A simulation method for cumulative brain injury under multiple shock waves

Abstract

The present invention provides a method for simulating cumulative brain damage under the action of multiple shock waves, and relates to the field of data processing technology. The method comprises: constructing an explosion shock wave model; constructing a head geometric model; characterizing the cerebrum, cerebellum, and brainstem in a solid region using a Mooney Rivlin model; characterizing the skin, muscles, and skull in a solid region using a linear elastic constitutive model; characterizing the cerebrospinal fluid in a fluid region using a fluid dynamic model; coupling the fluid region and the solid region using an embedded boundary method; calculating the explosion shock pressure when each round of explosion shock waves is transmitted to the head using the explosion shock wave model; and determining the cumulative skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure under each round of explosion shock waves using a finite element analysis method to calculate the cumulative brain damage value.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for simulating cumulative brain damage under the action of multiple shock waves. Background Art

[0002] In the event of an explosion, it is crucial for medical personnel to quickly and accurately predict and assess the severity of lung damage. This not only helps them take effective treatment measures within the golden rescue time, but also significantly improves the victim's chance of survival, avoiding further health deterioration or life-threatening situations. Similarly, the timely prediction and assessment of human craniocerebral injury is also a key link in medical rescue, especially in complex and high-risk scenarios such as explosions or multiple shock waves. For brain injuries that occur in these situations, accurate prediction can provide medical personnel with valuable information, allowing them to develop more effective treatment plans, protect the patient's nervous system function, and improve overall prognosis.

[0003] However, current simulations of cumulative brain damage under multiple shock waves typically use a simple linear elastic constitutive model. While computationally convenient, this model is overly simplistic and ignores the nonlinear elastic behavior exhibited by organs in real life. In reality, human tissue, especially brain tissue, possesses complex mechanical properties and exhibits significant nonlinear characteristics under conditions of high stress and large deformation.

[0004] Therefore, simulation methods that rely on linear elastic models may not be able to accurately capture the true response of brain tissue under the action of multiple shock waves, making it difficult to fully and accurately simulate the impact of blast injuries on the brain, affecting the accuracy of prediction of cumulative craniocerebral injury in an explosive environment. Summary of the Invention

[0005] In order to solve the technical problems that the simulation method of the existing technology relying on the linear elastic model may not accurately capture the real response of brain tissue under the action of multiple shock waves, and is difficult to fully and accurately simulate the impact of blast injury on the brain, thus affecting the accuracy of the prediction of cumulative brain damage under an explosive environment, the present invention provides a simulation method and system for cumulative brain damage under the action of multiple shock waves.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect

[0008] An embodiment of the present invention provides a method for simulating cumulative brain injury under multiple shock waves, comprising:

[0009] S1: Construct explosion shock wave model;

[0010] S2: constructing a head geometric model, wherein the head geometric model includes a solid region and a fluid region;

[0011] S3: The cerebrum, cerebellum, and brainstem in the solid region are characterized using the Mooney-Rivlin model within the Lagrange framework.

[0012] S4: characterizing the skin, muscles, and skull in the solid region using a linear elastic constitutive model within the Lagrange framework;

[0013] S5: Characterize the cerebrospinal fluid in the fluid region using a fluid dynamics model within the Euler framework;

[0014] S6: Couple the fluid region and the solid region by embedding the boundary method;

[0015] S7: Calculating the explosion shock pressure when each round of explosion shock wave is transmitted to the head using the explosion shock wave model;

[0016] S8: Loading the explosion shock pressure when each round of explosion shock waves is transmitted to the head into the head geometric model, and determining the cumulative skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure under the action of each round of explosion shock waves through finite element analysis;

[0017] S9: Calculate the cumulative brain injury value based on the skull displacement, skull acceleration, skull stress, skull strain and intracranial pressure under the action of each round of explosion shock waves.

[0018] Second aspect

[0019] An embodiment of the present invention provides a system for simulating cumulative craniocerebral injury under multiple shock waves, comprising:

[0020] processor;

[0021] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for simulating cumulative craniocerebral injury under the action of multiple shock waves as described in the first aspect is implemented.

[0022] The third aspect

[0023] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for simulating cumulative brain injury under the action of multiple shock waves as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] In the present invention, the Mooney Rivlin model is used to characterize the brain, cerebellum, and brainstem in the solid area, the linear elastic constitutive model is used to characterize the skin, muscles, and skull in the solid area, and the fluid dynamic model is used to characterize the cerebrospinal fluid in the fluid area. Different constitutive models are used for different parts of the head, which can accurately capture the complex dynamic response of the head under the action of multiple shock waves, comprehensively and accurately simulate the impact of blast injuries on the brain, improve the accuracy of the prediction of cumulative craniocerebral injury in an explosive environment, and provide a reference for the rapid assessment, treatment and protection of personnel injuries. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic flow chart of a method for simulating cumulative brain injury under multiple shock waves provided by an embodiment of the present invention;

[0028] Figure 2 A schematic structural diagram of a method for simulating cumulative brain injury under multiple shock waves provided by an embodiment of the present invention;

[0029] Figure 3 This is a structural diagram of a simulation system for simulating cumulative brain injury under multiple shock waves provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0031] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0032] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0033] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0034] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0035] Reference Manual Figure 1 , which shows a flow chart of a method for simulating cumulative brain injury under multiple shock waves provided by an embodiment of the present invention.

[0036] Reference Manual Figure 2 , which shows a structural schematic diagram of a method for simulating cumulative brain injury under the action of multiple shock waves provided by an embodiment of the present invention.

[0037] An embodiment of the present invention provides a method for simulating cumulative brain damage under multiple shock waves. The method can be implemented by a device for simulating cumulative brain damage under multiple shock waves, which can be a terminal or a server. The processing flow of the method for simulating cumulative brain damage under multiple shock waves may include the following steps:

[0038] S1: Construct explosion shock wave model.

[0039] In a possible implementation, the explosion shock wave model is specifically:

[0040]

[0041] Among them, p m represents the explosion shock pressure, t represents the time, p max represents the peak pressure generated by the explosion shock wave, τ represents the duration of the positive pressure of the explosion shock wave, and exp represents an exponential function with a natural constant as the base.

[0042] Optionally, the peak pressure generated by the explosion shock wave is specifically:

[0043]

[0044] in, represents the comparison distance, r represents the distance from the explosion center, and w represents the explosive equivalent.

[0045] Optionally, the duration of the positive pressure of the explosion shock wave is specifically:

[0046] τ=Br 1 / 2 w 1 / 6

[0047] Wherein, B represents the explosion environment parameter.

[0048] In this paper, the temporal evolution of an explosion shock wave can be accurately simulated by using a time-dependent explosion shock wave curve. This model captures the characteristic of the shock wave pressure gradually decaying after reaching its peak, accurately simulating the shock wave behavior in real-world scenarios. This provides a solid foundation for further damage analysis and protective design, greatly improving the reliability and practicality of predictions and analysis.

[0049] S2: Build the head geometry model.

[0050] Specifically, a three-dimensional modeling method is used to construct a head geometric model.

[0051] Optionally, the segmented 2D CT slices are stacked and the 3D geometry of each tissue is generated using a 3D reconstruction algorithm, which typically uses surface reconstruction techniques such as the Marching Cubes algorithm to generate a 3D surface mesh.

[0052] The head geometric model includes a solid area and a fluid area.

[0053] Further, solid regions include the cerebrum, cerebellum, brainstem, skin, muscles, and skull.

[0054] Further, the fluid region includes: cerebrospinal fluid.

[0055] S3: The cerebrum, cerebellum, and brainstem in solid regions are represented using the Mooney-Rivlin model within the Lagrange framework.

[0056] The Lagrange framework, also known as material description or substance description, is a method used in continuum mechanics to describe the deformation and motion of materials. Within this framework, the study focuses on tracking the motion and deformation of each material point over time. This description is particularly well-suited for studying solid materials and their behavior under large deformation conditions.

[0057] Among them, the Mooney Rivlin model is a constitutive model used to describe hyperelastic materials (such as rubber, soft tissue, etc.), and is particularly suitable for nonlinear elastic behavior under large deformation conditions.

[0058] In one possible implementation, the Mooney Rivlin model is specifically:

[0059]

[0060] Where U represents strain potential energy, C 10 、C 01 represents the material parameter related to temperature, J1 represents the first deviatoric strain invariant, J2 represents the second deviatoric strain invariant, D1 represents the material parameter related to bulk modulus, 1 / D1 represents the bulk modulus, J el represents the elastic volume ratio.

[0061] Optionally, the first deviatoric strain invariant is specifically:

[0062] J1=tr(C)

[0063] Where tr represents the matrix trace operation and C represents the Cauchy-Green deformation tensor.

[0064] Optionally, the second deviatoric strain invariant is specifically:

[0065]

[0066] Optionally, the Cauchy-Green deformation tensor is specified as:

[0067] C=F T F

[0068] Where F represents the deformation gradient tensor and T represents the matrix transpose operation.

[0069] Optionally, the deformation gradient tensor F is specifically:

[0070]

[0071] Where I represents the identity matrix, ε s represents solid displacement, and ▽ represents gradient operation.

[0072] In the present invention, brain tissues such as the cerebrum, cerebellum, and brainstem exhibit significant nonlinear elastic behavior, and these tissues do not follow simple linear elastic relationships under large deformations. The Mooney-Rivlin model is a model widely used in the analysis of hyperelastic materials (such as soft tissue and rubber) and can accurately capture the nonlinear response of these tissues under complex stress states. Compared with linear models, the Mooney-Rivlin model can better describe the deformation and stress distribution of brain tissue under external forces.

[0073] S4: The skin, muscles, and skull in the solid region are characterized by a linear elastic constitutive model within the Lagrange framework.

[0074] The linear elastic constitutive model is a mathematical model that describes the relationship between stress and strain in a material within its elastic range. It is the simplest and most widely used constitutive model. This model assumes a linear relationship between stress and strain in a material and that deformation is reversible, meaning that the material returns to its original shape after the external force is removed.

[0075] In a possible implementation, the linear elastic constitutive model is specifically:

[0076] σ ij =λ·tr(γ ij )·δ ij +2μ·γ ij

[0077] Among them, σ ij represents the stress tensor in the ij direction, λ and μ represent the material-related Lame constants, tr represents the matrix trace operation, γ ij represents the strain tensor in the ij direction, δ ij represents the Kronecker symbol, when i=j, δ ij =1, when i≠j ij = 0, used to filter specific tensor components.

[0078] Optionally, the strain tensor is specified as:

[0079]

[0080] in, represents the partial derivative operation, ε i represents the displacement component in the i direction, ε j represents the displacement component in the j direction, X i Indicates the initial coordinate component in the i direction, X j Represents the initial coordinate component in the j direction.

[0081] In this paper, a linear elastic constitutive model is used within the Lagrange framework to represent the skin, muscle, and skull within the solid domain. This effectively simplifies the analysis process, improves computational efficiency, and maintains sufficient accuracy. This approach is particularly suitable for biomechanical analysis under small deformation conditions, offering broad applicability and significant computational advantages.

[0082] S5: Under the Euler framework, the cerebrospinal fluid in the fluid area is characterized by a fluid dynamic model.

[0083] The Euler framework, also known as a spatial or Euler description, is a framework used in fluid mechanics and continuum mechanics to describe the motion of matter. Unlike the Lagrange framework (material description), the Euler framework focuses on observing how fixed points in space perceive the motion and changes of matter over time. This description is particularly common in studying the motion and deformation of fluids.

[0084] In a possible implementation, the fluid dynamics model is specifically:

[0085]

[0086] Where u represents the fluid velocity, t represents the time, represents the partial derivative operation, represents the gradient operation, σ l represents the fluid stress tensor, p f Indicates fluid pressure.

[0087] The fluid stress tensor is specifically:

[0088]

[0089] Where Re represents the fluid Reynolds number, and T represents the matrix transpose operation.

[0090] In this paper, we characterize cerebrospinal fluid within the Euler framework, enabling efficient and accurate simulation of the fluid's dynamic behavior within the complex intracranial environment. This approach is particularly well-suited for understanding the interaction between cerebrospinal fluid and surrounding tissues, helping to understand the impact of fluid dynamics on physiological and pathological phenomena such as intracranial pressure and brain tissue deformation.

[0091] Furthermore, fluid deformation parameters are introduced into the fluid region, and the solid displacement at the fluid-solid interface is propagated into the fluid through the fluid deformation parameters to accurately describe the fluid region.

[0092] It should be noted that the introduction of fluid deformation parameters allows the fluid region to more accurately reflect the deformation and motion of the solid at the fluid-solid interface. This coupling can capture the effect of solid deformation on fluid flow, thereby improving the accuracy of the simulation.

[0093] Optionally, S204 specifically includes:

[0094] The fluid deformation parameter of the fluid region is introduced to propagate the solid displacement at the fluid-solid interface into the fluid, thus accurately describing the stress tensor in the fluid region:

[0095] P f (ε f )=σ c (u,p f ,ε f)Φ(ε f ) T

[0096] Among them, P f represents the first-order Piola-Kirchhoff fluid stress tensor, ε f represents the fluid deformation parameter, σ c represents the Cauchy stress tensor, u represents the fluid velocity, and p f represents the fluid pressure, Φ represents the deformation, and T represents the matrix transpose operation.

[0097] Optionally, the Cauchy stress tensor is specified as:

[0098]

[0099] Where I represents the identity matrix, Re represents the fluid Reynolds number, and J represents the transformation Jacobian matrix.

[0100] Optionally, the deformation amount is specifically:

[0101]

[0102] It should be noted that for fluid-structure interaction problems with significant nonlinear deformation, using fluid deformation parameters can more naturally handle geometric nonlinear effects. The calculation of deformation amounts and the transformation Jacobian matrix allows the fluid region to adapt to the complex geometric changes caused by solid deformation.

[0103] Optionally, the fluid deformation parameters satisfy:

[0104]

[0105] Among them, ε f represents the fluid deformation parameter, Θ represents the expansion operator, and the expansion operator is specifically the Laplace equation.

[0106] In this invention, by setting the fluid deformation parameters to meet the above conditions, the conservation of the fluid volume during fluid-structure interaction is ensured. Laplace's equation is commonly used to describe divergence-free fields, ensuring that the fluid volume remains constant during deformation. This is crucial for simulating realistic fluid behavior, especially when dealing with incompressible fluids.

[0107] S6: The fluid region is coupled to the solid region through the embedded boundary method.

[0108] It should be noted that the embedded boundary method allows for accurate simulation of the continuity of physical quantities at the fluid-solid interface. Ensuring the continuity of displacement, velocity, and stress allows for a more accurate description of the interaction between fluid and solid.

[0109] In a possible implementation, S6 specifically includes: ensuring displacement continuity, velocity continuity, and stress continuity at the fluid-solid interface by an embedded boundary method, and coupling the fluid region and the solid region.

[0110] Optionally, the displacement continuity is specifically:

[0111] ε f =ε s

[0112] Among them, ε f represents the fluid deformation parameter, ε s represents the displacement of the solid.

[0113] Optionally, the speed continuity is specifically:

[0114]

[0115] Where u represents the fluid velocity, Represents partial derivative operation, and t represents time.

[0116] Optionally, the stress continuity is specifically:

[0117] n·P f (ε f )=n·P s (ε s )

[0118] Where n represents the normal vector of the fluid-solid interface, P f represents the first-order Piola-Kirchhoff fluid stress tensor, P s represents the first-order Piola-Kirchhoff solid stress tensor.

[0119] In this invention, displacement continuity, velocity continuity, and stress continuity are ensured, enabling realistic reproduction of the physical interactions between fluids and solids. At the fluid-solid interface, the interaction between fluids and solids affects the flow, deformation, and force transmission of the material. Maintaining continuity conditions is a fundamental requirement for simulating these interactions.

[0120] S7: Calculate the explosion shock pressure when each round of explosion shock wave is transmitted to the head through the explosion shock wave model.

[0121] S8: The blast shock pressures of each round of blast shock waves transmitted to the head are loaded into the head geometric model. Finite element analysis is used to determine the cumulative skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure under the action of each round of blast shock waves.

[0122] Finite element analysis (FEA) is a numerical calculation method used to solve boundary value problems in physics and engineering. It divides a complex continuous domain into a finite number of smaller, simpler elements, analyzes each element, and combines the results to determine the response of the entire domain.

[0123] In a possible implementation, S8 specifically includes sub-steps S801 to S805:

[0124] S801: Meshing the head geometric model.

[0125] Optionally, the grid form may be a triangle, a square or a hexagon.

[0126] It is important to note that by constructing a head geometry model and meshing it at the microscale, a detailed analysis of the stress and strain distribution within the alveoli can be performed. This level of detail can reveal the damage characteristics and possible damage mechanisms of the local area, providing more accurate results than macroscale analysis.

[0127] S802: Discretize the fluid region.

[0128] Optionally, S502 specifically includes: discretizing the fluid region according to the following formula:

[0129]

[0130] Among them, Ω f represents the fluid region, represents the gradient operation, σ represents the fluid stress tensor, u represents the fluid velocity, p f Indicates fluid pressure, P f represents the first-order Piola-Kirchhoff fluid stress tensor, ε f represents the fluid deformation parameter, Ψ f represents the fluid test function, d represents the differential operation, Γ represents the fluid-solid interface, : represents the inner product operation, and λ represents the Lagrange multiplier.

[0131] It should be noted that discretization allows complex fluid dynamics equations to be solved on a limited number of computing nodes, which makes it feasible to use computers to solve them, thereby accurately describing the field distribution of fluid velocity, pressure, etc.

[0132] S803: Discretize the solid region.

[0133] Optionally, S503 specifically includes: discretizing the solid region according to the following formula:

[0134]

[0135] Among them, Ω s represents the solid area, P s represents the first-order Piola-Kirchhoff solid stress tensor, ε s represents the alveolar wall displacement, Ψ s Represents a solid test function.

[0136] S804: Summarize the stress tensors of each point on the grid and construct a global stress tensor equation group.

[0137] By summing the stress tensors at each point on the mesh, we construct a global set of stress tensor equations for the entire system. This helps accurately reflect the mechanical behavior of the entire head geometry under external boundary conditions. Solving this global set of equations captures the stress distribution and deformation characteristics throughout the entire model.

[0138] S805: Using the explosion shock wave model, calculate the explosion shock pressure when each round of explosion shock waves is transmitted to the head as the initial condition, substitute it into the global stress tensor equation system, and solve the global stress tensor equation system to obtain the skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure at various locations in the head geometric model.

[0139] S9: Calculate the cumulative brain injury value based on the skull displacement, skull acceleration, skull stress, skull strain and intracranial pressure under the action of each round of explosion shock waves.

[0140] It should be noted that when external force acts on the head, the skull will displace, which reflects the overall deformation of the skull. If the skull displacement exceeds the normal physiological range, it may cause compression, shearing or tearing of brain tissue, resulting in brain damage.

[0141] Furthermore, in events such as explosions or collisions, the head is subjected to instantaneous impact forces, which manifest as accelerations. High accelerations can cause violent movement of brain tissue within the skull, resulting in shear stress and, in turn, injuries such as concussions and brain contusions.

[0142] Furthermore, stress is the distribution of forces within a material. When the head is subjected to external forces, the stress distribution within the skull reveals which areas experience the greatest forces. High-stress areas can become sites of fractures or cracks, posing a threat to brain tissue.

[0143] Furthermore, strain is the amount of deformation of the skull under external forces, which is directly related to the deformation and damage of the material. Excessive strain may exceed the elastic limit of the skull, resulting in irreversible damage (such as fracture).

[0144] Furthermore, intracranial pressure (ICP) is the pressure generated within the confined space of blood, cerebrospinal fluid, and brain tissue within the skull. External shock or blast waves can cause a dramatic increase in intracranial pressure, which can put immense stress on brain tissue and lead to life-threatening conditions such as cerebral edema and brain herniation.

[0145] In one possible implementation, S9 specifically includes calculating the cumulative brain injury value according to the following formula:

[0146] s k =β1ε k +β2a k +β3σ j +β4∈ k +β5p k

[0147] Among them, s k represents the cumulative brain damage value after the kth explosion shock wave, ε k represents the skull displacement after the kth explosion shock wave, β1 represents the weight coefficient of skull displacement, a k represents the skull acceleration after the kth explosion shock wave, β2 represents the weight coefficient of skull acceleration, σ k represents the skull stress after the kth explosion shock wave, β3 represents the weight coefficient of skull stress, ∈ k represents the skull strain after the kth explosion shock wave, β4 represents the weight coefficient of skull strain, and p k represents the intracranial pressure after the kth explosion shock wave, and β5 represents the weight coefficient of the intracranial pressure.

[0148] In the present invention, the impact of the explosion shock wave on the brain is evaluated by comprehensively considering multiple factors (such as skull displacement, acceleration, stress, strain and intracranial pressure), and the cumulative damage is dynamically tracked, thereby achieving an accurate quantitative assessment of brain injury. This not only improves the comprehensiveness and accuracy of the assessment, but also has strong adaptability and application prospects, which is of great significance for rapid decision-making and standardized evaluation in practical applications.

[0149] In a possible implementation, the weight coefficients of skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure are determined by a harmony search algorithm.

[0150] Among them, the Harmony Search Algorithm (HS) is a meta-heuristic optimization algorithm based on the music improvisation process. It simulates the process of musicians searching for harmonious music combinations when playing, and is used to solve various optimization problems.

[0151] Specifically, the deviation between the predicted value and the actual value of the cumulative brain injury value can be used to construct the mean square error loss function, and then the inverse of the mean square error loss function is used as the fitness function of the harmony search algorithm.

[0152] Initialize the harmony memory library. The harmony memory library includes multiple harmonies. Harmonies include multiple components. Each harmony represents a feasible weight parameter combination scheme, and each component represents a weight parameter combination:

[0153]

[0154] Among them, HM represents the harmony memory bank, D represents the harmony dimension, X i represents the i-th harmony, i = 1, 2, ..., HMS, HMS represents the size of the harmony memory bank, x ij represents the jth component in the i-th harmony, j = 1, 2, ..., D, and D represents the harmony dimension.

[0155] Generate a random number r1 and determine whether r1 is less than the probability of the harmony memory. If so, randomly generate a new harmony from the harmony memory. Otherwise, generate a new harmony through random selection.

[0156] Optionally, randomly generating new harmonies through the harmony memory library is specifically as follows:

[0157] X new =[x new，1 , x new，2 …, x new，D ]

[0158] x new，j ∈rand[x 1j , x 2j ,…,x HMS，j ]

[0159] Among them, X new Indicates new harmony, x new,j represents the jth component in the new harmony, j = 1, 2, ..., D, D represents the harmony dimension, rand represents random selection, x ij represents the jth component in the i-th harmony, i = 1, 2, ..., HMS, and HMS represents the size of the harmony memory bank.

[0160] In the present invention, the method of randomly generating new harmonies through the harmony memory bank can maintain the diversity of solutions, enhance the flexibility of search, maintain the effectiveness of the harmony memory bank, improve the convergence speed, balance exploration and development, and is simple and easy to implement.

[0161] Optionally, a new harmony is generated by random selection as follows:

[0162] xnew，j =r1[upper j -lower j ]+lower j

[0163] Among them, upper j Indicates the upper bound of the jth component, lower j represents the lower bound of the j-th component.

[0164] In the present invention, by randomly selecting a method for generating new harmonies, the coverage of the search space can be improved, the diversity of solutions can be enhanced, the implementation and calculation can be simplified, the robustness of the algorithm can be enhanced, a balance between exploration and development can be provided, and the search range can be dynamically adjusted.

[0165] Optionally, the upper and lower boundaries are:

[0166]

[0167] in, represents the upper bound of the j-th component at the t-th iteration, represents the upper bound of the jth component at the t-1th iteration, HM(:j) represents the jth component in the harmony memory library, and max[HM(:j)] represents the maximum value of the jth component in the harmony memory library. represents the lower bound of the j-th component at the t-th iteration, represents the lower bound of the j-th component at the t-1-th iteration, min[HM(:j)] represents the minimum value of the j-th component in the harmony memory library, and T represents the total number of iterations.

[0168] In this method, as the number of iterations increases, the upper and lower bounds gradually shrink, making the search range increasingly focused and improving the refinement of the optimization process. In the early stages of the iteration, the bounds are wide and the search range is large, enhancing global search capabilities and helping to explore the entire solution space and avoid being trapped in local optima. In the later stages of the iteration, the bounds shrink and the search range becomes more focused, facilitating a detailed search in the optimal areas of the solution space and improving the accuracy of the solution.

[0169] When randomly generating a new harmony from the harmony memory, generate a random number r2 and determine whether the random number r2 is less than the pitch fine-tuning probability. If so, generate a new harmony using pitch fine-tuning. Otherwise, keep the harmony unchanged.

[0170] Optionally, generating a new harmony by fine-tuning the pitch is specifically as follows:

[0171] x′ new，j =x new,j +r2BW+r3(x best，j -x worst，j)+x worst，j

[0172] Where x′ new，j represents the jth component of the harmony after fine-tuning the new harmony, x new,j Indicates that the jth component of the new harmony is randomly generated through the harmony memory library, BW represents the fine-tuning bandwidth, r3 represents a random number between -1 and 1, and x best,j represents the jth component of the harmony with the highest current fitness value, x worst,j Represents the jth component in the harmony with the lowest current fitness value.

[0173] In the present invention, the method of generating new harmonies by fine-tuning the pitch can improve the flexibility and adaptability of the optimization process, enhance the local search capability, increase the convergence speed, balance the global search and local search, and enhance the diversity of solutions.

[0174] The generated new harmony is compared with the harmony with the lowest fitness value in the harmony memory to determine whether the fitness value of the new harmony is greater than the fitness value of the harmony with the lowest fitness value in the harmony memory. If so, the harmony memory is updated by replacing the harmony with the new harmony. Otherwise, the harmony memory remains unchanged.

[0175] Determine whether the current number of iterations has reached the maximum number of iterations. If so, output the weight parameter combination with the highest current fitness. Otherwise, return to continue iteration.

[0176] The beneficial effects brought about by the technical solutions provided by the embodiments of the present invention include at least:

[0177] In the present invention, the Mooney Rivlin model is used to characterize the brain, cerebellum, and brainstem in the solid area, the linear elastic constitutive model is used to characterize the skin, muscles, and skull in the solid area, and the fluid dynamic model is used to characterize the cerebrospinal fluid in the fluid area. Different constitutive models are used for different parts of the head, which can accurately capture the complex dynamic response of the head under the action of multiple shock waves, comprehensively and accurately simulate the impact of blast injuries on the brain, improve the accuracy of the prediction of cumulative craniocerebral injury in an explosive environment, and provide a reference for the rapid assessment, treatment and protection of personnel injuries.

[0178] Reference Manual Figure 3 , which shows a structural schematic diagram of a simulation system for cumulative craniocerebral injury under multiple shock waves provided by the present invention.

[0179] The present invention further provides a system 20 for simulating cumulative brain damage under multiple shock waves, which is applied to the above-mentioned method for simulating cumulative brain damage under multiple shock waves, and comprises:

[0180] Processor 201.

[0181] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the method for simulating cumulative brain injury under the action of multiple shock waves as described in the method embodiment is implemented.

[0182] The simulation system 20 for simulating cumulative brain damage under multiple shock waves provided by the present invention can execute the above-mentioned simulation method for simulating cumulative brain damage under multiple shock waves and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on it.

[0183] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0184] In the present invention, the Mooney Rivlin model is used to characterize the brain, cerebellum, and brainstem in the solid area, the linear elastic constitutive model is used to characterize the skin, muscles, and skull in the solid area, and the fluid dynamic model is used to characterize the cerebrospinal fluid in the fluid area. Different constitutive models are used for different parts of the head, which can accurately capture the complex dynamic response of the head under the action of multiple shock waves, comprehensively and accurately simulate the impact of blast injuries on the brain, improve the accuracy of the prediction of cumulative craniocerebral injury in an explosive environment, and provide a reference for the rapid assessment, treatment and protection of personnel injuries.

[0185] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0186] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0187] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0188] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0189] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0190] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean 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.

[0191] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0192] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0193] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0194] 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0195] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0196] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0197] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a method for simulating cumulative brain injury under the action of multiple shock waves as described in the method embodiment.

[0198] The computer-readable storage medium provided by the present invention can realize the steps and effects of the method for simulating cumulative brain injury under multiple shock waves of the above-mentioned method embodiment. To avoid repetition, the present invention will not go into details.

[0199] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0200] In the present invention, the Mooney Rivlin model is used to characterize the brain, cerebellum, and brainstem in the solid area, the linear elastic constitutive model is used to characterize the skin, muscles, and skull in the solid area, and the fluid dynamic model is used to characterize the cerebrospinal fluid in the fluid area. Different constitutive models are used for different parts of the head, which can accurately capture the complex dynamic response of the head under the action of multiple shock waves, comprehensively and accurately simulate the impact of blast injuries on the brain, improve the accuracy of the prediction of cumulative craniocerebral injury in an explosive environment, and provide a reference for the rapid assessment, treatment and protection of personnel injuries.

[0201] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0202] There are a few points to note:

[0203] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0204] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0205] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0206] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for simulating cumulative brain damage under multiple shock waves, characterized in that: include: S1: Construct explosion shock wave model; S2: constructing a head geometric model, wherein the head geometric model includes a solid region and a fluid region; S3: The cerebrum, cerebellum, and brainstem in the solid region are characterized using the Mooney-Rivlin model within the Lagrange framework. S4: characterizing the skin, muscles, and skull in the solid region using a linear elastic constitutive model within the Lagrange framework; S5: Characterize the cerebrospinal fluid in the fluid region using a fluid dynamics model within the Euler framework; S6: Couple the fluid region and the solid region by embedding the boundary method; S7: Calculating the explosion shock pressure when each round of explosion shock wave is transmitted to the head using the explosion shock wave model; S8: Loading the explosion shock pressure when each round of explosion shock waves is transmitted to the head into the head geometric model, and determining the cumulative skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure under the action of each round of explosion shock waves through finite element analysis; S9: Calculate the cumulative brain injury value based on the skull displacement, skull acceleration, skull stress, skull strain and intracranial pressure under the action of each round of explosion shock waves.

2. The method for simulating cumulative craniocerebral injury under multiple shock waves according to claim 1, characterized in that: The explosion shock wave model is specifically: Among them, p m represents the explosion shock pressure, t represents the time, p max represents the peak pressure generated by the explosion shock wave, τ represents the duration of the positive pressure of the explosion shock wave, and exp represents the exponential function with a natural constant as the base; The peak pressure generated by the explosion shock wave is specifically: in, represents the comparison distance, r represents the distance from the explosion center, and w represents the explosive equivalent; The duration of the positive pressure of the explosion shock wave is specifically: τ=Br 1 / 2 w 1 / 6 Wherein, B represents the explosion environment parameter.

3. The method for simulating cumulative brain damage under multiple shock waves according to claim 1, characterized in that: The Mooney Rivlin model is specifically: Where U represents strain potential energy, C 10 、C 01 represents the material parameter related to temperature, J1 represents the first deviatoric strain invariant, J2 represents the second deviatoric strain invariant, D1 represents the material parameter related to bulk modulus, 1 / D1 represents the bulk modulus, J el represents the elastic volume ratio; The first deviatoric strain invariant is specifically: J1=tr(C) Where tr represents the matrix trace operation, C represents the Cauchy-Green deformation tensor; The second deviatoric strain invariant is specifically: The Cauchy-Green deformation tensor is specifically: C=F T F Where F represents the deformation gradient tensor, T Represents the matrix transpose operation; The deformation gradient tensor F is specifically: Where I represents the identity matrix, ε s represents the solid displacement, Represents the gradient operation.

4. The method for simulating cumulative craniocerebral injury under multiple shock waves according to claim 1, characterized in that: The linear elastic constitutive model is specifically: σ ij =λ·tr(γ ij )·δ ij +2μ·γ ij Among them, σ ij represents the stress tensor in the ij direction, λ and μ represent the material-related Lame constants, tr represents the matrix trace operation, γ ij represents the strain tensor in the ij direction, δ ij represents the Kronecker symbol, when i=j, δ ij =1, when i≠j ij =0, used to filter specific tensor components; The strain tensor is specifically: in, represents the partial derivative operation, ε i represents the displacement component in the i direction, ε j represents the displacement component in the j direction, X i Indicates the initial coordinate component in the i direction, X j Represents the initial coordinate component in the j direction.

5. The method for simulating cumulative brain damage under multiple shock waves according to claim 1, characterized in that: The fluid dynamic model is specifically: Where u represents the fluid velocity, t represents the time, represents the partial derivative operation, represents the gradient operation, σ l represents the fluid stress tensor, p f Indicates fluid pressure; The fluid stress tensor is specifically: Where Re represents the fluid Reynolds number, T Represents a matrix transpose operation.

6. The method for simulating cumulative brain damage under multiple shock waves according to claim 1, characterized in that: The S6 is specifically: By embedding the boundary method, displacement continuity, velocity continuity and stress continuity are guaranteed at the fluid-solid interface, and the fluid region and the solid region are coupled.

7. The method for simulating cumulative brain damage under multiple shock waves according to claim 6, characterized in that: The displacement continuity is specifically: e f =e s Among them, ε f represents the fluid deformation parameter, ε s represents the displacement of the solid; The speed continuity is specifically: Where u represents the fluid velocity, represents partial derivative operation, t represents time; The stress continuity is specifically: n·P f (e f )=n·P s (e s ) Where n represents the normal vector of the fluid-solid interface, P f represents the first-order Piola-Kirchhoff fluid stress tensor, P s represents the first-order Piola-Kirchhoff solid stress tensor.

8. The method for simulating cumulative brain damage under multiple shock waves according to claim 1, characterized in that: S9 specifically: The cumulative brain injury value was calculated according to the following formula: s k =β1ε k +β2a k +β3σ k +β4∈ k +β5p k Among them, s k represents the cumulative brain damage value after the kth explosion shock wave, ε k represents the skull displacement after the kth explosion shock wave, β1 represents the weight coefficient of skull displacement, a k represents the skull acceleration after the kth explosion shock wave, β2 represents the weight coefficient of skull acceleration, σ k represents the skull stress after the kth explosion shock wave, β3 represents the weight coefficient of skull stress, ∈ k represents the skull strain after the kth explosion shock wave, β4 represents the weight coefficient of skull strain, and p k represents the intracranial pressure after the kth explosion shock wave, and β5 represents the weight coefficient of the intracranial pressure.

9. The method for simulating cumulative brain damage under multiple shock waves according to claim 8, characterized in that: The weight coefficients of skull displacement, skull acceleration, skull stress, skull strain and intracranial pressure are determined by a harmony search algorithm.

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