Automobile crash dummy abdomen safety test device and method

Abstract

The invention relates to the technical field of biomechanical detection and automobile safety evaluation, and discloses an automobile collision dummy abdomen safety test device and method. The testing device comprises a human body feature modeling module which is used for establishing a target population abdomen parameterized model based on statistical analysis, determining a key size design domain covering target population percentile and constructing a target population metrology database; the abdominal mechanical property simulation module is used for obtaining a human tissue relative density distribution function based on the hyperelastic constitutive model, taking the density distribution function as an input parameter for manufacturing a gradient honeycomb structure and simulating human abdominal mechanical properties through relative density gradient distribution in the thickness direction; the multi-mode sensor integration module adopts a distributed intelligent sensing network architecture and is composed of a sensing layer, a transmission layer and a processing layer. By integrating pressure, deformation, acceleration and tissue stress sensors and combining a real-time feedback algorithm, collision parameters are dynamically optimized, and the test precision and the biological fidelity are improved.

Description

Technical Field

[0001] This invention relates to a device and method for testing the abdominal safety of a car crash dummy, belonging to the field of biomechanical testing and car safety assessment technology. Background Technology

[0002] Car crash dummies are high-end testing devices for evaluating vehicle safety and are an important technical measure of vehicle safety performance. Using crash dummies instead of real people in crash tests can simulate human injuries under different conditions. Abdominal injuries are one of the main causes of serious injury or even death to occupants in traffic accidents. Currently, the crash test dummies widely used in car crash tests mainly simulate human responses through pre-set mechanical structures. However, their abdominal biomechanical feedback mechanisms are relatively simple, relying only on a limited number of sensors (such as pressure sensors) to collect data, making it difficult to comprehensively reflect the complex biomechanical responses of a real human body in a crash. Traditional methods have the following problems: 1) Insufficient data dimensions: Existing systems mostly use single-modal sensors (such as accelerometers or strain gauges), which cannot simultaneously acquire multi-dimensional data such as abdominal pressure distribution, deformation dynamics, and muscle tissue stress.

[0003] 2) Poor real-time feedback: Traditional testing systems rely on post-processing analysis and cannot adjust collision parameters in real time to simulate the dynamic damage evolution process.

[0004] 3) Low biofidelity: The abdominal structure of the dummy lacks dynamic response simulation of real human organs and soft tissues, resulting in a weak correlation between the test results and actual damage.

[0005] Existing patents, such as CN110243392B, propose a center-of-mass adjustment device based on inertial sensors, but do not involve multimodal biomechanical feedback; CN109406054A discloses a center-of-mass detection device with a cylindrical structure, but its method is not applicable to the biomechanical analysis of complex soft tissues. Therefore, there is an urgent need for a high-precision abdominal safety testing system that can integrate multimodal biomechanical data in real time and dynamically control collision parameters through feedback.

[0006] To overcome the problems of limited data dimensions, delayed feedback, and insufficient biorealism in existing abdominal collision testing systems, this invention provides a vehicle collision dummy abdominal safety testing device and method. By integrating pressure, deformation, acceleration, and tissue stress sensors, and combining them with a real-time feedback algorithm, collision parameters are dynamically optimized to improve testing accuracy and biorealism. Summary of the Invention

[0007] To address the aforementioned technical problems, the present invention provides a vehicle collision dummy abdominal safety testing device, characterized by comprising the following modules: The human body feature modeling module establishes a parameterized abdominal model of the target population based on statistical analysis, determines the key size design domain covering the percentile of the target population, and constructs a measurement database of the target population. An abdominal mechanical performance simulation module is provided. The abdominal mechanical performance simulation module obtains the relative density distribution function of human tissue based on a hyperelastic constitutive model. The density distribution function is used as the input parameter for manufacturing a gradient honeycomb structure and the mechanical properties of the human abdomen are simulated through the relative density gradient distribution in the thickness direction. A multimodal sensor integration module is provided, which adopts a distributed intelligent sensor network architecture and consists of a sensing layer, a transmission layer, and a processing layer. The sensing layer includes a pressure sensor array, an acceleration sensor array, and a displacement sensor array. The transmission layer includes a synchronous acquisition system. The processing layer includes a biomechanical parameter calculation module and a dynamic time warping module, wherein the dynamic time warping module is used to calculate the similarity between the experimental response and the biomechanical response corridor.

[0008] Furthermore, the distribution model of the key dimensions is as follows: in The mean values ​​of abdominal dimensions for the target population. Standard deviation This is the error function.

[0009] Furthermore, the critical dimension design domain is determined based on the abdominal thickness, and the design range of the abdominal thickness is determined by calculating the 5th and 95th percentile values:

[0010] .

[0011] Furthermore, the abdominal thickness distribution function T(z) is:

[0012] in Based on thickness, For amplitude parameters, This is the peak position. This is the distribution width parameter.

[0013] Furthermore, the abdominal mechanical property simulation module obtains the relative density distribution function of human tissue based on a hyperelastic constitutive model, including establishing the stress-strain relationship of abdominal tissue based on the hyperelastic constitutive model: the equivalent elastic modulus and the equivalent yield strength formula. in and The equivalent modulus and strength of the honeycomb structure, and For the modulus and strength of the matrix material, ρs represents the relative density of the honeycomb structure, and C1 and C2 represent the hyperelastic coefficients describing the hyperelastic behavior of soft tissues (skin, fat, and muscle) in the human abdomen. These coefficients are derived from the Ogden hyperelastic model and fitted using uniaxial compression-tension experiments on the abdominal tissues of Chinese adults: C1 = 0.1-0.5 MPa, C2 = 0.05-0.2 MPa. m represents the equivalent modulus index, a correlation index between "honeycomb equivalent modulus and relative density" in the Gibson-Ashby model, reflecting the sensitivity of structural stiffness to density. For hexagonal honeycomb structures, m = 1.0. n represents the equivalent strength index, a correlation index between "honeycomb equivalent strength and relative density" in the Gibson-Ashby model, reflecting the sensitivity of structural resistance to damage to density. For hexagonal honeycomb structures, n = 0.5. Matching the "low strength, high energy consumption" characteristics of the human fat layer, the equivalent modulus distribution of the dummy abdomen is matched with the modulus of human tissue to obtain the relative density distribution. .

[0014] Furthermore, the gradient honeycomb structure includes a surface dense layer, a transition layer, an energy absorption layer, and a core protective layer.

[0015] Furthermore, the simulation of the biomechanical properties of the human abdomen through the relative density gradient distribution in the thickness direction specifically involves... The location of the density distribution center These are the distribution parameters.

[0016] Furthermore, the sensor positions in the pressure sensor array are determined by the following formula: Among them, X p This represents the set of optimal placement locations for pressure sensors; Ω k These represent four biomechanically sensitive areas of the abdomen, which are determined based on the anatomical features and biomechanical properties of the human abdomen; w k These are the weighting coefficients for each region. This is the regularization parameter.

[0017] Furthermore, the accelerometer array consists of a triaxial MEMS accelerometer.

[0018] Furthermore, the displacement sensor array consists of high-precision laser displacement sensors, and the positions of the displacement sensors are determined based on biomechanical response characteristic points determined by finite element analysis and experimental verification.

[0019] Furthermore, the synchronous acquisition system adopts a master-slave triggering mechanism, in which the master acquisition unit generates a synchronous trigger signal, and each slave acquisition unit starts acquisition simultaneously after receiving the trigger signal.

[0020] Furthermore, the biomechanical parameter calculation module calculates biomechanical parameters based on the fused data, including force-displacement characteristic parameters, energy absorption parameters, and viscous response parameters.

[0021] Furthermore, the dynamic time warping module is used to calculate the similarity between the experimental response and the biomechanical response corridor, specifically as follows: Where s i For the experimental data points, r j Use the reference data points; the similarity index is calculated as follows: N represents the number of experimental data points, and M represents the number of reference data points.

[0022] In addition, the present invention also provides a method for testing the abdominal safety of a car crash dummy, characterized by comprising the following steps: Abdominal height was extracted from the established abdominal database of the target population. ,width ,thickness data; Statistical analysis was performed on the abdominal data, and the mean was calculated. and standard deviation ; Based on the normal distribution model, determine the critical dimension design domain; Using MRI data, the thickness variations of each tissue layer along the thickness direction z of the real human abdomen were measured and fitted to the key-size design domain. A function was then used to... Curve fitting was performed on the measured data to optimize the parameters. , , , The value; The fitted mechanical property gradient Mapping to the geometric gradient of the cellular structure, we obtain the relative density distribution function with respect to position z; In finite element analysis software, the Ogden hyperelastic model is used. and viscoelastic model parameters The constitutive relation of the abdominal material of the dummy is set; where μ is the shear modulus, describing the material's ability to resist shear deformation; α is the strain hardening exponent, describing the degree of hardening of the material with increasing strain; λ1, λ2, and λ3 are the principal elongations of the abdominal tissue in the x (width), y (height), and z (thickness) directions, respectively; σ(t) is the linear viscoelastic convolution constitutive model, indicating that the current stress σ(t) is determined by the "time cumulative effect" of the historical strain rate and relaxation modulus G(t-τ); G(t) is the exponential decay model of the relaxation modulus, describing the "stress relaxation" characteristics of the material; G0 is the initial relaxation modulus; G... ∞Long-term relaxation modulus, where β is the relaxation rate coefficient, controlling the rate at which the relaxation modulus decays over time; Virtual collision simulation was performed to calculate the force-displacement curve, energy absorption, and viscous response of the dummy's abdomen. The simulation results are compared with the actual human biomechanical response corridor. If the simulation curve falls outside the biomechanical response corridor, the parameters in the parameterized model are adjusted and the simulation is repeated until the response matches the biomechanical response corridor sufficiently.

[0023] The embodiments of the present invention have the following technical effects: This invention provides a device and method for testing the abdominal safety of a car crash dummy. By integrating pressure, deformation, acceleration, and tissue stress sensors and combining them with a real-time feedback algorithm, the device dynamically optimizes crash parameters, improving testing accuracy and biofidelity. It proposes a multimodal biomechanical data fusion technology, which uses adaptive feedback to adjust the crash energy distribution and achieve dynamic damage simulation. A closed-loop "sensing-analysis-feedback" system is constructed, and for the first time, a flexible electronic sensor array is combined with a biomechanical model for abdominal testing. This invention can be widely applied in fields such as automotive safety testing and medical device impact assessment, significantly improving testing efficiency and data reliability. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the overall structure of the gradient honeycomb structure abdominal module provided in an embodiment of the present invention; Figure 2 This is a detailed structural diagram of the gradient honeycomb structure abdominal module provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the multimodal sensor arrangement provided in an embodiment of the present invention; Figure 4 This is a flowchart of the method for testing the abdominal safety of a car crash dummy provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are part of this invention.

[0027] This invention establishes a parametric model of the abdomen specifically for Chinese anatomy, and extracts abdominal anatomical features of the Chinese population using large-scale CT and MRI scan data.

[0028] Based on statistical analysis methods, a parameterized model of the abdominal features of the Chinese human body was established: Size distribution model: ; in This represents the average abdominal dimensions of Chinese individuals. Standard deviation The error function is used. This model is used to determine the key size design domains covering the percentiles of the target population, such as the design range for abdominal thickness, which is determined by calculating the 5th and 95th percentile values.

[0029]

[0030] Tissue thickness distribution function:

[0031] This function describes the tissue thickness distribution along the thickness direction of the abdomen, where Based on thickness, For amplitude parameters, This is the peak position. These parameters represent the distribution width. After being obtained through fitting MRI data, these parameters are used to guide the design of the mechanical property distribution of gradient cellular structures.

[0032] Based on the hyperelastic constitutive model, the stress-strain relationship of abdominal tissues was established:

[0033] The Gibson-Ashby model described above translates tissue mechanical performance requirements into honeycomb structure parameters. To simulate the mechanical properties of the human abdomen, the equivalent modulus distribution of the dummy abdomen needs to match the modulus of human tissue, thus deriving the relative density distribution:

[0034] This function is directly used as an input parameter for manufacturing gradient honeycomb structures, controlling the density changes during the 3D printing process.

[0035] Simulate the biomechanical properties of the human abdomen using the relative density gradient distribution along the thickness direction: .

[0036] like Figure 1 and Figure 2 As shown, the abdominal module adopts a four-layer gradient honeycomb structure design:

[0037] The multimodal sensor system employs a distributed intelligent sensor network architecture, consisting of three clearly defined hierarchical layers: a sensing layer, a transmission layer, and a processing layer. This layered architecture ensures efficient data acquisition, reliable transmission, and intelligent processing. Figure 3 As shown, a total of 24 high-performance sensor nodes are deployed in the entire dummy abdominal testing system. These nodes are scientifically divided into three specialized sensor subarrays according to their functional characteristics and the physical quantities they measure. This division not only considers the technical characteristics of the sensors themselves, but also fully integrates the characteristics of the abdominal biomechanical response, ensuring comprehensive and accurate monitoring of the dynamic response of various key parts of the abdomen during the collision.

[0038] The mathematical expression of the entire sensor system can be represented as:

[0039] Where S represents the total sensing matrix, which integrates the measurement data from all sensors; S p This specifically represents the pressure sensing submatrix, which contains measurement information from all pressure sensors; S a It is an acceleration sensing sub-matrix that records data from each acceleration sensor; S d This represents the displacement sensing submatrix, which contains the measurement results from the displacement sensors. This matrix-based representation not only facilitates data organization and management but, more importantly, lays the mathematical foundation for subsequent multimodal data fusion processing.

[0040] For pressure sensing, twelve miniature piezoresistive pressure sensors were selected, characterized by high sensitivity, fast response, and good stability. Each sensor's measurement range was carefully set to 0-500 kPa, fully covering the pressure range the human abdomen might experience during a collision. The sensors have a resolution better than 0.1 kPa, meaning even the smallest pressure changes can be accurately detected. The sampling frequency was set to 10 kHz to ensure the capture of rapidly changing pressure signals during a collision.

[0041] The sensor placement scheme was optimized based on the distribution of abdominal mechanical properties, and a complex optimization algorithm was used to determine the optimal placement location.

[0042] In this optimization model, X p This represents the set of optimal placement locations for pressure sensors; Ω k These represent four biomechanically sensitive areas of the abdomen, determined based on the anatomical features and biomechanical properties of the human abdomen; w k This is a weighting coefficient for each region, reflecting its importance in the overall biomechanical response. This optimization model ensures that the pressure sensor can cover the entire abdominal region while providing higher measurement density in key areas with complex biomechanical responses.

[0043] Acceleration measurements were performed using eight triaxial MEMS accelerometers. These sensors are small, lightweight, and low-power, making them ideal for embedding within the dummy's abdominal structure. Each accelerometer has a range of ±500 g, sufficient to cover the maximum acceleration that may occur during a collision. The sensors have a bandwidth of 0-5 kHz, ensuring accurate capture of high-frequency vibrational components during the collision.

[0044] These accelerometers are carefully embedded in key locations within the gradient honeycomb structure, forming a complete three-dimensional acceleration measurement network. The measurement data from all the accelerometers can be represented as a time-series matrix:

[0045] Among them, a x a y a z It represents the acceleration in the x, y, and z directions.

[0046] The displacement measurement system consists of four high-precision laser displacement sensors. These sensors employ a non-contact measurement principle, offering advantages such as high measurement accuracy, fast response speed, and no interference with the measured object. Each displacement sensor has a measurement range of 0-200 mm, fully covering the maximum deformation that the abdomen may experience during a collision. The measurement accuracy reaches 0.01 mm, ensuring the detection of minute deformation changes.

[0047] These displacement sensors were carefully positioned at key measurement points on the abdominal surface, points determined based on biomechanical response characteristics identified through finite element analysis and experimental verification. The displacement measurement data can be represented as:

[0048] Where d represents the displacement measurement data.

[0049] Each component represents the displacement-time history of a measurement point. These displacement data allow for precise understanding of the deformation pattern, amount of deformation, and deformation rate of the abdominal surface during a collision. This information is crucial for assessing the abdominal mechanical response and damage risk.

[0050] The data acquisition system employs an advanced distributed synchronous acquisition architecture, ensuring precise time synchronization across all 24 measurement channels. The system utilizes a master-slave triggering mechanism, where the master acquisition unit generates a synchronization trigger signal, and each slave acquisition unit begins acquisition simultaneously upon receiving the signal. This design ensures that the sampling time deviation across all channels is less than 1 microsecond, fully meeting the time synchronization accuracy requirements of collision testing.

[0051] Based on the fused data, several important biomechanical parameters were calculated, which describe the dynamic response characteristics of the abdomen from different perspectives.

[0052] The force-displacement characteristics are described by the following model:

[0053] Where F is the force, k is the linear stiffness coefficient, corresponding to the linear elastic deformation stage of the abdominal tissue; c is the damping coefficient, corresponding to the viscous dissipation of the tissue, which is related to the viscoelastic model η; m is the effective mass of the equivalent mass dummy abdominal module; and d is the displacement, corresponding to the deformation of the abdomen along the collision direction.

[0054] The force-displacement characteristic model comprehensively considers the contributions of elastic force, damping force, and inertial force, and can accurately describe the nonlinear mechanical behavior of the abdomen.

[0055] Energy absorption characteristics are calculated by integration:

[0056] E absorb Let d represent the total energy absorbed, and d represent the displacement, specifically the deformation of the abdomen along the collision direction. These parameters reflect the total energy absorbed by the abdomen during the collision and are important indicators for evaluating its protective performance.

[0057] The viscous response is calculated using the following formula:

[0058] VC: Viscosity index, reflecting the force-velocity coupling characteristics of soft tissue, is a key indicator for judging visceral injury; the viscous response parameter is closely related to the injury mechanism of soft tissue, ε(t) represents the velocity-time function, and D is the displacement during the loading process.

[0059] Dynamic Time Warping Algorithm:

[0060] The dynamic time warping algorithm is used to calculate the similarity between the experimental response and the biomechanical response corridor, where s i For the experimental data points, r j These are reference data points. The similarity index is calculated as follows:

[0061] This indicator is used to quantitatively verify the biosimulation of the dummy; a similarity of over 90% is considered to meet the requirements.

[0062] These mathematical models together form a complete technology chain from Chinese anatomy data to final product design, manufacturing, and verification, ensuring the biosimulation of the dummy's abdomen and the accuracy of testing.

[0063] like Figure 4 As shown, the present invention also provides a method for safety testing of the abdomen of a car crash dummy. It extracts anatomical feature parameters of the abdomen of the population, constructs a parameterized mathematical model, and uses a statistical distribution model to scientifically determine the key size range of the dummy's abdomen, ensuring that it covers the target population.

[0064] Step S1: Determine the design domain.

[0065] Step S11: Extract abdominal height data from a large number of samples from the established Chinese abdominal database. ,width ,thickness data; Step S12: Perform statistical analysis on these data and calculate the mean. and standard deviation ; Step S13: Based on the normal distribution model Choose an appropriate percentile (e.g., the 5th to 95th percentile) as the design domain; If the abdominal thickness is calculated mean = 200 mm, standard deviation = 10 mm.

[0066] The 5th percentile thickness in adult males is approximately:

[0067] The 95th percentile thickness is approximately:

[0068] Step S14: The thickness design domain for the dummy's abdomen can be determined to be 180-220 mm. This will directly guide the geometric modeling of the dummy's abdominal shell and internal structure.

[0069] Step S2: Use a parametric mathematical model to guide gradient design, specifically the design of a gradient honeycomb structure.

[0070] Step S21: Using MRI data, measure the thickness variation of each tissue layer (skin, fat, muscle) along the thickness direction z of the real human abdomen. Fit the data using the function.

[0071] Curve fitting was performed on the measured data to optimize the parameters. , , , The value of this function describes the location of the "peak" and the "width" of the tissue stiffness. Step S22: The fitted mechanical property gradient (obtained by the function) Description) mapped to the geometric gradient of the cellular structure (relative density) ); Through formula Perform the conversion; aim for the equivalent modulus of the dummy's abdomen. Distribution Modulus distribution of human tissue Since they match, it can be deduced that: ; Step S23: Obtain a relative density distribution function with respect to position z. This function will be used directly as an input file for 3D printing or manufacturing gradient honeycomb structures, controlling the density changes of the structure during the printing process, thereby creating biomimetic structures with mechanical properties highly matched to the human body.

[0072] Step S3: Verify the biomimicry.

[0073] In the computer-aided engineering simulation stage and after physical testing, the Ogden model and viscoelastic model are benchmarks for measuring whether the dummy's response is consistent with the biomechanical response of a real person.

[0074] Step S31: In finite element analysis software (such as LS-DYNA, Abaqus), the Ogden hyperelastic model... and viscoelastic model parameters Constitutive relations of the material used for the abdomen of the dummy; Step S32: Perform virtual collision simulation to calculate the force-displacement curve, energy absorption, and other responses of the dummy's abdomen; Step S33: Compare the simulation results with the real human biomechanical response corridors obtained from the experimental database; Step S34: If the simulation curve falls outside the corridor, adjust the parameters in the parametric model (e.g., fine-tune the density distribution of the cellular structure) and re-simulate until its response matches the biological response corridor sufficiently. Step S35: After the physical experiment, substitute the data collected by the sensor (such as force and acceleration) into the model, calculate the dynamic time warping (DTW) distance between it and the standard biomechanical response curve, and give a quantitative similarity score (such as 92%) to objectively prove its biosimulation degree.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle collision dummy abdominal safety testing device, characterized in that, Includes the following modules: The human body feature modeling module establishes a parameterized abdominal model of the target population based on statistical analysis, determines the key size design domain covering the percentile of the target population, and constructs a measurement database of the target population. An abdominal mechanical performance simulation module is provided. The abdominal mechanical performance simulation module obtains the relative density distribution function of human tissue based on a hyperelastic constitutive model. The density distribution function is used as the input parameter for manufacturing a gradient honeycomb structure and the mechanical properties of the human abdomen are simulated through the relative density gradient distribution in the thickness direction. A multimodal sensor integration module is provided, which adopts a distributed intelligent sensor network architecture and consists of a sensing layer, a transmission layer, and a processing layer. The sensing layer includes a pressure sensor array, an acceleration sensor array, and a displacement sensor array. The transmission layer includes a synchronous acquisition system. The processing layer includes a biomechanical parameter calculation module and a dynamic time warping module, wherein the dynamic time warping module is used to calculate the similarity between the experimental response and the biomechanical response corridor.

2. The vehicle collision dummy abdominal safety testing device according to claim 1, characterized in that, The distribution model for the critical dimensions is as follows: ;in The mean values ​​of abdominal dimensions for the target population. Standard deviation, Let be the error function. x It is the value that a random variable can take.

3. The vehicle collision dummy abdominal safety testing device according to claim 2, characterized in that, The critical dimension design domain is determined based on abdominal thickness, and the design range of abdominal thickness is determined by calculating the 5th and 95th percentile values: 。 4. The vehicle collision dummy abdominal safety testing device according to claim 1, characterized in that, The abdominal mechanical performance simulation module obtains the relative density distribution function of human tissue based on a hyperelastic constitutive model, including establishing the stress-strain relationship of abdominal tissue based on the hyperelastic constitutive model: ;in and These are the equivalent modulus and strength of the honeycomb structure, respectively. and These are the equivalent modulus and strength of the matrix material, respectively. For relative density, The relative density of the honeycomb structure. The matrix material density is the relative density, C1 and C2 are the hyperelastic material coefficients, and m and n are the correlation exponents. The equivalent modulus distribution of the dummy abdomen is matched with the modulus of human tissue to obtain the relative density distribution function. E human It is the equivalent modulus of human tissue.

5. The vehicle collision dummy abdominal safety testing device according to claim 4, characterized in that, The gradient honeycomb structure includes a surface dense layer, a transition layer, an energy absorption layer, and a core protective layer.

6. The vehicle collision dummy abdominal safety testing device according to claim 4, characterized in that, The simulation of the biomechanical properties of the human abdomen through the relative density gradient distribution in the thickness direction specifically refers to: ,in The location of the density distribution center Let be the distribution parameter, where ρ max For the maximum relative density, ρ min It represents the minimum relative density.

7. The vehicle collision dummy abdominal safety testing device according to claim 1, characterized in that, The displacement sensor array consists of high-precision laser displacement sensors, and the positions of the displacement sensors are determined based on biomechanical response characteristic points determined by finite element analysis and experimental verification.

8. The vehicle collision dummy abdominal safety testing device according to claim 1, characterized in that, The synchronous acquisition system adopts a master-slave triggering mechanism, in which the master acquisition unit generates a synchronous trigger signal, and each slave acquisition unit starts acquisition simultaneously after receiving the trigger signal.

9. The vehicle collision dummy abdominal safety testing device according to claim 1, characterized in that, The dynamic time warping module is used to calculate the similarity between the experimental response and the biomechanical response corridor, specifically as follows: in For experimental data points, Use the reference data points; the similarity index is calculated as follows: Where N is the number of experimental data points and M is the number of reference data points. The average value of the test data points. This is the average value of the reference data points.

10. A method for testing the abdominal safety of a car crash dummy using the vehicle crash dummy abdominal safety testing device according to any one of claims 1-9, characterized in that, Includes the following steps: Abdominal height was extracted from the established abdominal database of the target population. ,width ,thickness data; Statistical analysis was performed on the abdominal data, and the mean was calculated. and standard deviation ; Based on the normal distribution model, determine the critical dimension design domain; The thickness variation data of each tissue layer along the thickness direction z of the real human abdomen was obtained by measuring the critical dimension design domain and fitting the data. A function was then used. Curve fitting was performed on the measured data to optimize the parameters. , , , The value; The fitted mechanical property gradient Mapping to the geometric gradient of the cellular structure, we obtain the relative density distribution function with respect to position z; In finite element analysis software, the Ogden hyperelastic model is used. and viscoelastic model parameters The constitutive relation of the dummy abdominal material is set, where μ is the shear modulus, α is the strain hardening exponent, λ1, λ2, and λ3 are the principal elongation ratios of the abdominal tissue in the width, height, and thickness directions, respectively; σ(t) is the linear viscoelastic convolution constitutive relation, G(t) is the exponential decay model of the relaxation modulus, G0 is the initial relaxation modulus, and G... ∞ β is the long-term relaxation modulus, and β is the relaxation rate coefficient. A virtual collision simulation was performed to calculate the simulation results of the dummy's abdomen. The simulation results include force-displacement curves, energy absorption, and viscous response. The simulation results are compared with the actual human biomechanical response corridor. If the simulation curve falls outside the biomechanical response corridor, the parameters in the parameterized model are adjusted and the simulation is repeated until the simulation results meet the similarity requirements of the biomechanical response corridor.

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