Static balance analysis method, apparatus, device, and medium

Abstract

The application discloses a kind of static balance analysis method, device, equipment and medium, the method includes: solid rocket engine is scanned, obtains K including the two-dimensional slice image of the image of base component, the image of lining component and the image of first bonding area;According to K two-dimensional slice image, base component is three-dimensionally reconstructed, and optimization processing is carried out to obtain first optimized three-dimensional structure image;K lining main body area corresponding first gray interval is obtained;Lining component is three-dimensionally reconstructed to obtain second target three-dimensional structure image;First optimized three-dimensional structure image and second target three-dimensional structure image are carried out image fusion processing, to obtain the three-dimensional micro model of solid rocket engine;According to the three-dimensional micro model of solid rocket engine, the static balance analysis of solid rocket engine is carried out, to obtain the static balance analysis result of solid rocket engine, it is favorable to promote the accuracy when static balance analysis is carried out to solid rocket engine.

Description

Technical Field

[0001] This invention relates to the field of static equilibrium testing technology, and in particular to a static equilibrium analysis method, apparatus, equipment, and medium. Background Technology

[0002] Currently, static equilibrium analysis of solid rocket motors typically involves constructing a three-dimensional mesoscopic model from a two-dimensional finite element model using a particle-filling algorithm, and then performing analysis based on this model. However, the construction of this three-dimensional mesoscopic model not only simplifies the materials but also fails to achieve a realistic solid particle filling rate. Furthermore, the bonding interfaces constructed using this method are often composed of flat mesh elements, while real-world bonding interfaces are irregular interfaces resulting from chemical cross-linking and mutual erosion between two materials. Therefore, the three-dimensional mesoscopic model constructed in this way cannot reflect the true microstructural characteristics, leading to low accuracy and consequently, low accuracy in static equilibrium analysis of solid rocket motors. Summary of the Invention

[0003] This invention provides a static equilibrium analysis method, apparatus, equipment, and medium to solve the technical problem that the low accuracy of three-dimensional microscopic models leads to low accuracy in static equilibrium analysis of solid rocket motors.

[0004] Firstly, a static equilibrium analysis method is provided, including: The solid rocket motor is scanned to obtain K two-dimensional slice images. The two-dimensional slice images include images of the solid rocket motor's base component, liner component, and first bonding area, which is located between the base component and the liner component. The base component is reconstructed in three dimensions based on K two-dimensional slice images to obtain the first target three-dimensional structure image of the base component; The first target three-dimensional structure image is optimized to obtain a first optimized three-dimensional structure image; Obtain the gray values ​​corresponding to K liner body regions respectively to obtain K first gray values. The liner body region is the region of the liner component in the two-dimensional slice image. The K liner body regions correspond one-to-one with K two-dimensional slice images. Determine the grayscale intervals corresponding to the K first grayscale values ​​respectively, so as to obtain K first grayscale intervals; The sub-liner regions are determined from the first bonding region in the corresponding two-dimensional slice image based on the K first gray-level intervals to obtain K sub-liner regions. The liner component is reconstructed in three dimensions based on the regions of the liner component in K sub-liner regions and K two-dimensional slice images to obtain the second target three-dimensional structure image of the liner component; Image fusion processing is performed on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor; Static equilibrium analysis of the solid rocket motor was performed based on its three-dimensional microscopic model to obtain the static equilibrium analysis results.

[0005] Secondly, a static equilibrium analysis apparatus is provided, comprising: The scanning module is used to scan the solid rocket motor and obtain K two-dimensional slice images. The two-dimensional slice images include images of the solid rocket motor's base component, liner component, and first bonding area, which is located between the base component and the liner component. The processing module is used to perform three-dimensional reconstruction of the base component based on K two-dimensional slice images to obtain the first target three-dimensional structure image of the base component; The processing module is also used to optimize the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image; The acquisition module is used to acquire the gray values ​​corresponding to K liner body regions respectively, so as to obtain K first gray values. The liner body region is the region of the liner component in the two-dimensional slice image. The K liner body regions correspond one-to-one with K two-dimensional slice images. The determination module is used to determine the grayscale intervals corresponding to the K first grayscale values, so as to obtain the K first grayscale intervals; The determining module is also used to determine the sub-sub-layer region from the first bonding region in the corresponding two-dimensional slice image based on K first gray-scale intervals, so as to obtain K sub-sub-layer regions. The processing module is also used to perform three-dimensional reconstruction of the liner component based on the regions of the liner component in the K sub-liner regions and K two-dimensional slice images, so as to obtain a second target three-dimensional structure image of the liner component. The processing module is also used to perform image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor; and to perform static equilibrium analysis on the solid rocket motor based on the three-dimensional microscopic model of the solid rocket motor to obtain the static equilibrium analysis result of the solid rocket motor.

[0006] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described static equilibrium analysis method.

[0007] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described static equilibrium analysis method.

[0008] In the aforementioned static equilibrium analysis method, apparatus, equipment, and medium, by scanning the solid rocket motor, K two-dimensional slice images are obtained, including images of the solid rocket motor's base component, liner component, and first bonding area. Based on these K two-dimensional slice images, the base component can be reconstructed in three dimensions to obtain a first target three-dimensional structure image of the base component. This first target three-dimensional structure image is then optimized to obtain a first optimized three-dimensional structure image. K grayscale values ​​corresponding to the liner main body regions are obtained to obtain K first grayscale values. Grayscale intervals corresponding to these K first grayscale values ​​are determined to obtain K first grayscale intervals. Based on these K first grayscale intervals, the corresponding two-dimensional slice images are analyzed... Sub-liner regions are identified in the first bonding region to obtain K sub-liner regions. Further, based on the K sub-liner regions and the regions of the liner components in K two-dimensional slice images, the liner components are reconstructed in three dimensions to obtain a second target three-dimensional structural image of the liner components. Static equilibrium analysis is performed on the solid rocket motor based on its three-dimensional microscopic model to obtain the static equilibrium analysis results. Then, image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor that reflects the true microscopic structural features. This improves the accuracy of the three-dimensional microscopic model and thus enhances the accuracy of the static equilibrium analysis of the solid rocket motor. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a static equilibrium analysis method in one embodiment of the present invention; Figure 2 This is a schematic diagram of a two-dimensional slice image of a solid rocket motor according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a two-dimensional slice image of another solid rocket motor in one embodiment of the present invention; Figure 4 This is a schematic diagram of a first reference three-dimensional structure image in one embodiment of the present invention; Figure 5This is a schematic diagram of a grid-shaped particle in one embodiment of the present invention; Figure 6 This is a schematic diagram of another grid-shaped particle in one embodiment of the present invention; Figure 7 This is a before-and-after comparison of the three-dimensional structural image of a solid rocket motor in one embodiment of the present invention after optimization; Figure 8 This is a schematic diagram of a process for smoothing the outer boundary of particles in one embodiment of the present invention; Figure 9 This is a schematic diagram of a three-dimensional structure image of a second target in one embodiment of the present invention; Figure 10 This is a schematic diagram of a three-dimensional microscopic model of a solid rocket motor according to an embodiment of the present invention; Figure 11 This is a schematic diagram of a static equilibrium analysis device in one embodiment of the present invention; Figure 12 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 13 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] The static equilibrium analysis method provided in this invention can be executed by a static equilibrium analysis device. This device can be a terminal device or a server. Terminal devices can be, but are not limited to, various personal computers, laptops, smartphones, and tablets. Servers can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0013] To better understand the static equilibrium analysis method provided in this application, a brief introduction to existing static equilibrium analysis methods is given below. In existing methods, static equilibrium analysis typically involves constructing a three-dimensional mesoscopic model based on a two-dimensional finite element model using a particle-filling algorithm. During the construction of the three-dimensional mesoscopic model, not only are the materials simplified, but the solid particle filling rate in the resulting model also fails to reach realistic levels. Furthermore, the bonding interface constructed using this method is often a flat mesh element; however, the bonding interface under real-world conditions is an irregular interface resulting from the chemical cross-linking and mutual erosion of two materials. Therefore, the accuracy of constructing the bonding interface during the construction of the three-dimensional mesoscopic model is low, leading to low accuracy in the static equilibrium analysis of solid rocket motors.

[0014] To address the aforementioned problems, this application provides a static equilibrium analysis method. The method involves obtaining grayscale values ​​corresponding to the main liner region to obtain a first grayscale value. The main liner region is the region of the liner component in a two-dimensional slice image, with a one-to-one correspondence between the main liner region and the two-dimensional slice image. Grayscale intervals corresponding to the first grayscale values ​​are determined to obtain a first grayscale interval. Then, a sub-liner region is determined from the first bonding region in the corresponding two-dimensional slice image based on the first grayscale interval. The liner component is then reconstructed in three dimensions based on the sub-liner region and the region of the liner component in the two-dimensional slice image to obtain a second target three-dimensional structural image of the liner component. This allows the bonding interface in the constructed second target three-dimensional structural image to more realistically reflect the actual bonding interface, thereby improving the accuracy of subsequent determination of the three-dimensional microstructure model using the second target three-dimensional structural image, and ultimately improving the accuracy of static equilibrium analysis of solid rocket motors.

[0015] Please see Figure 1 As shown, Figure 1 A flowchart illustrating the static equilibrium analysis method provided in this embodiment of the invention includes the following steps: S10: Scan the solid rocket motor to obtain K two-dimensional slice images. The two-dimensional slice images include images of the solid rocket motor's base component, liner component, and first bonding area, which is located between the base component and the liner component.

[0016] Solid rocket motors are engines that use solid fuel for propulsion. A solid rocket motor ignites solid fuel to produce high-temperature, high-pressure combustion gases, which are then ejected through nozzles to generate thrust. Solid rocket motors can be used in rockets, missiles, spacecraft, etc., and this application does not limit their applications. It should be noted that the static equilibrium analysis equipment can scan the solid rocket motor to obtain K two-dimensional slice images of the motor, which can then be used to analyze the internal structure of the solid rocket motor (such as the base components and liner components).

[0017] It should be noted that each of the K two-dimensional slice images acquired by the static equilibrium analysis equipment can include an image of the solid rocket motor's base component, an image of the liner component, and an image of the first bonding area between the base component and the liner component. The solid rocket motor's base component is composed of solid propellant, which is typically a chemical substance existing in solid form, also known as solid fuel (usually a mixture of combustible material and oxidizer). The solid rocket motor's liner refers to the coating layer of the solid fuel, also known as a bushing or liner. The liner is typically located between the solid fuel and the combustion chamber, serving to enclose the solid fuel and provide structural support.

[0018] Optionally, the static equilibrium analysis equipment can obtain two-dimensional slice images of the solid rocket motor using computed tomography (CT) technology; this application is not limited in this regard. For example, such as... Figure 2 As shown, Figure 2 A schematic diagram of a two-dimensional slice image of a solid rocket motor is shown. Figure 2 It is understood that a two-dimensional slice image of a solid rocket motor can include three regions (i.e., the substrate region, the liner region, and the insulation layer region) and two bonding interfaces. That is, the two-dimensional slice image of a solid rocket motor can also include images of the insulation layer components and images of the bonding area between the liner components and the insulation layer components (such as referred to as the second bonding area), which this application does not limit. The insulation layer can be used to reduce heat conduction and heat loss, isolate the high-temperature combustion chamber from other structures, prevent heat radiation outward, and thereby improve the efficiency and performance of the propulsion system.

[0019] It should be noted that, in this embodiment, the description mainly uses the construction of a three-dimensional microstructure model through the substrate, liner, and first bonding region as an example, and does not constitute a limitation on this application. Optionally, the construction of the three-dimensional microstructure model may also include the treatment of the insulation layer. For specific steps, please refer to the detailed description in the embodiments of this application, which will not be repeated here.

[0020] S20: Reconstruct the base component in three dimensions based on K two-dimensional slice images to obtain the first target three-dimensional structural image of the base component.

[0021] It should be noted that, in order to ensure the accuracy of subsequent data, the static equilibrium analysis equipment can perform fine scanning on the solid rocket motor, so that the acquired K two-dimensional slice images can include more detailed data, thereby making the three-dimensional micro-model subsequently constructed by the static equilibrium analysis equipment more accurate.

[0022] Understandably, the static equilibrium analysis equipment can reconstruct the solid rocket motor's base component in three dimensions based on the base component image in each of the K acquired two-dimensional slice images, thereby obtaining the first target three-dimensional structural image of the base component.

[0023] From the above Figure 2 It can be seen that the matrix region is filled with a large number of particles, such as large, irregularly shaped ammonium perchlorate (AP) particles and small, nearly round aluminum (Al) powder. In other words, the static equilibrium analysis equipment can reconstruct the three-dimensional structure of the matrix region by using images of the matrix component in each of K two-dimensional slice images, thereby obtaining the first target three-dimensional structure image of the matrix component.

[0024] It should be understood that the aforementioned K two-dimensional slice images may include M first target two-dimensional slice images and N second target two-dimensional slice images; wherein, the first target two-dimensional slice image can be a two-dimensional slice image in a first direction, and the second target two-dimensional slice image can be a two-dimensional slice image in a second direction, and the first direction can be perpendicular to the second direction. For example, the first direction can be a horizontal direction, and the second direction can be a vertical direction, that is, the K two-dimensional slice images may include M first target two-dimensional slice images in the horizontal direction and N second target two-dimensional slice images in the vertical direction.

[0025] It is understandable that, since a solid rocket motor is a three-dimensional object, the static equilibrium analysis device can acquire two-dimensional slice images from three mutually perpendicular directions. In other words, the aforementioned K two-dimensional slice images can also include two-dimensional slice images corresponding to the three directions respectively, and this application does not impose any limitation on this. It should be noted that by acquiring two-dimensional slice images of the solid rocket motor corresponding to different directions from multiple directions, the static equilibrium analysis device can obtain more details of the various internal structures of the solid rocket motor. This allows the static equilibrium analysis device to obtain a three-dimensional microscopic model that better reflects the true microstructural features in subsequent steps, which is beneficial to improving the accuracy of the three-dimensional microscopic model.

[0026] Optionally, taking the above K two-dimensional slice images including two two-dimensional slice images corresponding to two directions as an example, step S20, that is, reconstructing the base component in three dimensions based on the K two-dimensional slice images to obtain the first target three-dimensional structure image of the base component, may include the following steps: S21: Extract features from M two-dimensional slice images of the first target to obtain gray values ​​corresponding to the M two-dimensional slice images of the first target respectively; S22: Determine the boundary information of the first particle region in the M first target two-dimensional slice images based on the gray values ​​corresponding to each of the M first target two-dimensional slice images; S23: Extract features from N two-dimensional slice images of the second target to obtain gray values ​​corresponding to the N two-dimensional slice images of the second target respectively; S24: Determine the boundary information of the second particle region in the N second target two-dimensional slice images based on the gray values ​​corresponding to the N second target two-dimensional slice images; S25: Based on the boundary information of the first particle region in M ​​first target two-dimensional slice images and the boundary information of the second particle region in N second target two-dimensional slice images, determine the three-dimensional boundary information of each particle in the matrix component; S26: Based on the three-dimensional boundary information of each particle in the matrix component, perform three-dimensional reconstruction on each particle in the matrix component to obtain the first reference three-dimensional structural image of the matrix component; S27: Fill the space in the first reference three-dimensional structure image except for the boundary indicated by the three-dimensional boundary information of each particle in the matrix component to obtain the first target three-dimensional structure image of the matrix component.

[0027] It should be noted that each different structure in the two-dimensional slice image obtained by the static equilibrium analysis device can be represented by a different grayscale value. For example, taking the presence of AP particles and Al powder in the two-dimensional slice image as an example, the grayscale value of AP particles and the grayscale value of Al powder can be different. It is understood that the grayscale value of AP particles and the grayscale value of the substrate can also be different, and this application does not impose any restrictions on this.

[0028] In other words, the static balance analysis device can extract features from two-dimensional slice images to obtain the grayscale value corresponding to each component in each two-dimensional slice image. Specifically, the static balance analysis device can extract features from M first target two-dimensional slice images and N second target two-dimensional slice images respectively to obtain the grayscale value corresponding to each component in the M first target two-dimensional slice images and the grayscale value corresponding to each component in the N second target two-dimensional slice images.

[0029] Optionally, when performing feature extraction on a specific particle (e.g., particle 1), the static equilibrium analysis device can adjust the grayscale threshold of the two-dimensional slice image to make the contrast between particle 1 and other materials more prominent, thereby making feature extraction on particle 1 more convenient and accurate.

[0030] It is understandable that when performing 3D reconstruction on a specific particle (such as particle 1), the static equilibrium analysis device can obtain the grayscale value corresponding to particle 1, and then determine the boundary of particle 1 based on the grayscale value. Based on this boundary, 3D reconstruction of particle 1 can be performed to obtain a 3D structural image corresponding to particle 1. Optionally, when performing 3D reconstruction on particle 2, the static equilibrium analysis device can perform the same steps as for particle 1 to perform 3D reconstruction on particle 2 to obtain a 3D structural image corresponding to particle 2. This will not be elaborated further in this application.

[0031] Specifically, the static equilibrium analysis device can determine the boundary information of the first particle region in M ​​first target 2D slice images based on the gray values ​​corresponding to the first target 2D slice images in the first direction; and determine the boundary information of the second particle region in N second target 2D slice images based on the gray values ​​corresponding to the second target 2D slice images in the second direction. Taking the 3D reconstruction of particle 1 by the static equilibrium analysis device as an example, the boundary information of the first particle region can be understood as the boundary information of particle 1 in the first direction, and the boundary information of the second particle region can be understood as the boundary information of particle 1 in the second direction.

[0032] Furthermore, the static equilibrium analysis device can determine the three-dimensional boundary information of each particle in the matrix component based on the boundary information of the first particle region in M ​​first target two-dimensional slice images and the boundary information of the second particle region in N second target two-dimensional slice images. For example, the static equilibrium analysis device can determine the three-dimensional boundary information of particle 1 based on the boundary information of particle 1 in the first direction and the boundary information of particle 1 in the second direction; the static equilibrium analysis device can determine the three-dimensional boundary information of particle 2 based on the boundary information of particle 2 in the first direction and the boundary information of particle 2 in the second direction. This application does not impose any limitations on this.

[0033] It is important to understand that 3D reconstruction of each particle in the matrix component refers to the process of reconstructing the 3D structure of each particle outward based on its 3D boundary information and center position information. Specifically, step S26, which involves reconstructing the 3D structure of each particle in the matrix component based on its 3D boundary information to obtain a first reference 3D structural image of the matrix component, includes the following steps: S261: Perform particle labeling processing on M first target two-dimensional slice images to obtain M third target two-dimensional slice images, where the third target two-dimensional slice images are two-dimensional slice images with the center position information of the first particles marked. S262: Perform particle labeling processing on N second target two-dimensional slice images to obtain N fourth target two-dimensional slice images. The fourth target two-dimensional slice images are two-dimensional slice images with the center position information of the second particles marked. S263: Based on M two-dimensional slice images of the third target and N two-dimensional slice images of the fourth target, determine the center position information of each particle in the matrix component; S264: Based on the three-dimensional boundary information of each particle in the matrix component and the center position information of each particle in the matrix component, perform three-dimensional reconstruction of each particle in the matrix component to obtain the first reference three-dimensional structural image of the matrix component.

[0034] It should be noted that when performing three-dimensional reconstruction of a particle (taking particle 1 as an example), the static equilibrium analysis equipment can perform particle marking processing on particle 1, thereby obtaining a two-dimensional slice image marked with the particle's center position information. For example, ... Figure 3 As shown, Figure 3 A two-dimensional slice image marked with particle center location information is shown. Since particle 1 can be an irregularly shaped particle, the particle center location information marked by the static equilibrium analysis device can be an approximate center location; this application does not impose any limitations on this. Optionally, the static equilibrium analysis device performs particle marking processing on particle 1, enabling the static equilibrium analysis device to identify and acquire particle 1, thereby allowing for further subsequent operation steps on particle 1; this application does not impose any limitations on this.

[0035] Specifically, the static equilibrium analysis equipment can perform particle labeling processing on M first target two-dimensional slice images in a first direction and N second target two-dimensional slice images in a second direction, thereby obtaining M third target two-dimensional slice images labeled with the center position information of the first particles, and N fourth target two-dimensional slice images labeled with the center position information of the second particles. Taking particle 1 as an example, the first particle center position information can be understood as the particle center position information obtained by labeling particle 1 in the first direction, and the second particle center position information can be understood as the particle center position information obtained by labeling particle 2 in the second direction; this application does not impose any limitations on this.

[0036] Furthermore, the static equilibrium analysis device can determine the center position information of each particle in the matrix component based on M two-dimensional slice images of the third target and N two-dimensional slice images of the fourth target. The center position information of each particle can be used to indicate the center position of the corresponding three-dimensional image, and this application does not limit this. For example, the static equilibrium analysis device can determine the center position information of particle 1 based on the particle center position information of particle 1 in the first direction and particle 1 in the second direction; the static equilibrium analysis device can determine the center position information of particle 2 based on the particle center position information of particle 2 in the first direction and particle 2 in the second direction, and this application does not limit this.

[0037] It should be noted that, to ensure the accuracy and precision of particle identification, the static equilibrium analysis device can determine the aforementioned three-dimensional boundary information and center position information for as many two-dimensional slices as possible. For example, the static equilibrium analysis device can perform the determination once for each two-dimensional slice. Optionally, to reduce the computational load of the device, the static equilibrium analysis device can determine the aforementioned three-dimensional boundary information and center position information for as few two-dimensional slices as possible while maintaining accuracy. For example, the static equilibrium analysis device can perform the determination once for every 10 two-dimensional slices. This application does not impose any limitation on this.

[0038] It is understandable that after the static equilibrium analysis equipment obtains the three-dimensional boundary information and the center position information of each particle in the matrix component, it can fill outwards from the center position indicated by the center position information of each particle until the three-dimensional boundary indicated by the three-dimensional boundary information of each particle is reached, thereby achieving the purpose of three-dimensional reconstruction of each particle in the matrix component, and thus obtaining the first reference three-dimensional structural image of the matrix component.

[0039] For example, such as Figure 4 As shown, Figure 4 A schematic diagram of a first reference three-dimensional structural image of a base component is shown. It should be noted that... Figure 4 Taking the example of a first reference three-dimensional structural image containing only one type of particle, i.e., a first reference three-dimensional structural image containing only a particle corresponding to a single grayscale value, does not constitute a limitation on this application. Optionally, if the first reference three-dimensional structural image includes multiple types of particles, the first reference three-dimensional structural image may include particles corresponding to multiple grayscale values, and this application does not impose any limitation on this.

[0040] For steps S261-S264, the static equilibrium analysis device performs particle labeling processing on M first target two-dimensional slice images to obtain M third target two-dimensional slice images labeled with the center position information of the first particles, and performs particle labeling processing on N second target two-dimensional slice images to obtain N fourth target two-dimensional slice images labeled with the center position information of the second particles. Thus, based on the M third target two-dimensional slice images and the N fourth target two-dimensional slice images, the center position information of each particle in the matrix component can be determined. Then, based on the three-dimensional boundary information of each particle in the matrix component and the center position information of each particle in the matrix component, the three-dimensional reconstruction of each particle in the matrix component can be performed to obtain a more accurate first reference three-dimensional structure image of the matrix component. This first reference three-dimensional structure image can better reflect the real microstructural features, which is beneficial to improving the accuracy of the three-dimensional microstructure model.

[0041] It should be noted that the static equilibrium analysis equipment can fill the space in the first reference three-dimensional structural image, excluding the boundaries indicated by the three-dimensional boundary information of each particle in the matrix component, to obtain another reference three-dimensional structural image of the matrix component (such as the second reference three-dimensional structural image). The second reference three-dimensional structural image of the matrix component can be understood as the three-dimensional structural image corresponding to the matrix material in the matrix component. Optionally, the matrix material can refer to the material of the main structure of the solid fuel in the solid fuel engine. The matrix material can be used to store and release energy and provide structural support for the matrix components of the solid fuel engine.

[0042] For example, taking an engine matrix component that includes AP particles, Al powder, other metals, pores, and other particles as an example, the static equilibrium analysis equipment can obtain a three-dimensional structural image of the AP particles, Al powder, other metals, pores, and other particles through the above-mentioned three-dimensional reconstruction method. In other words, after removing the aforementioned solid particles from the engine matrix component, the remaining material can be the matrix material. The static equilibrium analysis equipment can obtain a three-dimensional structural image of the matrix material by filling it with the matrix material. This application does not impose any limitations on this.

[0043] In other words, the static equilibrium analysis equipment can not only perform three-dimensional reconstruction of each particle in the matrix component to obtain a first reference three-dimensional structural image of the matrix component, but also perform three-dimensional reconstruction of the matrix material of the matrix component to obtain a second reference three-dimensional structural image of the matrix component. Based on these first and second reference three-dimensional structural images, a first target three-dimensional structural image of the matrix component can be obtained. It is understood that this first target three-dimensional structural image of the matrix component can be interpreted as a complete three-dimensional structural image corresponding to the matrix component, and this application does not impose any limitation on this.

[0044] For steps S21-S27, the static equilibrium analysis device extracts features from M first target two-dimensional slice images to obtain grayscale values ​​corresponding to each of the M first target two-dimensional slice images. Based on these grayscale values, the device further determines the boundary information of the first particle region in the M first target two-dimensional slice images. Similarly, it extracts features from N second target two-dimensional slice images to obtain grayscale values ​​corresponding to each of the N second target two-dimensional slice images. Based on these grayscale values, the device further determines the boundary information of the second particle region in the N second target two-dimensional slice images. Thus, based on the grayscale values ​​of the M first target two-dimensional slice images, the device can determine the boundary information of the second particle region in the M second target two-dimensional slice images. By using the boundary information of the first particle region in the sliced ​​image and the boundary information of the second particle region in N second target two-dimensional sliced ​​images, the three-dimensional boundary information of each particle in the matrix component is determined. Then, based on the three-dimensional boundary information of each particle in the matrix component, each particle in the matrix component is reconstructed in three dimensions to obtain the first reference three-dimensional structure image of the matrix component. The space in the first reference three-dimensional structure image other than the boundary indicated by the three-dimensional boundary information of each particle in the matrix component is filled to obtain a more accurate first target three-dimensional structure image of the matrix component. This first target three-dimensional structure image can better reflect the real microstructural features, which is beneficial to improving the accuracy of the three-dimensional microstructure model.

[0045] S30: Optimize the three-dimensional structure image of the first target to obtain the first optimized three-dimensional structure image.

[0046] Understandably, the static equilibrium analysis equipment can further optimize the obtained first target three-dimensional structure image to obtain a first optimized three-dimensional structure image, thereby minimizing the amount of computation in subsequent data processing while ensuring data accuracy.

[0047] It should be understood that optimizing the three-dimensional structure image of the first target can refer to the process of optimizing the three-dimensional structure image of the first target based on the mesh information of each particle in the matrix component. Specifically, step S30, which optimizes the three-dimensional structure image of the first target to obtain the first optimized three-dimensional structure image, includes the following steps: S311: Obtain the mesh information of each particle in the matrix component from the three-dimensional structural image of the first target. The mesh information includes the mesh boundary and voxel. S312: Determine a first set of particles and a second set of particles based on the mesh information of each particle in the matrix component. The first particle in the first set of particles is a particle in the matrix component whose mesh boundary overlaps with the mesh boundary of the adjacent particle is less than a first threshold or whose voxel count is less than a second threshold. The second particle in the second set of particles is a particle in the matrix component whose mesh boundary overlaps with the mesh boundary of the adjacent particle is greater than or equal to the first threshold and whose voxel count is greater than or equal to the second threshold. S313: The first particle in the first particle set is fused with the corresponding non-particle space in the first target three-dimensional structure image, and the second particle in the second particle set in the first target three-dimensional structure image is fused with the corresponding neighboring particles to obtain the first optimized three-dimensional structure image.

[0048] It should be noted that the mesh of each particle can be understood as the three-dimensional space grid occupied by each particle in the 3D image space. The mesh information of each particle can include the mesh boundary voxel corresponding to each particle in the 3D image space. The mesh boundary can be the boundary of the three-dimensional space grid occupied by each particle in the 3D space. A voxel can be used to represent the volume pixels of each particle in 3D space. A voxel can be a uniform cube, which is the smallest unit of spatial discretization. It can be understood that voxels in 3D image space are similar to pixels in 2D image space.

[0049] Understandably, by acquiring the mesh information of each particle in the matrix component, the static equilibrium analysis equipment can determine the specific boundary position (i.e., mesh boundary) and size (i.e., voxel) of each particle. Thus, it can optimize the first target three-dimensional structure image based on the mesh information of each particle, thereby obtaining the first optimized three-dimensional structure image.

[0050] Specifically, the static equilibrium analysis equipment can identify particles in the matrix component whose mesh boundary overlaps with the mesh boundary of neighboring particles by less than a first threshold, or whose voxel count is less than a second threshold, as first particles, thus obtaining a first particle set; and identify particles in the matrix component whose mesh boundary overlaps with the mesh boundary of neighboring particles by greater than or equal to the first threshold, and whose voxel count is greater than or equal to the second threshold, as second particles, thus obtaining a second particle set. The first threshold can be used to indicate the threshold corresponding to the overlap between the mesh boundary of a particle in the matrix component and the mesh boundary of a neighboring particle. This first threshold can be expressed as a percentage or a decimal, and this application does not limit its expression. The second threshold can be used to indicate the threshold corresponding to the voxel count of the particles in the matrix component. It is understood that the first threshold and the second threshold can be different, and this application does not limit their expression.

[0051] It should be noted that, for each first particle in the first particle set, i.e., a particle with a small degree of overlap with neighboring particles or a small voxel size, the static equilibrium analysis device can fuse each first particle with the matrix material in the matrix component (i.e., the corresponding non-particle space, which can be specifically understood as the non-particle space closest to the first particle). For each second particle in the second particle set, i.e., a particle with a large degree of overlap with neighboring particles and a large voxel size, the static equilibrium analysis device can fuse each second particle with its corresponding neighboring particles; this application does not impose any limitations on this.

[0052] It is understandable that the static equilibrium analysis equipment may contain errors or other problems in the three-dimensional structure image of the first target reconstructed from the two-dimensional slice image, which may result in some isolated particles (i.e., the aforementioned first and second particle sets). For example... Figure 5 As shown, Figure 5 A schematic diagram of particles in a grid pattern is shown. Figure 5 It can be seen that particles 1 and 4 can be particles in the first set of particles, and particles 2 and 3 can be particles in the second set of particles. These isolated particles will significantly increase the computational load and may even cause the computation to fail to converge.

[0053] Therefore, static equilibrium analysis equipment can undergo further fusion processing to minimize the complexity of subsequent calculations while preserving the true state of each particle. For example, in Figure 5 Based on this, the static equilibrium analysis equipment can fuse particles 1 and 4 with the matrix material, and fuse particles 2 and 3 with neighboring particles, thereby obtaining... Figure 6 The first optimized 3D structure image is shown.

[0054] It should be noted that, for ease of explanation, Figure 5 and Figure 6 The use of two-dimensional images for illustrative purposes does not constitute a limitation on this application. It is understood that in actual optimization processes, Figure 5 and Figure 6 The particles in it can be three-dimensional particles, that is... Figure 5 and Figure 6 The mesh in the image is a three-dimensional mesh, and this application does not impose any restrictions on it.

[0055] For example, such as Figure 7 As shown, Figure 7This paper presents a before-and-after comparison of optimizing a three-dimensional structural image of a solid rocket motor in three-dimensional space. It is understood that this application uses the optimization of each particle in the solid rocket motor matrix component using a static equilibrium analysis device as an example, and does not constitute a limitation on this application. Optionally, each component in the three-dimensional structural image of the solid rocket motor can be optimized using the above method, which will not be elaborated further here.

[0056] Optionally, the surface roughness of each particle in the matrix component obtained through 3D reconstruction by the static equilibrium analysis equipment may also affect the quantity and quality of the mesh. For example, ... Figure 8 As shown, the static equilibrium analysis equipment can smooth the outer boundary of particles using relevant algorithms to obtain smoother particles, thereby further reducing the computational load. This application does not impose any limitations on this. The principle and related steps of the smoothing process performed by the static equilibrium analysis equipment can be found in the aforementioned principle and related steps of optimizing each particle based on mesh information, which will not be repeated here.

[0057] For steps S311-S313, the static equilibrium analysis device obtains the mesh information of each particle in the matrix component from the first target three-dimensional structure image, and determines the first particle set and the second particle set based on the mesh information of each particle in the matrix component. Then, it fuses each first particle in the first particle set with the corresponding non-particle space in the first target three-dimensional structure image, and fuses the second particle in the second particle set in the first target three-dimensional structure image with the corresponding neighboring particles to obtain the first optimized three-dimensional structure image. In this way, while ensuring the true state of each particle, it minimizes the difficulty of subsequent calculations and helps to obtain a more accurate three-dimensional microstructure model.

[0058] It should be understood that optimizing the three-dimensional structure image of the first target can refer to the process of optimizing the three-dimensional structure image of the first target based on the mechanical influence of each particle in the matrix component. Specifically, step S30, which optimizes the three-dimensional structure image of the first target to obtain a first optimized three-dimensional structure image, includes the following steps: S321: Obtain the mechanical influence of each particle in the matrix component from the three-dimensional structural image of the first target; S322: Based on the mechanical influence of each particle in the matrix component, determine the third particle set. The third particle in the third particle set is the particle whose mechanical influence is less than the third threshold. S323: Fuse each third particle in the third particle set with the corresponding non-particle space in the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image.

[0059] The mechanical influence degree of each particle can be used to indicate how much the mechanical properties of each particle change due to the influence of other factors or materials. This mechanical influence degree can be called the mechanical property influence degree, and this application does not limit it to this. For example, a static equilibrium analysis device can obtain a small mechanical influence degree for Al powder, that is, the mechanical properties of Al powder change very little due to the influence of other factors or materials.

[0060] It is understandable that the static equilibrium analysis equipment can obtain a set of third particles by acquiring the third particles whose mechanical influence is less than the third threshold. Then, each third particle in the set of third particles can be fused with the matrix material in the matrix component (i.e., the corresponding non-particle space, which can be understood as the non-particle space closest to the third particle) to obtain the first optimized three-dimensional structure image.

[0061] The third threshold can be used to indicate the threshold corresponding to the mechanical influence of particles in the matrix component. This third threshold can be expressed as a percentage or a decimal, and this application does not impose any limitation on this. It should be noted that this third threshold may be different from the aforementioned first and second thresholds, and this application does not impose any limitation on this.

[0062] For steps S321-S323, the static equilibrium analysis device obtains the mechanical influence degree of each particle in the matrix component from the first target three-dimensional structure image. Based on the mechanical influence degree of each particle in the matrix component, it determines the third particle set corresponding to the third particle whose mechanical influence degree is less than the third threshold. Then, it fuses each third particle in the third particle set with the corresponding non-particle space in the first target three-dimensional structure image to obtain the first optimized three-dimensional structure image. This minimizes the difficulty of subsequent calculations while ensuring the true state of each particle, which is beneficial to obtaining a more accurate three-dimensional microstructure model.

[0063] It should be understood that optimizing the three-dimensional structure image of the first target can refer to the process of optimizing the three-dimensional structure image of the first target based on the volume of each particle in the matrix component. Specifically, step S30, which optimizes the three-dimensional structure image of the first target to obtain a first optimized three-dimensional structure image, includes the following steps: S331: Obtain the volume of each particle in the matrix component from the three-dimensional structural image of the first target; S332: Determine the fourth particle set based on the volume of each particle in the matrix component. The fourth particle in the fourth particle set is a particle whose volume is less than the fourth threshold. S333: Fuse each fourth particle in the fourth particle set with the corresponding non-particle space in the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image.

[0064] The volume of each particle indicates the three-dimensional spatial extent it occupies. The volume of each particle can be measured and calculated based on its shape and size. It should be noted that the volume of each particle is different from its voxel count; in 3D graphics and computer graphics, a series of voxels can be used to represent the volume of a particle.

[0065] It is understood that the static equilibrium analysis device can obtain a fourth particle set by acquiring fourth particles with volumes smaller than the fourth threshold. Each fourth particle in this set can then be fused with the matrix material in the matrix component (i.e., the corresponding non-particle space, specifically the non-particle space closest to the fourth particle) to obtain a first optimized three-dimensional structure image. The fourth threshold can be used to indicate the threshold corresponding to the volume of particles in the matrix component. This fourth threshold can be expressed in decimal form, and this application does not limit its representation. It should be noted that this fourth threshold can be different from the aforementioned first, second, and third thresholds, and this application does not limit its representation.

[0066] For steps S331-S333, the static equilibrium analysis device obtains the volume of each particle in the matrix component from the first target three-dimensional structure image. Based on the volume of each particle in the matrix component, it determines the fourth particle set corresponding to the fourth particle whose volume is less than the fourth threshold. Then, it fuses each fourth particle in the fourth particle set with the corresponding non-particle space in the first target three-dimensional structure image to obtain the first optimized three-dimensional structure image. This minimizes the difficulty of subsequent calculations while ensuring the true state of each particle, which is beneficial to obtaining a more accurate three-dimensional microstructure model.

[0067] It should be noted that the static equilibrium analysis equipment can select the optimization method that best matches the particles in the matrix components of the current solid rocket motor to perform the above optimization processing to obtain the first optimized three-dimensional structural image. Optionally, the static equilibrium analysis equipment can also select two or three of the above optimization methods to perform more comprehensive and complete optimization processing to obtain the first optimized three-dimensional structural image; this application does not impose any restrictions on this.

[0068] S40: Obtain the gray values ​​corresponding to the K liner main body regions respectively to obtain K first gray values. The liner main body region is the region of the liner component in the two-dimensional slice image. The K liner main body regions correspond one-to-one with the K two-dimensional slice images.

[0069] As mentioned above, static equilibrium analysis equipment can obtain the grayscale value corresponding to each component in a solid rocket motor. This means that static equilibrium analysis equipment can also obtain the grayscale value corresponding to the liner components. Specifically, the grayscale value corresponding to the liner components mainly refers to the grayscale value corresponding to the main area of ​​the liner.

[0070] It is understood that the static equilibrium analysis device can obtain the grayscale values ​​corresponding to K main areas of the liner from K two-dimensional slice images to obtain K first grayscale values. It should be noted that the static equilibrium analysis device can acquire the grayscale values ​​of the K two-dimensional slice images separately; however, it is understood that the K first grayscale values ​​corresponding to the K two-dimensional slice images can be the same grayscale value. Optionally, during the scanning process of the solid rocket motor, K two-dimensional slice images with slight differences may be obtained. Therefore, there may be slight errors between the K first grayscale values, which this application does not limit.

[0071] Optionally, to ensure the accuracy of the static balance analysis device during data processing, the static balance analysis device can acquire the gray values ​​corresponding to the K substrate main areas of the K two-dimensional slice images respectively, and determine the K first gray values ​​that correspond one-to-one with the K two-dimensional slice images.

[0072] S50: Determine the grayscale intervals corresponding to the K first grayscale values ​​respectively, so as to obtain K first grayscale intervals.

[0073] After the static equilibrium analysis device acquires K first grayscale values, it can determine the corresponding grayscale intervals for each of the K first grayscale values ​​to obtain K first grayscale intervals. A first grayscale interval can be used to indicate a grayscale interval close to a first grayscale value; that is, a first grayscale interval can be used to represent a grayscale interval within the error range of the first grayscale value.

[0074] It should be noted that the static equilibrium analysis equipment determines the first grayscale range by using the first grayscale value. It can then filter and judge other grayscale values ​​that are generated due to minor errors, thereby identifying other grayscale values ​​that are similar to the first grayscale value. This is beneficial for obtaining a more accurate three-dimensional structural image of the lining component in the subsequent three-dimensional reconstruction process.

[0075] S60: Determine the sub-liner region from the first bonding region in the corresponding two-dimensional slice image based on K first gray-level intervals to obtain K sub-liner regions.

[0076] The first bonding area can be an irregular area formed by bonding the base component and the liner component in a solid rocket motor. In other words, this first bonding area is obtained after the liner component and the base component undergo chemical cross-linking and mutual erosion during the bonding process.

[0077] It is understandable that the grayscale value corresponding to the first bonding region can include the grayscale value corresponding to the corresponding structure in the substrate component and the grayscale value corresponding to the main body region of the liner component. As mentioned above, the grayscale values ​​corresponding to the corresponding structure in the substrate component may be quite complex and diverse. Therefore, the static equilibrium analysis device can select the grayscale value corresponding to the main body region of the liner from the first bonding region to further separate the substrate component and the liner component in the first bonding region based on the grayscale value of the main body region of the liner. Specifically, the static equilibrium analysis device can determine the sub-liner regions from the first bonding region in the corresponding two-dimensional slice image based on K first grayscale intervals to obtain K sub-liner regions. Among them, the sub-liner region can be the region in the first bonding region that conforms to the first grayscale interval, that is, the region in the first bonding region that is close to the first grayscale value.

[0078] Optionally, when the two-dimensional slice image includes an image of the insulation layer component, the static equilibrium analysis device can select regions with gray values ​​conforming to the first gray value range from the second bonding region using the method described above to obtain K sub-liner regions on the insulation layer side (such as K second sub-liner regions), thereby separating the liner component from the insulation layer component in the second bonding region. Optionally, the static equilibrium analysis device can also select K corresponding sub-insulation layer regions on the insulation layer side from the second bonding interface based on the gray value corresponding to the main insulation layer region of the insulation layer component; this application does not limit this selection.

[0079] It should be noted that the static equilibrium analysis device can separate the region that meets the first grayscale range from the first adhesive region and / or the second adhesive region by determining K sub-liner regions from the first adhesive region in each two-dimensional slice image. This allows the three-dimensional structural image of the liner component to be determined in subsequent steps based on the K sub-liner regions and the liner body region.

[0080] S70: Based on the regions of the liner components in the K sub-liner regions and K two-dimensional slice images, the liner components are reconstructed in three dimensions to obtain the second target three-dimensional structural image of the liner components.

[0081] It should be noted that the static equilibrium analysis device can stitch together the main liner region and K sub-liner regions from K two-dimensional slice images to obtain K stitched two-dimensional slice images corresponding to the liner component. Optionally, the static equilibrium analysis device can also optimize the K stitched two-dimensional slice images corresponding to the liner component based on the regions of the liner component in the K two-dimensional slice images, such as filling gaps, to obtain K optimized two-dimensional slice images corresponding to the liner component. This application does not impose any limitations on this.

[0082] Furthermore, the static equilibrium analysis equipment can perform three-dimensional reconstruction of the liner component based on the obtained K optimized two-dimensional slice images, thereby obtaining a second target three-dimensional structural image. This second target three-dimensional structural image can be the three-dimensional structural image corresponding to the liner component. For example... Figure 9 As shown, Figure 9 A schematic diagram of a second target three-dimensional structure image is shown. It is understood that the second target three-dimensional structure image corresponding to the liner component can completely overlap with the first optimized three-dimensional structure image corresponding to the aforementioned base component, and this application does not impose any limitation on this.

[0083] In this embodiment, the example given is that the liner component does not contain any particulate structure, and this does not constitute a limitation on this application. Optionally, the liner component may also contain particulate structure. The static equilibrium analysis equipment can use a method for processing the particulate structure in the matrix component to process the particulate structure in the liner component, which will not be elaborated here.

[0084] S80: Perform image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor.

[0085] Understandably, the static equilibrium analysis equipment can fuse the first optimized three-dimensional structural image corresponding to the matrix component obtained from the three-dimensional reconstruction and the second target three-dimensional structural image corresponding to the liner component obtained from the three-dimensional reconstruction, thereby obtaining a three-dimensional microscopic model of the solid rocket motor.

[0086] In this application embodiment, the three-dimensional reconstruction of the insulation layer component is not described, and this does not constitute a limitation on this application. It should be noted that for the relevant content on the three-dimensional reconstruction of the insulation layer component, please refer to the detailed content on the three-dimensional reconstruction of the liner component described above, which will not be repeated here.

[0087] For example, such as Figure 10 As shown, Figure 10 A schematic diagram of a three-dimensional microscopic model of a solid rocket motor is shown. Among them, Figure 10The following description uses a three-dimensional structural diagram of a three-dimensional micro-model, including the base component, the liner component, and the insulation layer component, as an example and does not constitute a limitation on this application.

[0088] Optionally, after performing image fusion processing on the first optimized 3D structural image and the second target 3D structural image, the static equilibrium analysis equipment can further perform material property labeling processing on each component by obtaining the material properties corresponding to each component. Specifically, this may include the following steps: S81: Perform image fusion processing on the first optimized 3D structure image and the second target 3D structure image to obtain the third target 3D structure image; S82: Obtain the material properties of each component in the three-dimensional structural image of the third target; S83: Based on the material properties corresponding to each component in the three-dimensional structural image of the third target, perform material property labeling on each component in the three-dimensional structural image of the third target to obtain a three-dimensional microscopic model of the solid rocket motor.

[0089] The third target three-dimensional structural image can be a three-dimensional structural image obtained by fusing the base component and the liner component. Optionally, if a three-dimensional structural image corresponding to the reconstructed insulation layer component exists, the third target three-dimensional structural image can be a three-dimensional structural image obtained by fusing the base component, the liner component, and the insulation layer component; this application does not impose any restrictions on this.

[0090] It should be noted that the three-dimensional structural image of the third target includes the base component, the liner component, the first bonding region, etc. Each component in the three-dimensional structural image of the third target can have corresponding material properties. For example, each particle can have its own material properties, and the liner component can have its own material properties, etc. The static equilibrium analysis equipment can obtain the material properties corresponding to each component, and further perform material property labeling processing on each component based on these material properties, thereby obtaining a three-dimensional microscopic model of the solid rocket motor labeled with material properties.

[0091] For steps S81-S83, the static equilibrium analysis equipment performs image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain the third target three-dimensional structure image. This allows for the further acquisition of the material properties corresponding to each component in the third target three-dimensional structure image. Based on the material properties corresponding to each component in the third target three-dimensional structure image, material property labeling processing is performed on each component in the third target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor, which is beneficial for obtaining an accurate three-dimensional microscopic model.

[0092] S90: Perform static balance analysis on the solid rocket motor based on the three-dimensional microscopic model of the solid rocket motor to obtain the static balance analysis results of the solid rocket motor.

[0093] Static equilibrium analysis can be performed on the solid rocket motor based on the material properties of each component in the three-dimensional microscopic model and the strain information of each component in the three-dimensional microscopic model, so as to obtain the static equilibrium analysis results.

[0094] Since the three-dimensional microscopic model can reflect the particle boundary information of each component in the solid rocket motor, the strain information inside and between each component can be determined based on the particle boundary information. At the same time, since the solid rocket motor is in a stable state, the strain information and the stress information inside and between each component can be obtained to perform static equilibrium analysis on the solid rocket motor, obtain the factors that cause the solid rocket motor to be in static equilibrium, and the maximum critical condition required to break the static equilibrium.

[0095] Specifically, a three-dimensional mesoscopic model of the solid rocket motor under various stress conditions can be constructed. This model allows for the acquisition of strain information within and between the various components of the motor. Based on the stresses experienced by the motor and the strain information of its internal components, stress-strain curves can be constructed. Static equilibrium analysis can then be performed on these curves to determine the factors maintaining static equilibrium within the motor and the maximum critical condition required to disrupt this equilibrium. These factors can include the stress characteristics of the internal materials, while the maximum critical condition for disrupting this equilibrium can be characterized by the magnitude of external stress, such as the minimum stress required to break the motor's static equilibrium.

[0096] As can be seen, in the above scheme, the static equilibrium analysis equipment scans the solid rocket motor to obtain K two-dimensional slice images, including images of the solid rocket motor's base component, liner component, and first bonding area. Based on these K two-dimensional slice images, the base component can be reconstructed in three dimensions to obtain a first target three-dimensional structure image of the base component. This first target three-dimensional structure image is then optimized to obtain a first optimized three-dimensional structure image. The grayscale values ​​corresponding to K liner main body regions are obtained to obtain K first grayscale values. The grayscale intervals corresponding to these K first grayscale values ​​are determined to obtain K first grayscale intervals. Based on these K first grayscale intervals, the first grayscale value in the corresponding two-dimensional slice image is then analyzed. Sub-liner regions are identified within the bonding area to obtain K sub-liner regions. Further, based on the K sub-liner regions and the regions of the liner components in K two-dimensional slice images, the liner components are reconstructed in three dimensions to obtain a second target three-dimensional structural image of the liner components. Static equilibrium analysis is performed on the solid rocket motor based on its three-dimensional microscopic model to obtain the static equilibrium analysis results. Then, image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor that reflects the true microscopic structural features. This improves the accuracy of the three-dimensional microscopic model and, consequently, the accuracy of the static equilibrium analysis of the solid rocket motor.

[0097] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0098] In one embodiment, a static equilibrium analysis device is provided, which corresponds one-to-one with the static equilibrium analysis method described in the above embodiments. For example... Figure 11 As shown, the static equilibrium analysis device includes a scanning module 101, a processing module 102, an acquisition module 103, and a determination module 104. Detailed descriptions of each functional module are as follows: The scanning module 101 is used to scan the solid rocket motor and obtain K two-dimensional slice images. The two-dimensional slice images include images of the solid rocket motor's base component, liner component, and first bonding area, which is located between the base component and the liner component. The processing module 102 is used to perform three-dimensional reconstruction of the base component based on K two-dimensional slice images to obtain a first target three-dimensional structure image of the base component; The processing module 102 is also used to optimize the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image; The acquisition module 103 is used to acquire the gray values ​​corresponding to the K liner body regions respectively, so as to obtain K first gray values. The liner body region is the region of the liner component in the two-dimensional slice image. The K liner body regions correspond one-to-one with the K two-dimensional slice images. The determining module 104 is used to determine the grayscale intervals corresponding to the K first grayscale values ​​respectively, so as to obtain the K first grayscale intervals; The determining module 104 is further configured to determine the sub-sub-layer region from the first bonding region in the corresponding two-dimensional slice image based on the K first gray-scale intervals, so as to obtain the K sub-sub-layer regions. The processing module 102 is also used to perform three-dimensional reconstruction of the liner component based on the regions of the liner component in the K sub-liner regions and K two-dimensional slice images, so as to obtain a second target three-dimensional structure image of the liner component. The processing module 102 is further configured to perform image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor; and to perform static equilibrium analysis on the solid rocket motor based on the three-dimensional microscopic model of the solid rocket motor to obtain the static equilibrium analysis result of the solid rocket motor.

[0099] In one embodiment, the two-dimensional slice image includes M first target two-dimensional slice images and N second target two-dimensional slice images. The first target two-dimensional slice images are two-dimensional slice images in a first direction, and the second target two-dimensional slice images are two-dimensional slice images in a second direction. The first direction and the second direction are perpendicular. The processing module 102 is further configured to perform feature extraction on the M first target two-dimensional slice images to obtain grayscale values ​​corresponding to the M first target two-dimensional slice images respectively. The determining module 104 is further configured to determine the boundary information of the first particle region in the M first target two-dimensional slice images based on the grayscale values ​​corresponding to the M first target two-dimensional slice images respectively. The processing module 102 is further configured to perform feature extraction on the N second target two-dimensional slice images to obtain grayscale values ​​corresponding to the N second target two-dimensional slice images respectively. The determining module 104 is further configured to perform feature extraction on the N second target two-dimensional slice images to obtain grayscale values ​​corresponding to the N second target two-dimensional slice images respectively. The module 104 is further configured to determine the boundary information of the second particle region in the N second target two-dimensional slice images based on the gray values ​​corresponding to the N second target two-dimensional slice images respectively; the determination module 104 is further configured to determine the three-dimensional boundary information of each particle in the matrix component based on the boundary information of the first particle region in the M first target two-dimensional slice images and the boundary information of the second particle region in the N second target two-dimensional slice images; the processing module 102 is further configured to perform three-dimensional reconstruction on each particle in the matrix component based on the three-dimensional boundary information of each particle in the matrix component to obtain a first reference three-dimensional structure image of the matrix component; the processing module 102 is further configured to fill the space in the first reference three-dimensional structure image other than the boundary indicated by the three-dimensional boundary information of each particle in the matrix component to obtain a first target three-dimensional structure image of the matrix component.

[0100] In one embodiment, the processing module 102 is further configured to perform particle marking processing on M first target two-dimensional slice images to obtain M third target two-dimensional slice images, wherein the third target two-dimensional slice images are two-dimensional slice images marked with the center position information of the first particles; the processing module 102 is further configured to perform particle marking processing on N second target two-dimensional slice images to obtain N fourth target two-dimensional slice images, wherein the fourth target two-dimensional slice images are two-dimensional slice images marked with the center position information of the second particles; the determining module 104 is further configured to determine the center position information of each particle in the matrix component based on the M third target two-dimensional slice images and the N fourth target two-dimensional slice images; the processing module 102 is further configured to perform three-dimensional reconstruction of each particle in the matrix component based on the three-dimensional boundary information of each particle in the matrix component and the center position information of each particle in the matrix component to obtain a first reference three-dimensional structure image of the matrix component.

[0101] In one embodiment, the acquisition module 103 is further configured to acquire mesh information of each particle in the matrix component from the first target three-dimensional structure image, the mesh information including mesh boundaries and voxels; the determination module 104 is further configured to determine a first particle set and a second particle set based on the mesh information of each particle in the matrix component, wherein the first particle in the first particle set is a particle in the matrix component whose mesh boundary overlaps with the mesh boundary of a neighboring particle is less than a first threshold, or whose voxel count is less than a second threshold, and the second particle in the second particle set is a particle in the matrix component whose mesh boundary overlaps with the mesh boundary of a neighboring particle is greater than or equal to the first threshold, and whose voxel count is greater than or equal to the second threshold; the processing module 102 is further configured to perform fusion processing on each first particle in the first particle set and the corresponding non-particle space in the first target three-dimensional structure image, and to perform fusion processing on the second particle in the second particle set in the first target three-dimensional structure image and the corresponding neighboring particle, so as to obtain a first optimized three-dimensional structure image.

[0102] In one embodiment, the acquisition module 103 is further configured to acquire the mechanical influence degree of each particle in the matrix component from the first target three-dimensional structure image; the determination module 104 is further configured to determine a third particle set based on the mechanical influence degree of each particle in the matrix component, wherein the third particle in the third particle set is a particle whose mechanical influence degree is less than a third threshold; and the processing module 102 is further configured to perform fusion processing on each third particle in the third particle set with the corresponding non-particle space in the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image.

[0103] In one embodiment, the acquisition module 103 is further configured to acquire the volume of each particle in the matrix component from the first target three-dimensional structure image; the determination module 104 is further configured to determine a fourth particle set based on the volume of each particle in the matrix component, wherein the fourth particle in the fourth particle set is a particle with a volume less than a fourth threshold; and the processing module 102 is further configured to perform fusion processing on each fourth particle in the fourth particle set with the corresponding non-particle space in the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image.

[0104] In one embodiment, the processing module 102 is further configured to perform image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a third target three-dimensional structure image; the acquisition module 103 is further configured to acquire the material properties corresponding to each component in the third target three-dimensional structure image; the processing module 102 is further configured to perform material property marking processing on each component in the third target three-dimensional structure image according to the material properties corresponding to each component in the third target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor.

[0105] This invention provides an information processing device that, by scanning a solid rocket motor, obtains K two-dimensional slice images, including images of the solid rocket motor's base component, liner component, and first bonding region. Based on these K two-dimensional slice images, the base component can be reconstructed in three dimensions to obtain a first target three-dimensional structure image. This first target three-dimensional structure image is then optimized to obtain a first optimized three-dimensional structure image. The device also acquires the grayscale values ​​corresponding to K liner main body regions to obtain K first grayscale values, determines the grayscale intervals corresponding to these K first grayscale values, and then, based on these K first grayscale intervals, extracts the first bonding region from the corresponding two-dimensional slice images. Sub-liner regions are identified within the region to obtain K sub-liner regions. Further, based on the K sub-liner regions and the regions of the liner components in K two-dimensional slice images, the liner components are reconstructed in three dimensions to obtain a second target three-dimensional structural image of the liner components. Static equilibrium analysis is performed on the solid rocket motor based on its three-dimensional microscopic model to obtain the static equilibrium analysis results. Then, image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor that reflects the true microscopic structural features. This improves the accuracy of the three-dimensional microscopic model and thus enhances the accuracy of the static equilibrium analysis of the solid rocket motor.

[0106] Specific limitations regarding the static equilibrium analysis apparatus can be found in the limitations of the static equilibrium analysis method described above, and will not be repeated here. Each module in the aforementioned static equilibrium analysis apparatus can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0107] In one embodiment, a computer device is provided, which may be a static equilibrium analysis device, and its internal structure diagram may be as follows: Figure 12 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of the static equilibrium analysis device side in a static equilibrium analysis method.

[0108] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 13 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a static equilibrium analysis method on the client side.

[0109] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: The solid rocket motor is scanned to obtain K two-dimensional slice images. The two-dimensional slice images include images of the solid rocket motor's base component, liner component, and first bonding area, which is located between the base component and the liner component. The base component is reconstructed in three dimensions based on K two-dimensional slice images to obtain the first target three-dimensional structure image of the base component; The first target three-dimensional structure image is optimized to obtain a first optimized three-dimensional structure image; Obtain the gray values ​​corresponding to K liner body regions respectively to obtain K first gray values. The liner body region is the region of the liner component in the two-dimensional slice image. The K liner body regions correspond one-to-one with K two-dimensional slice images. Determine the grayscale intervals corresponding to the K first grayscale values ​​respectively, so as to obtain K first grayscale intervals; The sub-liner regions are determined from the first bonding region in the corresponding two-dimensional slice image based on the K first gray-level intervals to obtain K sub-liner regions. The liner component is reconstructed in three dimensions based on the regions of the liner component in K sub-liner regions and K two-dimensional slice images to obtain the second target three-dimensional structure image of the liner component; Image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor; static equilibrium analysis is performed on the solid rocket motor based on the three-dimensional microscopic model to obtain the static equilibrium analysis results of the solid rocket motor.

[0110] This invention provides a computer device that, by scanning a solid rocket motor, obtains K two-dimensional slice images, including images of the solid rocket motor's base component, liner component, and first bonding region. Based on these K two-dimensional slice images, the base component can be reconstructed in three dimensions to obtain a first target three-dimensional structure image. This first target three-dimensional structure image is then optimized to obtain a first optimized three-dimensional structure image. The device also acquires the grayscale values ​​corresponding to K liner main body regions to obtain K first grayscale values, determines the grayscale intervals corresponding to these K first grayscale values, and then extracts the first bonding region from the corresponding two-dimensional slice images based on these K first grayscale intervals. Sub-liner regions are identified within the region to obtain K sub-liner regions. Further, based on the K sub-liner regions and the regions of the liner components in K two-dimensional slice images, the liner components are reconstructed in three dimensions to obtain a second target three-dimensional structural image of the liner components. Static equilibrium analysis is performed on the solid rocket motor based on its three-dimensional microscopic model to obtain the static equilibrium analysis results. Then, image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor that reflects the true microscopic structural features. This improves the accuracy of the three-dimensional microscopic model and thus enhances the accuracy of the static equilibrium analysis of the solid rocket motor.

[0111] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: The solid rocket motor is scanned to obtain K two-dimensional slice images. The two-dimensional slice images include images of the solid rocket motor's base component, liner component, and first bonding area, which is located between the base component and the liner component. The base component is reconstructed in three dimensions based on K two-dimensional slice images to obtain the first target three-dimensional structure image of the base component; The first target three-dimensional structure image is optimized to obtain a first optimized three-dimensional structure image; Obtain the gray values ​​corresponding to K liner body regions respectively to obtain K first gray values. The liner body region is the region of the liner component in the two-dimensional slice image. The K liner body regions correspond one-to-one with K two-dimensional slice images. Determine the grayscale intervals corresponding to the K first grayscale values ​​respectively, so as to obtain K first grayscale intervals; The sub-liner regions are determined from the first bonding region in the corresponding two-dimensional slice image based on the K first gray-level intervals to obtain K sub-liner regions. The liner component is reconstructed in three dimensions based on the regions of the liner component in K sub-liner regions and K two-dimensional slice images to obtain the second target three-dimensional structure image of the liner component; Image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor; static equilibrium analysis is performed on the solid rocket motor based on the three-dimensional microscopic model to obtain the static equilibrium analysis results of the solid rocket motor.

[0112] This invention provides a computer-readable storage medium that, by scanning a solid rocket motor, obtains K two-dimensional slice images, including images of the solid rocket motor's base component, liner component, and first bonding region. Based on these K two-dimensional slice images, the base component can be reconstructed in three dimensions to obtain a first target three-dimensional structure image of the base component. This first target three-dimensional structure image is then optimized to obtain a first optimized three-dimensional structure image. The invention also involves acquiring K grayscale values ​​corresponding to K liner main body regions to obtain K first grayscale values, determining grayscale intervals corresponding to these K first grayscale values, and then selecting a first grayscale region from the corresponding two-dimensional slice images based on these K first grayscale intervals. Sub-liner regions are identified within the bonding area to obtain K sub-liner regions. Further, based on the K sub-liner regions and the regions of the liner components in K two-dimensional slice images, the liner components are reconstructed in three dimensions to obtain a second target three-dimensional structural image of the liner components. Static equilibrium analysis is performed on the solid rocket motor based on its three-dimensional microscopic model to obtain the static equilibrium analysis results. Then, image fusion processing is performed on the first optimized three-dimensional structural image and the second target three-dimensional structural image to obtain a three-dimensional microscopic model of the solid rocket motor that reflects the true microscopic structural features. This improves the accuracy of the three-dimensional microscopic model and, consequently, the accuracy of the static equilibrium analysis of the solid rocket motor.

[0113] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the static balance analysis device side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0114] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0116] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A static equilibrium analysis method, characterized in that, The method includes: The solid rocket motor is scanned to obtain K two-dimensional slice images. The two-dimensional slice images include images of the base component, the liner component, and the first bonding area between the base component and the liner component of the solid rocket motor. The base component is reconstructed in three dimensions based on K two-dimensional slice images to obtain a first target three-dimensional structural image of the base component; The first target three-dimensional structure image is optimized to obtain a first optimized three-dimensional structure image; Obtain the gray values ​​corresponding to K lining main areas respectively to obtain K first gray values. The lining main area is the area of ​​the lining component in the two-dimensional slice image. The K lining main areas correspond one-to-one with K two-dimensional slice images. Determine the grayscale intervals corresponding to the K first grayscale values ​​respectively to obtain K first grayscale intervals; Based on the K first grayscale intervals, sub-liner regions are determined from the first bonding region in the corresponding two-dimensional slice image to obtain K sub-liner regions. The liner component is reconstructed in three dimensions based on the regions of the liner component in the K sub-liner regions and the K two-dimensional slice images to obtain a second target three-dimensional structural image of the liner component; Image fusion processing is performed on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor; Static equilibrium analysis of the solid rocket motor is performed based on the three-dimensional mesoscopic model of the solid rocket motor to obtain the static equilibrium analysis results of the solid rocket motor. The K two-dimensional slice images include M first target two-dimensional slice images and N second target two-dimensional slice images. The first target two-dimensional slice images are two-dimensional slice images in a first direction, and the second target two-dimensional slice images are two-dimensional slice images in a second direction. The first direction is perpendicular to the second direction. The step of performing three-dimensional reconstruction of the base component based on the K two-dimensional slice images to obtain a first target three-dimensional structure image of the base component includes: Feature extraction is performed on M two-dimensional slice images of the first target to obtain gray values ​​corresponding to the M two-dimensional slice images of the first target respectively; Based on the gray values ​​corresponding to the M first target two-dimensional slice images, the boundary information of the first particle region in the M first target two-dimensional slice images is determined; Feature extraction is performed on N two-dimensional slice images of the second target to obtain gray values ​​corresponding to each of the N two-dimensional slice images of the second target; Based on the gray values ​​corresponding to the N two-dimensional slice images of the second target, the boundary information of the second particle region in the N two-dimensional slice images of the second target is determined; Based on the boundary information of the first particle region in M ​​first target two-dimensional slice images and the boundary information of the second particle region in N second target two-dimensional slice images, the three-dimensional boundary information of each particle in the matrix component is determined. Based on the three-dimensional boundary information of each particle in the matrix component, each particle in the matrix component is reconstructed in three dimensions to obtain a first reference three-dimensional structural image of the matrix component; The space in the first reference three-dimensional structure image, excluding the boundary indicated by the three-dimensional boundary information of each particle in the matrix component, is filled to obtain the first target three-dimensional structure image of the matrix component. The step of reconstructing each particle in the matrix component in three dimensions based on the three-dimensional boundary information of each particle in the matrix component to obtain a first reference three-dimensional structural image of the matrix component includes: Particle labeling processing is performed on M first target two-dimensional slice images to obtain M third target two-dimensional slice images, wherein the third target two-dimensional slice images are two-dimensional slice images with the center position information of the first particles marked. Particle labeling processing is performed on N second target two-dimensional slice images to obtain N fourth target two-dimensional slice images, wherein the fourth target two-dimensional slice images are two-dimensional slice images with the center position information of the second particles marked. Based on M two-dimensional slice images of the third target and N two-dimensional slice images of the fourth target, the center position information of each particle in the matrix component is determined; Based on the three-dimensional boundary information of each particle in the matrix component and the center position information of each particle in the matrix component, each particle in the matrix component is reconstructed in three dimensions to obtain a first reference three-dimensional structural image of the matrix component.

2. The static equilibrium analysis method according to claim 1, characterized in that, The optimization processing of the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image includes: The mesh information of each particle in the matrix component is obtained from the first target three-dimensional structure image, and the mesh information includes mesh boundaries and voxels; A first set of particles and a second set of particles are determined based on the mesh information of each particle in the matrix component. The first particle in the first set of particles is a particle in the matrix component whose mesh boundary overlaps with the mesh boundary of a neighboring particle by less than a first threshold or whose voxel count is less than a second threshold. The second particle in the second set of particles is a particle in the matrix component whose mesh boundary overlaps with the mesh boundary of a neighboring particle by greater than or equal to the first threshold and whose voxel count is greater than or equal to the second threshold. Each first particle in the first particle set is fused with the corresponding non-particle space in the first target three-dimensional structure image, and the second particle in the second particle set in the first target three-dimensional structure image is fused with the corresponding neighboring particles to obtain a first optimized three-dimensional structure image.

3. The static equilibrium analysis method according to claim 1, characterized in that, The optimization processing of the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image includes: The mechanical influence of each particle in the matrix component is obtained from the three-dimensional structural image of the first target. Based on the mechanical influence of each particle in the matrix component, a third particle set is determined, wherein the third particle in the third particle set is a particle whose mechanical influence is less than a third threshold. Each third particle in the third particle set is fused with the corresponding non-particle space in the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image.

4. The static equilibrium analysis method according to claim 1, characterized in that, The optimization processing of the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image includes: The volume of each particle in the matrix component is obtained from the first target three-dimensional structure image; Based on the volume of each particle in the matrix component, a fourth particle set is determined, wherein the fourth particle in the fourth particle set is a particle with a volume smaller than a fourth threshold. Each fourth particle in the fourth particle set is fused with the corresponding non-particle space in the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image.

5. The static equilibrium analysis method according to claim 1, characterized in that, The step of performing image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor includes: The first optimized 3D structure image and the second target 3D structure image are subjected to image fusion processing to obtain the third target 3D structure image; Obtain the material properties corresponding to each component in the three-dimensional structural image of the third target; Based on the material properties corresponding to each component in the three-dimensional structural image of the third target, material property labeling is performed on each component in the three-dimensional structural image of the third target to obtain the three-dimensional microscopic model of the solid rocket motor.

6. A static equilibrium analysis device, characterized in that, The static equilibrium analysis device includes: The scanning module is used to scan the solid rocket motor to obtain K two-dimensional slice images. The two-dimensional slice images include images of the base component, the liner component, and the first bonding area between the base component and the liner component of the solid rocket motor. The processing module is used to perform three-dimensional reconstruction of the base component based on K two-dimensional slice images to obtain a first target three-dimensional structure image of the base component; The processing module is further configured to optimize the first target three-dimensional structure image to obtain a first optimized three-dimensional structure image. The acquisition module is used to acquire the gray values ​​corresponding to K lining main areas respectively to obtain K first gray values. The lining main area is the area of ​​the lining component in the two-dimensional slice image. The K lining main areas correspond one-to-one with K two-dimensional slice images. The determining module is used to determine the grayscale intervals corresponding to the K first grayscale values ​​respectively, so as to obtain K first grayscale intervals; The determining module is further configured to determine the sub-sub-layer region from the first bonding region in the corresponding two-dimensional slice image based on the K first gray-scale intervals, so as to obtain K sub-sub-layer regions. The processing module is further configured to perform three-dimensional reconstruction of the liner component based on the regions of the liner component in the K sub-liner regions and K two-dimensional slice images, so as to obtain a second target three-dimensional structure image of the liner component. The processing module is further configured to perform image fusion processing on the first optimized three-dimensional structure image and the second target three-dimensional structure image to obtain a three-dimensional microscopic model of the solid rocket motor; and to perform static equilibrium analysis on the solid rocket motor based on the three-dimensional microscopic model of the solid rocket motor to obtain the static equilibrium analysis results of the solid rocket motor. The K two-dimensional slice images include M first target two-dimensional slice images and N second target two-dimensional slice images. The first target two-dimensional slice images are two-dimensional slice images in a first direction, and the second target two-dimensional slice images are two-dimensional slice images in a second direction. The first direction is perpendicular to the second direction. The step of performing three-dimensional reconstruction of the base component based on the K two-dimensional slice images to obtain a first target three-dimensional structure image of the base component includes: Feature extraction is performed on M two-dimensional slice images of the first target to obtain gray values ​​corresponding to the M two-dimensional slice images of the first target respectively; Based on the gray values ​​corresponding to the M first target two-dimensional slice images, the boundary information of the first particle region in the M first target two-dimensional slice images is determined; Feature extraction is performed on N two-dimensional slice images of the second target to obtain gray values ​​corresponding to each of the N two-dimensional slice images of the second target; Based on the gray values ​​corresponding to the N two-dimensional slice images of the second target, the boundary information of the second particle region in the N two-dimensional slice images of the second target is determined; Based on the boundary information of the first particle region in M ​​first target two-dimensional slice images and the boundary information of the second particle region in N second target two-dimensional slice images, the three-dimensional boundary information of each particle in the matrix component is determined. Based on the three-dimensional boundary information of each particle in the matrix component, each particle in the matrix component is reconstructed in three dimensions to obtain a first reference three-dimensional structural image of the matrix component; The space in the first reference three-dimensional structure image, excluding the boundary indicated by the three-dimensional boundary information of each particle in the matrix component, is filled to obtain the first target three-dimensional structure image of the matrix component. The step of reconstructing each particle in the matrix component in three dimensions based on the three-dimensional boundary information of each particle in the matrix component to obtain a first reference three-dimensional structural image of the matrix component includes: Particle labeling processing is performed on M first target two-dimensional slice images to obtain M third target two-dimensional slice images, wherein the third target two-dimensional slice images are two-dimensional slice images with the center position information of the first particles marked. Particle labeling processing is performed on N second target two-dimensional slice images to obtain N fourth target two-dimensional slice images, wherein the fourth target two-dimensional slice images are two-dimensional slice images with the center position information of the second particles marked. Based on M two-dimensional slice images of the third target and N two-dimensional slice images of the fourth target, the center position information of each particle in the matrix component is determined; Based on the three-dimensional boundary information of each particle in the matrix component and the center position information of each particle in the matrix component, each particle in the matrix component is reconstructed in three dimensions to obtain a first reference three-dimensional structural image of the matrix component.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the static equilibrium analysis method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the static equilibrium analysis method as described in any one of claims 1 to 5.

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