Composite material modeling method, system, equipment and medium

Abstract

The invention discloses a composite material modeling method, system, equipment and medium, and belongs to the technical field of three-dimensional modeling, and the method comprises the steps: filtering pixels corresponding to defect features through a gray value of a CT slice image according to the CT slice image of a composite material; a calculation unit of the CT image is determined, the ratio of the number of pixels corresponding to the defect features to the number of pixels of the calculation unit serves as an element value of a defect parameter matrix, and a defect proportion parameter matrix is obtained; building a composite material finite element model, and obtaining finite element model grid units with the same size as those represented by the proportional parameter matrix elements of the defects; and transmitting element values of the proportion parameter matrix of the defects to corresponding grid units of the established composite material finite element model to obtain a composite material three-dimensional reconstruction model. By means of the method, the finite element model with the defect distribution characteristics the same as those of an actual composite material can be established, the accuracy of finite element simulation is improved, the number of reconstructed model grids is small, and the calculation efficiency is high.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and more specifically to a composite material modeling method, system, equipment and medium. Background Art

[0002] Composite materials have broad application prospects due to their high specific strength, high specific modulus and corrosion resistance. Due to the complex structure of real composite materials and the limitations of the preparation process, the materials cannot be completely uniform and dense, and internal defects become an important factor affecting the performance of composite materials. In addition, different materials have different weaving methods and defect distributions, which leads to the inability to show the impact of the real internal structure on the macroscopic mechanical properties when establishing numerical simulation models.

[0003] In the prior art, simulation modeling of composite materials mainly includes: macroscopic method that regards composite materials as continuous homogeneous bodies and microscopic cell method that reflects the woven structure of materials. Among them, the macroscopic method is based on the assumption of continuous uniformity, regards the material as a continuum, introduces damage variables to describe the mechanical behavior of the composite material, and cannot consider the influence of the non-uniform distribution of defects inside the material; although the microscopic cell method can characterize the woven structure of composite materials, the number of grids is large, the calculation is difficult, and it cannot be extended to the entire component, which is not suitable for actual engineering applications.

[0004] In summary, the existing composite material modeling method has a large number of grids and is difficult to calculate during finite element simulation, and cannot be extended to the entire component, resulting in poor accuracy in composite material modeling. Summary of the invention

[0005] In response to the problems existing in the above-mentioned fields, the present invention proposes a composite material modeling method, system, device and medium. By determining the calculation unit of the CT slice image, the ratio of the number of pixels corresponding to the defect characteristics to the number of pixels in the calculation unit is used as the element value of the defect parameter matrix, and the defect proportional parameter matrix is ​​obtained. The composite material finite element model is meshed according to the number of calculation units in the CT slice image. A finite element model with the same defect distribution characteristics as the actual composite material can be established, thereby improving the accuracy of finite element simulation.

[0006] In order to solve the above technical problems, the present invention discloses a composite material modeling method, comprising the following steps: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, the pixels corresponding to the defect features are filtered by the gray value of the CT slice image; the calculation unit of the CT slice image is determined, and the ratio of the number of pixels corresponding to the defect features to the number of pixels in the calculation unit is used as the element value of the defect parameter matrix to obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0007] Preferably, the defect proportion parameter matrix obtained specifically includes: Obtain the grayscale values ​​of composite material boundaries and defects and their boundaries in CT slice images; The pixel grayscale contours are plotted through the function to obtain the defect characteristic distribution map of the composite material; Comparing the defect feature distribution map of the composite material with the acquired CT slice image of the composite material, and adjusting the parameters of the Laplace sharpening filter; Taking the pixel grayscale value of the CT slice image as a quantitative reference, all grayscale pixels are traversed within the pixel area of ​​the composite material, and the pixel area of ​​the preset size is used as the calculation unit; Obtaining a grayscale threshold for distinguishing defects, and determining the ratio of the number of grayscale pixels expressing microscopic defects of the composite material to the number of pixels in the calculation unit by filtering the number of pixels in each pixel matrix that are less than the grayscale threshold; Each pixel matrix represents an output element and contains a defect pixel ratio parameter. By calculating the ratio parameters of all pixel matrixes contained in the CT slice image, the defect ratio parameter matrix is ​​output.

[0008] Preferably, the step of obtaining a finite element model grid unit having the same size as that represented by a scale parameter matrix element of the defect comprises the following steps: According to the acquired proportional parameter matrix of defects, the order of the proportional parameter matrix is ​​determined, and the mesh density of the finite element model is set; When the number of meshes in the finite element model is consistent with the number of computational units contained in the composite material region in the CT slice image, a composite material finite element model having the same size as that represented by the elements of the proportional defect parameter matrix is ​​established.

[0009] Preferably, obtaining the three-dimensional reconstruction model of the composite material comprises the following steps: Obtain target defect parameter coordinates and defect parameter values; Divide the defect parameter values ​​into intervals; For each interval, filter the mesh element numbers of the finite element model corresponding to the defect parameter coordinates in the interval; Replace the keyword of the mesh unit number in the finite element model file, and establish a set of finite element model mesh units corresponding to each interval; Taking the performance parameters of the composite material without defects as the benchmark, the benchmark is weakened according to the interval where the defect parameters are located and assigned to the set of finite element model grid units: the set of finite element model grid units corresponding to each interval, the material performance parameter of the set of finite element model grid units = benchmark * interval upper limit; According to the calculated material parameters, different material properties and cross-section properties are set for each set of finite element model mesh units in the finite element software interface to obtain a three-dimensional reconstructed model of the composite material based on microscopic defects.

[0010] Preferably, the target defect parameter coordinates and defect parameter values ​​are obtained according to the defect proportional parameter matrix, by traversing the proportional parameter matrix, finding defect parameters and coordinates that are non-0 and non-1, and outputting the target defect parameter coordinates and defect parameter values ​​through a function.

[0011] Preferably, the defect parameter value is divided into 10 intervals, namely, 0~0.1, 0.1~0.2, ..., 0.9~1, according to the numerical value.

[0012] Preferably, the finite element model is established based on Abaqus / CAE finite element software.

[0013] Preferably, a composite material modeling system is also included, comprising: A CT slice image acquisition module for composite materials, used for acquiring CT slice images of composite materials; The defect parameter determination module is used to filter the pixels corresponding to the defect characteristics according to the gray value of the CT slice image of the composite material; determine the calculation unit of the CT slice image, and use the ratio of the number of pixels corresponding to the defect characteristics to the number of pixels in the calculation unit as the element value of the defect parameter matrix to obtain the defect ratio parameter matrix; A composite material finite element model building module is used to establish a composite material finite element model, mesh the composite material finite element model according to the number of calculation units in the CT slice image, and when the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, obtain a finite element model mesh unit of the same size as that represented by the proportional parameter matrix element of the defect; The composite material three-dimensional model reconstruction module is used to transfer the element values ​​of the defect proportional parameter matrix to the corresponding grid units of the established composite material finite element model to obtain the composite material three-dimensional reconstructed model.

[0014] Preferably, a computer device is also included, the computer device comprising a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor executes the following steps: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, the pixels corresponding to the defect features are filtered by the gray value of the CT slice image; the calculation unit of the CT slice image is determined, and the ratio of the number of pixels corresponding to the defect features to the number of pixels in the calculation unit is used as the element value of the defect parameter matrix to obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0015] Preferably, it further comprises a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the following steps: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, the pixels corresponding to the defect features are filtered by the gray value of the CT slice image; the calculation unit of the CT slice image is determined, and the ratio of the number of pixels corresponding to the defect features to the number of pixels in the calculation unit is used as the element value of the defect parameter matrix to obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The composite material modeling method proposed in the present invention obtains a CT slice image of the composite material, imports it into a recognition program, identifies and filters the grayscale pixels corresponding to the defect characteristics; uses an image pixel area of ​​a certain size as a calculation unit, determines the ratio of the number of defect pixels to the number of pixels in the calculation unit, and uses it as the element value of the defect parameter matrix, so that the CT slice image can clearly show the defect characteristics of the composite material. A composite material finite element model is established, and the composite material finite element model is meshed according to the size of the CT slice image processing pixel matrix, so that the finite element mesh size is the same as the size represented by the defect parameter matrix element. The element value of the defect ratio parameter matrix is ​​transferred to the corresponding mesh unit of the established composite material finite element model to obtain a three-dimensional reconstruction model of the composite material. The composite material modeling method for microscopic defects proposed in the present invention can establish a finite element model with the same defect distribution characteristics as the actual composite material, improve the accuracy of finite element simulation, and reconstruct the model with a small number of meshes and high calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of the composite material modeling method proposed in the present invention; Figure 2 A high-resolution CT slice image of a composite material modeling method based on mesoscopic defects provided by an embodiment of the present invention; Figure 3 This is a graph showing the CT slice image preprocessing result of the composite material modeling method based on mesoscopic defects provided in an embodiment of the present invention; Figure 4 A defect feature identification diagram of a composite material modeling method based on microscopic defects provided by an embodiment of the present invention; Figure 5 A composite material geometric model of a composite material modeling method based on mesoscopic defects provided in an embodiment of the present invention; Figure 6 A composite material finite element model of a composite material modeling method based on mesoscopic defects provided in an embodiment of the present invention; Figure 7 Different Elset material parameter calculation processes of the composite material modeling method based on microscopic defects provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the attached embodiment of the present invention Figure 1-Figure 7 , the technical solutions in the embodiments of the present invention are clearly and completely described. It should be understood that the terms described in the present invention are only used to describe specific implementation methods and are not used to limit the present invention.

[0019] like Figure 1 As shown, the present invention proposes a composite material modeling method, comprising the following steps: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, the pixels corresponding to the defect features are filtered by the gray value of the CT slice image; the calculation unit of the CT slice image is determined, and the ratio of the number of pixels corresponding to the defect features to the number of pixels in the calculation unit is used as the element value of the defect parameter matrix to obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0020] Specifically, the defect proportion parameter matrix is ​​obtained, including: Obtain the grayscale values ​​of composite material boundaries and defects and their boundaries in CT slice images; The pixel grayscale contours are plotted through the function to obtain the defect characteristic distribution map of the composite material; Compare the defect characteristic distribution map of the composite material with the acquired CT slice image of the composite material, and adjust the parameters of the function; Taking the pixel grayscale value of the CT slice image as a quantitative reference, all grayscale pixels are traversed within the pixel area of ​​the composite material, and the pixel area of ​​the preset size is used as the calculation unit; Obtaining a grayscale threshold for distinguishing defects, and determining the ratio of the number of grayscale pixels expressing microscopic defects of the composite material to the number of pixels in the calculation unit by filtering the number of pixels in each pixel matrix that are less than the grayscale threshold; Each pixel matrix represents an output element and contains a defect pixel ratio parameter. By calculating the ratio parameters of all pixel matrixes contained in the CT slice image, the defect ratio parameter matrix is ​​output.

[0021] Obtaining finite element model mesh elements of the same size as those represented by the scale parameter matrix elements of the defect includes the following steps: According to the acquired proportional parameter matrix of defects, the order of the proportional parameter matrix is ​​determined, and the mesh density of the finite element model is set; When the number of finite element model grids is consistent with the number of computational units contained in the composite material region in the CT slice image, a composite material finite element model having the same size as that represented by the elements of the proportional defect parameter matrix is ​​established.

[0022] Obtaining a three-dimensional reconstruction model of a composite material includes the following steps: Obtain target defect parameter coordinates and defect parameter values; Divide the defect parameter values ​​into intervals; For each interval, filter the mesh element numbers of the finite element model corresponding to the defect parameter coordinates in the interval; Replace the keyword of the mesh unit number in the finite element model inp file and establish a set of finite element model mesh units corresponding to each interval; Taking the performance parameters of the composite material without defects as the benchmark, the benchmark is weakened according to the interval of the defect parameters and assigned to the set: the set corresponding to each interval has the material parameter = benchmark * upper limit of the interval; According to the calculated material parameters, different material properties and section properties are set for each set in the Abaqus / CAE interface to obtain a three-dimensional reconstruction model of the composite material based on microscopic defects.

[0023] The target defect parameter coordinates and defect parameter values ​​are obtained based on the defect proportional parameter matrix. By traversing the proportional parameter matrix, non-0 and non-1 defect parameters and coordinates are found, and the target defect parameter coordinates and defect parameter values ​​are output through a function.

[0024] The present invention also provides a composite material modeling system, comprising: A CT slice image acquisition module for composite materials, used for acquiring CT slice images of composite materials; The defect parameter determination module is used to filter the pixels corresponding to the defect characteristics according to the gray value of the CT slice image of the composite material; determine the calculation unit of the CT slice image, and use the ratio of the number of pixels corresponding to the defect characteristics to the number of pixels in the calculation unit as the element value of the defect parameter matrix to obtain the defect ratio parameter matrix; A composite material finite element model building module is used to establish a composite material finite element model, mesh the composite material finite element model according to the number of calculation units in the CT slice image, and when the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, obtain a finite element model mesh unit of the same size as that represented by the proportional parameter matrix element of the defect; The composite material three-dimensional model reconstruction module is used to transfer the element values ​​of the defect proportional parameter matrix to the corresponding grid units of the established composite material finite element model to obtain the composite material three-dimensional reconstructed model.

[0025] The composite material modeling method proposed in the present invention can establish a finite element model with the same defect distribution characteristics as the actual composite material, improve the accuracy of finite element simulation, reconstruct a small number of model grids, and have high calculation efficiency.

[0026] Example In order to verify the feasibility of the method proposed in the present invention, a composite material modeling method based on microscopic defects is provided in an embodiment of the present invention for detailed description.

[0027] The composite material modeling method based on microscopic defects specifically includes the following steps: Step 1: Obtain high-resolution composite CT slice images Before imaging, use volatile detergent to clean the composite material to remove surface impurities and improve imaging quality. During imaging, perform tomographic scanning of the object to be observed along the thickness direction. The imaging density is uniform and the number of images is reasonable, so that the CT slice image can clearly show the internal microscopic defects of the composite material, such as Figure 2 shown.

[0028] Step 2: Preprocess the acquired CT slice images of the composite material The CT slice images to be processed are stored in the folder where the Matlab program is located. The image naming format is "slice image number.tif".

[0029] Use the function "I=imread('Image name.tif','PixelRegion',{[650,950],[27,2470]})" to read the image, enter the image file name, and set the vertical coordinate range of the read image pixels to [650,950] and the horizontal coordinate range to [27,2470].

[0030] The read image is passed to the function "imgGray=rgb2gray(I)", the image is converted into grayscale format, and the grayscale values ​​of the composite material boundary pixels and defects and their boundary pixels are read in the graph interface.

[0031] Define the Laplace sharpening filter "laplacianFilter = [0 -1 0; -1 4 -1; 0 -1 0]", adjust the parameters in the filter operator [0 -1 0; -1 4 -1; 0 -1 0], so that the image defect features can be clearly expressed without distortion, such as Figure 3 shown.

[0032] Use the "imcontour(modifiedImage,4)" function to draw the pixel grayscale contour line and obtain the defect characteristic distribution map of the composite material, such as Figure 4 As shown, compared with the original CT slice image of the composite material, the parameters of the Laplace sharpening filter in step 2 are adjusted to improve the recognition accuracy.

[0033] Step 3: Calculate the defect parameter matrix of the CT slice image of the composite material The pixel grayscale value of the CT slice image is used as a quantitative reference. In the pixel area of ​​the composite material, the loop statement “for i = 1:5:size(img, 1)” is used to traverse all grayscale pixels, and the 5×5 pixel area is set as the calculation unit.

[0034] According to the grayscale values ​​of the defects and their boundary pixels read in step 2, the grayscale threshold limit for distinguishing defects is set to 0.41, and the "count=sum(group(:)<=0.41)" function is used to filter the number of pixels in each calculation unit that are less than the grayscale threshold, and calculate the ratio of the number of grayscale pixels expressing the microscopic defects of the composite material to the number of pixels in the calculation unit.

[0035] In the image reading area, the number of calculation units = the total number of pixels in the image reading area / the total number of calculation unit pixels 25. Each calculation unit represents an output element, which contains a defect pixel ratio parameter. All calculation unit ratio parameters contained in the CT slice image are calculated. The defect ratio parameter variable set stored in the recognition program is opened and saved as an Excel file to obtain the defect parameter matrix. Part of the parameter matrix is ​​shown in Table 1.

[0036] Table 1 Partial defect parameter matrix of composite material modeling method based on microscopic defects

[0037] Step 4: Establish a composite material finite element model based on Abaqus / CAE finite element software The full-scale geometric model of the composite material is established in the component module of the Abaqus / CAE finite element software, such as Figure 5 shown.

[0038] According to the obtained defect parameter matrix order, the finite element model mesh density is set to make the finite element mesh the same size as the defect parameter matrix element representation, and the section properties, assembly properties, boundary conditions, etc. are set to obtain the composite material finite element model, such as Figure 6 shown.

[0039] Export the .inp file containing the composite material finite element model information. The .inp file contains the solver, material properties, mesh unit properties and other information of the established finite element model. By modifying the keyword content in the .inp file, you can directly complete the operations such as mesh unit set establishment and material property modification, which is convenient for the next step of model reconstruction.

[0040] Step 5: 3D reconstruction modeling of composite materials based on microscopic defects In Python, use the "inpfile=open("f: / filename.inp",'r+')" function to read the .inp file exported in step 4; use the "df = pd.read_excel(file_path)" function to read the defect parameter matrix excel file output in step 3.

[0041] Use "for row_index, row in df.iterrows():" to traverse the data in the Excel file, find the defect parameters and coordinates that are not 0 and not 1, and use the function results.append((row_index, col_index, value)) to output the coordinates and values ​​of the target defect parameters.

[0042] The defect ratio parameter is divided into 10 intervals according to size, namely 0~0.1, 0.1~0.2, …, 0.9~1; for each interval, the element (finite model grid unit) number corresponding to the parameter coordinates in the interval is filtered in the .inp file, and the Python function "newset1="**Elset, elset=Set-1, grid unit number"" is used to replace the keyword to establish the element number set Elset.

[0043] Taking the performance parameters of the material without defects as the benchmark, the benchmark is weakened according to the interval of the defect parameters and assigned to Elset: the Elset corresponding to each interval has a material parameter = benchmark * interval upper limit. The calculation process is as follows: Figure 7 As shown; the benchmark elastic modulus is set to 200 MPa, then the element elastic modulus corresponding to the interval of 0~0.1 is 200*0.1=20 MPa, and the element elastic modulus corresponding to the interval of 0.1~0.2 is 200*0.2=40 MPa; and so on, different material properties and section properties are set for each group of Elset in the Abaqus CAE interface to obtain a three-dimensional reconstruction model of the composite material based on mesoscopic defects.

[0044] The composite material modeling method proposed in the present invention obtains high-resolution CT scanning slice images of the composite material, uses the Matlab image recognition program to identify and filter the grayscale pixels corresponding to the defect characteristics; uses the 5th-order pixel matrix as the calibration unit, calculates the ratio of the number of defect pixels to the number of square matrix pixels, and uses it as the element value of the defect parameter matrix; establishes a composite material finite element model in the Abaqus finite element simulation software, so that the finite element grid size is the same as the size represented by the defect parameter matrix element; uses the Python language to transfer the element value of the defect parameter matrix to the corresponding grid unit of the Abaqus finite element model, and obtains a three-dimensional reconstruction model of the composite material based on mesoscopic defects. The established three-dimensional reconstruction model of the composite material based on mesoscopic defects can maintain the same defect distribution characteristics as the actual composite material. This method improves the accuracy of finite element simulation, and the number of reconstructed model grids is small and the calculation efficiency is high.

[0045] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0046] In addition, unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as commonly understood by those skilled in the art to which the present invention belongs. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0047] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0048] In addition, unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as commonly understood by those skilled in the art to which the present invention belongs. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

Claims

1. A composite material modeling method, characterized in that: The following steps are involved: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, pixels corresponding to defect features are filtered by gray value of the CT slice image; Determine the calculation unit of the CT slice image, take the ratio of the number of pixels corresponding to the defect feature to the number of pixels in the calculation unit as the element value of the defect parameter matrix, and obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

2. The composite material modeling method according to claim 1, characterized in that: The defect proportion parameter matrix obtained specifically includes: Obtain the grayscale values ​​of composite material boundaries and defects and their boundaries in CT slice images; The pixel grayscale contours are plotted through the function to obtain the defect characteristic distribution map of the composite material; Comparing the defect feature distribution map of the composite material with the acquired CT slice image of the composite material, and adjusting the parameters of the Laplace sharpening filter; Taking the pixel grayscale value of the CT slice image as a quantitative reference, all grayscale pixels are traversed within the pixel area of ​​the composite material, and the pixel area of ​​the preset size is used as the calculation unit; Obtaining a grayscale threshold for distinguishing defects, and determining the ratio of the number of grayscale pixels expressing microscopic defects of the composite material to the number of pixels in the calculation unit by filtering the number of pixels in each pixel matrix that are less than the grayscale threshold; Each pixel matrix represents an output element and contains a defect pixel ratio parameter. By calculating the ratio parameters of all pixel matrixes contained in the CT slice image, the defect ratio parameter matrix is ​​output.

3. The composite material modeling method according to claim 2, characterized in that: The step of obtaining a finite element model mesh unit having the same size as that represented by a proportional parameter matrix element of the defect comprises the following steps: According to the acquired proportional parameter matrix of defects, the order of the proportional parameter matrix is ​​determined, and the mesh density of the finite element model is set; When the number of meshes in the finite element model is consistent with the number of computational units contained in the composite material region in the CT slice image, a composite material finite element model having the same size as that represented by the elements of the proportional defect parameter matrix is ​​established.

4. The composite material modeling method according to claim 3, characterized in that: The method of obtaining the three-dimensional reconstruction model of the composite material comprises the following steps: Obtain target defect parameter coordinates and defect parameter values; Divide the defect parameter values ​​into intervals; For each interval, filter the mesh element numbers of the finite element model corresponding to the defect parameter coordinates in the interval; Replace the keyword of the mesh unit number in the finite element model file, and establish a set of finite element model mesh units corresponding to each interval; Taking the performance parameters of the composite material without defects as the benchmark, the benchmark is weakened according to the interval where the defect parameters are located and assigned to the set of finite element model grid units: the set of finite element model grid units corresponding to each interval, the material performance parameter of the set of finite element model grid units = benchmark * interval upper limit; According to the calculated material parameters, different material properties and cross-section properties are set for each set of finite element model mesh units in the finite element software interface to obtain a three-dimensional reconstructed model of the composite material based on microscopic defects.

5. The composite material modeling method according to claim 4, characterized in that: The target defect parameter coordinates and defect parameter values ​​are obtained according to the defect proportional parameter matrix, by traversing the proportional parameter matrix, finding defect parameters and coordinates that are not 0 and not 1, and outputting the target defect parameter coordinates and defect parameter values ​​through a function.

6. The composite material modeling method according to claim 5, characterized in that: The defect parameter value is divided into 10 intervals, namely, 0~0.1, 0.1~0.2, ..., 0.9~1, according to the numerical value.

7. The composite material modeling method according to claim 6, characterized in that: The finite element model is established based on Abaqus / CAE finite element software.

8. A composite material modeling system, characterized in that: include: A CT slice image acquisition module for composite materials, used for acquiring CT slice images of composite materials; A defect parameter determination module is used to filter pixels corresponding to defect features by grayscale values ​​of the CT slice image according to the CT slice image of the composite material; Determine the calculation unit of the CT slice image, take the ratio of the number of pixels corresponding to the defect feature to the number of pixels in the calculation unit as the element value of the defect parameter matrix, and obtain the defect ratio parameter matrix; A composite material finite element model building module is used to establish a composite material finite element model, mesh the composite material finite element model according to the number of calculation units in the CT slice image, and when the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, obtain a finite element model mesh unit of the same size as that represented by the proportional parameter matrix element of the defect; The composite material three-dimensional model reconstruction module is used to transfer the element values ​​of the defect proportional parameter matrix to the corresponding grid units of the established composite material finite element model to obtain the composite material three-dimensional reconstructed model.

9. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, pixels corresponding to defect features are filtered by gray value of the CT slice image; Determine the calculation unit of the CT slice image, take the ratio of the number of pixels corresponding to the defect feature to the number of pixels in the calculation unit as the element value of the defect parameter matrix, and obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following steps: Acquire CT slice images of the composite material; According to the CT slice image of the composite material, pixels corresponding to defect features are filtered by gray value of the CT slice image; Determine the calculation unit of the CT slice image, take the ratio of the number of pixels corresponding to the defect feature to the number of pixels in the calculation unit as the element value of the defect parameter matrix, and obtain the defect ratio parameter matrix; A composite material finite element model is established, and the composite material finite element model is meshed according to the number of calculation units in the CT slice image. When the number of finite element model meshes is consistent with the number of calculation units contained in the composite material region in the CT slice image, a finite element model mesh unit having the same size as that represented by the proportional parameter matrix element of the defect is obtained; The element values ​​of the defect proportional parameter matrix are transferred to the corresponding grid units of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

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