies, traditional monitoring methods often rely on discrete sensor arrangements, resulting in insufficient spatial resolution and an inability to fully cover the stress concentration and temperature variation areas of the structure. The calibration process used relies on fixed experimental conditions. The strain and temperature calibration steps usually performed in the calibration barrel fix the optical fiber in a specific position, which lacks the ability to adapt to dynamic changes. In practical applications, this method cannot fully reflect the true strain and temperature characteristics of the structure under different environmental conditions and loads.

[0005] Secondly, existing technologies mostly adopt a simple winding method for optical fiber arrangement, resulting in insufficient monitoring of local strain and temperature distribution, failing to fully utilize the advantages of distributed optical fiber, resulting in the inability to achieve high spatial resolution monitoring. In addition, the limitations of existing technologies in data processing and model establishment lead to a weak correlation between strain and temperature, thereby affecting the correction effect.

[0006] Therefore, it is necessary to provide a strain and temperature synchronous calibration method and device based on distributed optical fiber sensing to solve the above problems.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0008] The purpose of the present invention is to provide a method and device for synchronous calibration of strain and temperature based on distributed optical fiber sensing, so as to solve the problems raised in the above background technology.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A method and device 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 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 signals and temperature signals in the key area of ​​the structure to be measured;

[0012] Step 2: Using a laser to emit a pulsed light signal to the optical fiber sensor, collecting its echo signal as the light signal propagates along the optical fiber, and analyzing and preprocessing the collected echo signal. The strain value of each measurement node in the optical fiber is extracted based on the Rayleigh scattering information, and the temperature value of each measurement node in the optical fiber is analyzed based on the Raman scattering information.

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

[0014] Step 4: Set the calibration deviation threshold interval. 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 interval, the abnormal characteristics of the grid unit where the node is located are evaluated based on the spatial distribution and time change rate, the abnormal area is determined, and the configuration density of the measurement node is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

[0015] Furthermore, the key areas of the structure to be measured and the measurement spacing of the distributed optical fiber sensors are determined based on the following method:

[0016] Using engineering analysis methods to identify critical areas of the structure to be tested, finite element analysis is performed on the structure to be tested. First, a physical model is established based on the actual size of the structure to be tested, and corresponding boundary conditions and loads are applied. Then, the stress, strain distribution and deformation of the structure to be tested under different working conditions are simulated to identify areas with concentrated stress or obvious deformation. These areas are the weak links of the structure to be tested and are defined as critical areas.

[0017] The key area is divided into equidistant grid units of 10 cm*10 cm. A measurement node is set at the center of each grid unit. The spacing d0 between measurement nodes is 10 cm. Fiber optic sensors are deployed at the measurement nodes to obtain strain signals and temperature signals in the key areas of the structure to be measured.

[0018] Furthermore, the collected echo signals are analyzed and preprocessed, and the strain and temperature values ​​of each measurement node are obtained based on the Rayleigh scattering information and Raman scattering information in the echo signals. A mathematical model of the strain-temperature coupling relationship is established based on the following method:

[0019] The echo signal received by the fiber optic sensor is analyzed in the time and frequency domains by a highly sensitive photoelectric detector to identify and extract useful Rayleigh scattering 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 converted into the frequency domain using fast Fourier transform technology to separate different frequency components and extract the Rayleigh scattering signal with a specific wavelength change. 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 the Rayleigh scattering information and Raman scattering information, the strain value and the temperature value are matched to establish a mathematical model of the strain-temperature coupling relationship. The fitting formula is:

[0021] Δ∈=a0+a1t+a2T 2 +……+a n T n

[0022] Among them, T is the measured temperature value, a0, a1, a2…a e is the fitting coefficient, n is the order of the polynomial, and Δ∈ represents the strain value caused by temperature change;

[0023] The least squares method is used to solve the polynomial fitting coefficients so that the error between the fitting result and the experimental data is minimized. An error function is constructed and the error function is minimized to solve the fitting coefficients. The expression of the error function is:

[0024]

[0025] Where μ represents the error 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, ∈′ represents the strain prediction value of the model, ∈ j is the strain value of the j-th sample point.

[0026] Furthermore, a calibration compensation model is constructed to perform temperature compensation on the raw strain data collected in real time, and the strain measurement value corrected by the calibration compensation model is output. The method is based on:

[0027] Based on the mathematical model of strain-temperature coupling relationship, combined with the calculated fitting coefficients a0, a1, a2…a n The calibration compensation model is constructed based on the formula:

[0028]

[0029] in, It represents the strain value of the i-th measurement node caused by temperature change, which is used as the strain correction value. represents the temperature value measured by the i-th measurement node;

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

[0031]

[0032] in, represents the corrected strain value of the i-th measurement node, It represents the strain value actually monitored by 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, according to the following formula:

[0034]

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

[0036] Furthermore, the abnormal characteristics of the grid unit where the node is located are evaluated based on the spatial distribution and time change rate, the abnormal area is determined, and the configuration density of the measurement nodes is dynamically adjusted. The method is based on:

[0037] Set up the deviation threshold interval [y min ,y max ], when BD i <y min or BDi >y max When the strain change threshold τ is set ∈ and temperature change threshold τ T , and calculate the rate of change of strain and temperature in space and time, based on the 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 position of the measurement node, and t is the time when the strain and temperature values ​​of the measurement node are obtained;

[0040] Set the abnormal area determination conditions to:

[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 unit where the measurement node is located is marked as an abnormal grid, and the abnormal area is composed of adjacent abnormal grids;

[0044] According to the degree of change of abnormal grids, the spacing of measurement nodes is adjusted, and the spacing between adjacent abnormal grids is reduced to d new , and increase the number of measurement nodes in the abnormal grid, based on the formula:

[0045]

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

[0047] Among them, d new It represents the distance between measurement nodes after adjustment, f is the resolution factor, and α is the sensitivity coefficient, which is used to control the resolution adjustment range.

[0048] The present invention further provides a strain and temperature synchronous calibration device based on distributed optical fiber sensing, wherein the calibration device is used to execute the above-mentioned strain and temperature synchronous calibration method based on distributed optical fiber sensing, comprising:

[0049] an initial measurement node design module, which is used to determine a key area of ​​the structure to be measured, divide the key area into a plurality of 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 signals and temperature signals in the key area of ​​the structure to be measured;

[0050] a signal acquisition and data analysis module, which is used to use a laser to transmit a pulsed light signal to the optical fiber sensor, collect its echo signal during the process of the light signal propagating along the optical fiber, and analyze and preprocess the collected echo signal to extract the strain value of each measurement node in the optical fiber based on Rayleigh scattering information, and analyze the temperature value of each measurement node in the optical fiber based on Raman scattering information;

[0051] A calibration model and data correction module is used to perform data matching processing on the strain values ​​and temperature values ​​obtained from each measurement node, establish a mathematical model of the strain-temperature coupling relationship, construct a calibration compensation model using a polynomial curve fitting method, perform temperature compensation processing on the raw strain data collected 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 the calibration deviation threshold interval. 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 interval, the abnormal characteristics of the grid unit where the node is located are evaluated based on the spatial distribution and time change rate, the abnormal area is determined and the configuration density of the measurement node is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] By optimizing the layout of optical fibers and dynamically adjusting the number of measurement nodes, the present invention can achieve higher resolution in key areas, ensuring comprehensive monitoring of stress concentration and temperature differences, and improving data accuracy. The introduction of a real-time monitoring mechanism and adaptive measurement node adjustment can dynamically adjust the number and position of measurement nodes based on environmental changes and deviations in monitored data. A calibration model established using polynomial fitting enables precise and synchronous recording of strain and temperature data under different temperature and load conditions, avoiding the systematic errors caused by separate calibration in traditional methods.

[0055] By optimizing the optical fiber layout and combining the multi-distribution principle, the present invention ensures comprehensive coverage in areas with concentrated stress and large temperature differences, thereby improving the sensitivity and accuracy of monitoring. By introducing a dynamic adjustment mechanism, it allows flexible adjustment of measurement nodes based on real-time monitoring data, thereby improving the system's adaptability to environmental changes and monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the overall method 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 It is a schematic diagram of the system module flow of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0061] Example:

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

[0063] Step 1: 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 signals and temperature signals in the key area of ​​the structure to be measured;

[0064] Step 2: Using a laser to emit a pulsed light signal to the optical fiber sensor, collecting its echo signal as the light signal propagates along the optical fiber, and analyzing and preprocessing the collected echo signal. The strain value of each measurement node in the optical fiber is extracted based on the Rayleigh scattering information, and the temperature value of each measurement node in the optical fiber is analyzed based on the Raman scattering information.

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

[0066] Step 4: Set the calibration deviation threshold interval. 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 interval, the abnormal characteristics of the grid unit where the node is located are evaluated based on the spatial distribution and time change rate, the abnormal area is determined, and the configuration density of the measurement node is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

[0067] It should be noted that in step 1, the key areas of the structure to be tested refer to areas where strain and temperature will change significantly, such as connection parts and joints, load application points, areas with large temperature gradients, bending corners, and support points; stress concentration areas usually appear in places where the geometric shape changes more dramatically, such as mutations, holes, sharp angles, etc., and the stress values ​​in these areas are significantly higher than those in other areas. Therefore, stress concentration areas can be identified by calculating the stress intensity of different areas. A common method to quantify the degree of stress concentration is to use the stress concentration factor, which is the ratio of the maximum stress in a specific area to the stress in the unaffected part of the area. The larger the stress concentration factor value, the more severe the stress concentration; areas with obvious deformation can obtain the displacement of each node 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 that in other parts, these areas can be regarded as areas with obvious deformation.

[0068] It should be noted that by accurately calculating the spacing and distribution density of the initial measurement nodes, full coverage of sensors in key areas is ensured. This not only optimizes the layout of the optical fiber and can effectively capture subtle changes caused by stress concentration and temperature differences, but also can flexibly adjust the number and spacing of measurement nodes according to the physical properties of the structure to be measured and the expected strain and temperature change range, thereby improving the accuracy and reliability of the monitoring data, ensuring efficient real-time monitoring in complex environments, and providing a basis for subsequent adjustments to the spacing and number of measurement nodes.

[0069] Therefore, it is necessary to determine the critical areas of the structure to be measured and the measurement spacing of the distributed fiber optic sensors, based on the following method:

[0070] Using engineering analysis methods to identify critical areas of the structure to be tested, finite element analysis is performed on the structure to be tested. First, a physical model is established based on the actual size of the structure to be tested, and corresponding boundary conditions and loads are applied. Then, the stress, strain distribution and deformation of the structure to be tested under different working conditions are simulated to identify areas with concentrated stress or obvious deformation. These areas are the weak links of the structure to be tested and are defined as critical areas.

[0071] The key area is divided into equidistant grid units of 10 cm*10 cm. A measurement node is set at the center of each grid unit. The spacing d0 between measurement nodes is 10 cm. Fiber optic sensors are deployed at the measurement nodes to obtain strain signals and temperature signals in the key areas of the structure to be measured.

[0072] It should be noted that the Rayleigh scattering principle and the Raman scattering principle provide high-precision real-time monitoring capabilities. Rayleigh scattering directly reflects the strain state of the material through phase changes, making strain monitoring extremely sensitive and accurate. Raman scattering, on the other hand, reflects temperature changes through frequency changes of the optical signal, enabling accurate measurement over a wide temperature range. Calculating the strain and temperature values ​​of the measurement node using these two principles enables comprehensive monitoring of the structure under test, thereby ensuring its safety and reliability during use.

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

[0074] It's important to note that by collecting strain and temperature values ​​at each measurement node and simulating them using polynomial fitting, we can effectively reveal the relationship between the two, thereby forming a systematic, quantifiable model. This not only helps improve the resolution of monitoring data and reduce measurement errors caused by environmental changes, but also provides a more scientific basis for structural health monitoring, optimizes maintenance decisions, and extends the service life of structures.

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

[0076] The echo signal received by the fiber optic sensor is analyzed in the time and frequency domains by a highly sensitive photoelectric detector to identify and extract useful Rayleigh scattering 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 converted into the frequency domain using fast Fourier transform technology to separate different frequency components and extract the Rayleigh scattering signal with a specific wavelength change. 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 the Rayleigh scattering information and Raman scattering information, the strain value and the temperature value are matched to establish a mathematical model of the strain-temperature coupling relationship. The fitting formula is:

[0078] Δ∈=a0+a1T+a2T 2 +……+a n T n

[0079] Among them, T is the measured temperature value, a0, a1, a2…a n is the fitting coefficient, n is the order of the polynomial, and Δ∈ represents the strain value caused by temperature change;

[0080] The least squares method is used to solve the polynomial fitting coefficients so that the error between the fitting result and the experimental data is minimized. An error function is constructed and the error function is minimized to solve the fitting coefficients. The expression of the error function is:

[0081]

[0082] Where μ represents the error 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, ∈′ represents the strain prediction value of the model, ∈ jis the strain value of the j-th sample point; in the above formula, the least squares method is used for solution because for polynomial fitting, the quadratic form of the error function ensures that its optimal solution in the parameter space is unique, which makes the solution process more stable and easy to calculate, and the error is squared to ensure that all error terms are positive, so that any deviation will be amplified, especially larger deviations will be more valued. It should be noted that the sample point in the above experiment refers to the ideal measurement node 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 an established strain-temperature calibration model, measurement accuracy and system reliability can be significantly improved, eliminating the coupling effect between strain and temperature. This process enhances the efficiency of data analysis, reduces human error, supports scientific engineering decision-making, and adapts to applications under diverse environmental conditions. Furthermore, with the accumulation of data and the establishment of a feedback mechanism, the model can be continuously optimized to ensure continued reliability in structural health monitoring, thereby improving engineering safety and durability.

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

[0085] Based on the mathematical model of strain-temperature coupling relationship, combined with the calculated fitting coefficients a0, a1, a2…a n The calibration compensation model is constructed based on the formula:

[0086]

[0087] in, It represents the strain value of the i-th measurement node caused by temperature change, which is used as the strain correction value. represents the temperature value measured by the i-th measurement node;

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

[0089]

[0090] in, represents the corrected strain value of the i-th measurement node, It represents the strain value actually monitored by 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 will simultaneously affect the signal of the optical fiber. Strain will cause mechanical deformation of the optical fiber material, thereby affecting the phase or frequency of the optical signal, while temperature changes will change the thermal properties of the optical fiber material, thereby affecting the time delay, intensity and scattering characteristics of the optical signal. Since the effects of strain and temperature are mutually coupled, the signal captured by the optical fiber sensor is often the superposition effect of the two. If these two effects are not decoupled and the original sensing data is directly used, it may lead to large errors in the measurement results. For example, when the temperature change is small, the signal may mainly reflect the strain information, but when the temperature change is large, the signal may be dominated by the temperature effect, masking the actual 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 real-time monitored strain value is used for correction to obtain the final accurate calibration value. The fitting formula adopts a polynomial structure because in the temperature-strain relationship of many materials and structures, the effect of temperature on strain is not linear. The polynomial model can effectively capture this nonlinear relationship, and the high-order terms of the polynomial make the model more flexible, which can be adjusted according to the characteristics of the actual measurement data. By selecting the 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's important to note that calculating the calibration deviation factor quantifies the degree of deviation between real-time monitoring values ​​and corrected values, assessing the effectiveness of corrections, optimizing the monitoring system, helping to identify anomalies, and supporting engineers in making maintenance decisions based on reliable data. Furthermore, regularly calculating the BD enhances the reliability of the monitoring system, promotes industry standardization, improves technical uniformity and compatibility, and ultimately improves system performance and reliability.

[0094] Therefore, it is necessary to generate a calibration deviation coefficient based on the degree of deviation between the strain value and the corrected strain value at all measurement nodes of the optical fiber. The formula is:

[0095]

[0096] Among them, BD i Represents the calibration deviation coefficient of the i-th measurement node; in the above formula, the degree of error elimination is determined by measuring a quantitative indicator of the deviation between the corrected strain value and the original strain value. When the calibration deviation coefficient is close to 1, it means 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 means that the correction effect is poor and the model needs to be adjusted;

[0097] The reason why strain correction is needed is that the strain data obtained by the optical fiber sensor will be affected by temperature changes. It must be corrected through the strain-temperature calibration model to eliminate the temperature effect and ensure the accuracy of the strain data. By correcting the coupling effect of strain and temperature, the accuracy of the measurement results can be significantly improved, allowing the system to monitor the status of the structure in real time under dynamic environments. By continuously optimizing the calibration model and deviation coefficient, the system can adaptively adjust the number and spacing of measurement nodes, and maintain high reliability under different environmental conditions.

[0098] Table 1 - Calibration deviation coefficient calculation table

[0099]

[0100] As shown in Table 1, the present invention selected 20 measurement nodes for experimental result certification after calibration, and selected an optical fiber sensor for measuring grain and oil distribution for measurement experiments. The microstrain range is 0 to 140, and the BD value is calculated for each set of data to judge the accuracy of the strain value after model calibration. From the specific data in the table, the difference between the original strain and the corrected strain of each measurement node in each group shows 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 is and the higher the measurement accuracy is. As the stress value increases, the BD value shows an overall upward trend, indicating that the correction effect gradually increases and the deviation between the original strain value and the corrected strain value gradually decreases. In the first five groups of data, the BD value increases from 0.8000 to 0.9444, indicating that within a smaller strain range, the fitting correction effect of the model is weak, but as the actual measured strain value gradually increases, the BD value stabilizes above 0.95, indicating that the correction effect within this range is good; from the 16th to the 20th group, the DD value further approaches 1, indicating that the model has extremely small calibration deviation within this stress value measurement range, and can effectively eliminate the influence of temperature effect on strain data.

[0101] It should be noted that when the DD value is outside the set threshold range, monitoring the spatial and temporal rate of change of strain and temperature can promptly identify abnormal areas, ensuring timely feedback on the status of critical areas and the implementation of appropriate measures. By adjusting the number and density of measurement nodes, monitoring accuracy can be increased in abnormal areas, while maintaining the initial measurement distance in unchanged areas, effectively utilizing resources and avoiding unnecessary waste. This dynamic adjustment mechanism not only improves the responsiveness of the monitoring system but also provides important guarantees for maintaining the safety of the structure.

[0102] Therefore, it is necessary to evaluate the abnormal characteristics of the grid unit where the node is located based on the spatial distribution and time change rate, determine the abnormal area and dynamically adjust the configuration density of the measurement node. The method is based on:

[0103] Set up the deviation threshold interval [y min ,y max ], when DD i <y min or DD i >y max When the strain change threshold τ is set ∈ and temperature change threshold τ T , and calculate the rate of change of strain and temperature in space and time, based on the 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 position of the measurement node, and t is the time when the strain and temperature values ​​of the measurement node are obtained;

[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 interval is set to [0.95, 1.05]. The reason for setting this interval is that grain and oil distributed optical fiber sensors are usually used for real-time monitoring of various physical quantities in agricultural production, 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% has sufficient accuracy to make decisions. Otherwise, it may cause problems such as grain mildew and deterioration, resulting in quality loss. By setting this threshold, it is ensured that the measurement errors of these important parameters will not have an adverse effect on the storage environment monitoring of grain and oil.

[0107] Set the abnormal area determination conditions to:

[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 unit where the measurement node is located is marked as an abnormal grid, and the abnormal area is composed of adjacent abnormal grids;

[0111] According to the degree of change of abnormal grids, the spacing of measurement nodes is adjusted, and the spacing between adjacent abnormal grids is reduced to d new , and increase the number of measurement nodes in the abnormal grid, based on the formula:

[0112]

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

[0114] Among them, d new represents the adjusted measurement node spacing, f is the resolution factor, and α is the sensitivity coefficient, which is used to control the resolution adjustment range; in the above formula for adjusting the measurement node spacing, max[G ∈ (x,t),V ∈ (x,t),G T (x,t),V T (x, t)] The maximum value method is used to ensure that the measurement nodes can be adjusted more flexibly when the strain and temperature changes are significant. The addition of 1 is because if no change is detected, the measurement node spacing 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 variation of the spacing, making the adjustment process smoother and more controllable.

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

[0116] See also Figure 3 The present invention also provides a strain and temperature synchronous calibration device based on distributed optical fiber sensing, wherein the calibration device is used to execute the above-mentioned strain and temperature synchronous calibration method based on distributed optical fiber sensor, comprising:

[0117] an initial measurement node design module, which is used to obtain the initial measurement node spacing of the distributed optical fiber sensor based on the multi-distribution principle, set the distribution density of the measurement nodes based on the measurement node algorithm, select the optical fiber type and the obtained initial measurement node spacing and distribution density of the measurement nodes according to monitoring requirements, use computer-aided design software 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] A signal acquisition and data analysis module, which is used to use a laser to transmit a pulsed light signal to the optical fiber sensor, capture the echo signal during propagation in the optical fiber, record the time delay, intensity change, and phase change of the echo signal obtained by the measurement node, analyze the received echo signal, calculate the strain value of the measurement node in the optical fiber based on the Rayleigh scattering principle, and calculate the temperature value of the measurement node in the optical fiber using the Raman scattering principle;

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

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

[0121] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0122] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed by 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, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0124] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A strain and temperature synchronous calibration method based on distributed optical fiber sensing, characterized in that: The specific steps include: Step 1: 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 signals and temperature signals in the key area of ​​the structure to be measured; Step 2: Using a laser to emit a pulsed light signal to the optical fiber sensor, collecting its echo signal as the light signal propagates along the optical fiber, and analyzing and preprocessing the collected echo signal. The strain value of each measurement node in the optical fiber is extracted based on the Rayleigh scattering information, and the temperature value of each measurement node in the optical fiber is analyzed based on the Raman scattering information. Step 3: Perform data matching processing on the strain values ​​and temperature values ​​obtained at each measurement node, establish a mathematical model of the strain-temperature coupling relationship, use the polynomial curve fitting method to build a calibration compensation model, perform temperature compensation processing on the raw strain data collected in real time, and output the strain measurement value corrected by the calibration compensation model; Step 4: Set the calibration deviation threshold interval. 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 interval, the abnormal characteristics of the grid unit where the node is located are evaluated based on the spatial distribution and time change rate, the abnormal area is determined, and the configuration density of the measurement node is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

2. The method for synchronous calibration of strain and temperature based on distributed optical fiber sensing according to claim 1, characterized in that ,The key areas of the structure to be measured and the ,measurement spacing of the distributed fiber optic sensors are determined ,based on the following method: Using engineering analysis methods to identify critical areas of the structure to be tested, finite element analysis is performed on the structure to be tested. First, a physical model is established based on the actual size of the structure to be tested, and corresponding boundary conditions and loads are applied. Then, the stress, strain distribution and deformation of the structure to be tested under different working conditions are simulated to identify areas with concentrated stress or obvious deformation. These areas are the weak links of the structure to be tested and are defined as critical areas. The key area is divided into equidistant grid units of 10 cm*10 cm. A measurement node is set at the center of each grid unit. The spacing d0 between measurement nodes is 10 cm. Fiber optic sensors are deployed at the measurement nodes to obtain strain signals and temperature signals in the key areas of the structure to be measured.

3. The method for synchronous strain and temperature calibration based on distributed optical fiber sensing according to claim 1, characterized in that: The collected echo signals are analyzed and preprocessed, and the strain and temperature values ​​of each measurement node are obtained based on the Rayleigh scattering information and Raman scattering information in the echo signals. A mathematical model of the strain-temperature coupling relationship is established based on the following method: The echo signal received by the fiber optic sensor is analyzed in the time and frequency domains by a highly sensitive photoelectric detector to identify and extract useful Rayleigh scattering 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 converted into the frequency domain using fast Fourier transform technology to separate different frequency components and extract the Rayleigh scattering signal with a specific wavelength change. 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 the Rayleigh scattering information and Raman scattering information, the strain value and the temperature value are matched to establish a mathematical model of the strain-temperature coupling relationship. The fitting formula is: Δ∈=a0+a1T+a2T 2 +……+a n T n Among them, T is the measured temperature value, a0, a1, a2…a n is the fitting coefficient, n is the order of the polynomial, and Δ∈ represents the strain value caused by temperature change; The least squares method is used to solve the polynomial fitting coefficients so that the error between the fitting result and the experimental data is minimized. An error function is constructed and the error function is minimized to solve the fitting coefficients. The expression of the error function is: Where μ represents the error 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, ∈′ represents the strain prediction value of the model, ∈ j is the strain value of the j-th sample point.

4. The method for synchronous strain and temperature calibration based on distributed optical fiber sensing according to claim 3, characterized in that: Construct a calibration compensation model, perform temperature compensation on the raw strain data collected in real time, and output the strain measurement value corrected by the calibration compensation model. The method is based on: Based on the mathematical model of strain-temperature coupling relationship, combined with the calculated fitting coefficients a0, a1, a2…a n The calibration compensation model is constructed based on the formula: in, It represents the strain value of the i-th measurement node caused by temperature change, which is used as the strain correction value. represents the temperature value measured by the i-th measurement node; The actual monitored strain value of each measurement node is used as input, and the corrected strain value is output based on the constructed calibration compensation model. The formula is: in, represents the corrected strain value of the i-th measurement node, It represents the strain value actually monitored by the i-th measurement node in the optical fiber.

5. The method for synchronous strain and temperature calibration based on distributed optical fiber sensing according to claim 4, characterized in that: The calibration deviation coefficient is generated based on the degree of deviation between the strain value and the corrected strain value at all measurement nodes of the optical fiber. The formula is: Among them, BD i Represents the calibration deviation coefficient of the i-th measurement node.

6. The method for synchronous strain and temperature calibration based on distributed optical fiber sensing according to claim 5, characterized in that: Based on the spatial distribution and temporal change rate, the abnormal characteristics of the grid unit where the node is located are evaluated, the abnormal area is determined, and the configuration density of the measurement node is dynamically adjusted. The method is based on: Set up the deviation threshold interval [y min ,y max ], when BD i <y min or BD i >y max When the strain change threshold τ is set ∈ and temperature change threshold τ T , and calculate the rate of change of strain and temperature in space and time, based on the 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 position of the measurement node, and t is the time when the strain and temperature values ​​of the measurement node are obtained; Set the abnormal area determination conditions to: 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 unit where the measurement node is located is marked as an abnormal grid, and the abnormal area is composed of adjacent abnormal grids; According to the degree of change of abnormal grids, the spacing of measurement nodes is adjusted, and the spacing between adjacent abnormal grids is reduced to d new , and increase the number of measurement nodes in the abnormal grid, based on the formula: f=1+α*max[G ∈ (x,t),V ∈ (x,t),G T (x,t),V T (x,t)] Among them, d new It represents the distance between measurement nodes after adjustment, f is the resolution factor, and α is the sensitivity coefficient, which is used to control the resolution adjustment range.

7. 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 according to any one of claims 1 to 6, comprising: an initial measurement node design module, which is used to determine a key area of ​​the structure to be measured, divide the key area into a plurality of 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 signals and temperature signals in the key area of ​​the structure to be measured; a signal acquisition and data analysis module, which is used to use a laser to transmit a pulsed light signal to the optical fiber sensor, collect its echo signal during the process of the light signal propagating along the optical fiber, and analyze and preprocess the collected echo signal to extract the strain value of each measurement node in the optical fiber based on Rayleigh scattering information, and analyze the temperature value of each measurement node in the optical fiber based on Raman scattering information; A calibration model and data correction module is used to perform data matching processing on the strain values ​​and temperature values ​​obtained from each measurement node, establish a mathematical model of the strain-temperature coupling relationship, construct a calibration compensation model using a polynomial curve fitting method, perform temperature compensation processing on the raw strain data collected 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 the calibration deviation threshold interval. 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 interval, the abnormal characteristics of the grid unit where the node is located are evaluated based on the spatial distribution and time change rate, the abnormal area is determined and the configuration density of the measurement node is dynamically adjusted to achieve adaptive optimization of the distributed measurement network system.

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