Helmet Interior Structure Design Method Based on Fractal Geometry Model

Abstract

The present invention discloses a method for designing the internal structure of a helmet based on a fractal geometry model. The method comprises the following steps: S1: constructing an initial fractal geometry model; S2: defining a multi-objective optimization function; S3: performing preliminary material distribution planning based on the fractal geometry model and the multi-objective optimization function to generate an initial design scheme for the helmet's energy-absorbing structure; S4: obtaining a final design scheme for the helmet's energy-absorbing structure; S5: performing simulation verification on the final design scheme for the helmet's energy-absorbing structure; and S6: adjusting the fractal geometry model and material layout scheme based on the simulation results, including optimizing the fractal dimension, geometric unit size, and porosity parameters, to form an actual design scheme for the helmet's energy-absorbing structure that meets the optimization objectives. The present invention effectively resolves the contradiction between protective performance, lightweightness, and breathability in existing helmet designs, and has significant technical value and application prospects in the field of helmet design.

Description

Technical Field

[0001] The present invention relates to the technical field of special equipment, and in particular to a method for designing the internal structure of a helmet based on a fractal geometry model. Background Art

[0002] With the development of industrial design and safety technology, helmets have become important protective equipment for protecting personal safety and are widely used in transportation, construction, extreme sports and military fields. In these scenarios, helmets need to have good impact resistance to protect the head from injury, while also meeting the requirements of lightweight, breathability and comfort. Traditional helmet designs find it difficult to strike a balance between these performances.

[0003] At present, the internal structure of helmets usually adopts an integrated cushioning material design, such as polystyrene or other foam materials. The material protects the user's head by absorbing and dispersing impact energy, but there are obvious technical limitations. Traditional helmet design mainly relies on the physical properties of the material to improve protection capabilities. Therefore, it is necessary to increase the thickness of the material to improve the energy absorption effect, which directly leads to an increase in the weight of the helmet and affects the wearer's comfort. In addition, the dense distribution of the material limits the breathability of the helmet, which can easily cause discomfort to the user when worn for a long time or in a high temperature environment.

[0004] In recent years, some improved helmet designs have attempted to optimize performance by introducing new materials. However, these improvements have exposed the following challenges in practical applications: First, the high manufacturing costs of these new materials have hindered their widespread adoption. Second, traditional geometric design methods are insufficient for optimizing complex mechanical properties, making it difficult to effectively disperse impact energy while ensuring lightweight and breathable structures. Furthermore, traditional designs struggle to achieve multi-layered energy absorption when facing multi-directional impacts or uneven force distribution.

[0005] In summary, the existing technology has significant defects in the following aspects:

[0006] 1. The balance between protection and weight: Traditional helmets improve protection by increasing material thickness, but this also increases the weight of the helmet, affecting comfort when worn for long periods of time.

[0007] 2. Insufficient breathability: The dense material distribution limits the air circulation performance of the helmet, which can easily cause discomfort when used in high temperature or high humidity environments.

[0008] 3. Insufficient optimization of complex mechanical properties: Existing design methods are difficult to cope with multi-directional impacts and non-uniform force distribution, and the energy absorption effect is limited.

[0009] In response to the above problems, there is an urgent need for a helmet design method that can combine advanced geometric modeling technology and algorithm optimization to achieve multi-objective comprehensive optimization of protective performance, lightweight and breathability, and solve the shortcomings of existing technologies. Summary of the Invention

[0010] One purpose of the present invention is to propose a method for designing the internal structure of a helmet based on a fractal geometry model. The present invention effectively resolves the contradiction between protective performance, lightweight and breathability in existing helmet designs, and has important technical value and application prospects in the field of helmet design.

[0011] A method for designing the internal structure of a helmet based on a fractal geometry model according to an embodiment of the present invention includes the following steps:

[0012] S1. Construct an initial fractal geometry model, select Menger sponge as the basic fractal geometry of the helmet's internal energy absorption structure, set the initial parameters of the fractal geometry model based on the target protection requirements, and generate a three-dimensional geometric model with self-similarity and multi-level characteristics;

[0013] S2. Determine the performance optimization objectives of the helmet's internal structural design based on the initial fractal geometry model, and define a multi-objective optimization function based on energy absorption performance, structural weight, and breathability requirements;

[0014] S3. Preliminary planning of material distribution based on fractal geometry models and multi-objective optimization functions. The fractal geometry units are combined with the properties of the cushioning material to generate a preliminary material layout. The material layout is configured according to the density and porosity of the geometric units to generate an initial design scheme for the helmet's energy-absorbing structure.

[0015] S4. Optimize the initial design of the helmet energy absorption structure using a deep ant colony algorithm to obtain a final design of the helmet energy absorption structure;

[0016] S5. Conduct simulation verification of the final design of the helmet's energy-absorbing structure. Finite element simulation tools are used to simulate the energy-absorbing performance in different impact directions. This verification verifies the multi-level impact energy dispersion effect of the final design of the helmet's energy-absorbing structure, as well as the optimization effect of material distribution on the helmet's weight and breathability.

[0017] S6. Adjust the fractal geometry model and material layout plan based on the simulation results, including optimizing the fractal dimension, geometric unit size, and porosity parameters, to form an actual design plan for the helmet energy absorption structure that meets the optimization goals.

[0018] Optionally, S1 includes the following contents:

[0019] S11. Select Menger sponge as the basic fractal geometry of the helmet's internal energy absorption structure. According to the helmet's protective performance requirements, the basic form of the fractal geometry model is set. The Menger sponge's geometry has a cubic basic unit. A multi-level structure is generated through fractal iteration. The self-similarity of the multi-level structure disperses the impact energy step by step along the multi-level structure.

[0020] S12. Set the initial parameters of the fractal geometry model, including the fractal dimension , geometric unit size And the number of iterations N:

[0021] Fractal dimension Indicates the complexity of the geometric model. The fractal dimension is set according to the energy absorption requirements of the helmet. The higher the fractal dimension, the stronger the energy dispersion ability of the fractal geometric model.

[0022] Geometric unit size The side length of the basic cube unit is set according to the internal space limitation of the helmet and the accuracy of material distribution;

[0023] The number of iterations N determines the number of levels of the fractal geometry and is optimized based on the material distribution requirements and energy absorption capacity. The value of N ranges from 1 to 5.

[0024] S13. Generate a three-dimensional Menger sponge model using recursion:

[0025] ;

[0026] in, represents the fractal geometry model after the nth iteration, is the i-th mapping function, through the fractal geometry model The scaling and displacement realize the replication and arrangement of geometric units. The initial state is the basic cubic unit, with a size of ;

[0027] S14. In each iteration, the position and size of the geometric unit of the fractal geometric model are adjusted according to the scaling and displacement rules:

[0028] ;

[0029] Among them, x, y, z are the coordinates of the initial cube, are the offsets of the i-th scaling unit in three directions, and the offset value range is {-1, 0, 1}, which meets the arrangement rule of Menger sponge;

[0030] S15. After completing N iterations, a three-dimensional Menger sponge fractal geometric model with multi-level characteristics is generated, wherein the geometric units of each level are arranged according to a recursive formula, and the volume to surface area ratio of the three-dimensional Menger sponge fractal geometric model satisfies the following relationship:

[0031] ;

[0032] in, represents the total volume of the three-dimensional Menger sponge fractal geometry model after the Nth iteration, Represents the surface area of the three-dimensional Menger sponge fractal geometry model after the Nth iteration.

[0033] Optionally, S2 includes the following contents:

[0034] S21, based on the initial fractal geometry model , combined with the protective performance requirements of the helmet, determine the performance optimization goals of the helmet's internal structure design, including energy absorption performance, structural weight and breathability;

[0035] S22, improve the energy absorption performance so that the impact energy is absorbed by the fractal geometry model The energy absorption performance target is defined as maximizing the impact energy absorbed. :

[0036] ;

[0037] in, Fractal geometry model Absorbed impact energy, Fractal geometry model Internal local stress distribution function, V is the fractal geometry model The volume, Fractal geometry model The spatial position vector of the midpoint;

[0038] S23, achieve lightweight structure and optimize fractal geometry model The distribution of materials in the matrix reduces the overall weight, and the goal is defined as minimizing the total mass M:

[0039] ;

[0040] Among them, M is the fractal geometry model The total mass, is the density of the cushioning material, Fractal geometry model volume;

[0041] S24, improve the air permeability, make the fractal geometry model The pore structure has good air circulation characteristics, and the air permeability target is defined as maximizing the porosity P:

[0042] ;

[0043] Among them, P is the fractal geometry model The porosity, Fractal geometry model The solid part volume;

[0044] S25. Define the multi-objective optimization function F(x) based on the optimization requirements of comprehensive energy absorption performance, structural weight and breathability:

[0045] ;

[0046] in, is the weight coefficient of the optimization objective.

[0047] Optionally, S3 includes the following content:

[0048] S31, based on fractal geometry model And the multi-objective optimization function F(x), combined with the mechanical properties of the buffer material to establish the material distribution rules of the geometric unit and calculate the material density of the geometric unit :

[0049] ;

[0050] in, Fractal geometry model The material density of geometric unit i, is the local stress of geometric unit i, is the porosity of geometric unit i, and is the weight coefficient, which is used to adjust the influence of stress and porosity on material distribution;

[0051] S32, fractal geometry model The multi-level structure is divided into different density areas, according to the local stress of the geometric units in the area and porosity Distribution optimizes overall material utilization:

[0052] ;

[0053] in, For local areas The average material density, is the volume of geometric unit i, Fractal geometry model The kth region in is determined by the geometric unit stress and position;

[0054] S33. Calculate the overall energy absorption performance of the helmet energy absorption structure based on material distribution rules :

[0055] ;

[0056] in, is the total energy absorption performance of the helmet energy absorption structure, K is the fractal geometry model The total number of partitions, is the material energy absorption coefficient function of unit i, which is determined according to the characteristics of the cushioning material;

[0057] And generate the initial design of the helmet energy absorption structure:

[0058] ;

[0059] Where i represents the fractal geometry model The i-th geometric unit in m represents the fractal geometry model The total number of geometric cells in .

[0060] Optionally, S4 includes the following contents:

[0061] S41. Initial design of helmet energy absorption structure Input deep ant colony algorithm, fractal geometry model Geometric unit parameters and energy absorption performance To optimize the goal, construct the search space of the optimization problem:

[0062] ;

[0063] in, is the search space of design parameters, are the minimum and maximum densities of the cushioning material, are the minimum and maximum porosity of the geometric unit;

[0064] S42, fractal geometry model Represented as a graph structure:

[0065] ;

[0066] in, is a set of geometric unit nodes, is the set of Edges connecting nodes. is a node feature set, each node feature Represent stress, porosity, and material density of geometric elements;

[0067] S43. Use graph neural network to embed node features, extract multi-level correlation information in fractal geometry, and define node embedding vectors. :

[0068] ;

[0069] Among them, N(i) is the node The set of adjacent nodes of For the edge The characteristic mapping function is based on the fractal geometry model The topological relationship of adjacent nodes is weighted. Represents the node characteristics after embedding, fusing the stress, porosity and density information of each adjacent node;

[0070] S44. Model the solution construction process of the ant colony algorithm as a Markov decision process (S, A, P, R):

[0071] State S contains fractal parameters and material distribution , action A represents the adjustment of fractal parameters and material distribution, and the transition probability P is distributed by the strategy Determine that the reward R is determined by the objective function F(x), and define the action selection probability as:

[0072] ;

[0073] in, is the strategy parameter, is the set of optional actions in state S, Embedding vector for the combined node Feature mapping function with the target function F(x);

[0074] S45. Optimize strategy parameters using the policy gradient method Improve optimization efficiency:

[0075] ;

[0076] in, is the objective function of the expected return of the strategy, Performance indicator value of the path;

[0077] S46. Update the pheromone matrix to make the search process tend to the path with good performance. The pheromone update rules are as follows:

[0078] ;

[0079] in, For the edge The original pheromone concentration, is the pheromone volatility coefficient, is the objective function value of the current optimal path, is the feature similarity function based on the node embedding vector;

[0080] S47. After multiple iterations of path selection and pheromone updating, the optimal path that maximizes the objective function F(x) is selected from the path set to determine the final design of the helmet energy absorption structure:

[0081] ;

[0082] in, is the adjustment coefficient, which is used to balance the impact of the objective function value and pheromone distribution on path selection.

[0083] Optionally, S5 includes the following contents:

[0084] S51. Final design of helmet energy-absorbing structure Construct a finite element analysis model to transform the fractal geometry of the helmet's energy-absorbing structure into , material density distribution Input the material characteristic parameters into the simulation tool to establish a simulation model;

[0085] S52. Setting boundary conditions for impact simulation, simulating the energy dispersion and absorption performance of the helmet under multi-directional impact forces, and defining the moment and force in the impact direction;

[0086] S53, perform finite element meshing to create a fractal geometry model of the helmet's energy-absorbing structure Perform multi-level mesh division to refine the mesh units in the impact force area. The mesh division rules are as follows:

[0087] The impact area uses a high-density mesh with a cell size of ;

[0088] A low-density grid is used in other areas, with a cell size of ;

[0089] The meshing meets the convergence conditions of the overall calculation:

[0090] ;

[0091] in, is the energy absorption value calculated for the nth simulation, is the convergence threshold;

[0092] S54. Use finite element simulation tools to dynamically simulate and analyze the multi-level impact energy dispersion performance of the helmet energy absorption structure, and calculate the energy absorption efficiency of the structure in various impact directions. ;

[0093] S55. Evaluate the optimization effect of material distribution on helmet weight and calculate the total mass ;

[0094] S56. Verify the breathability of the helmet's energy-absorbing structure and evaluate the air circulation effect by calculating the porosity P.

[0095] S57. Based on the simulation results, a comprehensive evaluation report of the multi-level impact energy dispersion performance, total mass optimization effect, and breathability performance is output. If the simulation results do not meet the optimization requirements of the objective function F(x), the optimization process returns to step S4 to readjust the fractal parameters or material distribution.

[0096] The beneficial effects of the present invention are:

[0097] (1) The present invention applies fractal geometry models to the design of helmet energy absorption structures, and uses the self-similarity and multi-level characteristics of fractal geometry to construct a three-dimensional energy absorption structure in the form of Menger sponge. The energy absorption structure of traditional helmets usually adopts uniformly distributed material design, while the present invention uses fractal geometry to achieve the gradual dispersion and dissipation of impact energy in a multi-level network, which greatly improves the energy absorption performance. Simulation results show that compared with the traditional helmet design with uniformly distributed materials, the present invention improves the multi-directional impact energy dispersion efficiency by more than 22%, and significantly reduces the risk of injury to the head caused by single-point impact.

[0098] (2) The present invention introduces graph neural networks and reinforcement learning policy gradient methods into the ant colony algorithm, models the design problem of the helmet energy absorption structure as a Markov decision process, and extracts the correlation information of multi-level nodes in the fractal geometry model through the graph neural network to achieve efficient optimization of material distribution and structural parameters. Compared with traditional topology optimization methods, the present invention significantly improves the design efficiency and optimization effect. Experimental results show that the total mass of the optimized helmet is reduced by 18%, and a lightweight design is achieved while ensuring energy absorption performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0100] Figure 1 This is a flow chart of a method for designing the internal structure of a helmet based on a fractal geometry model proposed by the present invention;

[0101] Figure 2This is a schematic diagram of the optimization process of the helmet energy absorption structure using a deep ant colony algorithm in a helmet internal structure design method based on a fractal geometry model proposed in the present invention. DETAILED DESCRIPTION

[0102] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0103] refer to Figure 1-Figure 2 A method for designing the internal structure of a helmet based on a fractal geometry model comprises the following steps:

[0104] S1. Construct an initial fractal geometry model, select Menger sponge as the basic fractal geometry of the helmet's internal energy absorption structure, set the initial parameters of the fractal geometry model based on the target protection requirements, and generate a three-dimensional geometric model with self-similarity and multi-level characteristics;

[0105] S2. Determine the performance optimization objectives of the helmet's internal structural design based on the initial fractal geometry model, and define a multi-objective optimization function based on energy absorption performance, structural weight, and breathability requirements;

[0106] S3. Preliminary planning of material distribution based on fractal geometry models and multi-objective optimization functions. The fractal geometry units are combined with the properties of the cushioning material to generate a preliminary material layout. The material layout is configured according to the density and porosity of the geometric units to generate an initial design scheme for the helmet's energy-absorbing structure.

[0107] S4. Optimize the initial design of the helmet energy absorption structure using a deep ant colony algorithm to obtain a final design of the helmet energy absorption structure;

[0108] S5. Conduct simulation verification of the final design of the helmet's energy-absorbing structure. Finite element simulation tools are used to simulate the energy-absorbing performance in different impact directions. This verification verifies the multi-level impact energy dispersion effect of the final design of the helmet's energy-absorbing structure, as well as the optimization effect of material distribution on the helmet's weight and breathability.

[0109] S6. Adjust the fractal geometry model and material layout plan based on the simulation results, including optimizing the fractal dimension, geometric unit size, and porosity parameters, to form an actual design plan for the helmet energy absorption structure that meets the optimization goals.

[0110] In this embodiment, S1 includes the following contents:

[0111] S11. Select Menger sponge as the basic fractal geometry of the helmet's internal energy absorption structure. According to the helmet's protective performance requirements, the basic form of the fractal geometry model is set. The Menger sponge's geometry has a cubic basic unit. A multi-level structure is generated through fractal iteration. The self-similarity of the multi-level structure disperses the impact energy step by step along the multi-level structure.

[0112] S12. Set the initial parameters of the fractal geometry model, including the fractal dimension , geometric unit size And the number of iterations N:

[0113] Fractal dimension Indicates the complexity of the geometric model. The fractal dimension is set according to the energy absorption requirements of the helmet. The higher the fractal dimension, the stronger the energy dispersion ability of the fractal geometric model.

[0114] Geometric unit size The side length of the basic cube unit is set according to the internal space limitation of the helmet and the accuracy of material distribution;

[0115] The number of iterations N determines the number of levels of the fractal geometry and is optimized based on the material distribution requirements and energy absorption capacity. The value of N ranges from 1 to 5.

[0116] S13. Generate a three-dimensional Menger sponge model using recursion:

[0117] ;

[0118] in, represents the fractal geometry model after the nth iteration, is the i-th mapping function, through the fractal geometry model The scaling and displacement realize the replication and arrangement of geometric units. The initial state is the basic cubic unit, with a size of ;

[0119] S14. In each iteration, the position and size of the geometric unit of the fractal geometric model are adjusted according to the scaling and displacement rules:

[0120] ;

[0121] Among them, x, y, z are the coordinates of the initial cube, are the offsets of the i-th scaling unit in three directions, and the offset value range is {-1, 0, 1}, which meets the arrangement rule of Menger sponge;

[0122] S15. After completing N iterations, a three-dimensional Menger sponge fractal geometric model with multi-level characteristics is generated, wherein the geometric units of each level are arranged according to a recursive formula, and the volume to surface area ratio of the three-dimensional Menger sponge fractal geometric model satisfies the following relationship:

[0123] ;

[0124] in, represents the total volume of the three-dimensional Menger sponge fractal geometry model after the Nth iteration, Represents the surface area of the three-dimensional Menger sponge fractal geometry model after the Nth iteration.

[0125] In this embodiment, S2 includes the following contents:

[0126] S21, based on the initial fractal geometry model , combined with the protective performance requirements of the helmet, determine the performance optimization goals of the helmet's internal structure design, including energy absorption performance, structural weight and breathability;

[0127] S22, improve the energy absorption performance so that the impact energy is absorbed by the fractal geometry model The energy absorption performance target is defined as maximizing the impact energy absorbed. :

[0128] ;

[0129] in, Fractal geometry model Absorbed impact energy, Fractal geometry model Internal local stress distribution function, V is the fractal geometry model The volume, Fractal geometry model The spatial position vector of the midpoint;

[0130] S23, achieve lightweight structure and optimize fractal geometry model The distribution of materials in the matrix reduces the overall weight, and the goal is defined as minimizing the total mass M:

[0131] ;

[0132] Among them, M is the fractal geometry model The total mass, is the density of the cushioning material, Fractal geometry model volume;

[0133] S24, improve the air permeability, make the fractal geometry model The pore structure has good air circulation characteristics, and the air permeability target is defined as maximizing the porosity P:

[0134] ;

[0135] Among them, P is the fractal geometry model The porosity, Fractal geometry model The solid part volume;

[0136] S25. Define the multi-objective optimization function F(x) based on the optimization requirements of comprehensive energy absorption performance, structural weight and breathability:

[0137] ;

[0138] in, is the weight coefficient of the optimization objective.

[0139] In this embodiment, S3 includes the following contents:

[0140] S31, based on fractal geometry model And the multi-objective optimization function F(x), combined with the mechanical properties of the buffer material to establish the material distribution rules of the geometric unit and calculate the material density of the geometric unit :

[0141] ;

[0142] in, Fractal geometry model The material density of geometric unit i, is the local stress of geometric unit i, is the porosity of geometric unit i, and is the weight coefficient, which is used to adjust the influence of stress and porosity on material distribution;

[0143] S32, fractal geometry model The multi-level structure is divided into different density areas, according to the local stress of the geometric units in the area and porosity Distribution optimizes overall material utilization:

[0144] ;

[0145] in, For local areas The average material density, is the volume of geometric unit i, Fractal geometry model The kth region in is determined by the geometric unit stress and position;

[0146] S33. Calculate the overall energy absorption performance of the helmet energy absorption structure based on material distribution rules :

[0147] ;

[0148] in, is the total energy absorption performance of the helmet energy absorption structure, K is the fractal geometry model The total number of partitions, is the material energy absorption coefficient function of unit i, which is determined according to the characteristics of the cushioning material;

[0149] And generate the initial design of the helmet energy absorption structure:

[0150] ;

[0151] Where i represents the fractal geometry model The i-th geometric unit in m represents the fractal geometry model The total number of geometric cells in .

[0152] In this embodiment, S4 includes the following contents:

[0153] S41. Initial design of helmet energy absorption structure Input deep ant colony algorithm, fractal geometry model Geometric unit parameters and energy absorption performance To optimize the goal, construct the search space of the optimization problem:

[0154] ;

[0155] in, is the search space of design parameters, are the minimum and maximum densities of the cushioning material, are the minimum and maximum porosity of the geometric unit;

[0156] S42, fractal geometry model Represented as a graph structure:

[0157] ;

[0158] in, is a set of geometric unit nodes, is the set of Edges connecting nodes. is a node feature set, each node feature Represent stress, porosity, and material density of geometric elements;

[0159] S43. Use graph neural network to embed node features, extract multi-level correlation information in fractal geometry, and define node embedding vectors. :

[0160] ;

[0161] Among them, N(i) is the node The set of adjacent nodes of For the edge The characteristic mapping function is based on the fractal geometry model The topological relationship of adjacent nodes is weighted. Represents the node characteristics after embedding, fusing the stress, porosity and density information of each adjacent node;

[0162] S44. Model the solution construction process of the ant colony algorithm as a Markov decision process (S, A, P, R):

[0163] State S contains fractal parameters and material distribution , action A represents the adjustment of fractal parameters and material distribution, and the transition probability P is distributed by the strategy Determine that the reward R is determined by the objective function F(x), and define the action selection probability as:

[0164] ;

[0165] in, is the strategy parameter, is the set of optional actions in state S, Embedding vector for the combined node Feature mapping function with the target function F(x);

[0166] S45. Optimize strategy parameters using the policy gradient method Improve optimization efficiency:

[0167] ;

[0168] in, is the objective function of the expected return of the strategy, Performance indicator value of the path;

[0169] S46. Update the pheromone matrix to make the search process tend to the path with good performance. The pheromone update rules are as follows:

[0170] ;

[0171] in, For the edge The original pheromone concentration, is the pheromone volatility coefficient, is the objective function value of the current optimal path, is the feature similarity function based on the node embedding vector;

[0172] S47. After multiple iterations of path selection and pheromone updating, the optimal path that maximizes the objective function F(x) is selected from the path set to determine the final design of the helmet energy absorption structure:

[0173] ;

[0174] in, is the adjustment coefficient, which is used to balance the impact of the objective function value and pheromone distribution on path selection.

[0175] In this embodiment, S5 includes the following contents:

[0176] S51. Final design of helmet energy-absorbing structure Construct a finite element analysis model to transform the fractal geometry of the helmet's energy-absorbing structure into , material density distribution Input the material characteristic parameters into the simulation tool to establish a simulation model;

[0177] S52. Setting boundary conditions for impact simulation, simulating the energy dispersion and absorption performance of the helmet under multi-directional impact forces, and defining the moment and force in the impact direction;

[0178] S53, perform finite element meshing to create a fractal geometry model of the helmet's energy-absorbing structure Perform multi-level mesh division to refine the mesh units in the impact force area. The mesh division rules are as follows:

[0179] The impact area uses a high-density mesh with a cell size of ;

[0180] A low-density grid is used in other areas, with a cell size of ;

[0181] The meshing meets the convergence conditions of the overall calculation:

[0182] ;

[0183] in, is the energy absorption value calculated for the nth simulation, is the convergence threshold;

[0184] S54. Use finite element simulation tools to dynamically simulate and analyze the multi-level impact energy dispersion performance of the helmet energy absorption structure, and calculate the energy absorption efficiency of the structure in various impact directions. ;

[0185] S55. Evaluate the optimization effect of material distribution on helmet weight and calculate the total mass ;

[0186] S56. Verify the breathability of the helmet's energy-absorbing structure and evaluate the air circulation effect by calculating the porosity P.

[0187] S57. Based on the simulation results, a comprehensive evaluation report of the multi-level impact energy dispersion performance, total mass optimization effect, and breathability performance is output. If the simulation results do not meet the optimization requirements of the objective function F(x), the optimization process returns to step S4 to readjust the fractal parameters or material distribution.

[0188] Example 1:

[0189] Embodiment In an extreme sports event in August 2023, a helmet manufacturing company conducted a practical application test on the helmet designed based on the fractal geometry model and deep ant colony algorithm of the present invention. The goal of the event was to verify the protective performance, lightweight and breathability of the helmet under extreme conditions. The event was located in a desert terrain with an ambient temperature of up to 40°C. The athletes needed to wear helmets continuously for long-term mountain biking and rock climbing challenges.

[0190] On the day before the competition, the technical team registered the equipment of all contestants, including helmets of traditional design and helmets designed according to the present invention. 50 contestants were randomly selected for the test, 25 of whom wore traditional helmets and 25 wore helmets designed according to the present invention. The groups were numbered as Group A (traditional design) and Group B (designed according to the present invention).

[0191] During the test, the temperature, humidity, and impact force sensor data inside each athlete's helmet are recorded in real time. The equipment numbers are sensors ID001 to ID050. The data is uploaded to the cloud database at a frequency of 10 times per second for real-time analysis.

[0192] Event 1: Air permeability test under high temperature conditions

[0193] In the third hour of the cycling challenge, athlete ID013 (Group B) reported that the temperature inside the helmet was significantly lower than usual. Through sensor data analysis, it was found that under the conditions of an ambient temperature of 40°C and a humidity of 50%, the internal temperature of the helmet designed by the present invention worn by athlete ID013 was 37.2°C and the humidity was 65%, which was a significant improvement compared to the internal temperature (41.8°C) and humidity (78%) of the traditional helmet worn by athlete ID005 in Group A.

[0194] Further analysis of the data revealed that the porosity distribution of athlete ID013's helmet showed that the porosity of the top area of the helmet was 42%, and the porosity of the forehead area was 36%, which effectively promoted air circulation inside the helmet and reduced heat accumulation under high temperature conditions. After the event, athlete ID013 reported that the helmet maintained good comfort throughout the entire ride, and there was no stuffiness or excessive sweating on the head.

[0195] Event 2: Impact performance verification

[0196] During the rock climbing challenge, contestant ID026 (Group B) fell from a height of 2.5 meters due to a foot error, and the top of the helmet collided with hard rock. Sensor data showed that the instantaneous impact force on the top of the helmet reached 1568 N, while the internal sensor of the helmet recorded a dispersed impact force of 362 N, which is far below the safety threshold of the human head (800 N). In comparison, contestant ID018 of Group A fell under similar conditions with an impact force of 1896 N. The internal sensor of the helmet recorded a dispersed impact force of 735 N. Although it did not exceed the safety threshold, it caused obvious shock to the head.

[0197] Subsequent inspection found that the helmet worn by athlete ID026 still maintained the integrity of its overall structure after the collision. The multi-level energy-absorbing structure of the fractal geometry model effectively dispersed the impact energy to the various levels of the helmet network. However, traditionally designed helmets experienced local material compression and deformation under high impact forces, resulting in reduced energy absorption efficiency.

[0198] Event 3: Long-term wearing comfort test

[0199] The final stage of the competition was a 6-hour high-temperature off-road challenge, during which the contestants needed to wear helmets continuously to complete the race. Real-time data monitoring showed that the internal temperature of the helmets of Group B contestants remained at an average of 38°C and the humidity was 70% throughout the entire process. Compared with the internal temperature (43°C) and humidity (82%) of the helmets of Group A contestants, the helmet designed by the present invention had significant advantages.

[0200] In an interview after the competition, athlete ID034 (Group B) said that the lightweight design of the helmet meant that she did not feel noticeable fatigue when wearing it for long periods of time, and she did not experience any discomfort in her head or neck after the competition. Analysis showed that the total weight of the helmet designed with this invention was 425 g, while the average weight of the traditional design helmet in Group A was 560 g. The lightweight design significantly reduced the burden on the athletes.

[0201] In the test, 50 athletes were randomly selected, 25 of whom wore traditional helmets and 25 wore helmets designed according to the present invention. The comparison data of the groups A (traditional design) and B (design according to the present invention) are shown in Table 1 below:

[0202] Table 1 Comparison of test indicators

[0203] Test indicators Traditional helmet (Group A) Helmets of the present invention (Group B) Average internal temperature (℃) 42.5 37.8 Average internal humidity (%) 78 68 Collision impact force dispersion (N) 735 362 Total weight (g) 560 425 Long-term wearing comfort rating (10 points) 7.2 9.1

[0204] This example demonstrates the feasibility and effectiveness of the design method of the present invention through its application in real extreme sports events. Test results show that the helmet designed in this invention significantly outperforms traditional designs in terms of energy absorption, lightweighting, and breathability. It demonstrates exceptional performance under extreme conditions such as high temperatures, prolonged wear, and sudden impacts, providing enhanced safety and comfort for extreme sports enthusiasts.

[0205] The present invention applies fractal geometry models to the design of helmet energy-absorbing structures, and utilizes the self-similarity and multi-level characteristics of fractal geometry to construct a three-dimensional energy-absorbing structure in the form of a Menger sponge. The energy-absorbing structures of traditional helmets are usually designed with uniformly distributed materials, while the present invention uses fractal geometry to achieve the gradual dispersion and dissipation of impact energy in a multi-level network, greatly improving the energy absorption performance. Simulation results show that compared with the traditional helmet design with uniformly distributed materials, the present invention improves the multi-directional impact energy dispersion efficiency by more than 22%, while significantly reducing the risk of injury to the head from single-point impact.

[0206] This paper introduces graph neural networks and reinforcement learning policy gradient methods into the ant colony algorithm, models the design problem of the helmet energy absorption structure as a Markov decision process, and extracts the correlation information of multi-level nodes in the fractal geometry model through the graph neural network to achieve efficient optimization of material distribution and structural parameters. Compared with traditional topology optimization methods, this paper significantly improves the design efficiency and optimization effect. Experimental results show that the total mass of the optimized helmet is reduced by 18%, achieving a lightweight design while ensuring energy absorption performance.

[0207] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for designing the internal structure of a helmet based on a fractal geometry model, characterized in that: The steps include: S1. Construct an initial fractal geometry model, select Menger sponge as the basic fractal geometry of the helmet's internal energy absorption structure, set the initial parameters of the fractal geometry model based on the target protection requirements, and generate a three-dimensional geometric model with self-similarity and multi-level characteristics; S2. Determine the performance optimization objectives of the helmet's internal structural design based on the initial fractal geometry model, and define a multi-objective optimization function based on energy absorption performance, structural weight, and breathability requirements; S3. Preliminary planning of material distribution based on fractal geometry models and multi-objective optimization functions. The fractal geometry units are combined with the properties of the cushioning material to generate a preliminary material layout. The material layout is configured according to the density and porosity of the geometric units to generate an initial design scheme for the helmet's energy-absorbing structure. S4. Optimize the initial design of the helmet energy absorption structure using a deep ant colony algorithm to obtain a final design of the helmet energy absorption structure; S5. Conduct simulation verification of the final design of the helmet's energy-absorbing structure. Finite element simulation tools are used to simulate the energy-absorbing performance in different impact directions. This verification verifies the multi-level impact energy dispersion effect of the final design of the helmet's energy-absorbing structure, as well as the optimization effect of material distribution on the helmet's weight and breathability. S6. Adjust the fractal geometry model and material layout plan based on the simulation results, including optimizing the fractal dimension, geometric unit size, and porosity parameters, to form an actual design plan for the helmet energy absorption structure that meets the optimization goals; The S1 includes the following contents: S11. Select Menger sponge as the basic fractal geometry of the helmet's internal energy absorption structure. According to the helmet's protective performance requirements, the basic form of the fractal geometry model is set. The Menger sponge geometry has basic cubic units. A multi-level structure is generated through fractal iteration. The self-similarity of the multi-level structure disperses the impact energy step by step along the multi-level structure. S12. Set the initial parameters of the fractal geometry model, including the fractal dimension D f , geometric element size L0 and number of iterations N: Fractal dimension D f Indicates the complexity of the geometric model. The fractal dimension is set according to the energy absorption requirements of the helmet. The higher the fractal dimension, the stronger the energy dispersion ability of the fractal geometric model. The geometric unit size L0 is the side length of the basic cube unit, which is set according to the internal space limitation of the helmet and the material distribution accuracy; The number of iterations N determines the number of levels of the fractal geometry and is optimized based on the material distribution requirements and energy absorption capacity. The value of N ranges from 1 to 5. S13. Generate a three-dimensional Menger sponge model using recursion: Among them, S n Represents the fractal geometry model after the nth iteration, f i (S n ) is the i-th mapping function, through the fractal geometry model S n The scaling and displacement of the geometric units are used to realize the replication and arrangement of the geometric units. The initial state S0 is the basic cube unit with a size of S14. In each iteration, the position and size of the geometric unit of the fractal geometric model are adjusted according to the scaling and displacement rules: Among them, x, y, z are the coordinates of the initial cube, a i ,b i ,c i are the offsets of the i-th scaling unit in three directions, and the offset value range is {-1, 0, 1}, which meets the arrangement rule of Menger sponge; S15. After completing N iterations, a three-dimensional Menger sponge fractal geometric model with multi-level characteristics is generated, wherein the geometric units of each level are arranged according to a recursive formula, and the volume to surface area ratio of the three-dimensional Menger sponge fractal geometric model satisfies the following relationship: Among them, V N A represents the total volume of the three-dimensional Menger sponge fractal geometry model after the Nth iteration. N Represents the surface area of the three-dimensional Menger sponge fractal geometry model after the Nth iteration.

2. The method for designing the internal structure of a helmet based on a fractal geometry model according to claim 1, characterized in that: The S2 includes the following contents: S21, based on the initial fractal geometry model S n , combined with the protective performance requirements of the helmet, determine the performance optimization goals of the helmet's internal structure design, including energy absorption performance, structural weight and breathability; S22, improve the energy absorption performance so that the impact energy is absorbed by the fractal geometry model S n The energy absorption performance target is defined as maximizing the absorbed impact energy E. abs : Among them, E abs is the fractal geometry model S n Absorbed impact energy, σ(r) is the fractal geometry model S n The local stress distribution function inside, V is the fractal geometry model S n The volume of r is the fractal geometry model S n The spatial position vector of the midpoint; S23, achieve lightweight structure and optimize fractal geometry model S n The distribution of materials in the matrix reduces the overall weight, and the goal is defined as minimizing the total mass M: Among them, M is the fractal geometry model S n The total mass of the buffer material, ρ is the density of the buffer material, is the fractal geometry model S n volume; S24, improve the air permeability, make the fractal geometry model S n The pore structure has good air circulation characteristics, and the air permeability target is defined as maximizing the porosity P: Among them, P is the fractal geometry model S n Porosity, V solid is the fractal geometry model S n The solid part volume; S25. Define the multi-objective optimization function F(x) based on the optimization requirements of comprehensive energy absorption performance, structural weight and breathability: F(x)=w1·E abs -w2·M+w3·P; Among them, w1, w2, and w3 are the weight coefficients of the optimization target.

3. The method for designing the internal structure of a helmet based on a fractal geometry model according to claim 1, wherein: The S3 includes the following: S31, based on fractal geometry model S n And the multi-objective optimization function F(x), combined with the mechanical properties of the buffer material, establish the material distribution rules of the geometric unit and calculate the material density ρ of the geometric unit i : r i =a·s i +β·P i ; Among them, ρ i is the fractal geometry model S n Material density of geometric unit i, σ i is the local stress of geometric unit i, P i is the porosity of geometric unit i, α and β are weight coefficients used to adjust the effects of stress and porosity on material distribution; S32, the fractal geometry model S n The multi-level structure is divided into different density regions, and the local stress σ of the geometric unit in the region is i and porosity P i Distribution optimizes overall material utilization: Among them, ρ local is the local area R k The average material density, V i is the volume of geometric unit i, R k is the fractal geometry model S n The kth region in is determined by the geometric unit stress and position; S33. Calculate the overall energy absorption performance E of the helmet energy absorption structure based on material distribution rules mat : Among them, E mat is the total energy absorption performance of the helmet energy absorption structure, K is the fractal geometry model S n The total number of partitions, f abs (σ i ) is the material energy absorption coefficient function of unit i, which is determined according to the characteristics of the cushioning material; And generate the initial design of the helmet energy absorption structure: D init ={(S n ,ρ i ,P i ,AND mat )∣i∈{1,2,…,m}}; Where i represents the fractal geometry model S n The i-th geometric unit in m represents the fractal geometry model S n The total number of geometric cells in .

4. The method for designing the internal structure of a helmet based on a fractal geometry model according to claim 1, wherein: The S4 includes the following: S41, the initial design scheme of the helmet energy absorption structure D init Input deep ant colony algorithm, fractal geometry model S n The geometric unit parameters (ρ i ,P i ) and energy absorption performance E mat To optimize the goal, construct the search space of the optimization problem: in, is the search space of design parameters, ρ min ,ρ max is the minimum and maximum density of the cushioning material, P min ,P max are the minimum and maximum porosity of the geometric unit; S42, the fractal geometry model S n Represented as a graph structure: G(S n )=(V,E,H); Where V={v1,…,v m } is the set of geometric unit nodes, E={e ij } is the set of edges between nodes, H={h i } is a node feature set, each node feature h i =[σ i ,P i ,ρ i ] represents the stress, porosity and material density of the geometric unit; S43. Use graph neural network to embed node features, extract multi-level correlation information in fractal geometry, and define node embedding vector z i : Where N(i) is the node v i The set of adjacent nodes, φ(e ij ) is edge e ij The characteristic mapping function is based on the fractal geometry model S n The topological relationship of the adjacent node features is weighted, z i Represents the node characteristics after embedding, fusing the stress, porosity and density information of each adjacent node; S44. Model the solution construction process of the ant colony algorithm as a Markov decision process (S, A, P, R): The state S contains the fractal parameter (D f ,L0,N) and material distribution {ρ i }, action A represents the adjustment of fractal parameters and material distribution, the transition probability P is determined by the policy distribution P(A|S;θ), the reward R is determined by the objective function F(x), and the action selection probability is defined as: Among them, θ is the policy parameter, is the set of optional actions in state S, ψ(·) is the embedding vector z of the combined node i Feature mapping function with the target function F(x); S45. Use the policy gradient method to optimize the policy parameter θ to improve the optimization efficiency: Among them, J(θ) is the objective function of the expected return of the strategy, F(x S,A ) is the performance index value of the path; S46. Update the pheromone matrix to make the search process tend to the path with good performance. The pheromone update rules are as follows: in, is the original pheromone concentration of edge i→j, ρ1 is the pheromone volatility coefficient, is the objective function value of the current optimal path, ω(z i ,z j ) is a feature similarity function based on node embedding vectors; S47. After multiple iterations of path selection and pheromone updating, the optimal path that maximizes the objective function F(x) is selected from the path set to determine the final design of the helmet energy absorption structure: Among them, λ is the adjustment coefficient, which is used to balance the impact of the objective function value and pheromone distribution on path selection.

5. The method for designing the internal structure of a helmet based on a fractal geometry model according to claim 1, wherein: The S5 includes the following contents: S51, Final design of helmet energy absorption structure x * Construct a finite element analysis model and transform the fractal geometry of the helmet energy absorption structure into n,new , material density distribution ρ n,new Input the material characteristic parameters into the simulation tool to establish a simulation model; S52. Setting boundary conditions for impact simulation, simulating the energy dispersion and absorption performance of the helmet under multi-directional impact forces, and defining the moment and force in the impact direction; S53, perform finite element meshing to create a fractal geometry model of the helmet energy-absorbing structure n,new Perform multi-level mesh division to refine the mesh units in the impact force area. The mesh division rules are as follows: The impact area uses a high-density grid with a cell size of L min ; Other areas use a low-density grid with a cell size of L max ; The meshing meets the convergence conditions of the overall calculation: in, is the energy absorption value calculated for the nth simulation, ∈1 is the convergence threshold; S54. Use finite element simulation tools to perform dynamic simulation analysis on the multi-level impact energy dispersion performance of the helmet energy absorption structure, and calculate the energy absorption efficiency η of the structure in various impact directions. k ; S55. Evaluate the optimization effect of material distribution on the weight of the helmet and calculate the total mass M total ; S56. Verify the breathability of the helmet's energy-absorbing structure and evaluate the air circulation effect by calculating the porosity P. S57. Based on the simulation results, a comprehensive evaluation report of the multi-level impact energy dispersion performance, total mass optimization effect, and breathability performance is output. If the simulation results do not meet the optimization requirements of the objective function F(x), the optimization process returns to step S4 to readjust the fractal parameters or material distribution.

Images