Automobile crash dummy abdominal safety testing device and method

Abstract

The present application relates to the technical field of biomechanical detection and automobile safety evaluation, and discloses a device and method for testing the abdomen safety of a car crash dummy. The testing device comprises a human feature modeling module, a parameterized model of the abdomen of a target population is established based on statistical analysis, a key size design domain covering the target population percentile is determined, and an anthropometry database of the target population is constructed; an abdomen mechanical property simulation module, a human tissue relative density distribution function is obtained based on a super-elastic constitutive model, the density distribution function is taken as an input parameter for manufacturing a gradient honeycomb structure, and the abdomen mechanical properties of the human body are simulated through the relative density gradient distribution in the thickness direction; and a multi-modal sensor integration module, a distributed intelligent sensing network architecture is adopted, and the multi-modal sensor integration module is composed of a perception layer, a transmission layer and a processing layer. Through integration of pressure, deformation, acceleration and tissue stress sensors, and in combination with a real-time feedback algorithm, collision parameters are dynamically optimized, and the testing precision and biological fidelity are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of automobile crash dummy abdomen safety testing device and method, belong to biomechanics detection and automobile safety evaluation technical field. BACKGROUND

[0002] Automobile crash dummy is the high-end detection device for evaluating automobile safety, and is an important technical weight for measuring automobile safety performance. Using crash dummy to replace human for crash test can simulate human body injury under different conditions. Abdominal injury is one of the main factors leading to serious injury or even death of occupants in traffic accidents. Currently, the dummy model widely used in automobile crash test mainly simulates human body response through pre-set mechanical structure, but its abdominal biomechanics feedback mechanism is relatively single, relying only on limited sensors (such as pressure sensor) to collect data, which is difficult to fully reflect the complex biomechanical response of real human body in crash. The traditional method has the following problems:

[0003] 1) Insufficient data dimension: existing systems mostly use single-mode sensors (such as accelerometers or strain gauges), which cannot synchronously obtain multi-dimensional data such as abdominal pressure distribution, deformation dynamics, muscle tissue stress.

[0004] 2) Poor real-time feedback: traditional test systems rely on post-processing analysis and cannot adjust crash parameters in real time to simulate dynamic injury evolution process.

[0005] 3) Low biological fidelity: the abdominal structure of the dummy lacks dynamic response simulation of real human organs and soft tissues, resulting in weak correlation between test results and actual injury.

[0006] Existing patents such as CN110243392B propose a center of mass adjustment device based on inertial sensors, but do not involve multi-modal biomechanics feedback; CN109406054A discloses a center of mass detection device with a cylindrical structure, but its method is not suitable for complex soft tissue biomechanics analysis. Therefore, there is an urgent need for a high-precision abdominal safety test system that can integrate multi-modal biomechanics data in real time and dynamically feedback control crash parameters.

[0007] To overcome the problems of limited data dimension, feedback lag and insufficient biological fidelity of existing abdominal crash test systems, the present application provides an automobile crash dummy abdominal safety test device and method, which integrates pressure, deformation, acceleration and tissue stress sensors, combines real-time feedback algorithm, dynamically optimizes crash parameters, and improves test accuracy and biological fidelity. SUMMARY

[0008] To solve the above technical problems, the present application provides an automobile crash dummy abdominal safety test device, characterized by comprising the following modules:

[0009] a human body feature modeling module, which establishes a parameterized model of an abdominal region of a target population based on statistical analysis, determines a key dimension design domain covering a percentile of the target population, and constructs a metrology database of the target population;

[0010] an abdominal region mechanical property simulation module, which obtains a relative density distribution function of human tissue based on a hyperelastic constitutive model, takes the density distribution function as an input parameter for manufacturing a gradient honeycomb structure, and simulates mechanical properties of the abdominal region of the human body through a relative density gradient distribution in a thickness direction;

[0011] a multi-modal sensor integration module, which adopts a distributed intelligent sensor network architecture and is composed of a perception layer, a transmission layer, and a processing layer; the perception layer includes an array of pressure sensors, an array of acceleration sensors, and an array of displacement sensors, the transmission layer includes a synchronous acquisition system, and 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 a similarity of a test response and a biomechanical response corridor.

[0012] Further, the distribution model of the key dimension is wherein is a mean value of an abdominal region dimension parameter of the target population, is a standard deviation, is an error function.

[0013] Further, the key dimension design domain is determined based on an abdominal region thickness, and a design range of the abdominal region thickness is determined by calculating 5th and 95th percentile values:

[0014]

[0015] .

[0016] Further, the abdominal region thickness distribution function T(z) is:

[0017]

[0018] wherein is a basic thickness, is an amplitude parameter, is a peak position, is a distribution width parameter.

[0019] Further, the abdominal region mechanical property simulation module obtains a relative density distribution function of human tissue based on a hyperelastic constitutive model, which includes establishing a stress-strain relationship of the abdominal region tissue based on the hyperelastic constitutive model: equivalent elastic modulus and equivalent zone yield strength formula, wherein and Equivalent modulus and strength of honeycomb structure, and Modulus and strength of matrix material, Relative density of honeycomb structure, ρs is the density of matrix material, ρs is the relative density, C1, C2 are the elastic coefficients of the super-elastic material describing the super-elastic behavior of the abdominal soft tissue (skin, fat, muscle) of the human body, which are derived from the Ogden super-elastic model and fitted by the uniaxial compression and stretching experiment of the abdominal tissue of Chinese adults: C1=0.1-0.5MPa, C2=0.05-0.2MPa, m is the correlation index of the "honeycomb equivalent modulus and relative density" in the Gibson-Ashby model, which reflects the sensitivity of the structure stiffness to the density; the regular value of the hexagonal honeycomb structure is: m=1.0, n is the correlation index of the "honeycomb equivalent strength and relative density" in the Gibson-Ashby model, which reflects the sensitivity of the structure damage resistance to the density; the regular value of the hexagonal honeycomb structure is: n=0.5, which matches the "low strength and high energy consumption" characteristics of the human fat layer; the equivalent modulus distribution of the dummy abdominal region is matched with the modulus of the human tissue, and the relative density distribution is obtained: .

[0020] Further, the gradient honeycomb structure comprises a surface dense layer, a transition layer, an energy absorption layer and a core protection layer.

[0021] Further, the relative density gradient distribution in the thickness direction simulates the mechanical properties of the human abdominal region, and specifically is the center position of the density distribution is the distribution parameter.

[0022] Further, the sensor positions in the pressure sensor array are determined by the following formula: wherein X p represents the optimal arrangement position set of the pressure sensor; Ω k represents four mechanical sensitive regions of the abdominal region, which are determined according to the anatomical features and biomechanical properties of the human abdominal region; w k is the weight coefficient of each region, is a regularization parameter.

[0023] Further, the acceleration sensor array is composed of three-axis MEMS acceleration sensors.

[0024] Further, the displacement sensor array is composed of high-precision laser displacement sensors, and the displacement sensor positions are determined based on the biomechanical response characteristic points determined by finite element analysis and experimental verification.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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:

[0029] Abdominal height was extracted from the established abdominal database of the target population. ,width ,thickness data;

[0030] Statistical analysis was performed on the abdominal data, and the mean was calculated. and standard deviation ;

[0031] Based on the normal distribution model, determine the critical dimension design domain;

[0032] 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;

[0033] 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;

[0034] In finite element analysis software, the Ogden hyperelastic model is used. and viscoelastic model parameters The constitutive relation is set to be the dummy abdomen material; wherein μ is a shear modulus, which describes the ability of the material to resist shear deformation; α is a strain hardening index, which describes the hardening degree of the material with the increase of strain; λ1, λ2, λ3 are main elongation ratios, which are the deformation proportions of the abdominal tissue in the x (width), y (height), and z (thickness) directions; σ(t) is a convolution constitutive of linear viscoelasticity, which indicates that the current stress σ(t) is determined by the "time cumulative effect" of the historical strain rate and the relaxation modulus G(t-τ); G(t) is an exponential decay model of the relaxation modulus, which describes the characteristics of the "stress relaxation" of the material, G0 is an initial relaxation modulus, G ∞ a long-term relaxation modulus, and β is a relaxation rate coefficient, which controls the rate of relaxation modulus attenuation over time;

[0035] Virtual collision simulation is performed, and the force-displacement curve, energy absorption, and viscous response of the dummy abdomen are calculated;

[0036] The simulation results are compared with the real 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 performed again until the response is sufficiently consistent with the biomechanical response corridor.

[0037] The embodiments of the present application have the following technical effects:

[0038] The present application provides a kind of automobile crash dummy abdomen safety testing device and method, by integrating pressure, deformation, acceleration and tissue stress sensor, in combination with real-time feedback algorithm, dynamically optimize collision parameter, improve test precision and biological fidelity;Multi-modal biomechanical data fusion technology is proposed, and the distribution of collision energy is adjusted by adaptive feedback to realize dynamic damage simulation;A "perception-analysis-feedback" closed-loop system is constructed, which is the first time to combine flexible electronic sensor array and biomechanical model in abdominal testing, and can be widely applied in automobile safety testing, medical device impact evaluation and other fields, significantly improving test efficiency and data reliability. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the description of the specific embodiments or the prior art. Obviously, the drawings described below are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any inventive labor.

[0040] Figure 1 is the overall structure schematic diagram of the gradient honeycomb structure abdomen module provided by the embodiments of the present application;

[0041] Figure 2 is the detailed structure diagram of the gradient honeycomb structure abdomen module provided by the embodiments of the present application;

[0042] Figure 3 is a schematic diagram of a multi-modal sensor arrangement provided by an embodiment of the application;

[0043] Figure 4 is a flowchart of a method for testing the abdominal safety of a crash dummy in a car. DETAILED DESCRIPTION

[0044] So that the objects, technical solutions and advantages of the present application can be more clearly understood, the technical solutions of the present application will be described below in a clear and complete manner. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0045] The present application establishes an abdominal parameterized model specially for Chinese physical signs, extracts the abdominal anatomical feature parameters of Chinese population through large-scale CT and MRI scanning data.

[0046] Based on statistical analysis method, the abdominal feature parameterized model of Chinese human body is established:

[0047] Size distribution model: ;

[0048] wherein is the mean value of the abdominal size parameters of Chinese human body, is the standard deviation, is the error function. The model is used to determine the key size design domain covering the target population percentile, such as the design range of abdominal thickness determined by calculating the 5th and 95th percentile values:

[0049]

[0050]

[0051] Tissue thickness distribution function:

[0052]

[0053] The function describes the tissue thickness distribution along the abdominal thickness direction, wherein is the basic thickness, is the amplitude parameter, is the peak position, is the distribution width parameter. After these parameters are fitted by MRI data, they are used to guide the mechanical performance distribution design of the gradient honeycomb structure.

[0054] Based on the hyperelastic constitutive model, the stress-strain relationship of the abdominal tissue is established:

[0055]

[0056] The Gibson-Ashby model mentioned above converts the mechanical property requirements of the tissue into the parameters of the honeycomb structure. To achieve the simulation of the mechanical properties of the human abdomen, the equivalent modulus distribution of the dummy abdomen needs to be matched with the modulus of the human tissue, and thus the relative density distribution is derived:

[0057]

[0058] This function directly serves as an input parameter for manufacturing the gradient honeycomb structure, controlling the density change during 3D printing.

[0059] The mechanical properties of the human abdomen are simulated by the relative density gradient distribution in the thickness direction:

[0060] .

[0061] As shown in Figure 1 and Figure 2 , the abdomen module adopts a four-layer gradient honeycomb structure design:

[0062]

[0063] The multi-modal sensor system adopts a distributed intelligent sensor network architecture, which consists of three clear hierarchical structures: the perception layer, the transmission layer, and the processing layer. This hierarchical architecture ensures the efficiency of data acquisition, the reliability of transmission, and the intelligence of processing. As shown in Figure 3 , a total of 24 high-performance sensor nodes are arranged in the entire dummy abdomen test system. These nodes are scientifically divided into three specialized sensor sub-arrays according to their functional characteristics and measured physical quantities. This division not only considers the technical characteristics of the sensors themselves, but also fully combines the characteristics of the abdominal biomechanical response, ensuring that the dynamic response of each key part of the abdomen during the collision process can be comprehensively and accurately monitored.

[0064] The mathematical expression of the entire sensor system can be represented as:

[0065]

[0066] Where S represents the total sensor matrix, which integrates the measurement data of all sensors; S p is a pressure sensor sub-matrix, containing the measurement information of all pressure sensors; S a is an acceleration sensor sub-matrix, recording the data of each acceleration sensor; S dis the displacement sensing sub-matrix, which contains the measurement results of the displacement sensors. This matrix representation not only facilitates data organization and management, but more importantly, lays the mathematical foundation for subsequent multi-modal data fusion processing.

[0067] In terms of pressure sensing, 12 miniature piezoresistive pressure sensors were selected for their high sensitivity, fast response speed, and good stability. Each sensor's measurement range was carefully set to 0-500 kPa, fully covering the pressure range the human abdomen may experience during a crash. The sensor's resolution was better than 0.1 kPa, meaning even the smallest pressure changes could be accurately detected. The sampling frequency was set to 10 kHz, ensuring that rapidly changing pressure signals during a crash could be captured.

[0068] The sensor placement scheme was optimized based on the distribution of abdominal mechanical properties, using a complex optimization algorithm to determine the optimal placement positions:

[0069]

[0070] In this optimization model, X p represents the optimal set of pressure sensor placement positions; Ω k represents the four mechanically sensitive regions of the abdomen, determined based on human abdominal anatomy and biomechanical properties; w k is the weight coefficient of each region, reflecting the importance of the region in the overall biomechanical response. Through this optimization model, it is ensured that the pressure sensors can cover the entire abdominal region and provide higher measurement density in key regions with complex mechanical responses.

[0071] Acceleration measurement used 8 triaxial MEMS acceleration sensors, which are small in size, light in weight, and low in power consumption, making them ideal for embedding inside the dummy's abdominal structure. Each acceleration sensor had a range of ±500 g, sufficient to cover the maximum acceleration values that may occur during a crash. The sensor's bandwidth was 0-5 kHz, ensuring accurate capture of high-frequency vibration components during a crash.

[0072] These acceleration sensors were carefully embedded in key positions inside the gradient honeycomb structure, forming a complete three-dimensional acceleration measurement network. The measurement data of all acceleration sensors can be represented as a time series matrix:

[0073]

[0074] where a x , a y , a zAcceleration in x, y, z directions.

[0075] The displacement measurement system consists of 4 high-precision laser displacement sensors, which use non-contact measurement principles and have the advantages of high measurement accuracy, fast response speed, and no interference with the measured object. Each displacement sensor has a measurement range of 0-200 mm, which fully covers the maximum deformation that the abdomen may experience during the collision. The measurement accuracy is 0.01 mm, ensuring that small deformation changes can be detected.

[0076] These displacement sensors are carefully arranged on key measurement points on the abdominal surface, which are biodynamic response characteristic points determined based on finite element analysis and experimental verification. The displacement measurement data can be represented as:

[0077]

[0078] Where d represents the displacement measurement data.

[0079] Where each component represents the displacement time history of a measurement point. Through these displacement data, the deformation mode, deformation amount, and deformation speed of the abdominal surface during the collision can be accurately understood, which is crucial for evaluating the mechanical response and damage risk of the abdomen.

[0080] The data acquisition system uses an advanced distributed synchronous acquisition architecture, which ensures that all 24 measurement channels can achieve precise time synchronization. The system uses a master-slave triggering mechanism, where the master acquisition unit generates a synchronous trigger signal, and each slave acquisition unit starts collecting data simultaneously after receiving the trigger signal. This design ensures that the sampling time deviation of all channels is less than 1 microsecond, fully meeting the requirements of time synchronization accuracy for collision testing.

[0081] Based on the fused data, multiple important biomechanical parameters are calculated, which describe the dynamic response characteristics of the abdomen from different angles.

[0082] The force-displacement characteristic is described by the following model:

[0083]

[0084] 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, associated with the viscoelastic model η; m is the effective mass of the equivalent dummy abdomen module; d is the displacement, corresponding to the deformation of the abdomen along the collision direction.

[0085] The force-displacement characteristic model considers the contributions of elastic force, damping force, and inertial force, and can accurately describe the nonlinear mechanical behavior of the abdomen.

[0086] Energy absorption characteristics are calculated by integration:

[0087]

[0088] E absorb is the total energy absorption, d is the displacement, and the deformation of the abdomen along the collision direction. The parameter reflects the total amount of energy absorbed by the abdomen during the collision process, and is an important indicator for evaluating its protection performance.

[0089] Viscous response is calculated by the following formula:

[0090]

[0091] VC: viscous index, reflecting the "force-velocity" coupling characteristics of soft tissue, is a key indicator for judging internal organ 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 loading.

[0092] Dynamic time warping algorithm:

[0093] p

[0094] The dynamic time warping algorithm is used to calculate the similarity between the test response and the biomechanical response corridor, where s i is the test data point, and r j is the reference data point. The similarity index is calculated as:

[0095]

[0096] This index is used to quantitatively verify the biological simulation of the dummy, and when the similarity is higher than 90%, it is considered to meet the requirements.

[0097] These mathematical models together form a complete technical chain from Chinese anthropometric data to final product design, manufacturing and verification, ensuring the biological simulation and test accuracy of the dummy abdomen.

[0098] As Figure 4 shown, the present application also provides an automobile crash dummy abdomen safety test method, extracts the abdominal anatomical feature parameters of the population, constructs a parameterized mathematical model, and through a statistical distribution model, the key size range of the dummy abdomen can be scientifically determined to ensure that it covers the target population.

[0099] Step S1: determine the design domain.

[0100] Step S11: extract the abdominal height , width , thickness data of a large number of samples from the established Chinese abdominal database;

[0101] Step S12: Statistical analysis of the data, calculating the mean and standard deviation ;

[0102] Step S13: According to the normal distribution model , select the appropriate percentile (e.g., 5th to 95th percentile) as the design domain;

[0103] As calculated, the mean of the abdominal thickness = 200 mm, the standard deviation = 10 mm.

[0104] Then the 5th percentile thickness of adult males is about:

[0105]

[0106] The 95th percentile thickness is about:

[0107] Step S14: The thickness design domain of the dummy abdomen can be determined as 180-220 mm. This will directly guide the geometric modeling of the dummy abdomen shell and internal structure.

[0108] Step S2: Use parameterized mathematical models to guide gradient design, specifically the design of gradient honeycomb structures.

[0109] Step S21: Using MRI data, measure the thickness variation data of each tissue layer (skin, fat, muscle) along the thickness direction z of the real human abdomen, and fit: Use the function

[0110]

[0111] Curve fitting is performed on the measured data to optimize the values of parameters , , , This function describes the "peak" position and "distribution width" of the tissue stiffness;

[0112] Step S22: Map the fitted mechanical property gradient (described by the function ) to the geometric gradient (relative density ( ) of the honeycomb structure;

[0113] Convert by formula ; The distribution of the equivalent modulus of the dummy abdomen is expected to match the modulus distribution of human tissue, so we can deduce:

[0114] ;

[0115] Step S23: Obtain a relative density distribution function with respect to position z This function will be directly used as an input file for 3D printing or manufacturing gradient honeycomb structures, controlling the density variation of the structure during the printing process, so as to manufacture a biomimetic structure with high matching mechanical properties to the human body.

[0116] Step S3: Verify the biological simulation degree.

[0117] After the computer-aided engineering simulation stage and physical testing, the Ogden model and the viscoelastic model are the scales for measuring whether the dummy response is consistent with the biomechanical response of the real human body.

[0118] Step S31: In the finite element analysis software (such as LS-DYNA, Abaqus), set the parameters of the Ogden hyperelastic model and the viscoelastic model as the constitutive relation of the dummy abdominal material;

[0119] Step S32: Perform virtual collision simulation to calculate the force-displacement curve, energy absorption, and other responses of the dummy abdomen;

[0120] Step S33: Compare the simulation results with the biomechanical response corridor of the real human body obtained from the experimental database;

[0121] Step S34: If the simulation curve falls outside the corridor, adjust the parameters in the parameterized model (for example, fine-tune the density distribution of the honeycomb structure) and perform simulation again until the response is sufficiently consistent with the biological response corridor;

[0122] Step S35: After physical testing, substitute the data collected by the sensor (such as force, acceleration) into the model to calculate the dynamic time warping (DTW) distance with the standard biomechanical response curve, and quantitatively give a similarity score (such as 92%), thereby objectively proving the biological simulation degree.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not limited thereto; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for part or all of the technical features; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.​

Claims

1. An automotive crash test dummy abdomen safety testing device, characterized by, The system comprises the following modules: a human body feature modeling module, which establishes a parameterized model of the abdomen of a target population based on statistical analysis, determines a key dimension design domain covering the target population percentile, and constructs a target population metrology database; an abdomen mechanics performance simulation module, which obtains a human tissue relative density distribution function based on a hyper-elastic constitutive model, takes the density distribution function as an input parameter for manufacturing a gradient honeycomb structure, and simulates the mechanical properties of the human abdomen through a relative density gradient distribution in the thickness direction; a multi-modal sensor integration module, which adopts a distributed intelligent sensor network architecture and is composed of a perception layer, a transmission layer, and a processing layer; the perception layer comprises a pressure sensor array, an acceleration sensor array, and a displacement sensor array, the transmission layer comprises a synchronous acquisition system, and the processing layer comprises a biomechanics parameter calculation module and a dynamic time warping module, wherein the dynamic time warping module is used to calculate the similarity of the test response and the biomechanics response corridor.

2. The device for testing the safety of an automobile crash dummy's abdomen according to claim 1, wherein The distribution model of the critical dimension is: ; wherein is the mean value of the abdominal size parameter of the target population, is the standard deviation, is the error function, x is the value of the random variable.

3. The device for testing the abdominal safety of an automotive crash dummy according to claim 2, wherein The key dimension design domain is determined based on the abdomen thickness, and the design range of the abdomen thickness is determined by calculating the 5th and 95th percentile values: 。 4. The device for testing the abdominal safety of an automotive crash dummy according to claim 1, wherein The abdominal mechanical property simulation module obtains the relative density distribution function of human tissue based on the super-elastic constitutive model, and comprises the following steps: ; wherein and are respectively the equivalent modulus and strength of the honeycomb structure, and are respectively the equivalent modulus and strength of the matrix material, is the relative density, is the relative density of the honeycomb structure, is the relative density of the matrix material density, C1 and C2 are super-elastic material coefficients, and m and n are correlation indexes; the dummy abdomen equivalent modulus distribution is matched with the human tissue modulus, and the relative density distribution function is obtained: , wherein E human is the equivalent modulus of human tissue.

5. The device for testing the abdominal safety of an automotive crash dummy according to claim 4, wherein The gradient honeycomb structure comprises a surface dense layer, a transition layer, an energy absorption layer, and a core protection layer.

6. The device for testing the abdominal safety of an automotive crash dummy according to claim 4, wherein The relative density gradient distribution through the thickness direction simulates the mechanical properties of the human abdomen in particular: wherein is the density distribution center position, is the distribution parameter, wherein p max is the maximum relative density, p min is the minimum relative density.

7. The device for testing the abdominal safety of an automotive crash dummy according to claim 1, wherein The displacement sensor array is composed of high-precision laser displacement sensors, and the displacement sensor positions are determined based on biomechanics response characteristic points determined by finite element analysis and experimental verification.

8. The device for testing the abdominal safety of an automotive crash dummy according to claim 1, wherein The synchronous acquisition system adopts a master-slave triggering mechanism, and a synchronous trigger signal is generated by a master acquisition unit, and each slave acquisition unit starts acquisition at the same time after receiving the trigger signal.

9. The device for testing the abdominal safety of an automotive crash dummy according to claim 1, wherein The dynamic time warping module is used to calculate the similarity between the test response and the biomechanics response corridor, specifically: wherein is the test data point, is the reference data point; the similarity index is calculated as wherein N is the number of test data points, M is the number of reference data points, is the average value of the test data point, is the average value of the reference data point.

10. A method for testing the abdominal safety of an automotive crash dummy using the automotive crash dummy abdominal safety testing device according to any one of claims 1 to 9, characterized by, The system comprises the following steps: extracting abdominal height, width, and thickness data from the established target population abdominal database data;​​ Statistical analysis was performed on the abdominal data to calculate the mean and standard deviation ; According to the normal distribution model, the key dimension design domain is determined; The thickness variation data of each tissue layer of the real human abdomen along the thickness direction z in the critical dimension design domain is fitted by using the function The measured data is curve-fitted, and the values of the parameters , , , are optimized. The obtained mechanical property gradient is mapped to the geometric gradient of the honeycomb structure, obtaining a relative density distribution function as a function of 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. Virtual collision simulation is performed, and simulation results of the dummy abdomen are calculated, including force-displacement curve, energy absorption, and viscous response; The simulation results are compared with the real human biomechanics response corridor, if the simulation curve falls outside the biomechanics response corridor, the parameters in the parameterized model are adjusted, and the simulation is performed again until the simulation results meet the similarity requirements of the biomechanics response corridor.

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