Elastomer digital model material optimization method and system based on stress field matching

Abstract

The application discloses a kind of based on stress field matching elastomer digital model material optimization method and system, belong to the field of physical simulation.Model is converted to volume grid representation and marks target area displacement constraint;Unconstrained displacement vector and corresponding stress vector under current material parameter estimation are calculated;Target area displacement constraint is constraint condition, and constraint displacement vector under current material parameter estimation is calculated;According to stress vector and constraint displacement vector, optimized material parameter estimation is solved, and current material parameter estimation is updated to optimized material parameter estimation, and iteration optimization is repeated until optimization task is completed.The application combines the high fidelity result of displacement matching with the calculation efficiency of force matching, and is robust under different resolution conditions, improving the efficiency of material optimization in numerical calibration, coarsening and other applications.

Description

Technical Field

[0001] This invention belongs to the field of physical simulation, specifically relating to a material optimization method and system for digital models of elastic bodies based on stress field matching. Background Technology

[0002] Materials optimization is a crucial problem in computer graphics and computational physics, with wide applications in computer-aided design, virtual reality, and numerical equivalence and calibration. Materials optimization is one of the core problems in these diverse applications, and developing efficient methods for this task is essential for advancing these applications.

[0003] A typical material optimization problem is defined as determining appropriate material parameters within an elastic body such that the deformation of the body under a given force field matches the target displacement as closely as possible, either globally or locally. Some methods formalize this problem as minimizing the residuals of the force balance equations. Essentially, this involves comparing two elastic force fields: one derived from a simulation containing only the given force field, and the other from a simulation applying both the given force field and the target displacement constraint. This is called "force matching." While the numerical problems generated by this method are relatively easy to handle, the resulting material parameters often differ significantly from the displacements in the target region when used in forward simulations. Therefore, a more widely adopted modeling approach, "displacement matching," aims to directly minimize the displacement deviations corresponding to the material parameters. However, the dense Hessian matrix of this formula limits the use of second-order methods. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a material optimization method and system for digital models of elastic bodies based on stress field matching. By matching the stress fields derived from two simulation methods—one involving only a given force field and the other involving simultaneously applying a given force field and a target displacement constraint—this invention combines the high fidelity of displacement matching with the computational efficiency of force matching. The resulting displacement field generated by the material parameters matches the target displacement field much more closely than the displacement field generated by force matching.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a material optimization method for an elastic body digital model based on stress field matching, comprising the following steps:

[0007] S1 converts the digital model corresponding to the target elastic body into a volume mesh representation, using the volume mesh element as the basic unit, and marks the displacement constraints of the target region after applying given boundary conditions to the model.

[0008] S2, solves the unconstrained displacement vector and corresponding stress vector of the model under given boundary conditions and current material parameter estimates;

[0009] S3, constrain the target region displacement as a constraint condition, and calculate the constrained displacement vector of the model under given boundary conditions and current material parameter estimation by solving the static equilibrium equation with target region displacement constraint;

[0010] S4. Based on the stress vector obtained in S2 and the constraint displacement vector obtained in S3, the change in the material parameter estimate is calculated, and then the optimized material parameter estimate is obtained. The optimization objective is to minimize the difference between the stress vector obtained by the constraint displacement vector under the optimized material parameter estimate and the stress vector obtained in S2.

[0011] S5, update the current material parameter estimate to the optimized material parameter estimate, and repeat steps S2-S4 until the optimization task is completed.

[0012] Furthermore, the given boundary conditions include Neumann boundary conditions and Dirichlet boundary conditions.

[0013] Furthermore, using volumetric mesh elements as the basic unit, each mesh element corresponds to an independent material parameter, and the material parameters of all mesh elements are concatenated to form a material parameter vector to be optimized; the material parameter is any one of Young's modulus, Poisson's ratio, and Lamé coefficient.

[0014] Furthermore, S2 specifically refers to:

[0015] S21 defines the model as achieving static equilibrium under applied external forces and elastic internal forces under given boundary conditions and current material parameter estimates. The unconstrained displacement vector, which includes the displacement values ​​of all volume mesh element vertices, is obtained by solving the static equilibrium equations.

[0016] S22, based on the calculated unconstrained displacement vector, solve for the stress vector under the current material estimation parameters.

[0017] Furthermore, in S3, the static equilibrium equations with target region displacement constraints are as follows:

[0018] ;

[0019] ;

[0020] in, This is an estimate of the current material parameters; It is the constraint displacement vector that satisfies the displacement constraint of the target region under the current material parameter estimation, and includes the displacement values ​​of all vertices of the volume mesh element; It is a constraint introduced to constrain the target region. It is a selection matrix that includes all vertices of the target region. It is a target region displacement constraint, containing specified displacement values ​​for all vertices of the target region. The superscript T indicates transpose. It is an elastic internal force. It is the applied external force.

[0021] Furthermore, the stress vector is obtained by splicing the first type of Piola-Kirchhoff stresses at each Gaussian integration point in the model volume mesh.

[0022] Furthermore, the solution process for unconstrained and constrained displacement vectors determines the initial values ​​to be solved through a caching mechanism, which includes:

[0023] For an unconstrained displacement vector, record the unconstrained displacement vector solution that has the smallest difference between the displacement of the target region obtained in the historical loop and the given target region displacement constraint, and use it as the initial estimate for this optimization solution;

[0024] For the constraint displacement vector, the constraint displacement vector obtained in the previous iteration of optimization is used as the initial estimate for this optimization.

[0025] Furthermore, the optimization objective also includes a regularization constraint on the amount of change.

[0026] Furthermore, bounded constraints are introduced when updating material parameter estimates. If the change in the material parameter estimates exceeds the bounded constraint range, the change is replaced with a boundary value.

[0027] Secondly, the present invention provides a material optimization system for a digital model of an elastomer based on stress field matching, for implementing the aforementioned material optimization method for a digital model of an elastomer based on stress field matching.

[0028] The beneficial effects of this invention are as follows:

[0029] (1) The method of the present invention remodels the traditional material optimization problem into the form of stress matching. This novel modeling method has a better numerical solution strategy than the traditional displacement matching (such as being able to use the second-order Gauss-Newton method to solve it), and avoids the gradient vanishing problem caused by the existing displacement matching method when the initial material is too hard. It breaks through the limitation of the traditional displacement matching method that it needs to rely on a differentiable forward simulator.

[0030] (2) It improves the efficiency of material optimization, with an acceleration of about 5 to 10 times compared to traditional displacement matching methods;

[0031] (3) The parameter optimization efficiency is not affected by the volume mesh resolution, and has strong robustness and scalability, so that users do not need to reduce the resolution requirement in order to obtain efficient optimization.

[0032] (4) The present invention only requires that the forward simulation can obtain a sufficiently accurate displacement field so that the present invention can be seamlessly combined with various existing commercial software and fast forward simulation methods. Attached Figure Description

[0033] Figure 1 This is an overall framework diagram of a specific embodiment of the present invention.

[0034] Figure 2 This is a diagram showing the boundary conditions of the model in a specific embodiment of the present invention.

[0035] Figure 3 This is a diagram showing the displacement constraint markings for the target region of the model in a specific embodiment of the present invention.

[0036] Figure 4 This is the estimated model material distribution in a specific embodiment of the present invention.

[0037] Figure 5 This is a schematic diagram of the model material distribution for experimental condition settings and estimation in another embodiment of the present invention, wherein... Figure 5 (a) in the diagram is a schematic of boundary condition settings. Figure 5 (b) in the diagram is a schematic diagram of displacement constraints in the target area. Figure 5 (c) in the diagram is a schematic diagram of the evolution of model material estimation.

[0038] Figure 6 This is a comparison chart showing the performance differences between specific embodiments of the present invention and traditional methods.

[0039] Figure 7 This is a graph showing the experimental results of material parameter optimization efficiency versus different mesh resolutions in another embodiment of the present invention, wherein... Figure 7 (a) in the diagram is a schematic diagram of the model volume mesh generation at different resolutions. Figure 7 (b) in the figure is a diagram showing the optimization accuracy of material parameters at different volume mesh resolutions. Figure 7 (c) in the figure is the material parameter optimization efficiency diagram under different volume mesh resolutions. Detailed Implementation

[0040] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Technical features in the various embodiments of the present invention can be combined accordingly without mutual conflict.

[0041] This invention proposes a material optimization method for elastic bodies based on stress field matching digital models. The overall framework is as follows: Figure 1 As shown, it includes the following steps:

[0042] S1 converts the digital model corresponding to the target elastic body into a volume mesh representation and sets the boundary conditions of the model to ensure that the material distribution of the model can be estimated in the virtual environment later.

[0043] The specific steps are as follows:

[0044] S11. The digital model corresponding to the target elastic body is discretized into a volume mesh representation (tetrahedral or hexahedral) using existing tools such as tetwild, tetgen (for generating tetrahedral discrete elements) or polycube (for generating hexahedral discrete elements), and modeled using the finite element method. An elastic body is a material structure that can deform under stress and return to its original shape when the external force is removed. Its physical behavior is characterized by assigning the corresponding material constitutive model (such as the Saint-Venant-Kirchhoff, Neo-Hookean, or Mooney-Rivlin model) to the discrete mesh elements. Under given boundary conditions, its static or dynamic response is simulated by solving the discretized control system.

[0045] S12, from the volume mesh of step S11, mark the user-defined boundary conditions, such as von Neumann boundary conditions (used to apply external forces or stresses) and Dirichlet boundary conditions (used to define fixed points). The Dirichlet boundary conditions are implemented by forcibly specifying the displacement of the vertices of the volume mesh boundary (or interior), and the fixed points are the points where the displacement is forcibly set to 0. The von Neumann boundary conditions are used to define the forces or stresses on the elements of the volume mesh boundary (or interior).

[0046] Figure 2 In a specific embodiment of the present invention, the red area on the left is set as a fixed constraint (Dirichlet boundary condition), that is, the displacement of the volume mesh vertices in this area is forced to be 0, and the red arrow on the right represents the external force applied from top to bottom (Neumann boundary condition).

[0047] S13, mark the mesh vertices contained in the target region and the displacement constraints of the target region from the volume mesh of step S11. Assume the number of vertices in the target region is m, and the total number of vertices in the model volume mesh is n, and define a... Selection matrix The selection matrix This is used to select m target region vertices from the total vertices; simultaneously, the displacement constraints set by the user for the m target region vertices are denoted as... , where R represents a real number and 3 represents the three-dimensional spatial coordinates (x, y, z).

[0048] Figure 3 In a specific embodiment of the present invention, the white area is a defined target area, and the white markers within this area are the corresponding vertices of the target area. Each point is then compared with... Figure 2 The displacement obtained by comparing the initial position in the image is the expected displacement of each vertex, which includes spatial coordinate components in the x, y, and z directions.

[0049] S14, the material distribution of the model is discretized as independent material parameters within each element of the volume mesh, and these are concatenated into a material parameter vector. (e.g., Young's modulus, Poisson's ratio, Lamé coefficient, etc.), this vector contains the material parameters corresponding to all elements in the volume mesh. For the material parameter optimization task to be solved in this invention, the objective is to obtain suitable parameters. This minimizes the displacement deviation between the displacement field containing only the given force field and the displacement field constrained by the given target region.

[0050] S2, based on the given boundary conditions and the current material parameter vector p, calculates the stress field distribution of the model through finite element simulation.

[0051] S21, First, it is necessary to solve for the displacement vector under the given boundary conditions: numerically solve the static equilibrium equations established for the entire model using Newton's iteration method until the equations converge to obtain the displacement vector. :

[0052]

[0053] in, It is the displacement vector formed by concatenating all vertices of the volume mesh. , These are the elastic internal forces calculated based on the constitutive relations specified by the user. It is the external force applied to the model.

[0054] S22, based on the calculated displacement vector In the current material parameter vector The stress at the Gaussian integral point in each element of the model mesh is calculated, and the stress vector corresponding to the model mesh is obtained by concatenating the results. The stress calculation here uses the first Piola-Kirchhoff stress. It should be noted that the first Piola-Kirchhoff stress is an empirical model widely used in nonlinear mechanical analysis, describing the distribution of the current external force over a reference area. It represents the force per unit area of ​​the reference configuration under the current configuration. This first Piola-Kirchhoff stress is used to calculate the stress under the current material parameter vector. and displacement vector The stress vector is given below, and the principle behind it will not be elaborated here.

[0055] In the material optimization loop, for each update of the material parameter vector in S4... Then, the above equilibrium equations need to be solved repeatedly. To accelerate this process, this invention uses a caching mechanism to record the best estimate of the displacement vector obtained in the historical loop (i.e., the displacement vector solution with the smallest difference between the displacement in the target region and the target displacement specified by the user), and uses it as the initial estimate of the displacement vector when solving S21 in this optimization, thereby accelerating the solution of the equilibrium equations.

[0056] S3 uses the static condensed method of finite element simulation to divide the degrees of freedom of the entire model into internal degrees of freedom and boundary degrees of freedom. The displacement constraint of the target area is taken as the internal degree of freedom, and the displacement vector generated by the model under the given boundary conditions using the current material is calculated.

[0057] The static equilibrium equations with target region displacement constraints were obtained using the static condensation method:

[0058]

[0059] in, The constraint forces introduced to constrain the target region are determined by the Lagrange multiplier method. In the current material vector The displacement vector that satisfies the displacement constraints of the target region is given by the superscript T, which is the transpose.

[0060] For nonlinear materials, solving this set of static equilibrium equations is transformed into numerically solving this constrained optimization problem using Newton's iteration method: The displacement vector that satisfies the displacement constraints of the target region is obtained. ,in, The elastic potential energy of the entire model is given by Obtained through conversion.

[0061] For each update of the material parameter vector in S4 Then, the above constrained optimization problem needs to be solved again, using the same caching mechanism, and employing the displacement vector obtained in the previous iteration of optimization. As an initial estimate, it can accelerate the solution of constrained optimization problems.

[0062] S4 aims to minimize the stress field deviation by using a nonlinear least squares algorithm to optimize the current material parameter estimation, so that the difference between the stress field obtained by the displacement vector of S3 under the material parameters and the stress field obtained by S2 is minimized.

[0063] In this embodiment, the stress vector obtained from S2 The displacement vector obtained from S3 Solve for the change in the material parameter vector. to satisfy The stress vector obtained for the configuration and To get as close as possible, i.e., to solve the nonlinear least squares problem, the calculation formula is as follows:

[0064]

[0065] in, It is the q-th Gaussian integration point in the model's volume mesh. It is the weight of the q-th Gaussian integration point in the model's volumetric mesh. It is the first Piola-Kirchhoff stress at the qth Gaussian integration point in the model mesh; Let Frobenius norm be denoted as the square root of the sum of the squares of all matrix elements. For the weight parameters determined using the Levenberg-Marquardt method, This is a regularization constraint to ensure the robustness of the calculation results.

[0066] Solving for the material parameter vector When solving optimization problems, it is still necessary to consider their bounded constraints, i.e. , These are the upper and lower bounds of the material parameter vector, respectively, specified by the user. or At that time, Updated to or .

[0067] S5, material parameter vector Updated to Repeat steps S2-S4 until... Convergence (i.e.) , (This is either a user-specified threshold) or meets the user's requirements for the model material (i.e., using the material parameter vector). Displacement of the target region obtained under given boundary conditions Displacement constraints with the target region deviation Less than the user-specified threshold If the number of iterations exceeds the number specified by the user (e.g., 200 times), the task is considered complete.

[0068] The calculation formula is as follows:

[0069]

[0070] Figure 4This is the distribution of material parameters (Young's modulus in this specific embodiment) on each cell of the volume mesh obtained by optimization convergence in a specific embodiment of the present invention. Red indicates that the material parameter of the cell is larger and the corresponding material is harder, and the darker the color, the higher the hardness; blue indicates that the parameter of the cell is smaller and the corresponding material is softer, and the lighter the color, the softer the material.

[0071] This invention further verifies through experiments that, compared to traditional displacement matching methods, this invention can effectively achieve a faster computational speedup. For example... Figure 5 As shown, in this specific embodiment, a solid cuboid is defined, which contains the letter "STRESS" made of different materials (with unknown properties). In the initial settings of material optimization, the model is assumed to be made of uniform materials (i.e., the letter is made of the same material as the cuboid). Figure 5 (a) in the figure represents the boundary conditions set in this specific embodiment: the bottom surface of the model is fixed, and an external force is applied to the top surface from top to bottom, i.e., the direction indicated by the red arrow. Figure 5 In Figure (b), the displacement field of the target region under the "real" material parameter properties (Ground Truth) serves as the displacement constraint for the target region. The white dots in the figure are the marker points of the vertices of the target region. In this specific embodiment, the real displacement data of the vertices of the target region is obtained through virtual experiments (i.e., simulation using real material parameters). In practical applications, these displacement data can be obtained through experimental measurement.

[0072] Figure 5 (c) in the figure represents the iterative sequence of material parameter optimization estimation using the method of the present invention. The three stages of material parameters from the initial stage of iteration to convergence are extracted from top to bottom. Taking the middle section of the cuboid as an example, the greener the color, the harder the material, and the grayer the color, the softer the material. As the material parameters converge, the word "STRESS" gradually becomes clear. The results prove that the method of the present invention can effectively distinguish the different material estimates in the model.

[0073] Figure 6 The material optimization efficiency of this specific embodiment was further compared with two typical numerical methods for traditional displacement matching (LBFGS and nonlinear conjugate gradient). The horizontal axis represents the time (in hours) for parameter optimization, and the vertical axis represents the displacement deviation of the target region. The results show that the material parameter optimization efficiency of this invention has a significant acceleration effect compared with traditional typical numerical methods, and is approximately 5 to 10 times faster than traditional displacement matching methods.

[0074] like Figure 7 As shown, in another specific embodiment, the present invention further investigated the relationship between the volume mesh resolution after model discretization and the convergence rate of the material parameter optimization of the present invention. The specific experimental setup is as follows: Figure 7 As shown in (a):

[0075] Data generation: First, the entire model is set as the target region (i.e. all vertices are used as marker points). The "real" material parameters of different target regions of the model (blue represents region 1, green represents region 2, yellow represents region 3, and red represents region 4) are manually set. Based on the "real" material parameters, virtual experiments are conducted to obtain the displacement data of each target region under specific boundary conditions as displacement constraints.

[0076] Boundary conditions: Set the bottom surface of region 1 as a fixed constraint, and apply a downward external force to the bottom surface of region 4 (as shown by the black arrow in the figure).

[0077] Optimization settings: During the optimization process, the initial values ​​of material parameters in all regions of the model are set to uniform values, and the material parameters are solved through material optimization.

[0078] The results are as follows Figure 7 As shown in (b) and (c) in the figure. Figure 7 In (b) of the diagram, the vertical axis represents the relative error between the material parameters of this invention and the artificially set "real" material parameters, the horizontal axis represents the number of mesh cells at different resolutions, and the curve color corresponds to the region color in the model. Experimental results show that, at different mesh resolutions, the relative error between the material parameters optimized by the method of this invention and the artificially set "real" parameters does not exceed 0.1%. Figure 7 In (c), the vertical axis represents the number of optimization iterations of the material parameters according to the present invention, and the horizontal axis represents the number of mesh elements at different resolutions. Experimental results show that regardless of the mesh resolution used, the present invention can converge to the target accuracy in only 3 iterations.

[0079] In summary, the convergence rate of material parameter optimization using the method of this invention is essentially independent of mesh resolution, indicating that this invention has strong robustness and scalability. This eliminates the need for users to painstakingly refine the mesh to achieve efficient optimization, greatly saving preprocessing time and computational costs.

[0080] Based on the same inventive concept, this invention also provides a material optimization system for an elastic body digital model based on stress field matching, comprising:

[0081] The finite element modeling module is used to convert the digital model corresponding to the target elastic body into a volume mesh representation, and to mark the target region displacement constraint after applying given boundary conditions to the model using volume mesh elements as the basic unit.

[0082] The first finite element simulation calculation module is used to solve the unconstrained displacement vector and the corresponding stress vector of the model under given boundary conditions and current material parameter estimates.

[0083] The second finite element simulation calculation module is used to solve the static equilibrium equation with target region displacement constraint and calculate the constraint displacement vector of the model under given boundary conditions and current material parameter estimation.

[0084] The stress field matching optimization module is used to solve for the change in material parameter estimates based on the stress vector and constraint displacement vector, and then obtain the optimized material parameter estimates.

[0085] The result detection module is used to identify whether the detection optimization task has been completed and to update the current material parameter estimate to the optimized material parameter estimate.

[0086] For the system embodiments, since they basically correspond to the method embodiments, relevant details can be found in the descriptions of the method embodiments; the implementation methods of the remaining modules will not be repeated here. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0087] The system embodiments of the present invention can be applied to any device with data processing capabilities, such as a computer or other similar device. The system embodiments can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution.

[0088] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. Those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A stress field matching based elastomeric digital model material optimization method, characterized in that, The method comprises the following steps: S1, converting a digital model corresponding to a target elastomer into a volume mesh representation, marking a target region displacement constraint after a given boundary condition is applied to the model, taking a volume mesh unit as a basic unit; S2, solving an unconstrained displacement vector and a corresponding stress vector of the model under the given boundary condition and a current material parameter estimation; S3, taking the target region displacement constraint as a constraint condition, calculating a constrained displacement vector of the model under the given boundary condition and the current material parameter estimation by solving a static equilibrium equation with the target region displacement constraint; S4, solving a variation of the material parameter estimation according to the stress vector obtained in S2 and the constrained displacement vector obtained in S3, and further obtaining an optimized material parameter estimation, wherein an optimization target is that a difference between a stress vector obtained by the constrained displacement vector under the optimized material parameter estimation and the stress vector obtained in S2 is minimum; S5, updating the current material parameter estimation to the optimized material parameter estimation, and repeating steps S2-S4 until an optimization task is completed.

2. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, The given boundary condition comprises a Neumann boundary condition and a Dirichlet boundary condition.

3. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, Taking a volume mesh unit as a basic unit, each mesh unit corresponds to an independent material parameter, and material parameters of all mesh units are spliced into a material parameter vector to be optimized; the material parameter is any one of a Young's modulus, a Poisson's ratio and a Lame coefficient.

4. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, S2 is specifically as follows: S21, defining that the model realizes static equilibrium under an applied external force and an elastic internal force under the given boundary condition and the current material parameter estimation, obtaining an unconstrained displacement vector by solving a static equilibrium equation, and the unconstrained displacement vector comprises displacement values of all vertexes of the volume mesh units; S22, solving a stress vector under the current material estimation parameter according to the calculated unconstrained displacement vector.

5. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, In S3, the static equilibrium equation with the target region displacement constraint is as follows: ； ； wherein, is the current material parameter estimate; is the constraint displacement vector satisfying the target region displacement constraint under the current material parameter estimate, containing displacement values of all volume mesh element vertices; is the constraint force introduced by the constraint target region, is the selection matrix containing all vertices of the target region, is the target region displacement constraint, containing specified displacement values of all vertices of the target region, and the upper index T is the transpose, is the elastic internal force, is the applied external force.

6. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, The stress vector is spliced from first-type Pio-la-Kirchhoff stresses of all Gauss integral points in the model volume mesh.

7. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, The solving process of the unconstrained displacement vector and the constrained displacement vector determines an initial value to be solved through a cache mechanism, and the cache mechanism comprises: For the unconstrained displacement vector, recording an unconstrained displacement vector solution with a minimum difference between a displacement of the target region obtained in a historical cycle and the given target region displacement constraint as an initial estimation value for this time of optimization and solving; For the constrained displacement vector, using a constrained displacement vector solved in a last cycle of optimization as an initial estimation value for this time of optimization and solving.

8. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, The optimization target further comprises a regular term constraint about the variation.

9. The stress field matching based elastomeric digital model material optimization method of claim 1, wherein, A bounded constraint condition is introduced when the material parameter estimation is updated, and if the variation of the material parameter estimation exceeds the bounded constraint range, the variation is replaced by a boundary value.

10. A stress field matching based elastomeric digital model material optimization system for implementing the stress field matching based elastomeric digital model material optimization method of claim 1, characterized by, The system comprises: a finite element modeling module, which is used to convert a digital model corresponding to a target elastomer into a volume mesh representation, and mark a target region displacement constraint after a given boundary condition is applied to the model, taking a volume mesh unit as a basic unit; a first finite element simulation calculation module, which is used to solve an unconstrained displacement vector and a corresponding stress vector of the model under the given boundary condition and a current material parameter estimation; a second finite element simulation calculation module, which is used to calculate a constrained displacement vector of the model under the given boundary condition and the current material parameter estimation by solving a static equilibrium equation with a target region displacement constraint; and a material parameter estimation updating module, which is used to solve a variation of the material parameter estimation according to the stress vector obtained in the first finite element simulation calculation module and the constrained displacement vector obtained in the second finite element simulation calculation module, and further obtain an optimized material parameter estimation, wherein an optimization target is that a difference between a stress vector obtained by the constrained displacement vector under the optimized material parameter estimation and the stress vector obtained in the first finite element simulation calculation module is minimum. a second finite element simulation calculation module configured to solve a static force balance equation with target region displacement constraints to calculate a constraint displacement vector of the model under given boundary conditions and a current material parameter estimation; a stress field matching optimization module configured to solve a variation of the material parameter estimation according to the stress vector and the constraint displacement vector, and to obtain an optimized material parameter estimation; a result detection module configured to identify whether an optimization task is completed and to update the current material parameter estimation as the optimized material parameter estimation.

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