Composite material modeling method, system, device and medium

Abstract

The present invention discloses a composite material modeling method, system, device, and medium, belonging to the field of three-dimensional modeling technology. The method filters pixels corresponding to defect features based on a CT slice image of the composite material using the grayscale value of the CT slice image; determines the calculation unit of the CT image, uses the ratio of the number of pixels corresponding to the defect features to the number of pixels in the calculation unit as the element value of the defect parameter matrix, and obtains the defect proportional parameter matrix; establishes a finite element model of the composite material, obtains finite element model grid units of the same size as the elements represented by the defect proportional parameter matrix; transfers the element values ​​of the defect proportional parameter matrix to the corresponding grid units of the established composite material finite element model, and obtains a three-dimensional reconstructed model of the composite material. This method can establish a finite element model with the same defect distribution characteristics as the actual composite material, improves the accuracy of finite element simulation, and reconstructs a model with a small number of grids and high computational efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional modeling, and more particularly 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. However, due to the complex structure of real composite materials and the limitations of the preparation process, the materials cannot be completely uniformly dense. Internal defects become a significant factor affecting the performance of composite materials. In addition, the different weaving methods of different materials lead to different defect distributions. As a result, the influence of the actual internal structure on the macroscopic mechanical properties cannot be fully reflected in the numerical simulation model.

[0003] Prior art simulation modeling of composite materials primarily involves two approaches: the macroscopic approach, which treats the composite material as a continuous homogeneous body, and the microscopic cellular approach, which reflects the material's woven structure. The macroscopic approach, based on the assumption of continuous homogeneity, treats the material as a continuum and introduces damage variables to describe the mechanical behavior of the composite material. However, it fails to consider the impact of the non-uniform distribution of internal defects. While the microscopic cellular approach can characterize the woven structure of composite materials, it requires a large number of grid cells, is computationally difficult, and cannot be generalized to the entire component, making it unsuitable for practical 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, equipment 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] To solve the above technical problems, the present invention discloses a composite material modeling method, comprising the following steps:

[0007] Acquire CT slice images of the composite material;

[0008] According to the CT slice image of the composite material, the pixels corresponding to the defect characteristics are filtered by the grayscale 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 characteristics 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;

[0009] A composite material finite element model is established, and the composite material finite element model is meshed according to the number of computational units in the CT slice image. When the number of finite element model meshes is consistent with the number of computational 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 scale parameter matrix element of the defect is obtained;

[0010] The element values ​​of the defect scale parameter matrix are transferred to the corresponding grid elements of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0011] Preferably, the defect proportion parameter matrix obtained specifically includes:

[0012] Obtain the grayscale values ​​of composite material boundaries and defects and their boundaries in CT slice images;

[0013] By drawing pixel grayscale contours through the function, the defect characteristic distribution map of the composite material is obtained;

[0014] 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;

[0015] The pixel grayscale value of the CT slice image is used as a quantitative reference. Within the pixel area of ​​the composite material, all grayscale pixels are traversed and the pixel area of ​​the preset size is used as the calculation unit.

[0016] 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;

[0017] Each pixel matrix represents an output element and contains a defect pixel ratio parameter. By calculating the ratio parameters of all pixel matrices contained in the CT slice image, the defect ratio parameter matrix is ​​output.

[0018] Preferably, 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:

[0019] According to the obtained defect scale parameter matrix, the order of the scale parameter matrix is ​​determined and the mesh density of the finite element model is set;

[0020] 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.

[0021] Preferably, obtaining the three-dimensional reconstructed model of the composite material comprises the following steps:

[0022] Obtain target defect parameter coordinates and defect parameter values;

[0023] Divide the defect parameter values ​​into intervals;

[0024] For each interval, filter the mesh element numbers of the finite element model corresponding to the defect parameter coordinates in the interval;

[0025] 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;

[0026] Taking the performance parameters of the defect-free composite material as the benchmark, the benchmark is weakened according to the interval of the defect parameters and assigned to the set of finite element model mesh elements: the set of finite element model mesh elements corresponding to each interval, the material performance parameter of the set of finite element model mesh elements = benchmark * interval upper limit;

[0027] According to the calculated material parameters, different material properties and cross-sectional properties are set for each set of finite element model grid cells in the finite element software interface to obtain a three-dimensional reconstructed model of the composite material based on microscopic defects.

[0028] Preferably, the target defect parameter coordinates and defect parameter values ​​are obtained based on the defect proportional parameter matrix, by traversing the proportional parameter matrix, finding non-0 and non-1 defect parameters and coordinates, and outputting the target defect parameter coordinates and defect parameter values ​​through a function.

[0029] 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.

[0030] Preferably, the finite element model is established based on Abaqus / CAE finite element software.

[0031] Preferably, a composite material modeling system is further included, comprising:

[0032] A CT slice image acquisition module for composite materials, used for acquiring CT slice images of composite materials;

[0033] The defect parameter determination module is used to filter pixels corresponding to defect features based on the grayscale 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 features 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;

[0034] A composite material finite element model construction 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 finite element model mesh units of the same size as those represented by the scale parameter matrix elements of the defect;

[0035] 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.

[0036] Preferably, a computer device is further 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, causing the processor to perform the following steps:

[0037] Acquire CT slice images of the composite material;

[0038] According to the CT slice image of the composite material, the pixels corresponding to the defect characteristics are filtered by the grayscale 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 characteristics 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;

[0039] A composite material finite element model is established, and the composite material finite element model is meshed according to the number of computational units in the CT slice image. When the number of finite element model meshes is consistent with the number of computational 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 scale parameter matrix element of the defect is obtained;

[0040] The element values ​​of the defect scale parameter matrix are transferred to the corresponding grid elements of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0041] Preferably, the present invention 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 is caused to perform the following steps:

[0042] Acquire CT slice images of the composite material;

[0043] According to the CT slice image of the composite material, the pixels corresponding to the defect characteristics are filtered by the grayscale 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 characteristics 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;

[0044] A composite material finite element model is established, and the composite material finite element model is meshed according to the number of computational units in the CT slice image. When the number of finite element model meshes is consistent with the number of computational 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 scale parameter matrix element of the defect is obtained;

[0045] The element values ​​of the defect scale parameter matrix are transferred to the corresponding grid elements of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The composite material modeling method proposed in the present invention obtains a CT slice image of the composite material and imports it into a recognition program to identify and filter the grayscale pixels corresponding to the defect characteristics; using a certain size of image pixel area as a calculation unit, the ratio of the number of defect pixels to the number of pixels in the calculation unit is determined 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 finite element model of the composite material is established, and the finite element model of the composite material is meshed according to the size of the pixel matrix processed by the CT slice image, so that the finite element mesh size is the same as the size represented by the element of the defect parameter matrix. 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 reconstructed 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 computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the composite material modeling method proposed in the present invention;

[0049] Figure 2 A high-resolution CT slice image of the composite material modeling method based on microscopic defects provided by an embodiment of the present invention;

[0050] Figure 3 This is a graph showing the CT slice image preprocessing results of the composite material modeling method based on microscopic defects provided in an embodiment of the present invention;

[0051] Figure 4 A defect feature recognition diagram of a composite material modeling method based on microscopic defects provided by an embodiment of the present invention;

[0052] Figure 5 A composite material geometric model for the composite material modeling method based on microscopic defects provided in an embodiment of the present invention;

[0053] Figure 6 A composite material finite element model according to a composite material modeling method based on microscopic defects provided in an embodiment of the present invention;

[0054] Figure 7 This is a calculation process for different Elset material parameters of a composite material modeling method based on microscopic defects provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following is a combination of the embodiments 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 used in the present invention are only used to describe specific implementation methods and are not intended to limit the present invention.

[0056] like Figure 1 As shown, the present invention proposes a composite material modeling method, comprising the following steps:

[0057] Acquire CT slice images of the composite material;

[0058] According to the CT slice image of the composite material, the pixels corresponding to the defect characteristics are filtered by the grayscale 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 characteristics 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;

[0059] A composite material finite element model is established, and the composite material finite element model is meshed according to the number of computational units in the CT slice image. When the number of finite element model meshes is consistent with the number of computational 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 scale parameter matrix element of the defect is obtained;

[0060] The element values ​​of the defect scale parameter matrix are transferred to the corresponding grid elements of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material.

[0061] Specifically, the defect proportion parameter matrix is ​​obtained, including:

[0062] Obtain the grayscale values ​​of composite material boundaries and defects and their boundaries in CT slice images;

[0063] By drawing pixel grayscale contours through the function, the defect characteristic distribution map of the composite material is obtained;

[0064] 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;

[0065] The pixel grayscale value of the CT slice image is used as a quantitative reference. Within the pixel area of ​​the composite material, all grayscale pixels are traversed and the pixel area of ​​the preset size is used as the calculation unit.

[0066] 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;

[0067] Each pixel matrix represents an output element and contains a defect pixel ratio parameter. By calculating the ratio parameters of all pixel matrices contained in the CT slice image, the defect ratio parameter matrix is ​​output.

[0068] 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:

[0069] According to the obtained defect scale parameter matrix, the order of the scale parameter matrix is ​​determined and the mesh density of the finite element model is set;

[0070] 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.

[0071] Obtaining a three-dimensional reconstruction model of a composite material includes the following steps:

[0072] Obtain target defect parameter coordinates and defect parameter values;

[0073] Divide the defect parameter values ​​into intervals;

[0074] For each interval, filter the mesh element numbers of the finite element model corresponding to the defect parameter coordinates in the interval;

[0075] 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;

[0076] Taking the performance parameters of the defect-free composite material as the benchmark, the benchmark is weakened according to the interval of the defect parameter and assigned to the set: the set corresponding to each interval has the material parameter = benchmark * interval upper limit;

[0077] Based on the calculated material parameters, different material properties and cross-sectional properties are set for each set in the Abaqus / CAE interface to obtain a three-dimensional reconstructed model of the composite material based on microscopic defects.

[0078] The target defect parameter coordinates and defect parameter values ​​are obtained based on the defect proportional parameter matrix. By traversing the proportional parameter matrix, the defect parameters and coordinates that are non-0 and non-1 are found, and the target defect parameter coordinates and defect parameter values ​​are output through the function.

[0079] The present invention also provides a composite material modeling system, comprising:

[0080] A CT slice image acquisition module for composite materials, used for acquiring CT slice images of composite materials;

[0081] The defect parameter determination module is used to filter pixels corresponding to defect features based on the grayscale 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 features 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;

[0082] A composite material finite element model construction 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 finite element model mesh units of the same size as those represented by the scale parameter matrix elements of the defect;

[0083] 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.

[0084] 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 the model with a small number of grids and high calculation efficiency.

[0085] Example

[0086] In order to verify the feasibility of the method proposed in the present invention, an embodiment of the present invention provides a composite material modeling method based on microscopic defects as an example for detailed description.

[0087] The composite material modeling method based on microscopic defects specifically includes the following steps:

[0088] Step 1: Acquire high-resolution composite CT slice images

[0089] Before imaging, use volatile detergent to clean the composite material to remove surface impurities and improve imaging quality. During imaging, perform a tomographic scan 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.

[0090] Step 2: Preprocess the acquired CT slice images of the composite material

[0091] The CT slice images to be processed are stored in the folder where the Matlab program is located, and the image naming format is "slice image sequence number.tif".

[0092] 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].

[0093] The read image is passed to the function "imgGray=rgb2gray(I)" to convert the image 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.

[0094] Define the Laplace sharpening filter "laplacianFilter = [0 -1 0; -1 4 -1; 0 -1 0]", and 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.

[0095] 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.

[0096] Step 3: Calculate the defect parameter matrix of the CT slice image of the composite material

[0097] The pixel grayscale value of the CT slice image is used as the 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.

[0098] According to the grayscale values ​​of the defects and their boundary pixels read in step 2, the grayscale threshold for distinguishing defects is set to 0.41. 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 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 is calculated.

[0099] 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 and contains a defect pixel ratio parameter. Calculate the ratio parameters of all calculation units contained in the CT slice image. Open the defect ratio parameter variable set stored in the recognition program and save it as an Excel file to obtain the defect parameter matrix. Part of the parameter matrix is ​​shown in Table 1.

[0100] Table 1 Partial defect parameter matrix of composite material modeling method based on microscopic defects

[0101]

[0102] Step 4: Establish a composite material finite element model based on Abaqus / CAE finite element software

[0103] A 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.

[0104] According to the obtained defect parameter matrix order, the finite element model mesh density is set so that the finite element mesh has the same size as the defect parameter matrix element, and the section properties, assembly properties, boundary conditions, etc. are set to obtain the composite material finite element model, such as Figure 6 shown.

[0105] Export the .inp file containing the composite material finite element model information. The .inp file contains information such as the solver, material properties, and mesh element properties of the established finite element model. By modifying the keyword content in the .inp file, you can directly complete operations such as mesh element set establishment and material property modification, facilitating the next step of model reconstruction.

[0106] Step 5: 3D reconstruction modeling of composite materials based on microscopic defects

[0107] 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.

[0108] Use "for row_index, row in df.iterrows():" to traverse the data in the Excel file, find the defect parameters and coordinates that are non-0 and non-1, and use the function results.append((row_index, col_index, value)) to output the coordinates and values ​​of the target defect parameters.

[0109] The defect ratio parameter is divided into 10 intervals according to size: 0-0.1, 0.1-0.2, …, 0.9-1. For each interval, the element (finite model grid unit) number corresponding to the parameter coordinate in the interval is filtered in the .inp file. The Python function "newset1="**Elset, elset=Set-1, grid unit number"" is used to replace the keyword to establish the element number set Elset.

[0110] 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 the material parameter = benchmark * interval upper limit. The calculation process is as follows Figure 7 As shown in the figure; if the benchmark elastic modulus is set to 200 MPa, the element elastic modulus corresponding to the range of 0~0.1 is 200*0.1=20 MPa, and the element elastic modulus corresponding to the range 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 microscopic defects.

[0111] The composite material modeling method proposed in this invention obtains high-resolution CT scan slice images of the composite material and uses a Matlab image recognition program to identify and filter grayscale pixels corresponding to defect features. Using a 5th-order pixel matrix as a calibration unit, the ratio of the number of defect pixels to the number of pixels in the matrix is ​​calculated, which is used as the element value of the defect parameter matrix. A finite element model of the composite material is established in Abaqus finite element simulation software, with the finite element mesh size being the same as the size represented by the elements of the defect parameter matrix. The element values ​​of the defect parameter matrix are transferred to the corresponding mesh elements of the Abaqus finite element model using Python, resulting in a three-dimensional reconstructed model of the composite material based on microscopic defects. The established three-dimensional reconstructed model of the composite material based on microscopic defects maintains the same defect distribution characteristics as the actual composite material. This method improves the accuracy of finite element simulations, reduces the number of meshes in the reconstructed model, and improves computational efficiency.

[0112] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

[0113] In addition, unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning 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.

[0114] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

[0115] In addition, unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning 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, the pixels corresponding to the defect features are filtered by the gray value of the CT slice image; Determine the calculation unit of the CT 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 proportion parameter matrix; Establish a composite material finite element model, mesh the composite material finite element model according to the number of computational units in the CT image, and when the number of finite element model meshes is consistent with the number of computational units contained in the composite material region in the CT image, obtain finite element model mesh units of the same size as those represented by the scale parameter matrix elements of the defect; The element values ​​of the defect scale parameter matrix are transferred to the corresponding grid elements of the established composite material finite element model to obtain a three-dimensional reconstructed model of the composite material; The defect proportion parameter matrix obtained specifically includes: Obtain the grayscale values ​​of composite material boundaries and defects and their boundaries in CT images; By drawing pixel grayscale contours through the function, the defect characteristic distribution map of the composite material is obtained; 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; The pixel grayscale value of the CT slice image is used as a quantitative reference. Within the pixel area of ​​the composite material, all grayscale pixels are traversed 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 matrices contained in the CT slice image, the defect ratio parameter matrix is ​​output; The method of obtaining a finite element model mesh unit having the same size as that represented by the defect scale parameter matrix element comprises the following steps: According to the obtained defect scale parameter matrix, the order of the scale 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 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; The method of obtaining a three-dimensional reconstruction model of a 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 defect-free composite material 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 mesh elements: the set of finite element model mesh elements corresponding to each interval has a material parameter = benchmark * interval upper limit; Based on the calculated material parameters, different material properties and cross-sectional properties are set for each set of finite element model mesh elements in the finite element software interface to obtain a three-dimensional reconstructed model of the composite material based on microscopic defects; The target defect parameter coordinates and defect parameter values ​​are obtained by traversing the defect proportional parameter matrix to find non-0 and non-1 defect parameters and coordinates, and outputting the target defect parameter coordinates and defect parameter values ​​through a function; The defect parameter values ​​are divided into 10 intervals, namely 0 to 0.1, 0.1 to 0.2, ..., 0.9 to 1, according to the numerical values.

2. The composite material modeling method according to claim 1, characterized in that: The finite element model is established based on Abaqus / CAE finite element software.

3. 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, configured to filter pixels corresponding to defect features by grayscale values ​​of the CT slice image based on the CT slice image of the composite material; Determine the calculation unit of the CT 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 proportion parameter matrix; A composite material finite element model construction 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 image, and obtain finite element model mesh units of the same size as those represented by the scale parameter matrix elements of the defect 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 image; A composite material three-dimensional model reconstruction module is used to transfer the element values ​​of the defect scale parameter matrix to the corresponding grid elements of the established composite material finite element model to obtain a composite material three-dimensional reconstructed model; The defect parameter determination module is also used to obtain the grayscale values ​​of the composite material boundary and defects and their boundaries in the CT image; draw pixel grayscale contour lines through the function to obtain the defect characteristic distribution map of the composite material; Compare the defect feature distribution map of the composite material with the acquired CT slice image of the composite material and adjust the parameters of the function; use the pixel grayscale value of the CT slice image as a quantitative reference, and traverse all grayscale pixels within the pixel area of ​​the composite material, with the pixel area of ​​the preset size as the calculation unit; Obtain a grayscale threshold for distinguishing defects, and determine the ratio of the number of grayscale pixels expressing microscopic defects in the composite material to the number of pixels in the computational unit by filtering the number of pixels in each pixel matrix that is 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 matrices contained in the CT slice image, a defect ratio parameter matrix is ​​output. The composite material finite element model construction module is further configured to determine the order of the proportional parameter matrix and set the finite element model grid density based on the acquired defect proportional parameter matrix; when the number of finite element model grids is consistent with the number of calculation units contained in the composite material region in the CT image, establish a composite material finite element model having the same size as that represented by the elements of the proportional defect parameter matrix; The composite material three-dimensional model reconstruction module is also used to obtain target defect parameter coordinates and defect parameter values; The defect parameter values ​​are divided into intervals; for each interval, the finite element model mesh unit numbers corresponding to the defect parameter coordinates in the interval are filtered; the keywords of the mesh unit numbers are replaced in the finite element model file to establish a set of finite element model mesh units corresponding to each interval; the performance parameters of the composite material without defects are used as a benchmark, the benchmark is weakened according to the interval where the defect parameter is located, and the benchmark is assigned to the set of finite element model mesh units: the set of finite element model mesh units corresponding to each interval has a material parameter = benchmark * interval upper limit; According to the material parameters obtained by calculation, different material properties and cross-sectional properties are set for each set of finite element model grid units in the finite element software interface to obtain a three-dimensional reconstruction model of the composite material based on microscopic defects; the target defect parameter coordinates and defect parameter values ​​are obtained according to the proportional parameter matrix of the defects, and the defect parameters and coordinates that are non-0 and non-1 are found by traversing the proportional parameter matrix, and the target defect parameter coordinates and defect parameter values ​​are output through a function; the defect parameter values ​​are divided into intervals into 10 intervals of 0 to 0.1, 0.1 to 0.2... 0.9 to 1, which are evenly divided according to the numerical values.

4. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1 to 2 is implemented.

5. 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 method according to any one of claims 1 to 2 is implemented.

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