A non-contact microscopic strain measurement method, device, electronic device and medium applicable to particle-reinforced composites

Abstract

The present invention provides a non-contact microscopic strain measurement method, device, electronic device and medium applicable to particle-reinforced composites. The method includes generating a set of circular frames using an adaptive algorithm according to the gray-scale difference between particles and the matrix, where each circular frame in the set of circular frames contains one of the particles; calculating the rigid body motion of the particles in each circular frame in the set of circular frames to obtain a set of particle displacement data; and fitting the strain of the matrix between each of the particles using the moving least squares method according to the set of particle displacement data. This method does not require manual preparation of speckles and can accurately measure the surface deformation of particle-reinforced composites only by relying on the gray-scale difference between the reinforcing phase and the matrix.

Description

Technical Field

[0001] The present invention belongs to the technical field of measurement, and relates to a method for measuring the strain of a particle-reinforced composite material, and particularly to a non-contact microscopic strain measurement method, device, electronic device and medium applicable to a particle-reinforced composite material. Background Art

[0002] As a heterogeneous multiphase material, the particle-reinforced composite material is characterized in that the reinforcing phase is discontinuous particles. Compared with homogeneous single-phase materials, the deformation of particle-reinforced composite materials is more complex at the microscopic scale. To study the failure mechanism of particle-reinforced composite materials and the particle reinforcement effect, it is necessary to measure the strain field non-uniformity caused by the multiphase structure of the material at the microscopic scale. The conventional measurement method is Digital Image Correlation (DIC), which obtains the surface displacement and deformation information by comparing the images of the material surface before and after deformation. However, for materials with not very obvious surface gray-scale differences, artificial speckles need to be prepared on the material surface.

[0003] The particles on the surface of the particle-reinforced composite material can provide the speckles required by the DIC technology, but the quality of the speckles is not high and cannot meet the requirements of accurate deformation measurement. If the method of artificially preparing speckles is adopted, the speckles will affect the observation of the reinforcing phase particles. Summary of the Invention

[0004] To solve the technical problems existing in the prior art, the present invention provides a non-contact microscopic strain measurement method, device, electronic device and medium applicable to a particle-reinforced composite material. This method does not require artificial preparation of speckles and only relies on the gray-scale difference between the reinforcing phase and the matrix to achieve accurate measurement of the surface deformation of the particle-reinforced composite material.

[0005] To achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] One of the objects of the present invention is to provide a non-contact microscopic strain measurement method applicable to a particle-reinforced composite material. The measurement method includes:

[0007] According to the gray-scale difference between the particles and the matrix, an adaptive algorithm is used to generate a set of circular frames, and each circular frame in the set of circular frames contains one of the particles;

[0008] The rigid body motion of the particles in each circular frame in the set of circular frames is calculated by using the DIC algorithm to obtain a set of particle displacement data;

[0009] According to the set of particle displacement data, the strain of the matrix between each of the particles is fitted by using the moving least squares method.

[0010] As a preferred technical solution of the present invention, the number of gray-level gradients inside the circular frame is represented by the sum of the squares of the gray-level gradients within the circular frame, as shown in Equation 1;

[0011] Equation 1;

[0012] Among them, SSSIG represents the sum of the squares of the gray-level gradients of all pixels in the circular frame, ∇f_i represents the gray-level gradient of the i-th pixel within the circular frame, and i is the pixel number.

[0013] As a preferred technical solution of the present invention, the adaptive algorithm includes:

[0014] Select a continuous pixel region where the gray level of each pixel in the continuous pixel region is greater than the first threshold, assuming that the gray value of the particle is greater than the gray value of the matrix;

[0015] Calculate the weighted gray-level center of the region as the center of the circular frame and give the initial radius of the circular frame;

[0016] Calculate the sum of the squares of the gray-level gradients within the circular frame, denoted as SSSIG0; increase the radius of the circular frame by one pixel, and calculate the sum of the squares of the gray-level gradients within the circular frame again, denoted as SSSIG1; if (SSSIG1 - SSSIG0) / SSSIG0 is less than the second threshold and SSSIG1 is greater than the third threshold, then output the circular frame; if (SSSIG1 - SSSIG0) / SSSIG0 is greater than the second threshold, then increase the radius by one pixel and calculate the sum of the squares of the gray-level gradients within the circular frame, denoted as SSSIG2, and judge the relationship between (SSSIG2 - SSSIG1) / SSSIG1 and the second threshold; repeat the above steps until (SSSIG n -SSSIG n-1 ) / SSSIG n-1 is less than the second threshold and SSSIG n is greater than the third threshold, then output the circular frame; if SSSIG n is less than the third threshold, then abandon the region, where n is an integer greater than or equal to 1.

[0017] As a preferred technical solution of the present invention, the first threshold is the gray level at the junction of the matrix and the particle;

[0018] The second threshold is the lowest relative increment of the sum of the squares of the gray-level gradients;

[0019] The third threshold is the lowest sum of the squares of the gray-level gradients within the circular frame.

[0020] As a preferred technical solution of the present invention, the DIC algorithm is used to calculate the rigid body motion of the particles in each circular frame of the circular frame group.

[0021] As a preferred technical solution of the present invention, the method for fitting calculation by moving least squares method includes:

[0022] Define a moving window around the fitting calculation point of the matrix, and the moving window includes at least three particles;

[0023] Select the representation function of the displacement field of the moving window, use the least squares method to fit the displacements of the particles in the moving window, obtain the displacement field of the fitting calculation point, and take the derivative of the displacement field of the fitting calculation point to obtain the strain of the fitting calculation point.

[0024] As a preferred technical solution of the present invention, the representation function of the displacement field of the moving window includes polynomial function or trigonometric function.

[0025] The second object of the present invention is to provide a non-contact microscopic strain measurement device applicable to particle-reinforced composites. The measurement device includes:

[0026] An adaptive particle selection module, which is used to generate a set of circular frames by using an adaptive algorithm according to the gray difference between the particles and the matrix, and each circular frame in the set of circular frames contains one of the particles;

[0027] A particle rigid body motion measurement module, which is used to calculate the rigid body motion of the particles in each circular frame in the set of circular frames to obtain a set of particle displacement data;

[0028] An overall strain measurement module, which is used to obtain the strain of the matrix between each pair of particles by fitting calculation using the moving least squares method according to the set of particle displacement data.

[0029] The third object of the present invention is to provide an electronic device, and the electronic device includes:

[0030] At least one processor and a memory communicatively connected to at least one processor;

[0031] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the non-contact microscopic strain measurement method applicable to particle-reinforced composites provided by the first object of the present invention.

[0032] The fourth object of the present invention is to provide a computer-readable storage medium, and the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the non-contact microscopic strain measurement method applicable to particle-reinforced composites provided by the first object of the present invention when executed by a processor.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] (1) The present invention provides a non-contact microscopic strain measurement method applicable to particle-reinforced composites. Since this method does not require the preparation of artificial speckles, it avoids the influence of artificial speckles on the observation of the microscopic structure of the composites.

[0035] (2) The present invention provides a non-contact microscopic strain measurement method applicable to particle-reinforced composites. This method highly optimizes the solution region for the rigid body motion of the circular frame, that is, a single particle, which contains as much gray-level gradient as possible and less noise, thereby improving the solution accuracy of the rigid body motion of a single particle.

[0036] (3) The present invention provides a non-contact microscopic strain measurement method applicable to particle-reinforced composites. The displacement and strain fields of the matrix between particles are calculated by the moving least squares method, and the flexibility of fitting can be adjusted by selecting different polynomial orders or basis functions. Description of the Drawings

[0037] Figure 1 It is a schematic flow chart of the adaptive algorithm for generating a set of circular frames in the specific embodiment of the present invention;

[0038] Figure 2 It is the adaptive circular frame generated in the specific embodiment of the present invention;

[0039] Figure 3 It is the displacement field calculated from the adaptive circular frame generated in the specific embodiment of the present invention;

[0040] Figure 4 It is the strain field obtained by fitting the particle displacement field in the specific embodiment of the present invention.

[0041] The following further details the present invention. However, the following examples are merely simple examples of the present invention and do not represent or limit the scope of the protection of the present invention. The scope of protection of the present invention shall be subject to the claims. Specific Embodiments

[0042] The technical solution of the present invention will be further described below through specific embodiments.

[0043] The specific embodiment of the present invention provides a non-contact microscopic strain measurement method applicable to particle-reinforced composites. This measurement method includes:

[0044] According to the gray-level difference between particles and the matrix, an adaptive algorithm is used to generate a set of circular frames, and each circular frame in the set of circular frames contains one of the said particles;

[0045] Using the DIC algorithm to calculate the rigid body motion of the particles in each circular frame in the set of circular frames to obtain a set of particle displacement data;

[0046] Based on the particle displacement data set, the strain of the matrix between each pair of the particles is obtained by fitting using the moving least squares method.

[0047] In the present invention, by framing the motion region of the particles and optimizing and solving this motion region, i.e., the circular frame, such that the circular frame contains as much gray-scale gradient as possible and ensures that there is as little noise as possible within the circular frame, the solution accuracy of the particle motion is improved, and there is no need to prepare artificial speckles, thereby avoiding the influence of artificial speckles on the observation of the microstructure of the composite material.

[0048] In a specific embodiment of the present invention, the materials of the particles and the matrix are not specifically limited, and the particles and the matrix applicable to the production of particle-reinforced composite materials are all applicable to the non-contact microscopic strain measurement method provided by the present invention. By way of example, the material of the particles may be aluminum alloy, and the material of the matrix may be ceramic particles (such as TiB2).

[0049] In the present invention, the gray-scale gradient within the circular frame determines the accuracy of calculating the rigid body motion of the particles by the DIC algorithm. Therefore, the circular frame needs to encompass the boundaries of the particles and the matrix as much as possible; at the same time, the size of the circular frame should not be too large to avoid including too much image noise inside the circular frame.

[0050] In a specific embodiment of the present invention, the number of gray-scale gradients inside the circular frame is represented by the sum of the squares of the gray-scale gradients within the circular frame, as shown in Equation 1;

[0051] Equation 1;

[0052] where SSSIG represents the sum of the squares of the gray-scale gradients within the circular frame, represents the i-th gray-scale gradient value within the circular frame, i represents the number of gray-scale gradients within the circular frame, and i is an integer not less than.

[0053] In a specific embodiment of the present invention, the flow of the adaptive algorithm for generating the circular frame group is as Figure 1 shown, and it specifically includes:

[0054] Select a continuous pixel region where the gray scale of each pixel in the continuous pixel region is greater than the first threshold, assuming that the gray scale value of the particles is greater than that of the matrix;

[0055] Calculate the gray-scale weighted center of the region as the center of the circular frame and give the initial radius of the circular frame;

[0056] Calculate the sum of the squares of the gray-scale gradients within the circular frame, denoted as SSSIG0; increase the radius of the circular frame by one pixel, and calculate the sum of the squares of the gray-scale gradients within the circular frame again, denoted as SSSIG1; if (SSSIG1 - SSSIG0) / SSSIG0 is less than the second threshold and SSSIG1 is greater than the third threshold, then output the circular frame; if (SSSIG1 - SSSIG0) / SSSIG0 is greater than the second threshold, then increase the radius by one pixel and calculate the sum of the squares of the gray-scale gradients within the circular frame, denoted as SSSIG2, and determine the relationship between (SSSIG2 - SSSIG1) / SSSIG1 and the second threshold; repeat the above steps until (SSSIG n -SSSIG n-1 ) / SSSIG n-1 is less than the second threshold and SSSIG n is greater than the third threshold, then output the circular frame; if SSSIG n is less than the third threshold, then abandon the area, where n is an integer greater than or equal to 1.

[0057] In a specific embodiment of the present invention, the first threshold is set based on the average gray-scale values of the matrix and the particles, ensuring that the selected circular frame is close to the junction of the particles and the matrix.

[0058] In a specific embodiment of the present invention, the calculation method of the weighted center of the gray scale in the continuous pixel region is as follows:

[0059]

[0060]

[0061] where , , are the x, y coordinates and gray-scale value of the pixel respectively.

[0062] In a specific embodiment of the present invention, the initial radius of the circular frame is set according to the estimated average size of the particles in the image. For example, it can be set to 5.

[0063] In a specific embodiment of the present invention, the relationship between the sum of the squares of the gray-scale gradients within the circular frame at the initial radius and the sum of the squares of the gray-scale gradients within the circular frame at the first radius (initial radius + 1 pixel) is expressed as the relationship between (SSSIG1 - SSSIG0) / SSSIG0 and the second threshold. If (SSSIG1 - SSSIG0) / SSSIG0 is less than the second threshold, then proceed to the next judgment; if (SSSIG1 - SSSIG0) / SSSIG0 is greater than or equal to the second threshold, then continue to increase the radius and repeat this judgment until (SSSIG n -SSSIG n-1 ) / SSSIGn-1 When it is less than the second threshold, proceed to the next judgment. SSSIG n-1 It represents the sum of the squared gray level gradients inside the circular frame at the (n - 1)-th radius (initial radius + n - 1 pixels), where n represents the sum of the squared gray level gradients inside the circular frame at the n-th radius (initial radius + n pixels), and n is an integer greater than or equal to 1.

[0064] In a specific embodiment of the present invention, the second threshold is set according to the actual situation. If it is too large, the circular frame may be too small to enclose the entire particle; if it is too small, the circular frame may be too large and include too much matrix. Generally, it is set to 10%. During the actual operation, a second threshold can be preset first, and then adjusted according to the size of the circular frame.

[0065] In a specific embodiment of the present invention, when (SSSIG n -SSSIG n-1 ) / SSSIG n-1 is less than the second threshold (n is an integer greater than or equal to 1), judge the magnitude relationship between SSSIG n and the third threshold. If SSSIG n is greater than the third threshold, output the circular frame; if SSSIG n is less than the third threshold, discard the circular frame.

[0066] In a specific embodiment of the present invention, the third threshold is set according to the actual situation. If it is too small, the sum of the squared gray level gradients included in the circular frame is insufficient, affecting the DIC measurement accuracy; if it is too large, the number of available circular frames is too small, affecting the subsequent displacement field fitting. During the actual operation, a third threshold can be preset first, and then adjusted according to the number of circular frames. For example, it can be set to 5×10 8 .

[0067] In a specific embodiment of the present invention, since the stiffness of the reinforcing particles is much higher than that of the matrix and can be regarded as a rigid body without considering deformation, the rigid body motion of the particles can adopt the conventional rigid body motion DIC algorithm.

[0068] In a specific embodiment of the present invention, the moving least squares method is an algorithm for interpolating and smoothing data, which applies the least squares method in a local area.

[0069] In a specific embodiment of the present invention, the method for fitting calculation by the moving least squares method includes:

[0070] Define a moving window around the fitting calculation point of the matrix, and the moving window includes at least two particles;

[0071] Select the representation function of the displacement field of the moving window, use the least squares method to fit the displacements of the particles in the moving window, obtain the displacement field of the fitting calculation points, and take the derivative of the displacement field of the fitting calculation points to obtain the strain of the fitting calculation points.

[0072] In a specific embodiment of the present invention, the moving window is a local area around the fitting calculation point, and this local area contains several particles, and the displacement data of each particle has been obtained in the previous steps.

[0073] In a specific embodiment of the present invention, the representation function of the displacement field of the moving window can be a polynomial function, a trigonometric function, etc. Preferably, it is a polynomial function. In order to reflect the uniform displacement mode, the representation function needs to include a constant term. In order to reflect the uniform strain mode, the representation function needs to include the first-order terms of x and y; in order to reflect the non-uniformity of the strain field, the representation function also needs to include the higher-order terms of x and y. Here, only up to the second-order terms are taken. The moving window contains at least three particles, and the three particles have six degrees of freedom, so they correspond to six basis functions of the selected fitting function. As an example, the representation function of the displacement field can be

[0074]

[0075] where ~ are the basis function coefficients.

[0076] A specific embodiment of the present invention provides a non-contact microscopic strain measurement device applicable to particle-reinforced composites. This measurement device includes:

[0077] An adaptive particle selection module, which is used to generate a set of circular frames by using an adaptive algorithm according to the gray-scale difference between the particles and the matrix. Each circular frame in the set of circular frames contains one of the said particles;

[0078] A particle rigid body motion measurement module, which is used to calculate the rigid body motion of the particles in each circular frame in the set of circular frames to obtain a set of particle displacement data;

[0079] An overall strain measurement module, which is used to fit and calculate the strain of the matrix between each particle according to the set of particle displacement data by using the moving least squares method.

[0080] In a specific embodiment of the present invention, the adaptive particle selection module, the particle rigid body motion measurement module, and the overall strain measurement module can be connected by any connection method that realizes electromagnetic signal transmission under the condition of meeting the operation sequence, and no specific limitation is made here.

[0081] A specific embodiment of the present invention provides an electronic device. This electronic device includes:

[0082] At least one processor and a memory communicatively connected to the at least one processor;

[0083] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the non-contact microscopic strain measurement method applicable to particulate-reinforced composites provided by the specific embodiments of the present invention.

[0084] The specific embodiments of the present invention provide a computer-readable storage medium storing computer instructions for causing a processor to implement the non-contact microscopic strain measurement method applicable to particulate-reinforced composites provided by the specific embodiments of the present invention when executed.

[0085] To better illustrate the present invention and facilitate understanding of its technical solutions, typical but non-limiting embodiments of the present invention are as follows:

[0086] Embodiment 1

[0087] This embodiment provides a non-contact microscopic strain measurement method applicable to particulate-reinforced composites, wherein the matrix is aluminum alloy, the particles are TiB2 ceramic particles with a particle size of 1 - 10 μm, and the mass percentage of the particles is 6%. The measurement method includes:

[0088] According to the gray-scale difference between the particles and the matrix, an adaptive algorithm is used to generate a set of circular frames, and each circular frame in the set of circular frames contains one particle;

[0089] The adaptive algorithm includes:

[0090] Select a continuous pixel region where the gray scale of each pixel in the continuous pixel region is greater than a first threshold;

[0091] Calculate the gray-scale weighted center of the region as the center of the circular frame and give an initial radius to the circular frame;

[0092] Calculate the sum of the squares of the gray-scale gradients within the circular frame, denoted as SSSIG0; increase the radius of the circular frame by one pixel and calculate the sum of the squares of the gray-scale gradients within the circular frame again, denoted as SSSIG1; if (SSSIG1 - SSSIG0) / SSSIG0 is less than a second threshold and SSSIG1 is greater than a third threshold, then output the circular frame; if (SSSIG1 - SSSIG0) / SSSIG0 is greater than the second threshold, then increase the radius by one pixel and calculate the sum of the squares of the gray-scale gradients within the circular frame, denoted as SSSIG2, and judge the relationship between (SSSIG2 - SSSIG1) / SSSIG1 and the second threshold; repeat the above steps until (SSSIG n -SSSIG n-1 ) / SSSIG n-1less than the second threshold, but due to SSSIG n less than the third threshold, so this area is abandoned.

[0093] Embodiment 2

[0094] This embodiment uses the same adaptive algorithm as in Embodiment 1 to generate a set of circular frames, but reselects a continuous pixel area, and the gray level of each pixel in the continuous pixel area is greater than the first threshold;

[0095] Calculate the gray-level weighted center of the area as the center of the circular frame, and give an initial radius to the circular frame;

[0096] Calculate the sum of the squares of the gray-level gradients within the circular frame, denoted as SSSIG0; increase the radius of the circular frame by one pixel, and calculate the sum of the squares of the gray-level gradients within the circular frame again, denoted as SSSIG1; if (SSSIG1 - SSSIG0) / SSSIG0 is less than the second threshold, and SSSIG1 is greater than the third threshold, then output this circular frame; if (SSSIG1 - SSSIG0) / SSSIG0 is greater than the second threshold, then increase the radius by one pixel again, calculate the sum of the squares of the gray-level gradients within the circular frame, denoted as SSSIG2, and judge the relationship between (SSSIG2 - SSSIG1) / SSSIG1 and the second threshold; repeat the above steps until (SSSIG n -SSSIG n-1 ) / SSSIG n-1 is less than the second threshold, and SSSIG n is greater than the third threshold, so output this circular frame.

[0097] Repeat the processes of Embodiment 1 and Embodiment 2 until a set of circular frames is obtained, and the result is as Figure 2 shown.

[0098] Embodiment 3

[0099] This embodiment uses the DIC algorithm to calculate the rigid body motion of each particle in the set of circular frames, and the result is as Figure 3 shown.

[0100] Embodiment 4

[0101] This embodiment uses the calculation results of Embodiment 3 and the moving least squares method to calculate the overall strain of the particle-reinforced composite material. The method of fitting calculation by the moving least squares method includes:

[0102] Define a moving window around the fitting calculation point of the matrix, and the moving window includes at least two particles;

[0103] Select the polynomial or basis function of the displacement field of the moving window, and use the least squares method to fit the displacements of the particles in the moving window to obtain the displacement field of the fitting calculation points. Take the derivative of the displacement field of the fitting calculation points to obtain the strain of the fitting calculation points.

[0104] In this embodiment, the selected polynomial or basis function is .

[0105] The matrix of the particle-reinforced composite material adopted in this embodiment is aluminum alloy, the particles are TiB2, the particle size is 1-10 μm, and the mass percentage of the particles is 6%

[0106] The overall strain calculation result of this particle-reinforced composite material is as Figure 4 shown.

[0107] Example 5

[0108] This embodiment provides a non-contact microscopic strain measurement device applicable to particle-reinforced composite materials, which is used for the non-contact microscopic strain measurement method applicable to particle-reinforced composite materials in Embodiments 1-4. This measurement device includes:

[0109] An adaptive particle selection module, which is used to generate a set of circular frames by using an adaptive algorithm according to the gray-scale difference between the particles and the matrix. Each circular frame in the set of circular frames contains one of the said particles;

[0110] A particle rigid body motion measurement module, which is used to calculate the rigid body motion of the particles in each circular frame in the set of circular frames to obtain a set of particle displacement data;

[0111] An overall strain measurement module, which is used to obtain the strain of the matrix between each pair of particles by using the interpolation method according to the set of particle displacement data.

[0112] The applicant declares that the present invention uses the above embodiments to illustrate the detailed structural features of the present invention, but the present invention is not limited to the above detailed structural features, that is, it does not mean that the present invention must rely on the above detailed structural features to be implemented. Those skilled in the art should understand that any improvement to the present invention, the equivalent replacement of the components selected by the present invention, the addition of auxiliary components, and the selection of specific methods, etc., all fall within the protection scope and the disclosure scope of the present invention.

[0113] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept scope of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0114] In addition, it should be noted that, for the various specific technical features described in the above specific embodiments, they can be combined in any appropriate manner without conflict. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

[0115] In addition, any combination can be made between various different embodiments of the present invention, as long as it does not violate the idea of the present invention, and it should also be regarded as the content disclosed by the present invention.

Claims

1. A non-contact microscopic strain measurement method suitable for particle reinforced composite materials, characterized in that: The measuring method comprises: According to the grayscale difference between the particle and the matrix, a circle frame group is generated by using an adaptive algorithm, each circle frame in the circle frame group contains one particle; Calculating the rigid body motion of the particles in each circular frame in the circular frame group to obtain a particle displacement data group; According to the particle displacement data set, the strain of the matrix between each of the particles is calculated by using the moving least squares method; The number of grayscale gradients inside the circular frame is represented by the sum of squares of the grayscale gradients inside the circular frame, as shown in Formula 1; Formula 1; Among them, SSSIG represents the sum of the squares of the grayscale gradients of all pixels in the circle. Represents the grayscale gradient of the i-th pixel in the circle, where i is the pixel number; The adaptive algorithm includes: Selecting a continuous pixel region, wherein the grayscale of each pixel in the continuous pixel region is greater than a first threshold, assuming that the grayscale value of the particle is greater than the grayscale value of the matrix; Calculating the grayscale weighted center of the region as the center of the circular frame, and giving the initial radius of the circular frame; Calculate the sum of squares of grayscale gradients in the circular frame, recorded as SSSIG0; increase the radius of the circular frame by one pixel, calculate the sum of squares of grayscale gradients in the circular frame again, recorded as SSSIG1; if (SSSIG1-SSSIG0) / SSSIG0 is less than the second threshold, and SSSIG1 is greater than the third threshold, output the circular frame; if (SSSIG1-SSSIG0) / SSSIG0 is greater than the second threshold, increase the radius by one pixel again, calculate the sum of squares of grayscale gradients in the circular frame, recorded as SSSIG2, and determine the relationship between (SSSIG2-SSSIG1) / SSSIG1 and the second threshold; repeat the above steps until (SSSIG n -SSSIG n-1 ) / SSSIG n-1 is less than the second threshold, and SSSIG n is greater than the third threshold, then the circle is output; if SSSIG n If the value is less than a third threshold, the region is abandoned, where n is an integer greater than or equal to 1; The first threshold is the gray value at the junction of the matrix and the particle, ensuring that all pixels in the circle are at the particle; The second threshold is the minimum relative increment of the grayscale gradient square sum, ensuring that the grayscale gradient square sum in the selected circular frame does not increase significantly with the increase of the radius; The third threshold is the sum of squares of grayscale gradients in the lowest circular frame, ensuring that there is enough sum of squares of grayscale gradients in the selected circular frame to ensure the DIC measurement accuracy.

2. The measuring method according to claim 1, characterized in that: The DIC algorithm is used to calculate the rigid body motion of the particles in each circle in the circle group.

3. The measuring method according to claim 1, characterized in that: The moving least squares fitting calculation method includes: defining a moving window around the fitting calculation point of the matrix, wherein the moving window includes at least three particles; A representation function of the displacement field of the moving window is selected, and the displacement of the particles in the moving window is fitted using the least squares method to obtain the displacement field of the fitting calculation point, and the strain of the fitting calculation point is obtained by derivation of the displacement field of the fitting calculation point.

4. The measuring method according to claim 3, characterized in that: The representation function of the displacement field of the moving window includes a polynomial function or a trigonometric function.

5. A non-contact microscopic strain measurement device suitable for particle reinforced composite materials, characterized in that: The measuring device is used to run the non-contact microscopic strain measurement method applicable to particle-reinforced composite materials according to any one of claims 1 to 4, and the measuring device comprises: An adaptive particle selection module, used to generate a circle frame group using an adaptive algorithm according to the grayscale difference between the particle and the matrix, each circle frame in the circle frame group contains one particle; A particle rigid body motion measurement module is used to calculate the rigid body motion of the particles in each circular frame in the circular frame group to obtain a particle displacement data group; The overall strain measurement module is used to obtain the strain of the matrix between each of the particles by fitting and calculating the particle displacement data group using the moving least square method.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the contactless micro-strain measurement method applicable to particle-reinforced composite materials as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the non-contact microscopic strain measurement method applicable to particle-reinforced composite materials as described in any one of claims 1 to 4 when executed.

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