A method and system for optimizing the crashworthiness of a rail vehicle energy-absorbing structure

Abstract

The present invention relates to the technical field of structural optimization, and discloses a method and system for optimizing the crashworthiness performance of an energy-absorbing structure of a rail vehicle, comprising: determining corresponding simulation parameters affecting energy absorption and a multi-dimensional collision mechanical response data set of the energy-absorbing structure; constructing a multi-dimensional collision response prediction model; defining a pattern stability constraint based on a decoding result of an image feature extraction network, defining a load stability constraint based on a decoding result of a curve feature extraction network, and defining an index matching constraint based on an output result of the multi-dimensional collision response prediction model; constructing a multi-objective crashworthiness optimization model of the energy-absorbing structure based on the pattern stability constraint, the load stability constraint, and the index matching constraint, and solving the model to obtain a Pareto solution set; solving the optimal feasible solution of the Pareto solution set, and using the optimal feasible solution as a crashworthiness optimization result of the target energy-absorbing structure; the present invention solves the problem of low reliability of optimization results in existing crashworthiness optimization methods.

Description

Technical Field

[0001] The present invention relates to the technical field of structural optimization, and in particular to a method and system for optimizing the crashworthiness of an energy-absorbing structure of a rail vehicle. Background Art

[0002] Energy-absorbing structures used in rail vehicles absorb impact energy through plastic deformation during a train collision, thereby protecting drivers and passengers. However, due to the diverse forms, numerous structural parameters, significant coupling interactions, and highly nonlinear collision response of energy-absorbing structures, crashworthiness optimization has always been a core challenge in energy-absorbing structure design. Currently, surrogate model-driven optimization methods, such as the "Multi-Objective Optimization Method for Crashworthiness Performance of Conical Energy-Absorbing Structures Based on Machine Learning" (CN202211151523.7), primarily employ surrogate models (including response surface methods, kriging, radial basis functions, and in this case, response surface models) for crashworthiness performance indicators (such as energy absorption and peak force), but fail to account for constraints such as deformation patterns and load fluctuations. While the optimization results ensure that average load and energy absorption meet design expectations, they may result in non-ideal conditions such as unstable deformation patterns and severe load fluctuations, severely impacting design iteration and the efficiency of new product development. Consequently, existing crashworthiness optimization methods suffer from low reliability. Summary of the Invention

[0003] The present invention provides a method and system for optimizing the crashworthiness performance of an energy-absorbing structure of a rail vehicle, so as to solve the problem of low reliability of optimization results in existing crashworthiness optimization methods.

[0004] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0005] In a first aspect, the present invention provides a method for optimizing the crashworthiness performance of an energy-absorbing structure of a rail vehicle, comprising:

[0006] Determine the simulation parameters that affect energy absorption according to the type of target energy-absorbing structure, and perform numerical simulation based on the simulation parameters to obtain a multi-dimensional collision mechanical response dataset of the energy-absorbing structure;

[0007] Constructing a multi-dimensional collision response prediction model based on a deep learning framework and the multi-dimensional collision mechanical response dataset of the energy-absorbing structure, wherein the multi-dimensional collision response prediction model includes: an image feature extraction network, a curve feature extraction network, and a feature vector prediction network;

[0008] Defining a pattern stability constraint based on the decoding result of the image feature extraction network, defining a load stability constraint based on the decoding result of the curve feature extraction network, and defining an index matching constraint based on the output result of the multi-dimensional collision response prediction model;

[0009] A multi-objective crashworthiness optimization model for an energy-absorbing structure is constructed based on the mode stability constraint, the load stability constraint, and the index matching constraint, and a non-dominated sorting genetic algorithm is used to solve the model to obtain a Pareto solution set;

[0010] The optimal feasible solution of the Pareto solution set is solved based on the entropy-weighted VIKOR multi-attribute decision-making method, and the optimal feasible solution is used as the optimization result of the crashworthiness performance of the target energy-absorbing structure.

[0011] Optionally, the performing of numerical simulation based on simulation parameters to obtain a multi-dimensional collision mechanical response dataset of the energy-absorbing structure includes:

[0012] Conduct impact tests and corresponding numerical simulations on the target energy absorbing structure according to its type and simulation parameters, and calibrate the numerical simulation model;

[0013] The experimental design is carried out based on the numerical simulation model after experimental benchmarking, and a multi-dimensional collision mechanical response data set of the energy-absorbing structure is generated by integrating experiment and simulation.

[0014] Optionally, the constructing of a multi-dimensional collision response prediction model based on a deep learning framework and the multi-dimensional collision mechanical response dataset of the energy-absorbing structure includes:

[0015] The multi-dimensional collision mechanical response dataset of the energy-absorbing structure is randomly divided into a training set, a validation set, and a test set in a ratio of 8:1:1;

[0016] A deep learning model is constructed through a deep learning framework, and the deep learning model is trained using training sets, validation sets, and test sets to obtain a multi-dimensional collision response prediction model.

[0017] Optionally, the image feature extraction network is used to construct an autoencoder network with stacked convolutional layers as the main architecture, and extract the deformation pattern image feature vector, wherein the deformation pattern image feature vector is: ;

[0018] The curve feature extraction network is used to construct an autoencoder network with stacked long and short-term memory layers as the main structure, and extract the load characteristic curve feature vector, wherein the load characteristic curve feature vector is: ;

[0019] The feature vector prediction network is used to construct a vector prediction network with a fully connected layer as the main body, and fuses the deformation model image feature vector, the load characteristic curve feature vector and the crashworthiness index feature vector directly extracted from the multi-dimensional collision mechanical response data set of the energy-absorbing structure into an output vector.

[0020] Optionally, defining a pattern stability constraint based on a decoding result of the image feature extraction network includes:

[0021] After obtaining the prediction result of the output vector through the multi-dimensional collision response prediction model, the feature vector portion of the deformation model image is input into the image feature extraction network for decoding to obtain the pattern image prediction result;

[0022] The pattern stability constraint is defined based on the pattern image prediction results. The stability judgment basis is different according to the type of target energy-absorbing structure. When the pattern is stable, the constraint value is returned to 0, and when the pattern is unstable, the constraint value is returned to 1.

[0023] Optionally, defining a load stability constraint based on a decoding result of the curve feature extraction network includes:

[0024] After obtaining the prediction result of the output vector through the multi-dimensional collision response prediction model, the characteristic vector portion of the load characteristic curve is input into the curve feature extraction network for decoding to obtain the load curve prediction result;

[0025] The load stability constraint is defined based on the load curve prediction results, where the load stability constraint satisfies the following relationship:

[0026] ;

[0027] Where S 2 represents the load stability constraint value, θ is the preset constraint value of this indicator, N represents the number of sampling points of the load curve, Fi represents the load value at any sampling time, Represents the average value of the load curve. , the return constraint value is 0, otherwise the return constraint value is 1.

[0028] Optionally, defining an indicator matching constraint based on an output result of the multi-dimensional collision response prediction model includes:

[0029] The crashworthiness index prediction result is directly output from the crashworthiness index vector part, and the index matching constraint is defined based on the crashworthiness index prediction result. According to the difference in the target energy absorption structure type, the matching judgment basis is different. When the matching meets the requirements, the constraint value is returned to 0, and when the mode is unstable, the constraint value is returned to 1.

[0030] Optionally, the multi-objective crashworthiness optimization model of the energy-absorbing structure satisfies the following relationship:

[0031] ;

[0032] Where, Obj i represents the optimization goal, V stands for Structural parameters, VL 、V U Represent the upper and lower boundaries of the structural parameters, P and P L and P U Represent the crashworthiness index and its upper and lower boundaries respectively, represents the mode stability constraint, Represents the load fluctuation constraint.

[0033] Optionally, the entropy-weighted VIKOR multi-attribute decision-making method is used to solve the optimal feasible solution of the Pareto solution set, including:

[0034] S1. Construct a standardized solution set that satisfies the following relationship:

[0035] ;

[0036] Where, x ij represents the original feasible solution, z ij represents the standardized feasible solution;

[0037] S2. Determine the positive ideal solution and the negative ideal solution in the standardized solution set. The determination method satisfies the following relationship:

[0038]

[0039] ;

[0040] Where Z + represents the set of positive ideal solutions, Z i + Representative i The positive ideal solution of the target, Z - represents the set of negative ideal solutions, Z i - Representative i Negative ideal solution of a target;

[0041] S3. Calculate the weight of each optimization objective based on the entropy value. The calculation includes:

[0042] The contribution of the calculated target in the feasible solution satisfies the following relationship:

[0043] ;

[0044] Where, w ij represents the contribution to the feasible solution, x ij represents the original feasible solution;

[0045] Calculate the entropy value of the target, which satisfies the following relationship:

[0046] ;

[0047] Where, e j represents the entropy value, w ij represents the contribution in the feasible solution;

[0048] Calculate the weight of the target, which satisfies the following relationship:

[0049] ;

[0050] Where, ω j represents the weight value, e j represents the entropy value;

[0051] S4. Calculate the group contribution value and individual regret value of each feasible solution. The calculation of the group contribution value satisfies the following relationship:

[0052] ;

[0053] Where, S i represents the group contribution value, ω j Represents the weight value, Z j + Representative j The positive ideal solution of the target, Z j - Representative j The negative ideal solution of the target, z ij represents the standardized feasible solution;

[0054] The calculation of its individual regret value satisfies the following relationship:

[0055] ;

[0056] Where, R i represents the individual regret value, ω j Represents the weight value, Z j + Representative j The positive ideal solution of the target, Z j - Representative j The negative ideal solution of the target, z ij represents the standardized feasible solution;

[0057] S5. Calculate the contribution rate of each feasible solution, which satisfies the following relationship:

[0058] ;

[0059] Where, Q i represents the contribution rate, S + 、 S - Represent the group contribution values ​​of positive ideal solution and negative ideal solution respectively, R + 、 R - Represent the individual regret values ​​of positive ideal solutions and negative ideal solutions respectively.

[0060] S6. Sort the contribution rates of the feasible solutions and obtain the optimal feasible solution in the Pareto solution set based on the sorting results.

[0061] In a second aspect, an embodiment of the present application provides a system for optimizing the crashworthiness performance of an energy-absorbing structure of a rail vehicle, including a processor and a memory;

[0062] Memory for storing computer programs;

[0063] The processor is configured to implement any one of the method steps described in the first aspect when executing a program stored in the memory.

[0064] Beneficial effects:

[0065] The present invention provides a method for optimizing the crashworthiness of rail vehicle energy-absorbing structures. By applying the proposed optimization method, the optimization results can ensure that the energy-absorbing structure indicators meet the requirements, while maintaining a stable deformation mode and smooth load fluctuations, thereby greatly improving the reliability of the optimization results. In the final decision-making stage, a multi-attribute decision-making method based on entropy-weighted VIKOR is proposed. By calculating the entropy value of each optimization target and assigning weights, the interference of human factors in the multi-target decision-making process is effectively avoided, and the decision results obtained are more objective. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flow chart of a method for optimizing the crashworthiness of a rail vehicle energy-absorbing structure according to a preferred embodiment of the present invention;

[0067] Figure 2 This is a schematic diagram of a network framework of a multi-dimensional collision response prediction model according to a preferred embodiment of the present invention;

[0068] Figure 3 Schematic diagram of the image feature extraction network architecture of a preferred embodiment of the present invention;

[0069] Figure 4 A schematic diagram of a curve feature extraction network architecture according to a preferred embodiment of the present invention;

[0070] Figure 5 Schematic diagram of the optimization results of the thin-walled circular tube energy absorption structure in preferred embodiment 1 of the present invention;

[0071] Figure 6 Schematic diagram of constraint optimization results of the inflatable energy absorbing structure in preferred embodiment 2 of the present invention;

[0072] Figure 7 This is a schematic diagram of the optimization results of the honeycomb-filled evil energy structure in the preferred embodiment 3 of the present invention. DETAILED DESCRIPTION

[0073] The following is a clear and complete description of the technical solutions of the present invention. It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0074] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "a" do not indicate a quantity limitation, but rather indicate the existence of at least one. Words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0075] See Figure 1-7 The present invention provides a method for optimizing the crashworthiness of a rail vehicle energy-absorbing structure, including:

[0076] Determine the simulation parameters that affect energy absorption according to the type of target energy-absorbing structure, and perform numerical simulation based on the simulation parameters to obtain a multi-dimensional collision mechanical response dataset of the energy-absorbing structure;

[0077] Constructing a multi-dimensional collision response prediction model based on a deep learning framework and the multi-dimensional collision mechanical response dataset of the energy-absorbing structure, wherein the multi-dimensional collision response prediction model includes: an image feature extraction network, a curve feature extraction network, and a feature vector prediction network;

[0078] Defining a pattern stability constraint based on the decoding result of the image feature extraction network, defining a load stability constraint based on the decoding result of the curve feature extraction network, and defining an index matching constraint based on the output result of the multi-dimensional collision response prediction model;

[0079] A multi-objective crashworthiness optimization model for an energy-absorbing structure is constructed based on the mode stability constraint, the load stability constraint, and the index matching constraint, and a non-dominated sorting genetic algorithm is used to solve the model to obtain a Pareto solution set;

[0080] The optimal feasible solution of the Pareto solution set is solved based on the entropy-weighted VIKOR multi-attribute decision-making method, and the optimal feasible solution is used as the optimization result of the crashworthiness performance of the target energy-absorbing structure.

[0081] In response to the above content, the following three examples, including thin-walled circular tube energy absorption structure, bulging energy absorption structure, reduced diameter energy absorption structure and honeycomb filling energy absorption structure, illustrate the effectiveness of the method proposed in this scheme.

[0082] Example 1: Optimization of thin-walled circular tube energy absorption structure

[0083] In the thin-walled circular tube structure optimization problem, key structural parameters include wall thickness t, radius r, and material property parameters A, B, and n. The optimization goal is to maximize specific energy absorption (SEA) and minimize peak crushing force (PCF). The boundary ranges and constraints of each structural parameter are shown in the following equations.

[0084]

[0085] The NSGA algorithm is used to solve the above optimization problem, where the number of iterations is set to 200 and the population size is 50. After optimization, the optimal feasible solution is calculated according to the multi-attribute decision-making method proposed in the solution of the present invention. In order to further verify the reliability of the optimization results obtained by the proposed multi-dimensional collision mechanics response prediction model, a finite element simulation model of the optimal feasible solution thin-walled circular tube structure is established, and its collision mechanics response is calculated and compared with the predicted optimization results. The results are as follows Figure 5 shown.

[0086] The results show that the deep learning prediction framework for the multi-dimensional collision mechanical response of energy-absorbing structures proposed in this invention can accurately predict the various collision responses of thin-walled circular tube structures. After applying it to fusion constraint optimization, the deformation mode of the obtained results is stable, the load fluctuation is controllable, and the crashworthiness indicators meet the requirements, which fully demonstrates the effectiveness of this scheme.

[0087] Example 2: Optimization of the inflatable energy absorption structure

[0088] In the optimization problem of bulging structure, the key structural parameters include wall thickness t, inner diameter r e , outer diameter r m , bulging cone angle α , friction coefficient u The optimization goal is to maximize specific energy absorption (SEA) and minimize peak crushing force (PCF). The boundary ranges and constraints of each structural parameter are shown in the following formula.

[0089]

[0090] The NSGA algorithm is used to solve the above optimization problem, where the number of iterations is set to 200 and the population size is 50. After optimization, the optimal feasible solution is calculated according to the multi-attribute decision-making method proposed in the solution of the present invention. A finite element simulation model of the optimal feasible solution inflatable energy absorption structure is established, and its collision mechanical response is calculated and compared with the predicted optimization results. The results are as follows Figure 6 shown.

[0091] The results show that the deformation mode of the optimized inflatable energy-absorbing structure is stable, the load fluctuation is controllable, and the crashworthiness index meets the requirements, which fully demonstrates the effectiveness of this scheme.

[0092] Example 3: Optimization of honeycomb-filled energy-absorbing structure

[0093] In the optimization problem of honeycomb filling structure, the key structural parameters include the strength of the filling honeycomb block P 1. P 2. P 3. Coating wall thickness T , Covering height L , fill the gap S The optimization goal is to maximize the specific energy absorption (SEA) and minimize the peak crushing force (PCF). The boundary ranges and constraints of each structural parameter are shown in the following formula.

[0094]

[0095] The NSGA algorithm is used to solve the above optimization problem, where the number of iterations is set to 200 and the population size is 50. After optimization, the optimal feasible solution is calculated according to the multi-attribute decision-making method proposed in the solution of the present invention. A finite element simulation model of the optimal feasible solution honeycomb-filled energy-absorbing structure is established, and its collision mechanical response is calculated and compared with the predicted optimization results. The results are as follows Figure 6 shown

[0096] The embodiment of the present application also provides a system for optimizing the crashworthiness performance of a rail vehicle energy-absorbing structure, including a processor and a memory;

[0097] Memory for storing computer programs;

[0098] The processor is configured to implement any one of the method steps described in the method for optimizing the crashworthiness performance of the energy-absorbing structure of a rail vehicle when executing the program stored in the memory.

[0099] The above-mentioned system for optimizing the crashworthiness performance of the energy-absorbing structure of a rail vehicle can implement various embodiments of the above-mentioned method for optimizing the crashworthiness performance of the energy-absorbing structure of a rail vehicle and can achieve the same beneficial effects, which will not be described in detail here.

[0100] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing the crashworthiness of a rail vehicle energy-absorbing structure, characterized in that: include: Determine the simulation parameters that affect energy absorption according to the type of target energy-absorbing structure, and perform numerical simulation based on the simulation parameters to obtain a multi-dimensional collision mechanical response dataset of the energy-absorbing structure; Constructing a multi-dimensional collision response prediction model based on a deep learning framework and the multi-dimensional collision mechanical response dataset of the energy-absorbing structure, wherein the multi-dimensional collision response prediction model includes: an image feature extraction network, a curve feature extraction network, and a feature vector prediction network; Defining a pattern stability constraint based on the decoding result of the image feature extraction network, defining a load stability constraint based on the decoding result of the curve feature extraction network, and defining an index matching constraint based on the output result of the multi-dimensional collision response prediction model; A multi-objective crashworthiness optimization model for an energy-absorbing structure is constructed based on the mode stability constraint, the load stability constraint, and the index matching constraint, and a non-dominated sorting genetic algorithm is used to solve the model to obtain a Pareto solution set; The optimal feasible solution of the Pareto solution set is solved based on the entropy-weighted VIKOR multi-attribute decision-making method, and the optimal feasible solution is used as the optimization result of the crashworthiness performance of the target energy-absorbing structure.

2. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: The multi-dimensional collision mechanical response data set of the energy-absorbing structure obtained by performing numerical simulation based on the simulation parameters includes: Conduct impact tests and corresponding numerical simulations on the target energy absorbing structure according to its type and simulation parameters, and calibrate the numerical simulation model; The experimental design is carried out based on the numerical simulation model after experimental benchmarking, and a multi-dimensional collision mechanical response data set of the energy-absorbing structure is generated by integrating experiment and simulation.

3. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: The multi-dimensional collision response prediction model is constructed based on the deep learning framework and the multi-dimensional collision mechanical response dataset of the energy-absorbing structure, including: The multi-dimensional collision mechanical response dataset of the energy-absorbing structure is randomly divided into a training set, a validation set, and a test set in a ratio of 8:1:1; A deep learning model is constructed through a deep learning framework, and the deep learning model is trained using training sets, validation sets, and test sets to obtain a multi-dimensional collision response prediction model.

4. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, wherein: The image feature extraction network is used to construct an autoencoder network with stacked convolutional layers as the main architecture, and extract the deformation mode image feature vector, wherein the deformation mode image feature vector is: ; The curve feature extraction network is used to construct an autoencoder network with stacked long and short-term memory layers as the main structure, and extract the load characteristic curve feature vector, wherein the load characteristic curve feature vector is: ; The feature vector prediction network is used to construct a vector prediction network with a fully connected layer as the main body, and fuses the deformation model image feature vector, the load characteristic curve feature vector and the crashworthiness index feature vector directly extracted from the multi-dimensional collision mechanical response data set of the energy-absorbing structure into an output vector.

5. The method for optimizing the crashworthiness performance of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: Defining a pattern stability constraint based on a decoding result of the image feature extraction network includes: After obtaining the prediction result of the output vector through the multi-dimensional collision response prediction model, the feature vector portion of the deformation model image is input into the image feature extraction network for decoding to obtain the pattern image prediction result; The pattern stability constraint is defined based on the pattern image prediction results. The stability judgment basis is different according to the type of target energy-absorbing structure. When the pattern is stable, the constraint value is returned to 0, and when the pattern is unstable, the constraint value is returned to 1.

6. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: Defining a load stability constraint based on a decoding result of the curve feature extraction network includes: After obtaining the prediction result of the output vector through the multi-dimensional collision response prediction model, the characteristic vector portion of the load characteristic curve is input into the curve feature extraction network for decoding to obtain the load curve prediction result; The load stability constraint is defined based on the load curve prediction results, where the load stability constraint satisfies the following relationship: ； Where S 2 represents the load stability constraint value, θ is the preset constraint value of this indicator, N represents the number of sampling points of the load curve, Fi represents the load value at any sampling time, Represents the average value of the load curve. , the return constraint value is 0, otherwise the return constraint value is 1.

7. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: The defining of the indicator matching constraint based on the output result of the multi-dimensional collision response prediction model includes: The crashworthiness index prediction result is directly output from the crashworthiness index vector part, and the index matching constraint is defined based on the crashworthiness index prediction result. Among them, the matching judgment basis varies depending on the type of target energy absorption structure. When the matching meets the requirements, the constraint value returned is 0, and when the mode is unstable, the constraint value returned is 1.

8. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: The multi-objective crashworthiness optimization model of the energy-absorbing structure satisfies the following relationship: ； Where, Obj i represents the optimization goal, V represents the structural parameters, V L 、V U Represent the upper and lower boundaries of the structural parameters, P and P L and P U Represent the crashworthiness index and its upper and lower boundaries respectively, represents the mode stability constraint, Represents the load fluctuation constraint.

9. The method for optimizing the crashworthiness of a railway vehicle energy-absorbing structure according to claim 1, characterized in that: The entropy-weighted VIKOR multi-attribute decision-making method is used to solve the optimal feasible solution of the Pareto solution set, including: S1. Construct a standardized solution set that satisfies the following relationship: ； Where, x ij represents the original feasible solution, z ij represents the standardized feasible solution; S2. Determine the positive ideal solution and the negative ideal solution in the standardized solution set. The determination method satisfies the following relationship: ； Where Z + represents the set of positive ideal solutions, Z i + Representative i The positive ideal solution of the target, Z - represents the set of negative ideal solutions, Z i - Representative i Negative ideal solution of a target; S3. Calculate the weight of each optimization objective based on the entropy value. The calculation includes: The contribution of the calculated target in the feasible solution satisfies the following relationship: ； Where, w ij represents the contribution to the feasible solution, x ij represents the original feasible solution; Calculate the entropy value of the target, which satisfies the following relationship: ； Where, e j represents the entropy value, w ij represents the contribution in the feasible solution; Calculate the weight of the target, which satisfies the following relationship: ； Where, ω j represents the weight value, e j represents the entropy value; S4. Calculate the group contribution value and individual regret value of each feasible solution. The calculation of the group contribution value satisfies the following relationship: ； Where, S i represents the group contribution value, ω j Represents the weight value, Z j + Representative j The positive ideal solution of the target, Z j - Representative j The negative ideal solution of the target, z ij represents the standardized feasible solution; The calculation of its individual regret value satisfies the following relationship: ； Where, R i represents the individual regret value, ω j Represents the weight value, Z j + Representative j The positive ideal solution of the target, Z j - Representative j The negative ideal solution of the target, z ij represents the standardized feasible solution; S5. Calculate the contribution rate of each feasible solution, which satisfies the following relationship: ； Where, Q i represents the contribution rate, S + 、 S - Represent the group contribution values ​​of positive ideal solution and negative ideal solution respectively, R + 、 R - Represent the individual regret values ​​of positive ideal solutions and negative ideal solutions respectively; S6. Sort the contribution rates of the feasible solutions and obtain the optimal feasible solution in the Pareto solution set based on the sorting results.

10. A crashworthiness optimization system for a rail vehicle energy-absorbing structure, characterized in that: Including processor and memory; Memory for storing computer programs; A processor, configured to implement the steps of any one of the methods described in claims 1-9 when executing a program stored in a memory.

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