A method and system for authenticating a digital human body model of a car crash

Abstract

This application provides a method and system for certifying digital human body models in automotive crashes. The method involves constructing a multi-level verification index system; establishing a benchmark certification database for uncertainty indicators; running the digital human body model to be certified under standardized crash conditions to generate a model response vector; calculating the certification score of the digital human body model based on the benchmark certification database and the model response vector; determining the certification result of the digital human body model based on the certification score; and using the multi-level verification index system to certify the digital human body model in automotive crashes to improve the comprehensiveness and accuracy of the certification. Furthermore, by establishing a benchmark certification database for uncertainty indicators, using the benchmark certification database and the model response vector obtained from running the digital human body model to be certified, calculating the certification score, and determining the certification result of the digital human body model based on the certification score, the method further considers the certification results of uncertainty indicators, thereby improving the accuracy of the certification.

Description

Technical Field

[0001] This application relates to the field of automotive crash human body model technology, specifically to a method and system for certifying automotive crash digital human body models. Background Technology

[0002] Human Body Models (HBMs) are a core tool in modern automotive safety simulation and analysis, used to predict the biomechanical response of the human body during a collision, and to assist in the design and optimization of vehicle safety systems. Currently, mainstream HBMs include finite element models such as GHBMC, THUMS, and VIVA, which are widely used in evaluation systems such as Euro NCAP, US NCAP, and C-NCAP.

[0003] However, existing HBM verification methods have significant drawbacks: most certification systems only focus on global dynamic responses (such as head acceleration and chest compression), lacking a systematic evaluation of local tissue levels (such as organ strain and bone stress) and the consistency of anatomical structure motion. Furthermore, biological experimental data itself is highly variable, and most existing methods ignore this uncertainty, directly using a single threshold for judgment, resulting in insufficient reliability of certification results. Therefore, there is an urgent need for a quantifiable digital human model certification method with uncertainty handling capabilities. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method and system for authenticating a digital human body model in a vehicle collision.

[0005] According to one aspect of this application, a method for certifying a digital human body model in a car crash is provided, comprising: constructing a multi-level verification index system; wherein the multi-level verification index system includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set, wherein the global dynamic response index set is used to evaluate the overall mechanical response of the model during a crash, the local tissue deformation mechanical index set is used to evaluate the authenticity of the mechanical response of the model at the tissue level, the anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the model's internal anatomical structures and the real biological response during a crash, and the damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk; establishing a benchmark certification database of uncertainty indicators in the multi-level verification index system; wherein at least some benchmark indicators in the benchmark certification database are characterized by probability distributions; running the digital human body model to be certified under standardized crash conditions to generate a model response vector; calculating the certification score of the digital human body model to be certified based on the benchmark certification database and the model response vector; and determining the certification result of the digital human body model to be certified based on the certification score.

[0006] In one embodiment, establishing a benchmark certification database for uncertainty indicators in the multi-level verification indicator system includes: if the uncertainty indicator is a continuous indicator, then establishing the probability distribution of the uncertainty indicator as a normal distribution; wherein the parameters of the normal distribution are determined by processing multi-source biomechanical experimental data using Bayesian statistical methods.

[0007] In one embodiment, calculating the authentication score of the digital human body model to be authenticated based on the benchmark authentication database and the model response vector includes: calculating the authentication score of the digital human body model to be authenticated based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index.

[0008] In one embodiment, calculating the authentication score of the digital human body model to be authenticated based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index includes: calculating the normalized likelihood score of the uncertainty index based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index; and calculating the authentication score of the digital human body model to be authenticated based on the normalized likelihood score of the uncertainty index.

[0009] In one embodiment, calculating the normalized likelihood score of the uncertainty index based on the vector element values ​​corresponding to the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index includes: the formula for calculating the normalized likelihood score of the uncertainty index is:

[0010] ;

[0011] in, For the first i The normalized likelihood score of an uncertainty indicator. For the first i The probability density function of an uncertainty index. For the corresponding number in the model response vector i The vector element values ​​of an uncertainty index, For the first i The expected value of the probability distribution of an uncertainty indicator. For the first i The lower limit reference value of the probability distribution of an uncertainty indicator.

[0012] In one embodiment, the formula for calculating the lower limit reference value is:

[0013] ;

[0014] in, For the first i The standard deviation of the probability distribution of an uncertainty indicator.

[0015] In one embodiment, calculating the authentication score of the digital human body model to be authenticated based on the normalized likelihood score of the uncertainty index includes: the calculation formula for the authentication score is:

[0016] ;

[0017] in, For certification scoring, It is a very small positive number. For the first i The weight of each uncertainty indicator, This represents the total number of uncertainty indicators.

[0018] In one embodiment, determining the authentication result of the digital human body model to be authenticated based on the authentication score includes: if the authentication score is greater than a preset authentication threshold, then the digital human body model to be authenticated is determined to have passed authentication.

[0019] In one embodiment, the authentication method for the vehicle collision digital human body model further includes: if there are vector elements in the response vector that exceed the corresponding biological trust range, then it is determined that the authentication of the digital human body model to be authenticated has failed.

[0020] According to another aspect of this application, a certification system for a digital human body model in a car crash is provided, comprising: a verification index construction module for constructing a multi-level verification index system; wherein the multi-level verification index system includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set, wherein the global dynamic response index set is used to evaluate the overall mechanical response of the model during a crash, the local tissue deformation mechanical index set is used to evaluate the authenticity of the model's mechanical response at the tissue level, and the anatomical structure kinematic consistency index set is used to quantify the motion morphology of the model's internal anatomical structures during a crash and its correlation with real biological responses. The system includes a set of damage biomechanical threshold indicators to assess the model's ability to predict damage risk; a certification data establishment module to establish a benchmark certification database for uncertainty indicators in the multi-level verification indicator system; wherein at least some benchmark indicators in the benchmark certification database are characterized by probability distributions; a response vector generation module to run the digital human body model to be certified under standardized collision conditions and generate a model response vector; a certification score calculation module to calculate the certification score of the digital human body model to be certified based on the benchmark certification database and the model response vector; and a certification result determination module to determine the certification result of the digital human body model to be certified based on the certification score.

[0021] This application provides a certification method and system for a digital human body model used in automotive collisions. It constructs a multi-level verification index system, which includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during a collision; the local tissue deformation mechanical index set is used to evaluate the realism of the model's mechanical response at the tissue level; the anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the model's internal anatomical structures and the actual biological response during a collision; and the damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk. A benchmark certification database for uncertainty indicators within the multi-level verification index system is established. The process involves establishing a benchmark certification database with at least some benchmark indicators represented by probability distributions; running the digital human body model to be certified under standardized collision conditions to generate model response vectors; calculating the certification score of the digital human body model based on the benchmark certification database and the model response vectors; determining the certification result of the digital human body model based on the certification score; using a multi-level verification indicator system to certify the vehicle collision digital human body model to improve the comprehensiveness and accuracy of the certification; establishing a benchmark certification database for uncertainty indicators; calculating the certification score using the benchmark certification database and the model response vectors obtained by running the digital human body model to be certified under standardized collision conditions; determining the certification result of the digital human body model based on the certification score; and further considering the certification results of uncertainty indicators to improve the accuracy of the certification. Attached Figure Description

[0022] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0023] Figure 1 This is a flowchart illustrating an exemplary embodiment of the present application for the authentication method of a digital human body model in a car crash.

[0024] Figure 2 This is a schematic diagram of the structure of an authentication system for a digital human body model in a car crash provided in an exemplary embodiment of this application. Detailed Implementation

[0025] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0026] Figure 1 This is a flowchart illustrating an exemplary embodiment of the authentication method for a digital human body model involved in a car crash, as provided in this application. Figure 1 As shown, the authentication method for this digital human body model in a car crash includes the following steps:

[0027] Step 110: Construct a multi-level verification indicator system.

[0028] The multi-level validation index system includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during the collision process. The local tissue deformation mechanical index set is used to evaluate the authenticity of the mechanical response of the model at the tissue level. The anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the internal anatomical structure of the model and the real biological response during the collision process. The damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk. Specifically, the global dynamic response index set includes macroscopic biomechanical parameters such as head injury criteria, chest compression, pelvic composite acceleration, and sternal displacement relative to the spine. The local tissue deformation mechanical index set includes maximum principal strain of brain tissue, maximum principal strain of rib cortical bone, maximum first principal stress of lumbar ligaments, and equivalent stress of cavities such as the heart. The anatomical structure kinematic consistency index set calculates the similarity measure of key anatomical structures by comparing simulation results with dynamic medical images (such as high-speed X-rays and dynamic CT scans). Specifically, it includes: Dessian similarity coefficient based on three-dimensional geometric segmentation, such as chest cavity contour, organ volume overlap, Hausdorff distance, maximum error of quantified contour boundary matching, and trajectory error of specific anatomical point pairs (such as vertebral body center and rib head). The injury biomechanical threshold index set includes injury risk probability based on response quantities, such as the probability of chest injury of AIS3+ grade or above based on chest compression, the concussion risk probability based on brain tissue strain level, and the risk of lumbar spine injury based on ligament force.

[0029] Step 120: Establish a benchmark certification database for uncertainty indicators in a multi-level verification indicator system.

[0030] In this benchmark certification database, at least some benchmark indicators are characterized by probability distributions. This application systematically integrates multi-source, heterogeneous biomechanical databases, with each database storing the probability distribution type and parameters for each indicator. Furthermore, probability distributions are used to characterize the inherent variability and uncertainty of each benchmark indicator, thus forming a benchmark certification database for uncertain indicators.

[0031] Step 130: Run the digital human body model to be certified under standardized collision conditions to generate the model response vector.

[0032] This application employs a series of standardized virtual collision certification scenarios, such as: 56 km / h frontal rigid barrier collision, 50 km / h deformable barrier offset collision, and 32 km / h side-moving barrier collision. The boundary conditions (barrier attributes, constraint system parameters, initial velocity, impact angle, etc.) for each scenario must be strictly defined to ensure consistency in each certification test. The standardized collision scenarios are simulated by running the digital human body model to be certified. After simulation, the output file is automatically parsed, and the response data of the digital human body model under all predetermined indicators is accurately extracted according to the defined index set. This automatically assembles a complete model response vector, avoiding errors and subjective biases that may be introduced by manual extraction.

[0033] Step 140: Calculate the certification score of the digital human body model to be certified based on the benchmark certification database and the model response vector.

[0034] After obtaining the model response vector, this application combines the benchmark certification database and the model response vector to comprehensively calculate the certification score of the digital human body model to be certified.

[0035] Step 150: Determine the certification result of the digital human body model to be certified based on the certification score.

[0036] This application determines the certification result of the digital human body model to be certified based on the certification score of the digital human body model to be certified, so as to achieve the quantification and accuracy of the certification result.

[0037] This application provides a certification method for a digital human body model used in automotive collisions. This method constructs a multi-level verification index system, which includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during a collision. The local tissue deformation mechanical index set is used to evaluate the realism of the model's mechanical response at the tissue level. The anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the model's internal anatomical structures and the actual biological response during a collision. The damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk. A benchmark certification database for uncertainty indicators within the multi-level verification index system is established. The benchmark certification database contains at least some benchmark indicators characterized by probability distributions; the digital human body model to be certified is run under standardized collision conditions to generate model response vectors; based on the benchmark certification database and model response vectors, the certification score of the digital human body model to be certified is calculated; based on the certification score, the certification result of the digital human body model to be certified is determined; a multi-level verification indicator system is used to certify the vehicle collision digital human body model to improve the comprehensiveness and accuracy of certification, and a benchmark certification database is established for uncertainty indicators. Using the benchmark certification database and the model response vectors obtained by running the digital human body model to be certified under standardized collision conditions, the certification score is calculated, and the certification result of the digital human body model to be certified is determined based on the certification score. The certification result of uncertainty indicators is further considered to improve the accuracy of certification.

[0038] In one embodiment, the specific implementation of step 120 above may be as follows: if the uncertainty index is a continuous index, then the probability distribution of the uncertainty index is established as a normal distribution; wherein, the parameters of the normal distribution are determined by processing multi-source biomechanical experimental data using Bayesian statistical methods.

[0039] Specifically, for continuous response quantities (such as chest compression and acceleration), a normal distribution is preferably used for modeling. For example, a normal distribution can be obtained by processing multi-source experimental data using Bayesian statistical methods. This method can combine prior information (such as historical data and expert knowledge) with current observation data to obtain the posterior distribution of parameters, thus more fully reflecting the dispersion of data and cognitive uncertainty. For discrete or binary indicators (such as whether a fracture occurs under a specific load), Bernoulli or Beta distributions are used for characterization. Furthermore, a biologically reliable range based on physiological limits and a large amount of prior knowledge is defined for each indicator. For example, the reliable range for chest compression can be set to [0, 100] mm. Responses exceeding this range are considered non-physical or erroneous. The reliable range is used to quickly screen models with serious errors.

[0040] In one embodiment, step 140 can be implemented by calculating the authentication score of the digital human body model to be authenticated based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index.

[0041] This application calculates the certification score of the digital human body model to be certified by comprehensively calculating the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index, thereby quantifying the certification result of the digital human body model to be certified.

[0042] In one embodiment, step 140 can be implemented as follows: based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index, calculate the normalized likelihood score of the uncertainty index; based on the normalized likelihood score of the uncertainty index, calculate the authentication score of the digital human body model to be authenticated.

[0043] This application quantifies the degree of conformity of the model response vector to the probability distribution of the benchmark index by calculating the normalized likelihood score of the uncertainty index and, based on the normalized likelihood score, calculating the certification score of the digital human body model to be certified.

[0044] In one embodiment, step 140 can be specifically implemented as follows: the formula for calculating the normalized likelihood score of the uncertainty index is:

[0045] ;

[0046] in, For the first i The normalized likelihood score of an uncertainty indicator. For the first i The probability density function of an uncertainty index. For the corresponding number in the model response vector i The vector element values ​​of an uncertainty index, For the first i The expected value of the probability distribution of an uncertainty indicator. For the first i The lower limit reference value of the probability distribution of an uncertainty indicator.

[0047] In one embodiment, the formula for calculating the lower limit reference value is:

[0048] ;

[0049] in, For the first i The standard deviation of the probability distribution of the uncertainty index. In the formula for the normalized likelihood fraction above, the numerator is... The probability value at the left tail of the distribution The difference in probability values, This is typically considered the extreme lower limit of a normal biological response. The denominator is the mean. The probability value at the left tail The difference in probability values ​​is used for normalization. Normalized to the [0,1] interval. =1 indicates that the model response is completely consistent with the expected value (mean) of the baseline distribution. =0 indicates that the model response is at or above 3. The lower bound represents an event with an extremely low probability, 0 < <1 indicates that the response is within a reasonable range, and the closer it is to 1, the better it matches the baseline expectation.

[0050] In one embodiment, step 140 can be specifically implemented as follows: the formula for calculating the authentication score is:

[0051] ;

[0052] in, For certification scoring, For a very small positive number (e.g., e), -10 ), to prevent when any A value of 0 results in a zero score for the entire certification process, in order to maintain the mathematical stability of the formula. For the first i The weights of the uncertainty indicators are determined using objective weighting methods such as the analytic hierarchy process (AHP) or the entropy weighting method. This represents the total number of uncertainty indicators. This application employs a weighted geometric mean algorithm to aggregate the normalized likelihood scores of all indicators and calculate a single comprehensive certification score for the model.

[0053] In one embodiment, step 150 can be implemented as follows: if the authentication score is greater than a preset authentication threshold, then the digital human body model to be authenticated is determined to have passed authentication.

[0054] This application can determine whether the digital human body model to be certified has passed the certification by setting a preset certification threshold and calculating the certification score after obtaining the certification score, by judging whether the certification score is greater than the preset certification threshold.

[0055] In one embodiment, the authentication method for the above-mentioned digital human body model in a car collision may further include: if there are vector elements in the response vector that exceed the corresponding biological trust range, then it is determined that the digital human body model to be authenticated has failed authentication.

[0056] This application iterates through each element value in the model's response vector, checking whether it exceeds the corresponding biologically reliable range. If it does, the certification process is immediately terminated, and the model certification is deemed unsuccessful. This process serves as a rapid and rigorous filtering step to efficiently identify and eliminate models that produce non-physical or unreliable results due to modeling errors, improper parameter settings, or computational instability. If the model does not exceed the corresponding biologically reliable range, a certification score is further calculated to certify the model.

[0057] Figure 2 This is a schematic diagram of the structure of an authentication system for a digital human body model in a car crash, provided in an exemplary embodiment of this application. Figure 2 As shown, the certification system 20 for the digital human body model in a car crash includes: a verification index construction module 21, used to construct a multi-level verification index system; wherein, the multi-level verification index system includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during the crash process, the local tissue deformation mechanical index set is used to evaluate the authenticity of the mechanical response of the model at the tissue level, and the anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the internal anatomical structure of the model and the real biological response during the crash. The system includes: a set of damage biomechanical threshold indicators to assess the model's ability to predict damage risk; a certification data establishment module 22 to establish a benchmark certification database for uncertainty indicators in a multi-level verification indicator system; wherein at least some benchmark indicators in the benchmark certification database are characterized by probability distributions; a response vector generation module 23 to run the digital human body model to be certified under standardized collision conditions and generate model response vectors; a certification score calculation module 24 to calculate the certification score of the digital human body model to be certified based on the benchmark certification database and the model response vectors; and a certification result determination module 25 to determine the certification result of the digital human body model to be certified based on the certification score.

[0058] This application provides a certification system for a digital human body model in a car collision. A multi-level verification index system is constructed through a verification index construction module 21. This system includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during a collision. The local tissue deformation mechanical index set is used to evaluate the authenticity of the model's mechanical response at the tissue level. The anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the model's internal anatomical structures and the actual biological response during a collision. The damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk. A certification data establishment module 22 establishes a benchmark certification database for the uncertainty indicators in the multi-level verification index system. The benchmark certification database... The database contains at least some benchmark indicators characterized by probability distributions; the response vector generation module 23 runs the digital human body model to be certified under standardized collision conditions to generate model response vectors; the certification score calculation module 24 calculates the certification score of the digital human body model to be certified based on the benchmark certification database and the model response vectors; the certification result determination module 25 determines the certification result of the digital human body model to be certified based on the certification score; a multi-level verification index system is used to certify the digital human body model in car collisions to improve the comprehensiveness and accuracy of certification, and a benchmark certification database is established for uncertain indicators. The certification score is calculated using the benchmark certification database and the model response vectors obtained by running the digital human body model to be certified under standardized collision conditions, and the certification result of the digital human body model to be certified is determined based on the certification score. The certification result of uncertain indicators is further considered to improve the accuracy of certification.

[0059] In one embodiment, the authentication data establishment module 22 can be further configured such that: if the uncertainty index is a continuous index, the probability distribution of the uncertainty index is established as a normal distribution; wherein, the parameters of the normal distribution are determined by processing multi-source biomechanical experimental data using Bayesian statistical methods.

[0060] In one embodiment, the authentication score calculation module 24 can be further configured to: calculate the authentication score of the digital human body model to be authenticated based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index.

[0061] In one embodiment, the authentication score calculation module 24 can be further configured to: calculate the normalized likelihood score of the uncertainty index based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index; and calculate the authentication score of the digital human body model to be authenticated based on the normalized likelihood score of the uncertainty index.

[0062] In one embodiment, the authentication scoring calculation module 24 can be further configured such that the formula for calculating the normalized likelihood score of the uncertainty index is:

[0063] ;

[0064] in, For the first i The normalized likelihood score of an uncertainty indicator. For the first i The probability density function of an uncertainty index. For the corresponding number in the model response vector i The vector element values ​​of an uncertainty index, For the first i The expected value of the probability distribution of an uncertainty indicator. For the first i The lower limit reference value of the probability distribution of an uncertainty indicator.

[0065] In one embodiment, the formula for calculating the lower limit reference value is:

[0066] ;

[0067] in, For the first i The standard deviation of the probability distribution of an uncertainty indicator.

[0068] In one embodiment, the authentication score calculation module 24 can be further configured such that the authentication score is calculated using the following formula:

[0069] ;

[0070] in, For certification scoring, It is a very small positive number. For the first i The weight of each uncertainty indicator, This represents the total number of uncertainty indicators.

[0071] In one embodiment, the authentication result determination module 25 can be further configured to: if the authentication score is greater than a preset authentication threshold, then determine that the digital human body model to be authenticated has passed the authentication.

[0072] In one embodiment, the above-mentioned vehicle collision digital human body model authentication system 20 can be further configured to: if there are vector elements in the response vector that exceed the corresponding biological trust range, then it is determined that the digital human body model to be authenticated has failed authentication.

[0073] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0074] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0075] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0076] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0077] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0078] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0079] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0080] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0081] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for certifying a digital human body model in a car crash, characterized in that, include: A multi-level validation index system is constructed, comprising a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during the collision process. The local tissue deformation mechanical index set is used to evaluate the authenticity of the model's mechanical response at the tissue level. The anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the model's internal anatomical structures and the actual biological response during the collision process. The damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk. Establish a benchmark certification database for uncertainty indicators in the multi-level verification indicator system; wherein, at least some benchmark indicators in the benchmark certification database are characterized by probability distributions; Run the digital human body model to be certified under standardized collision conditions and generate the model response vector; Based on the benchmark authentication database and the model response vector, the authentication score of the digital human body model to be authenticated is calculated; Based on the authentication score, the authentication result of the digital human body model to be authenticated is determined.

2. The authentication method for a digital human body model in a car crash according to claim 1, characterized in that, The establishment of the benchmark certification database for uncertainty indicators in the multi-level verification indicator system includes: If the uncertainty index is a continuous index, then the probability distribution of the uncertainty index is established as a normal distribution; wherein, the parameters of the normal distribution are determined by processing multi-source biomechanical experimental data using Bayesian statistical methods.

3. The authentication method for a digital human body model in a car crash according to claim 1, characterized in that, The process of calculating the authentication score of the digital human model to be authenticated based on the benchmark authentication database and the model response vector includes: The authentication score of the digital human body model to be authenticated is calculated based on the vector element values ​​corresponding to the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index.

4. The authentication method for a digital human body model in a car crash according to claim 3, characterized in that, The calculation of the authentication score for the digital human model to be authenticated, based on the vector element values ​​corresponding to the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index, includes: Based on the vector element values ​​of the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index, the normalized likelihood score of the uncertainty index is calculated. The authentication score of the digital human body model to be authenticated is calculated based on the normalized likelihood score of the uncertainty index.

5. The authentication method for a digital human body model in a car crash according to claim 4, characterized in that, The step of calculating the normalized likelihood score of the uncertainty index based on the vector element values ​​corresponding to the uncertainty index in the model response vector and the probability distribution corresponding to the uncertainty index includes: The formula for calculating the normalized likelihood score of the uncertainty index is as follows: ； in, For the first i The normalized likelihood score of an uncertainty indicator. For the first i The probability density function of an uncertainty index. For the corresponding number in the model response vector i The vector element values ​​of an uncertainty index, For the first i The expected value of the probability distribution of an uncertainty indicator. For the first i The lower limit reference value of the probability distribution of an uncertainty indicator.

6. The authentication method for a digital human body model in a car crash according to claim 5, characterized in that, The formula for calculating the lower limit reference value is as follows: ； in, For the first i The standard deviation of the probability distribution of an uncertainty indicator.

7. The authentication method for a digital human body model in a car crash according to claim 5, characterized in that, The calculation of the authentication score for the digital human body model to be authenticated based on the normalized likelihood score of the uncertainty index includes: The formula for calculating the certification score is as follows: ； in, For certification scoring, It is a very small positive number. For the first i The weight of each uncertainty indicator, This represents the total number of uncertainty indicators.

8. The authentication method for a digital human body model in a car crash according to claim 1, characterized in that, The determination of the authentication result of the digital human body model to be authenticated based on the authentication score includes: If the authentication score is greater than the preset authentication threshold, then the digital human body model to be authenticated is determined to have passed the authentication.

9. The authentication method for a digital human body model in a car crash according to claim 1, characterized in that, The authentication method for the digital human body model in a car crash also includes: If any element in the response vector exceeds the corresponding biometric trust range, then the digital human body model to be certified is determined to have failed certification.

10. A certification system for a digital human body model in a car crash, characterized in that, include: A validation index construction module is used to construct a multi-level validation index system. This multi-level validation index system includes a global dynamic response index set, a local tissue deformation mechanical index set, an anatomical structure kinematic consistency index set, and a damage biomechanical threshold index set. The global dynamic response index set is used to evaluate the overall mechanical response of the model during a collision. The local tissue deformation mechanical index set is used to evaluate the realism of the model's mechanical response at the tissue level. The anatomical structure kinematic consistency index set is used to quantify the consistency between the motion morphology of the model's internal anatomical structures and the actual biological response during a collision. The damage biomechanical threshold index set is used to evaluate the model's ability to predict damage risk. The authentication data establishment module is used to establish a benchmark authentication database for uncertainty indicators in the multi-level verification indicator system; wherein, at least some benchmark indicators in the benchmark authentication database are characterized by probability distributions; The response vector generation module is used to run the digital human body model to be certified under standardized collision conditions and generate the model's response vector. The authentication score calculation module is used to calculate the authentication score of the digital human body model to be authenticated based on the benchmark authentication database and the model response vector. The authentication result determination module is used to determine the authentication result of the digital human body model to be authenticated based on the authentication score.

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