An integrated nondestructive testing method combining acoustic emission, DIC and infrared technology

Abstract

The present invention belongs to the technical field of nondestructive testing, and discloses an integrated nondestructive testing method combining acoustic emission, DIC and infrared technology, which collects damage-related data of materials or components; pre-processes the collected data, pre-processes the acoustic emission data through bandpass filters and feature extraction, uses Brown distortion model to correct images and distortion coefficient feature extraction to pre-process DIC emission data, and processes infrared data pre-processing modules through infrared images, radiometry, blackbody calibration and feature extraction; and fuses the data obtained after pre-processing through Kalman filters and corresponding sensors. The present invention integrates the joint application of acoustic emission, DIC and infrared technologies, and their joint use can complement each other to overcome the limitations of a single technology, so as to more accurately identify and evaluate the damage state of materials, and improve the efficiency and accuracy of data processing.

Description

Technical Field

[0001] The invention belongs to the technical field of nondestructive testing, and specifically relates to an integrated nondestructive testing method combining acoustic emission, DIC and infrared technologies. Background Art

[0002] Acoustic Emission (AE) is a non-destructive testing technology based on the phenomenon that when a material undergoes plastic deformation or crack expansion under stress, it releases strain energy in the form of elastic waves. This technology uses the received acoustic emission signals to perform dynamic non-destructive testing of materials or components. It has the advantages of high sensitivity and precision, continuous online monitoring, and strong environmental adaptability, and is widely used in petrochemical, material testing, aerospace, and aviation industries.

[0003] The DIC non-contact full-field measurement system is a method that uses optical imaging technology, combined with computer vision and geometric measurement principles, to measure objects in the entire field without contacting the object being measured. This method can not only provide 3D data in the entire field, but also, compared to traditional strain gauge technology, it does not require grinding and pasting on the surface of the object, and can measure strain data in the entire field, not just the strain at a single point.

[0004] Infrared Non-destructive Testing (IR) is a detection method based on the principle of infrared thermal radiation. It analyzes the surface and internal defects or structures of the workpiece being tested by scanning, recording or observing the temperature changes of the detected surface. This technology has the advantages of non-contact, convenience, high efficiency, intuitiveness, large detection area and long-distance non-contact detection. Infrared non-destructive testing technology uses active thermal excitation sources to thermally excite the test piece. Active thermal excitation sources include flash lamps, lasers, LEDs, halogen lamps, ultrasound, electromagnetic excitation, etc. These thermal excitation sources are used to break the original temperature balance on the surface of the test piece and form a new temperature field distribution on the surface of the test piece. The infrared thermal imager collects infrared image sequences before and after excitation, and processes the collected infrared images to detect defects in the test piece.

[0005] Traditional nondestructive testing technologies, such as acoustic emission technology, DIC full-field measurement system and infrared technology, each have certain limitations. For example, although acoustic emission technology can detect small changes inside the material, it is difficult to provide detailed geometric deformation information; DIC technology can provide full-field deformation data, but its detection ability for small cracks is limited; although infrared technology can detect surface and internal defects, its monitoring ability for dynamic processes is weak. Therefore, a detection system integrating multiple technologies is needed to overcome the limitations of a single technology and achieve more comprehensive detection. In practical applications, it is a technical challenge to effectively fuse and analyze the data of acoustic emission, DIC and infrared technologies. Data from different technologies have different characteristics and formats, and advanced algorithms and software need to be developed to achieve synchronous data acquisition, processing and analysis in order to more accurately identify and evaluate the damage state of the material. Traditional nondestructive testing methods often require multiple steps and equipment, which not only increases the time and cost of detection, but also reduces the efficiency of detection. The present invention aims to achieve a one-stop detection solution by integrating acoustic emission, DIC and infrared technologies, reducing the time and equipment required for detection, improving detection efficiency and reducing costs. Summary of the invention

[0006] In view of the shortcomings of the background technology, the present invention integrates acoustic emission, DIC, and infrared technologies into one to overcome the limitations of a single technology, and develops advanced algorithms and software to achieve simultaneous collection, processing, and analysis of data from the three technologies, so as to more accurately identify and evaluate the damage state of the material, thereby realizing a one-stop and more comprehensive detection solution.

[0007] The purpose of the present invention is to propose an integrated nondestructive testing method combining acoustic emission, DIC and infrared technology. The steps of the method are as follows:

[0008] Step S1, collecting damage-related data of materials or components;

[0009] Step S2, performing data preprocessing on the collected data, wherein the data preprocessing includes: an acoustic emission data preprocessing module, a DIC data preprocessing module and an infrared data preprocessing module;

[0010] Step S3, fusing the data obtained after preprocessing by acoustic emission, DIC and infrared technology to obtain an accurate damage value.

[0011] Preferably, the steps of the acoustic emission data preprocessing module in step S2 are as follows:

[0012] Step Sa1, using a bandpass filter to eliminate or reduce environmental noise, the calculation formula is as follows:

[0013] ;

[0014] in, represents the original acoustic emission signal, represents the signal after bandpass filtering;

[0015] Step Sa2, extracting signal features from the signal filtered by the bandpass filter, the signal features include amplitude A, energy E, duration D, and the calculation formula is as follows:

[0016] ;

[0017] ;

[0018] ;

[0019] In the formula It means to calculate the maximum value of the filtered signal. Indicates the integration of the square of the filtered signal, and the integration interval is arrive , Indicates the start time of the calculation signal and end time The difference between

[0020] Preferably, the steps of the DIC data preprocessing module in step S2 are as follows:

[0021] Step Sb1, use the Brown distortion model to correct the image to eliminate distortion. The calculation formula of the coordinates of the image after correction is as follows:

[0022] ;

[0023] ;

[0024] ;

[0025] In the formula is the distance from the pixel to the center of the image, and are the coordinates in the undistorted ideal image, and are the coordinates in the distorted image, , and is the radial distortion coefficient, and is the tangential distortion coefficient;

[0026] Step Sb2, calculating the estimation of the distortion coefficient through a calibration process, and using a calibration plate to obtain feature points in the image and the corresponding world coordinates.

[0027] Preferably, the calibration process steps of the distortion coefficient estimation are as follows:

[0028] Step Sb21, detecting feature points in the calibration image, which will be used to calculate the distortion coefficients in subsequent steps;

[0029] Step Sb22, matching the detected feature points with known geometric features on the calibration plate;

[0030] Step Sb23, using the least square method to estimate the distortion coefficient so that the error between the position of the feature point in the distorted image and the actual detected position is minimized;

[0031] Step Sb24, extracting displacement and strain field , the calculation formula is as follows:

[0032] ;

[0033] in, represents the displacement in the x-axis direction, represents the displacement in the y-axis direction, represents the normal strain along the x-axis, represents the normal strain along the y-axis, represents the shear strain in the xy-axis direction.

[0034] Preferably, the steps of the infrared data preprocessing module in step S2 are as follows:

[0035] Step Sc1, calibrating the infrared image, performing non-uniformity correction to eliminate the fixed pattern noise of the infrared detector and improve the image quality;

[0036] Step Sc2, correcting the radiation intensity, converting the radiation intensity of the infrared image into an actual temperature value;

[0037] Step Sc3, by using a black body radiation source with a known temperature to calibrate the infrared camera, the temperature distribution information of the black body radiation source is extracted to further improve the accuracy and reliability of the infrared camera.

[0038] Preferably, the steps in step Sc1 are as follows:

[0039] Step Sc11, in the absence of a target object, collecting a set of reference images at a uniform temperature to obtain the response characteristics of the infrared detector at the uniform temperature;

[0040] Step Sc12, after statistical analysis of the reference image, calculate the median or average value of the reference image to obtain a non-uniformity correction map;

[0041] Step Sc13, subtracting the non-uniformity correction map from the actual measured image to eliminate fixed pattern noise;

[0042] Preferably, the specific steps of radiometry correction in step Sc2 are as follows:

[0043] Step Sc21, determine the emissivity of the object being measured ;

[0044] Step Sc22, the radiation intensity is derived to obtain the black body radiation brightness, and the radiation intensity of the infrared image is converted into absolute temperature using Planck's law. The formula is as follows:

[0045] ;

[0046] Among them, M( T) is the blackbody radiation brightness, is Planck's constant, is the speed of light, is the wavelength, is the Boltzmann constant, is the absolute temperature;

[0047] Step Sc23, according to the response characteristics of the infrared detector and the known emissivity, the black body radiation brightness of the actual object is obtained, and then the actual temperature of the object is calculated using Planck's law.

[0048] Preferably, the steps in step S3 are as follows:

[0049] Step Sd1, define the state vector of the system , the calculation formula of the state vector is as follows:

[0050] ;

[0051] In the formula Indicates The state variable is The value of the moment;

[0052] Step Sd2, initialize the Kalman filter and initialize the state estimation and the covariance matrix , the formula is as follows:

[0053] ;

[0054] ;

[0055] in, is the system at time step The initial state estimate at Is the system status With the initial state estimate The covariance matrix of the errors between

[0056] Step Sd3, for each moment , the prediction steps performed include:

[0057] The calculation formula for the predicted state is:

[0058] ;

[0059] The formula for calculating the forecast covariance is:

[0060] ;

[0061] in, is the predicted state at time k, is the state transition matrix, is the valuation state at time k-1, is the control input matrix, is the control vector, is the forecast covariance matrix at time k, is the valuation covariance matrix at time k-1, is the process noise covariance matrix;

[0062] Step Sd4, for each sensor data, the update steps performed include:

[0063] The calculation formula for calculating the Kalman gain is:

[0064] ;

[0065] The calculation formula for updating the state estimate is:

[0066] ;

[0067] The calculation formula for updating the covariance matrix is:

[0068] ;

[0069] in, is the Kalman gain, is the observation matrix, It is The measurement vector at time, is the measurement noise covariance matrix, is the updated state estimate, is the updated covariance matrix;

[0070] Step Sd5: Since the three sensors measure different state variables, an observation matrix is ​​defined for each sensor. , including: For AE sensors, Corresponding to the observation of stress state variables; for DIC sensors, Corresponding to the observation of displacement state variables; for IR sensors, Observe the temperature state variable;

[0071] Step Sd6, at each time step, set the corresponding and Implement updated state estimation for each sensor measurement value, repeat the prediction step and update step for each new measurement data point, analyze the difference between the new measurement data and the predicted value, and adjust the accuracy of the state estimation.

[0072] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned acoustic emission, DIC and infrared technology combined integrated non-destructive testing method.

[0073] Compared with the prior art, the present invention proposes an integrated nondestructive testing method combining acoustic emission, DIC and infrared technologies. The method can more comprehensively monitor the damage state of materials or components through the combined application of acoustic emission, DIC and infrared technologies. The acoustic emission technology can capture tiny changes inside the material, the DIC technology provides full-field deformation data, and the infrared technology can detect surface and internal defects. The combination of the three makes the detection result more accurate and comprehensive. Since acoustic emission, DIC and infrared technologies each have different detection principles and advantages, it is necessary to develop advanced algorithms and software to realize synchronous data collection, processing and analysis. Their combined use can complement each other to overcome the limitations of a single technology, so as to more accurately identify and evaluate the damage state of the material, thereby improving the efficiency and accuracy of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The present invention is an overall flow chart of an integrated nondestructive testing method combining acoustic emission, DIC and infrared technologies. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present application to further clearly and completely describe the technical solutions in the embodiments of the present application. It should be noted that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0076] In order to make the invention objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application are further described in detail in conjunction with the drawings in the specification: In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the advantages of the present invention will be further illustrated by comparing the embodiments in conjunction with the drawings and specific implementation methods.

[0077] The present invention proposes an integrated nondestructive testing method combining acoustic emission, DIC and infrared technology. The overall flow chart of the method is as follows: Figure 1 As shown, the steps of this method are described in detail below:

[0078] Step S1: Collecting data related to damage of materials or components

[0079] Step S2, performing data preprocessing on the collected data, wherein the data preprocessing includes: an acoustic emission data preprocessing module, a DIC data preprocessing module and an infrared data preprocessing module;

[0080] Furthermore, the steps of the acoustic emission data preprocessing module in step S2 are as follows:

[0081] Step Sa1, using a bandpass filter to eliminate or reduce environmental noise, wherein the bandpass filter is used to retain signal components within a specific frequency range while suppressing other frequency components, thereby improving the signal-to-noise ratio. The calculation formula is as follows:

[0082] ;

[0083] in, represents the original acoustic emission signal, represents the signal after bandpass filtering;

[0084] Step Sa2, extracting signal features from the filtered signal, the signal features include amplitude A, energy E, duration D, and the calculation formula is as follows:

[0085] ;

[0086] ;

[0087] ;

[0088] In the formula It means to calculate the maximum value of the filtered signal. Indicates the integration of the square of the filtered signal, and the integration interval is arrive , Indicates the start time of the calculation signal and end time The difference between

[0089] Furthermore, the steps of the DIC data preprocessing module in step S2 are as follows:

[0090] Step Sb1, use the Brown distortion model to correct the image to eliminate distortion. The calculation formula of the coordinates of the image after correction is as follows:

[0091] ;

[0092] ;

[0093] ;

[0094] In the formula is the distance from the pixel to the center of the image, and are the coordinates in the undistorted ideal image, and are the coordinates in the distorted image, , and is the radial distortion coefficient, and is the tangential distortion coefficient;

[0095] Step Sb2, calculating the estimation of the distortion coefficient through a calibration process, and using a calibration plate to obtain feature points in the image and the corresponding world coordinates.

[0096] Furthermore, the calibration process steps of the distortion coefficient estimation are as follows:

[0097] Step Sb21, detecting feature points in the calibration image, including corner points or edges, which will be used to calculate distortion coefficients in subsequent steps;

[0098] Step Sb22, matching the detected feature points with known geometric features on the calibration plate;

[0099] Step Sb23, using the least square method to estimate the distortion coefficient so that the error between the position of the feature point in the distorted image and the actual detected position is minimized;

[0100] Step Sb24, extracting displacement and strain field , the calculation formula is as follows:

[0101] ;

[0102] in, represents the displacement in the x-axis direction, represents the displacement in the y-axis direction, represents the normal strain along the x-axis, represents the normal strain along the y-axis, represents the shear strain in the xy-axis direction.

[0103] Furthermore, the steps of the infrared data preprocessing module in step S2 are as follows:

[0104] Step Sc1, calibrating the infrared image, performing non-uniformity correction to eliminate the fixed pattern noise of the infrared detector and improve the image quality;

[0105] Step Sc2, correcting the radiation intensity, converting the radiation intensity of the infrared image into an actual temperature value;

[0106] Step Sc3, by using a black body radiation source with a known temperature to calibrate the infrared camera, the temperature distribution information of the black body radiation source is extracted to further improve the accuracy and reliability of the infrared camera.

[0107] Furthermore, the steps in step Sc1 are as follows:

[0108] Step Sc11, in the absence of a target object, collecting a set of reference images at a uniform temperature to obtain the response characteristics of the infrared detector at the uniform temperature;

[0109] Step Sc12, after statistical analysis of the reference image, calculate the median or average value of the reference image to obtain a non-uniformity correction map;

[0110] Step Sc13, subtracting the non-uniformity correction map from the actual measured image to eliminate fixed pattern noise;

[0111] Furthermore, the steps in step Sc2 are as follows:

[0112] Step Sc21, determine the emissivity of the object being measured ;

[0113] Step Sc22, the radiation intensity is derived to obtain the black body radiation brightness, and the radiation intensity of the infrared image is converted into absolute temperature using Planck's law. The formula is as follows:

[0114] ;

[0115] Among them, M( T) is the blackbody radiation brightness, is Planck's constant, is the speed of light, is the wavelength, is the Boltzmann constant, is the absolute temperature;

[0116] Step Sc23, according to the response characteristics of the infrared detector and the known emissivity, the black body radiation brightness of the actual object is obtained, and then the actual temperature of the object is calculated using Planck's law.

[0117] Step S3, fusing the data obtained after preprocessing by acoustic emission, DIC and infrared technology to obtain an accurate damage value.

[0118] Furthermore, the specific steps of data fusion in step S3 are as follows:

[0119] Step Sd1, define the state vector of the system , the calculation formula of the state vector is as follows:

[0120] ;

[0121] In the formula Indicates The state variable is The value of the moment;

[0122] Step Sd2, initialize the Kalman filter and initialize the state estimation and the covariance matrix , the formula is as follows:

[0123] ;

[0124] ;

[0125] in, is the system at time step The initial state estimate at Is the system status With the initial state estimate The covariance matrix of the errors between

[0126] Step Sd3, for each moment , the prediction steps performed include:

[0127] The predicted state is used to predict the current state based on the state and control input at the previous moment. The calculation formula is:

[0128] ;

[0129] The predicted covariance is used to predict the covariance matrix of the current moment based on the covariance matrix of the previous moment and the process noise. The calculation formula is:

[0130] ;

[0131] in, is the predicted state at time k, is the state transfer matrix, which is used to describe the change of system state from k-1 to k. is the valuation state at time k-1, is the control input matrix, which is used to describe the influence of the control input on the system state. is the control vector, representing the control input at time k, is the forecast covariance matrix at time k, is the valuation covariance matrix at time k-1, is the process noise covariance matrix, which represents the uncertainty of system state changes;

[0132] Step Sd4, for each sensor data, the update steps performed include:

[0133] The Kalman gain is calculated to determine the influence of the observation on the state estimation. It reflects the trade-off between the reliability of the observation and the prediction error. The calculation formula is:

[0134] ;

[0135] Update the state estimation, fuse the predicted state with the actual observation value, and obtain a more accurate state estimation. The calculation formula is:

[0136] ;

[0137] Updating the covariance matrix reflects the uncertainty of the state estimation. By updating the covariance matrix, the accuracy of the state estimation can be evaluated. The calculation formula is:

[0138] ;

[0139] in, is the Kalman gain, is the observation matrix, It is The measurement vector at time, is the measurement noise covariance matrix, is the updated state estimate, is the updated covariance matrix;

[0140] Step Sd5: Since the three sensors measure different state variables, an observation matrix is ​​defined for each sensor. , including: For AE sensors, Corresponding to the observation of stress state variables; for DIC sensors, Corresponding to the observation of displacement state variables; for IR sensors, Observe the temperature state variable;

[0141] Step Sd6, at each time step, set the corresponding and Implement updated state estimation for each sensor measurement value. For each new measurement data point, repeat the prediction step and update step, analyze the difference between the new measurement data and the predicted value, adjust the precision of the state estimation, and ensure the accuracy of the state estimation.

[0142] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned acoustic emission, DIC and infrared technology combined integrated non-destructive testing method.

[0143] The present invention integrates a detection system of multiple technologies through the combined application of acoustic emission, DIC and infrared technologies to overcome the limitation of a single technology, collects data related to the damage of materials or components; pre-processes the collected data, pre-processes the acoustic emission data through a bandpass filter and feature extraction, pre-processes the DIC emission data using a Brown distortion model to correct the image and extract the distortion coefficient feature, and processes the infrared data pre-processing module through infrared images, radiometry and blackbody calibration; and fuses the data obtained after pre-processing through a Kalman filter and a corresponding sensor to achieve more comprehensive detection, thereby improving the efficiency and accuracy of data processing.

[0144] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An integrated nondestructive testing method using acoustic emission, DIC and infrared technology, characterized in that: include: Step S1, collecting damage-related data of materials or components; Step S2, performing data preprocessing on the collected data, the data preprocessing includes: Steps of acoustic emission data preprocessing module: Step Sa1, using a bandpass filter to eliminate or reduce environmental noise, the calculation formula is as follows: ； in, represents the original acoustic emission signal, represents the signal after bandpass filtering; Step Sa2, extracting signal features from the signal filtered by the bandpass filter, the signal features include amplitude A, energy E and duration D, and the calculation formula is as follows: ； ； ； In the formula It means to calculate the maximum value of the filtered signal. Indicates the integration of the square of the filtered signal, and the integration interval is arrive , Indicates the start time of the calculation signal and end time The difference between Steps of DIC data preprocessing module: Step Sb1, use the Brown distortion model to correct the image to eliminate distortion. The calculation formula of the coordinates of the image after correction is as follows: ； ； ； In the formula is the distance from the pixel to the center of the image, and are the coordinates in the undistorted ideal image, and are the coordinates in the distorted image, , and is the radial distortion coefficient, and is the tangential distortion coefficient; Step Sb2, calculating the estimation of the distortion coefficient through a calibration process, and using a calibration plate to obtain feature points in the image and corresponding world coordinates; Steps of infrared data preprocessing module: Step Sc1, calibrating the infrared image and performing non-uniformity correction to eliminate the fixed pattern noise of the infrared detector; Step Sc2, correcting the radiation intensity, converting the radiation intensity of the infrared image into an actual temperature value; Step Sc3, calibrating the infrared camera by using a black body radiation source with a known temperature to extract information on the temperature distribution of the black body radiation source; Step S3, fusing the data obtained after preprocessing by acoustic emission, DIC and infrared technology to obtain an accurate damage value.

2. The acoustic emission, DIC and infrared technology combined integrated nondestructive testing method according to claim 1 is characterized in that: The calibration process steps of the distortion coefficient estimation in step Sb2 are as follows: Step Sb21, detecting feature points in the calibration plate image; Step Sb22, matching the detected feature points with known geometric features on the calibration plate; Step Sb23, using the least square method to estimate the distortion coefficient so that the error between the position of the feature point in the distorted image and the actual detected position is minimized; Step Sb24, extract displacement and strain field , the calculation formula is as follows: ； in, represents the displacement in the x-axis direction, represents the displacement in the y-axis direction, represents the normal strain along the x-axis, represents the normal strain along the y-axis, represents the shear strain in the xy-axis direction.

3. The method of nondestructive testing using acoustic emission, DIC and infrared technology as claimed in claim 1, characterized in that: The steps in step Sc1 are as follows: Step Sc11, in the absence of a target object, collecting a set of reference images at a uniform temperature to obtain the response characteristics of the infrared detector at the uniform temperature; Step Sc12, after statistical analysis of the reference image, calculate the median or average value of the reference image to obtain a non-uniformity correction map; Step Sc13, subtracting the non-uniformity correction image from the actual measured image to eliminate fixed pattern noise.

4. The method of nondestructive testing using acoustic emission, DIC and infrared technology combined in one body according to claim 1, characterized in that: The steps in step Sc2 are as follows: Step Sc21, determine the emissivity of the object being measured ; Step Sc22, the radiation intensity is derived to obtain the black body radiation brightness, and the radiation intensity of the infrared image is converted into absolute temperature using Planck's law. The formula is as follows: ; in, is the blackbody radiation brightness, is Planck's constant, is the speed of light, is the wavelength, is the Boltzmann constant, is the absolute temperature; Step Sc23, according to the response characteristics of the infrared detector and the known emissivity, the black body radiation brightness of the actual object is obtained, and then the actual temperature of the object is calculated using Planck's law.

5. The acoustic emission, DIC and infrared technology combined integrated nondestructive testing method according to claim 1 is characterized in that: The steps of data fusion in step S3 are as follows: Step Sd1, define the state vector of the system , the calculation formula of the state vector is as follows: ; In the formula Indicates The state variable is The value of the moment; Step Sd2, initialize the Kalman filter and initialize the state estimation and the covariance matrix , the formula is as follows: ; ; in, is the system at time step The initial state estimate at Is the system status With the initial state estimate The covariance matrix of the errors between Step Sd3, for each moment , the prediction steps performed include: The calculation formula for the predicted state is: ; The forecast covariance is calculated as: ; in, It is at the moment The predicted state of is the state transition matrix, It is at the moment -1 valuation status, is the control input matrix, is the control vector, It is at the moment The prediction covariance matrix of It is at the moment -1 estimate covariance matrix, is the process noise covariance matrix; Step Sd4, for each sensor data, the update steps performed include: The calculation formula for calculating the Kalman gain is: ; The calculation formula for updating the state estimate is: ; The calculation formula for updating the covariance matrix is: ; in, is the Kalman gain, is the observation matrix, It is The measurement vector at time, is the measurement noise covariance matrix, is the updated state estimate, is the updated covariance matrix; Step Sd5, define an observation matrix for each sensor , including: For AE sensors, Corresponding to the observation of stress state variables; for DIC sensors, Corresponding to the observation of displacement state variables; for IR sensors, Observe the temperature state variable; Step Sd6, at each time step, set the corresponding and Implement updated state estimation for each sensor measurement value, repeat the prediction step and update step for each new measurement data point, analyze the difference between the new measurement data and the predicted value, and adjust the accuracy of the state estimation.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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