A method, device, medium and product for optimizing a shaped charge liner structure
By combining Latin hypercube sampling and Bayesian optimization with a Gaussian process regression model, a surrogate model is constructed, which solves the problem of low efficiency in the design of shaped charge shields in existing technologies and achieves global optimal design and intelligent optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
- Filing Date
- 2026-06-01
- Publication Date
- 2026-06-26
AI Technical Summary
Existing technologies rely on engineers' experience and costly trial-and-error simulation cycles in the design of shaped charge shields, resulting in low efficiency, high computational costs, and a tendency to get stuck in local optima, lacking intelligent guidance.
By combining Latin hypercube sampling and Bayesian optimization with a Gaussian process regression model, a surrogate model is constructed. The globally optimal shaped charge liner structural parameters are then found through intelligent cyclic searching, which reduces computational costs and improves design efficiency.
It significantly reduces computational costs and design cycles, finds the globally optimal design solution through automated loops, reduces reliance on engineers' experience, and achieves intelligent design.
Smart Images
Figure CN122287164A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of structural design, and in particular to a method, equipment, medium, and product for optimizing the structure of a shaped charge shroud. Background Technology
[0002] The core of shaped charge blasting technology lies in the design of the shaped charge liner. The structural parameters of the shaped charge liner (such as cone angle and wall thickness) directly affect the morphology and penetration capability of the shaped charge jet. Traditional design methods rely heavily on engineers' experience and costly "trial and error-simulation" cycles, i.e., manually adjusting parameters, performing numerical simulations, evaluating performance, and then adjusting again. This process has inherent drawbacks such as low efficiency, high computational cost, susceptibility to local optima, and lack of intelligent guidance.
[0003] With the development of computer technology, how to automatically and efficiently find the globally optimal or near-optimal drug liner structural parameters through a limited number of numerical simulations has become an urgent problem to be solved in this field.
[0004] Therefore, there is an urgent need to provide an intelligent determination method that can reduce computational costs, shorten design cycles, and automatically perform global optimization. Summary of the Invention
[0005] The purpose of this application is to provide a method, equipment, medium, and product for optimizing the structure of a shaped charge shroud, which can reduce the computational cost of determining the structural parameters of the shaped charge shroud and improve the efficiency and intelligence of the optimization of the shaped charge shroud structure.
[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for optimizing the structure of a shaped charge liner, the method comprising: Obtain the value range of the structural parameters of the shaped charge liner; the structural parameters include: the cone angle of the shaped charge liner and the wall thickness of the shaped charge liner; Using structural parameters as design variables and maximizing the performance index value as the objective, an objective function is constructed; the performance index value is the penetration depth on a standard steel target. Based on the range of structural parameters, the Latin hypercube sampling method is used to determine multiple initial sample points; and according to the objective function, numerical simulation is used to determine the performance index values corresponding to the initial sample points, thereby constructing an initial sample library. Based on the initial sample library, a surrogate model is constructed; the surrogate model is used to predict performance index values and corresponding uncertainties based on structural parameters. Based on the surrogate model and the range of structural parameters, candidate sample points are determined using Bayesian optimization loops; and numerical simulations are used to determine the corresponding performance index values. The initial sample library and surrogate model are updated based on candidate sample points and their corresponding performance index values; and the steps of determining candidate sample points based on Bayesian optimization loops according to the range of values of surrogate model and structural parameters are returned; and the corresponding performance index values are determined by numerical simulation until the termination condition is met; and the sample point corresponding to the maximum performance index value is taken as the target structural parameter. The shaped charge shield is designed and optimized based on the target structural parameters.
[0007] Optionally, the cone angle of the shaped charge liner is in the range of 40°-70°; the wall thickness of the shaped charge liner is in the range of 0.4mm-0.8mm.
[0008] Optionally, numerical simulations can be performed using finite element software; finite element software includes ANSYS and AUTODYN.
[0009] Optionally, the surrogate model adopts a Gaussian process regression model and uses a radial basis function as the kernel function.
[0010] Optionally, using the formula Determine the kernel function ; in, For the input design variable vector, Let M be the signal variance, and M be a diagonal matrix. For noise variance, Let T be the Kronecker function, and the superscript T is the transpose.
[0011] Optionally, based on the surrogate model and the range of values for the structural parameters, candidate sample points are determined using a Bayesian optimization loop, specifically including: Using formula Determine the desired improvement function ; in, For design variables, and The proxy model has the following design variables: The mean and standard deviation of the objective function predicted at that time. The best observation of the objective function in the current sample library. For balancing parameters, and These are the cumulative distribution function and probability density function of the standard normal distribution, respectively, with parameters... .
[0012] Secondly, this application provides a device for optimizing the structure of a shaped charge liner, the device comprising: The value range acquisition module is used to acquire the value range of the structural parameters of the shaped charge liner; the structural parameters include: the cone angle of the shaped charge liner and the wall thickness of the shaped charge liner; The objective function construction module is used to construct an objective function with structural parameters as design variables and the goal of maximizing the performance index value; the performance index value is the penetration depth on a standard steel target. The initial sample library construction module is used to determine multiple initial sample points based on the range of structural parameters using the Latin hypercube sampling method; and to determine the performance index values corresponding to the initial sample points using numerical simulation according to the objective function, thereby constructing the initial sample library. The surrogate model construction module is used to construct a surrogate model based on the initial sample library; the surrogate model is used to predict performance index values and corresponding uncertainties based on structural parameters. The optimization loop module is used to determine candidate sample points based on the range of values of the surrogate model and structural parameters using Bayesian optimization loops; and to determine the corresponding performance index values using numerical simulation. The target structure parameter determination module is used to update the initial sample library and surrogate model based on candidate sample points and corresponding performance index values; and return to the optimization loop module until the termination condition is met; and take the sample point corresponding to the maximum performance index value as the target structure parameter. The shaped charge shroud optimization module is used to optimize the design of the shaped charge shroud based on the target structural parameters.
[0013] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the shaped charge shroud structure optimization method described above.
[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for optimizing the shaped charge shroud structure.
[0015] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for optimizing the shaped charge shroud structure.
[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, device, medium, and product for optimizing the structure of a shaped charge shroud. By constructing a surrogate model to replace most of the costly numerical simulations, and utilizing Bayesian optimization to intelligently recommend promising candidate sample points, it rapidly approximates the optimal solution with a minimal number of simulations, significantly reducing computational costs and design cycle time. This application effectively balances "exploration" (searching unknown regions) and "utilization" (refined searching in known favorable regions) to avoid getting trapped in local optima, thereby finding a globally superior design solution. Furthermore, the technical solution of this application can form an automated closed loop of "initial sampling - model learning - intelligent recommendation - verification and update," reducing reliance on engineer experience and realizing intelligent design processes. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a schematic diagram of a method for optimizing the structure of a shaped charge shroud in one embodiment of this application; Figure 2 This is a schematic diagram of the overall process of a method for optimizing the structure of a shaped charge shroud in one embodiment of this application; Figure 3 This is a schematic diagram of Latin hypercube sampling; Figure 4 Flowchart for training a Gaussian process regression model; Figure 5 Optimize the loop flowchart for Bayesian methods; Figure 6 A schematic diagram illustrating the convergence of the Bayesian optimization loop process. Detailed Implementation
[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0020] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0021] In one exemplary embodiment, such as Figure 1As shown, a method for optimizing the structure of a shaped charge shroud is provided, which includes the following steps S101 to S107. Wherein: S101, Obtain the value range of the structural parameters of the shaped charge liner; the structural parameters include: the cone angle of the shaped charge liner and the wall thickness of the shaped charge liner; S102, using structural parameters as design variables and maximizing the performance index value as the objective function, constructs an objective function; the performance index value is the penetration depth on a standard steel target. S103. Based on the range of structural parameters, the Latin hypercube sampling method is used to determine multiple initial sample points; and according to the objective function, numerical simulation is used to determine the performance index values corresponding to the initial sample points, thereby constructing an initial sample library. S104, Construct a surrogate model based on the initial sample library; the surrogate model is used to predict performance index values and corresponding uncertainties based on structural parameters; S105. Based on the surrogate model and the range of values for structural parameters, candidate sample points are determined using Bayesian optimization loops; and corresponding performance index values are determined using numerical simulation. S106, update the initial sample library and surrogate model according to the candidate sample points and the corresponding performance index values; and return the steps of determining candidate sample points based on the range of values of the surrogate model and structural parameters, using Bayesian optimization loop; and determining the corresponding performance index values using numerical simulation, until the termination condition is met; and take the sample point corresponding to the maximum performance index value as the target structural parameter; S107, the shaped charge shield is designed with optimized structural parameters.
[0022] like Figure 2 As shown, the overall process of this application begins with parametric modeling and ends with the output of the optimal solution. Its core is a data-driven intelligent loop, and the specific process is as follows: Step S1, Parametric Modeling and Optimization Problem Definition: Determine the design variables, optimization objectives, and constraints of the shaped charge shroud; The following section uses a certain type of emulsion explosive shaped charge blasting device as an application scenario to optimize the structure of the shaped charge liner: (1) Design variables: cone angle α (range 40°-70°) and wall thickness δ (range 0.4mm-0.8mm) of the shaped charge shroud.
[0023] (2) Optimization objective: Maximize the penetration depth P (unit: mm) on a standard steel target.
[0024] (3) Constraints: This embodiment is a single-objective unconstrained optimization. In practical applications, other process constraints can be added.
[0025] Step S2, Initial sample library construction: Multiple initial sample points are generated within the design space (range of values) using the Latin hypercube sampling method, and their performance index values are obtained through numerical simulation; like Figure 3 As shown, the Latin hypercube sampling method is used to generate multiple initial sample points. The specific process is as follows: the value range of each design variable is randomly divided, ensuring that only one sample exists within each division interval. Finally, these samples are randomly combined to form initial sample points uniformly distributed throughout the design space. Latin hypercube sampling can efficiently cover the entire design space with a relatively small number of sample points.
[0026] In this embodiment, for simplicity, five initial sample points are generated, and their penetration depths are obtained through numerical simulation using finite element software such as ANSYS and AUTODYN, thus constructing an initial sample library. An example of the initial sample library is shown in Table 1. Table 1
[0027] As shown in Table 1, the historical best solution (From sample 4).
[0028] Step S3, surrogate model construction and training: Train a Gaussian Process Regression (GPR) model based on the initial sample library; the Gaussian Process Regression model is used to predict performance indicators and provide uncertainty estimates; like Figure 4 As shown, GPR can provide not only the predicted mean μ(x), but also the predicted standard deviation σ(x) (an estimate of uncertainty). Using formula Determine the kernel function ; in, For the input design variable vector, Let M be the signal variance, and M be a diagonal matrix whose diagonal elements are... , Let d be the length scale hyperparameter corresponding to the d-th design variable. For noise variance, Let T be the Kronecker function, and the superscript T is the transpose.
[0029] Based on the initial sample library in Table 1, the optimal hyperparameters of the GPR model are automatically learned by maximizing the marginal likelihood function. Under the optimal hyperparameters of the GPR model, the currently observed training data has the highest probability, thus allowing the surrogate model to best fit the existing data and balance model complexity. Assuming the optimized result is: the length scale of the cone angle... =8.5 (cone angle) =0.15 (wall thickness), signal variance =28.0, noise variance =0.1. Wherein, the length of the cone angle is measured... Length scale much larger than wall thickness This indicates that in the design space of this embodiment, the penetration depth is more sensitive to changes in wall thickness, and the function changes more drastically in the wall thickness dimension.
[0030] Step S4, Bayesian optimization loop: Based on the Gaussian process regression model, the next candidate sampling point is found through the expected improvement function (collection function), numerical simulation is performed for verification, and the sample library and surrogate model are iteratively updated; like Figure 5 As shown, by utilizing the uncertainty information provided by the surrogate model, it intelligently balances "exploration" (searching unknown areas) and "utilization" (refined searching in known good areas).
[0031] First iteration: Using the trained GPR model, the value of the desired improvement function (acquisition function) is calculated throughout the entire design space.
[0032] Using formula Determine the desired improvement function ; in, For design variables, and The proxy model has the following design variables: The mean and standard deviation of the objective function predicted at that time. The best observation of the objective function in the current sample library. For balancing parameters, and These are the cumulative distribution function and probability density function of the standard normal distribution, respectively, with parameters... ; Calculations revealed that the sampling point x = (49.0°, 0.59 mm) The value is the largest, with a predicted mean μ = 55.5 mm and a prediction standard deviation σ = 2.1 mm at this point. Numerical simulation of the sampling point yields a penetration depth P = 55.8 mm.
[0033] The new sampling point (49.0, 0.59, 55.8) is added to the initial sample set, increasing the initial sample set to 6 points. The GPR model is then retrained using the updated initial sample set, and its hyperparameters are updated; the historical best solution is updated to 55.8 mm.
[0034] Second iteration: The updated GPR model recommends sampling points (48.5°, 0.62 mm), and the simulation yields P=56.5 mm.
[0035] Third iteration: The model recommended sampling point (50.5°, 0.58 mm), and the simulation yielded P=55.9 mm.
[0036] The termination condition is set as "performance improvement is less than 0.2 mm for three consecutive iterations". If there is no performance improvement after the third iteration, and the subsequent two iterations do not exceed 56.5 mm, the loop terminates.
[0037] Step S5: Output the results. When the termination condition is met, output the historical optimal solution as the final design scheme.
[0038] After the loop ends, the best-performing point is selected from all simulated points (initial 5 + iterative 5 = 10) as the final design. The final optimal design in this embodiment is (α = 48.5°, δ = 0.62 mm), with a penetration depth of 56.5 mm.
[0039] To verify the effectiveness of this application, simulations were conducted using uniform sampling at 10 points within the same design space, and the optimal penetration depth was found to be 54.9 mm. Under the same number of simulations, the design scheme found in this application showed a 2.9% performance improvement, fully demonstrating its superiority. Figure 6 As shown, the Bayesian optimization loop process exhibits typical convergence behavior: the initial optimal performance improves rapidly, then enters a fine search phase and gradually stabilizes, thus intuitively demonstrating the high efficiency of this application in finding the global optimum.
[0040] This application provides an efficient and intelligent design path for shaped charge propellants, which has significant industrial application value.
[0041] Based on the same inventive concept, this application also provides a shaped charge liner structure optimization device for implementing the above-mentioned shaped charge liner structure optimization method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more shaped charge liner structure optimization device embodiments provided below can be found in the limitations of the shaped charge liner structure optimization method above, and will not be repeated here.
[0042] In one exemplary embodiment, a device for optimizing the structure of a shaped charge shroud is provided, comprising: The value range acquisition module is used to acquire the value range of the structural parameters of the shaped charge liner; the structural parameters include: the cone angle of the shaped charge liner and the wall thickness of the shaped charge liner; The objective function construction module is used to construct an objective function with structural parameters as design variables and the goal of maximizing the performance index value; the performance index value is the penetration depth on a standard steel target. The initial sample library construction module is used to determine multiple initial sample points based on the range of structural parameters using the Latin hypercube sampling method; and to determine the performance index values corresponding to the initial sample points using numerical simulation according to the objective function, thereby constructing the initial sample library. The surrogate model construction module is used to construct a surrogate model based on the initial sample library; the surrogate model is used to predict performance index values and corresponding uncertainties based on structural parameters. The optimization loop module is used to determine candidate sample points based on the range of values of the surrogate model and structural parameters using Bayesian optimization loops; and to determine the corresponding performance index values using numerical simulation. The target structure parameter determination module is used to update the initial sample library and surrogate model based on candidate sample points and corresponding performance index values; and return to the optimization loop module until the termination condition is met; and take the sample point corresponding to the maximum performance index value as the target structure parameter. The shaped charge shroud optimization module is used to optimize the design of the shaped charge shroud based on the target structural parameters.
[0043] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for optimizing the structure of a shaped charge shroud.
[0044] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0045] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0046] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0047] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0048] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0049] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0050] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.
[0051] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0052] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for optimizing the structure of a shaped charge shroud, characterized in that, The method for optimizing the shaped charge liner structure includes: Obtain the value range of the structural parameters of the shaped charge liner; the structural parameters include: the cone angle of the shaped charge liner and the wall thickness of the shaped charge liner; Using structural parameters as design variables and maximizing the performance index value as the objective, an objective function is constructed; the performance index value is the penetration depth on a standard steel target. Based on the range of structural parameters, the Latin hypercube sampling method is used to determine multiple initial sample points; and according to the objective function, numerical simulation is used to determine the performance index values corresponding to the initial sample points, thereby constructing an initial sample library. Based on the initial sample library, a surrogate model is constructed; the surrogate model is used to predict performance index values and corresponding uncertainties based on structural parameters. Based on the surrogate model and the range of structural parameters, candidate sample points are determined using Bayesian optimization loops; and numerical simulations are used to determine the corresponding performance index values. The initial sample library and surrogate model are updated based on candidate sample points and their corresponding performance index values; and the steps of determining candidate sample points based on Bayesian optimization loops according to the range of values of surrogate model and structural parameters are returned; and the corresponding performance index values are determined by numerical simulation until the termination condition is met; and the sample point corresponding to the maximum performance index value is taken as the target structural parameter. The shaped charge shield is designed and optimized based on the target structural parameters.
2. The method for optimizing the structure of a shaped charge shroud according to claim 1, characterized in that, The cone angle of the shaped charge liner is in the range of 40°-70°; the wall thickness of the shaped charge liner is in the range of 0.4mm-0.8mm.
3. The method for optimizing the structure of a shaped charge shroud according to claim 1, characterized in that, Numerical simulations are performed using finite element software; finite element software includes ANSYS and AUTODYN.
4. The method for optimizing the structure of a shaped charge shroud according to claim 1, characterized in that, The surrogate model adopts a Gaussian process regression model and uses radial basis functions as kernel functions.
5. The method for optimizing the structure of a shaped charge shroud according to claim 4, characterized in that, Using formula Determine the kernel function ; in, For the input design variable vector, Let M be the signal variance, and M be a diagonal matrix. For noise variance, Let T be the Kronecker function, and the superscript T is the transpose.
6. The method for optimizing the structure of a shaped charge shroud according to claim 1, characterized in that, Based on the surrogate model and the range of structural parameters, candidate sample points are determined using a Bayesian optimization loop, specifically including: Using formula Determine the desired improvement function ; in, For design variables, and The proxy model has the following design variables: The mean and standard deviation of the objective function predicted at that time. The best observation of the objective function in the current sample library. For balancing parameters, and These are the cumulative distribution function and probability density function of the standard normal distribution, respectively, with parameters... .
7. A device for optimizing the structure of a shaped charge shroud, characterized in that, The energy-concentrating shaped charge liner structure optimization equipment includes: The value range acquisition module is used to acquire the value range of the structural parameters of the shaped charge liner; the structural parameters include: the cone angle of the shaped charge liner and the wall thickness of the shaped charge liner; The objective function construction module is used to construct an objective function with structural parameters as design variables and the goal of maximizing the performance index value; the performance index value is the penetration depth on a standard steel target. The initial sample library construction module is used to determine multiple initial sample points based on the range of structural parameters using the Latin hypercube sampling method; and to determine the performance index values corresponding to the initial sample points using numerical simulation according to the objective function, thereby constructing the initial sample library. The surrogate model construction module is used to construct a surrogate model based on the initial sample library; the surrogate model is used to predict performance index values and corresponding uncertainties based on structural parameters. The optimization loop module is used to determine candidate sample points based on the range of values of the surrogate model and structural parameters using Bayesian optimization loops; and to determine the corresponding performance index values using numerical simulation. The target structure parameter determination module is used to update the initial sample library and surrogate model based on candidate sample points and corresponding performance index values; and return to the optimization loop module until the termination condition is met; and take the sample point corresponding to the maximum performance index value as the target structure parameter. The shaped charge shroud optimization module is used to optimize the design of the shaped charge shroud based on the target structural parameters.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the shaped charge shroud structure optimization method according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for optimizing the shaped charge shroud structure as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for optimizing the shaped charge shroud structure as described in any one of claims 1-6.