Rib fracture risk prediction method and system of a finite element human body model

Abstract

The application provides a rib fracture risk prediction method and system of a finite element human body model, a finite element human body model is constructed through measured data; a strain response matrix is obtained based on the finite element human body model; an injury risk function representing the statistical relationship between a representative strain value and a rib fracture risk probability is constructed based on the strain response matrix; a parameter vector of the injury risk function is optimized based on the strain response matrix, and an optimized injury risk function is obtained; the rib fracture risk probability of a human body model with different representative strain values is predicted based on the optimized injury risk function; the measured data is converted into a strain response matrix and the injury risk function is constructed based on the finite element human body model, and the injury risk function is optimized by using the strain response matrix to improve the prediction accuracy, and the injury risk function can be used to quickly predict the probability of rib fracture in the finite element human body model in the actual prediction process, thereby improving the prediction accuracy and efficiency.

Description

Technical Field

[0001] This application relates to the field of prediction technology for finite element human body models, specifically to a method and system for predicting rib fracture risk using finite element human body models. Background Technology

[0002] In the field of vehicle safety design, finite element human body models have gradually become a core tool for evaluating the performance of constraint systems. Finite element human body models can provide internal biomechanical responses (such as skeletal strain), and theoretically can achieve more accurate damage prediction (such as rib fracture prediction). Currently, the most common method is to directly apply the mechanical failure properties of ex vivo skeletal materials (such as the ultimate strain of cortical bone) to the simulation results of finite element human body models. However, the resulting finite element human body model is a highly simplified mechanical system, and its internal strain field is affected by multiple factors such as mesh density, material constitutive model, connection definition, and contact algorithm, leading to a systematic deviation between the predicted strain values ​​and the real human body. Therefore, there is an urgent need for a finite element human body model that can realistically reflect the strain capacity of the human body and a corresponding method for predicting rib fractures. Summary of the Invention

[0003] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method and system for predicting rib fracture risk using a finite element human body model.

[0004] According to one aspect of this application, a method for predicting rib fracture risk using a finite element human body model is provided, comprising: collecting measured data from a real collision experiment; wherein the measured data includes a load vector, a dummy feature vector, and a dummy damage vector; constructing a finite element human body model based on the measured data; obtaining a strain response matrix representing representative strain values ​​of human ribs based on the finite element human body model; constructing a damage risk function representing the statistical relationship between representative strain values ​​and the probability of rib fracture risk based on the strain response matrix; optimizing the parameter vector of the damage risk function based on the strain response matrix to obtain an optimized damage risk function; and predicting the probability of rib fracture risk for human body models with different representative strain values ​​based on the optimized damage risk function.

[0005] In one embodiment, constructing a finite element human body model based on the measured data includes: calculating the inertial load or displacement load of the finite element human body model based on the measured data.

[0006] In one embodiment, obtaining the strain response matrix characterizing the representative strain values ​​of human ribs based on the finite element human body model includes: calculating the representative strain value of each rib in the finite element human body model; and obtaining the strain response matrix based on the representative strain values ​​of all ribs in the finite element human body model.

[0007] In one embodiment, calculating the representative strain value of each rib in the finite element human body model includes: identifying the shell elements of the cortical bone of each rib in the finite element human body model; calculating the absolute peak strain time of each shell element for each rib; and calculating the representative strain value of the corresponding rib based on the absolute peak strain times of all shell elements of each rib.

[0008] In one embodiment, constructing a damage risk function based on the strain response matrix to characterize the statistical relationship between representative strain values ​​and the probability of rib fracture includes: the damage risk function is: ;in, As a representative strain value, AGE As a covariate representing age, For scale parameters, k For shape parameters, For parameter vectors, , The intercept term represents the logarithmic scaling parameter at the baseline age. This is a coefficient representing age.

[0009] In one embodiment, optimizing the parameter vector of the damage risk function based on the strain response matrix to obtain the optimized damage risk function includes: calculating the overall risk probability for each experiment based on the strain response matrix; and optimizing the parameter vector of the damage risk function based on the overall risk probability of all experiments to obtain the optimized damage risk function.

[0010] In one embodiment, calculating the overall risk probability for each experiment based on the strain response matrix includes: calculating the fracture probability of a single rib in each experiment based on the strain response matrix corresponding to each experiment and the damage risk function; and calculating the fracture probability of multiple ribs based on the fracture probability of a single rib to obtain the overall risk probability.

[0011] In one embodiment, optimizing the parameter vector of the damage risk function based on the overall risk probability of all experiments to obtain the optimized damage risk function includes: solving a maximum likelihood estimation optimization problem based on the overall risk probability of all experiments to obtain the optimal parameter vector of the damage risk function; wherein, the maximum likelihood estimation optimization problem is: ;in, For the optimal parameter vector, For parameter vectors, It is the maximum likelihood function. , For the current parameter vector, the th i The overall risk probability of this experiment. For the firsti The damage results of this experiment, The number of experiments.

[0012] In one embodiment, predicting the rib fracture risk probability of a human model with different representative strain values ​​based on the optimized damage risk function includes: inputting the representative strain value of the human model and the covariate representing age into the optimized damage risk function to predict the rib fracture risk probability.

[0013] According to another aspect of this application, a rib fracture risk prediction system based on a finite element human body model is provided, comprising: a measured data acquisition module for acquiring measured data from real collision experiments; wherein the measured data includes a load vector, a dummy feature vector, and a dummy damage vector; a human body model construction module for constructing a finite element human body model based on the measured data; a strain matrix determination module for obtaining a strain response matrix characterizing representative strain values ​​of human ribs based on the finite element human body model; a damage function construction module for constructing a damage risk function characterizing the statistical relationship between representative strain values ​​and the probability of rib fracture based on the strain response matrix; a damage function optimization module for optimizing the parameter vector of the damage risk function based on the strain response matrix to obtain an optimized damage risk function; and a fault probability prediction module for predicting the probability of rib fracture for human body models with different representative strain values ​​based on the optimized damage risk function.

[0014] This application provides a method and system for predicting rib fracture risk using a finite element human body model. The method involves collecting measured data from real collision experiments, including load vectors, dummy feature vectors, and dummy damage vectors. Based on the measured data, a finite element human body model is constructed. Based on the finite element human body model, a strain response matrix representing the representative strain values ​​of the human ribs is obtained. Based on the strain response matrix, a damage risk function representing the statistical relationship between the representative strain values ​​and the probability of rib fracture risk is constructed. Based on the strain response matrix, the parameter vector of the damage risk function is optimized to obtain an optimized damage risk function. Based on the optimized damage risk function, the probability of rib fracture risk for human body models with different representative strain values ​​is predicted. The method utilizes measured data from experiments to construct the finite element human body model, improving model accuracy. Based on the finite element human body model, the measured data is converted into a strain response matrix, and a damage risk function is constructed based on the strain response matrix. Simultaneously, the damage risk function is optimized using the strain response matrix to improve its prediction accuracy. In actual prediction, the damage risk function can quickly predict the probability of rib fracture in the finite element human body model, thereby improving prediction accuracy and efficiency. Attached Figure Description

[0015] 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.

[0016] Figure 1 This is a flowchart illustrating a method for predicting rib fracture risk using a finite element human body model, provided in an exemplary embodiment of this application.

[0017] Figure 2 This is a schematic diagram of the structure of a rib fracture risk prediction system based on a finite element human body model provided in an exemplary embodiment of this application. Detailed Implementation

[0018] 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.

[0019] Figure 1 This is a flowchart illustrating a method for predicting rib fracture risk using a finite element human body model, provided in an exemplary embodiment of this application. Figure 1 As shown, the method for predicting rib fracture risk using this finite element human body model includes the following steps:

[0020] Step 110: Collect measured data from real collision experiments.

[0021] The measured data includes load vectors, dummy feature vectors, and dummy damage vectors. The load vectors precisely describe the experimental input conditions; for impact tests, this includes the impactor mass and velocity; for trolley tests, it includes displacement-time or force-time curves. The dummy feature vectors include key biomechanical attributes of the dummy used in the experiment, such as simulated age, weight, and chest depth, used for biomechanical scaling correction of the simulation to ensure biofidelity between the simulation and the experiment. The dummy damage vector is a binary array of length 24, precisely marking whether each rib has fractured and obtaining the overall damage level. This database of measured data ensures the comparability between subsequent simulation matching and real experiments, providing a foundation for establishing a data-driven damage risk function.

[0022] Step 120: Construct a finite element human body model based on measured data.

[0023] This application obtains the above-mentioned measured data through actual experimental testing, and constructs a finite element human body model based on the measured data to obtain a model that is as close as possible to the real scene, thereby improving the accuracy of subsequent predictions.

[0024] Step 130: Based on the finite element human body model, obtain the strain response matrix that represents the representative strain values ​​of the human ribs.

[0025] After constructing the finite element human body model, the measured data is converted into a strain response matrix that represents the strain values ​​of the human ribs, based on the finite element human body model simulation. This yields the data structure and data type required for finite element human body model simulation.

[0026] Step 140: Based on the strain response matrix, construct a damage risk function that characterizes the statistical relationship between representative strain values ​​and the probability of rib fracture risk.

[0027] This application seeks an optimal parametric damage risk function that can most accurately describe the statistical relationship between the strain signal extracted from the finite element human body model and the actual damage probability.

[0028] Step 150: Based on the strain response matrix, optimize the parameter vector of the damage risk function to obtain the optimized damage risk function.

[0029] This application optimizes the parameter vector of the damage risk function by using the strain response matrix to obtain the optimized damage risk function, thereby obtaining a more accurate statistical relationship between the strain signal and the true damage probability.

[0030] Step 160: Based on the optimized damage risk function, predict the probability of rib fracture risk for human models with different representative strain values.

[0031] After optimizing the damage risk function, the probability of rib fracture risk of human models with different representative strain values ​​is predicted based on the optimized damage risk function, so as to improve the prediction accuracy of rib fracture risk probability.

[0032] This application provides a method for predicting rib fracture risk using a finite element human body model. The method involves collecting measured data from real collision experiments, including load vectors, dummy feature vectors, and dummy damage vectors. Based on the measured data, a finite element human body model is constructed. Based on the finite element human body model, a strain response matrix representing the representative strain values ​​of the human ribs is obtained. Based on the strain response matrix, a damage risk function representing the statistical relationship between the representative strain values ​​and the probability of rib fracture risk is constructed. Based on the strain response matrix, the parameter vector of the damage risk function is optimized to obtain an optimized damage risk function. Based on the optimized damage risk function, the probability of rib fracture risk for human body models with different representative strain values ​​is predicted. The method utilizes measured data from experiments to construct the finite element human body model, thereby improving the model's accuracy. Based on the finite element human body model, the measured data is converted into a strain response matrix, and a damage risk function is constructed according to the strain response matrix. Simultaneously, the damage risk function is optimized using the strain response matrix to improve its prediction accuracy. In actual prediction, the damage risk function can quickly predict the probability of rib fracture in the finite element human body model, thus improving prediction accuracy and efficiency.

[0033] In one embodiment, step 120 can be implemented by calculating the inertial load or displacement load of the finite element human body model based on measured data.

[0034] For inertial loads (such as pendulums), the impactor mass is scaled based on body weight using measured data to maintain momentum density similarity. For displacement loads (such as slide table tests), the input displacement curve is scaled based on chest depth using measured data to match chest compression rate. Multiple sets of complete simulation result files are generated after completing several simulations.

[0035] In one embodiment, step 130 can be implemented by: calculating the representative strain value of each rib in the finite element human body model; and obtaining the strain response matrix based on the representative strain values ​​of all ribs in the finite element human body model.

[0036] This application calculates the representative strain value of each rib in the finite element human body model and combines the representative strain values ​​of all ribs in the finite element human body model to obtain a strain response matrix. For example, the rows of the strain response matrix represent the experimental data for each time, and each column represents the representative strain value of a rib.

[0037] In one embodiment, step 130 can be implemented as follows: identifying the shell elements of the cortical bone of each rib in the finite element human body model; calculating the absolute peak strain time of each shell element for each rib; and calculating the representative strain value of the corresponding rib based on the absolute peak strain times of all shell elements of each rib.

[0038] The shell elements of all rib cortical bone in the finite element human body model are identified. For each shell element, the time histories of the maximum and minimum principal strains at its integration point are calculated, and then the absolute peak strain time histories (the maximum value of the maximum and minimum principal strain time histories) are generated. For each rib, the peak values ​​of all its shell elements on the absolute peak strain time histories are collected to form a set of peak strains. The 95th percentile of the set of peak strains of all shell elements of the rib is taken to obtain the representative strain value of the rib. This quantile can robustly characterize the high strain region of the rib, while effectively filtering numerical noise caused by element distortion, hourglass mode, or local contact.

[0039] In one embodiment, step 140 can be implemented as follows: the damage risk function is: ;in, As a representative strain value, AGE As a covariate representing age, For scale parameters, k For shape parameters, For parameter vectors, .

[0040] This application controls the steepness of the curve by setting shape parameters, wherein, k The larger the value, the steeper the curve, meaning a narrower transition zone from "safety" to "failure"; scale parameter Representative characteristic strain, The intercept term represents the logarithmic scaling parameter at the baseline age. The coefficient representing age decreases with age, meaning that older adults have a higher risk of fractures at the same strain level.

[0041] In one embodiment, step 150 can be implemented as follows: based on the strain response matrix, calculate the overall risk probability of each experiment; based on the overall risk probability of all experiments, optimize the parameter vector of the damage risk function to obtain the optimized damage risk function.

[0042] This application calculates the overall risk probability of each experiment based on the strain response matrix, and optimizes the parameter vector of the damage risk function based on the overall risk probability of all experiments to obtain the optimized damage risk function.

[0043] In one embodiment, step 150 can be implemented as follows: based on the strain response matrix and damage risk function corresponding to each experiment, calculate the fracture probability of a single rib in each experiment; based on the fracture probability of a single rib, calculate the fracture probability of multiple ribs to obtain the overall risk probability.

[0044] For a single experiment, this application calculates the overall probability risk of ≥X (e.g., X=3) rib fractures in that single experiment based on the strain response matrix and damage risk function corresponding to that experiment. The specific calculation process is as follows:

[0045] First, the risk of a single rib fracture is calculated: for each rib, its single-rib fracture probability is calculated. Specifically, the strain response matrix of a single experiment can be input into the damage risk function to calculate the risk probability of a single rib fracture. Then, multi-rib probability aggregation is performed: given 24 independent Bernoulli trials with different probabilities (each probability representing the fracture probability of each rib), the probability distribution of the total number of fractures is calculated. Using a Poisson binomial distribution model and a fast algorithm based on discrete Fourier transform, the probability of a total number of fractures of K (K being a positive integer) is accurately calculated. Finally, the cumulative risk probability of K ≥ X is calculated and returned, which is the overall risk probability.

[0046] In one embodiment, step 150 can be implemented as follows: based on the overall risk probability of all experiments, solve the maximum likelihood estimation optimization problem to obtain the optimal parameter vector of the damage risk function; wherein, the maximum likelihood estimation optimization problem is: ;in, For the optimal parameter vector, For parameter vectors, It is the maximum likelihood function. , For the current parameter vector, the th i The overall risk probability of this experiment. For the first i The damage results of this experiment, The number of experiments.

[0047] This application employs a maximum likelihood estimation framework to find a set of parameters θ such that the observed damage outcome is most likely to occur under these parameters. Indicates the first i The damage results of this experiment, =1 indicates that an event with ≥3 fractures has occurred. =0 indicates that the event of ≥3 fractures did not occur. This application obtains a set of optimal parameter vectors by solving the above optimization problem. ( (a set of parameters in the equation), thus obtaining the optimized damage risk function.

[0048] In one embodiment, step 160 can be implemented by inputting the representative strain value of the human body model and the covariate representing age into the optimized injury risk function to predict the probability of rib fracture risk.

[0049] In actual prediction, the representative strain vector of the ribs in the finite element human body model under the target scenario is extracted, and the representative strain vector and the age of the target occupant are input into the optimized damage risk function to directly calculate the probability of ≥X rib fractures under the scenario.

[0050] Optionally, this application can generate a comprehensive report based on the predicted probability of ≥X rib fractures in the scenario, including numerical risk, rib probability cloud map (for guiding design improvements), etc.

[0051] Actual prediction results: This application sets initial parameters θ={3.356, -2.868, -0.017}, and after optimization, obtains the optimal parameters: θ*={k=3.3562, β=-3.0665, β1=-0.0179}. Simulations of a full-vehicle collision at 56 km / h predict a 27.9% risk of ≥3 rib fractures in a 65-year-old male occupant. This predicted value is consistent with actual epidemiological statistics and biomechanical expectations for this age group (risk increases significantly with age), demonstrating the biofidelity and predictive rationality of the method.

[0052] Figure 2 This is a schematic diagram of the structure of a rib fracture risk prediction system based on a finite element human body model provided in an exemplary embodiment of this application. Figure 2 As shown, the rib fracture risk prediction system 20 for the finite element human body model includes: a measured data acquisition module 21, used to acquire measured data from real collision experiments; wherein, the measured data includes load vector, dummy feature vector, and dummy damage vector; a human body model construction module 22, used to construct a finite element human body model based on the measured data; a strain matrix determination module 23, used to obtain a strain response matrix representing the representative strain values ​​of the human ribs based on the finite element human body model; a damage function construction module 24, used to construct a damage risk function representing the statistical relationship between the representative strain values ​​and the probability of rib fracture based on the strain response matrix; a damage function optimization module 25, used to optimize the parameter vector of the damage risk function based on the strain response matrix to obtain the optimized damage risk function; and a fault probability prediction module 26, used to predict the probability of rib fracture for human body models with different representative strain values ​​based on the optimized damage risk function.

[0053] This application provides a rib fracture risk prediction system based on a finite element human body model. The system acquires measured data from real collision experiments via a measured data acquisition module 21. The measured data includes load vectors, dummy feature vectors, and dummy damage vectors. A human body model construction module 22 constructs a finite element human body model based on the measured data. A strain matrix determination module 23 obtains a strain response matrix representing the representative strain values ​​of the human ribs based on the finite element human body model. A damage function construction module 24 constructs a damage risk function representing the statistical relationship between the representative strain values ​​and the probability of rib fracture based on the strain response matrix. A damage function optimization module 25 optimizes the damage function based on the strain response matrix. The parameter vector of the damage risk function is used to obtain the optimized damage risk function. The fault probability prediction module 26 predicts the rib fracture risk probability of the human body model with different representative strain values ​​based on the optimized damage risk function. A finite element human body model is constructed using experimentally measured data to improve the accuracy of the model. Based on the finite element human body model, the measured data is converted into a strain response matrix, and a damage risk function is constructed based on the strain response matrix. At the same time, the damage risk function is optimized using the strain response matrix to improve its prediction accuracy. In the actual prediction process, the damage risk function can quickly predict the probability of rib fracture in the finite element human body model, thereby improving the prediction accuracy and efficiency.

[0054] In one embodiment, the human body model construction module 22 can be further configured to: calculate the inertial load or displacement load of the finite element human body model based on measured data.

[0055] In one embodiment, the strain matrix determination module 23 can be further configured to: calculate the representative strain value of each rib in the finite element human body model; and obtain the strain response matrix based on the representative strain values ​​of all ribs in the finite element human body model.

[0056] In one embodiment, the strain matrix determination module 23 can be further configured to: identify the shell elements of the cortical bone of each rib in the finite element human body model; calculate the absolute peak strain time of each shell element for each rib; and calculate the representative strain value of the corresponding rib based on the absolute peak strain time of all shell elements of each rib.

[0057] In one embodiment, the damage function construction module 24 can be further configured such that the damage risk function is: ;in, As a representative strain value, AGE As a covariate representing age, For scale parameters, k For shape parameters, For parameter vectors, , The intercept term represents the logarithmic scaling parameter at the baseline age. This is a coefficient representing age.

[0058] In one embodiment, the damage function optimization module 25 can be further configured to: calculate the overall risk probability of each experiment based on the strain response matrix; optimize the parameter vector of the damage risk function based on the overall risk probability of all experiments, and obtain the optimized damage risk function.

[0059] In one embodiment, the damage function optimization module 25 can be further configured to: calculate the fracture probability of a single rib in each experiment based on the strain response matrix and damage risk function corresponding to each experiment; and calculate the fracture probability of multiple ribs based on the fracture probability of a single rib to obtain the overall risk probability.

[0060] In one embodiment, the damage function optimization module 25 can be further configured to: solve the maximum likelihood estimation optimization problem based on the overall risk probability of all experiments to obtain the optimal parameter vector of the damage risk function; wherein, the maximum likelihood estimation optimization problem is: ;in, For the optimal parameter vector, For parameter vectors, It is the maximum likelihood function. , For the current parameter vector, the th i The overall risk probability of this experiment. For the first i The damage results of this experiment, The number of experiments.

[0061] In one embodiment, the fault probability prediction module 26 can be further configured to: input the representative strain value of the human body model and the covariate representing age into the optimized damage risk function to predict the probability of rib fracture risk.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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 rib fracture risk prediction method of a finite element human body model, characterized by, The method comprises the following steps: Collecting measured data in a real collision experiment; wherein the measured data comprises a load vector, a dummy characteristic vector, and a dummy damage vector; Based on the measured data, a finite element human body model is constructed; Based on the finite element human body model, a strain response matrix representing the representative strain value of the human rib is obtained; Based on the strain response matrix, a damage risk function representing the statistical relationship between the representative strain value and the rib fracture risk probability is constructed; Based on the strain response matrix, the parameter vector of the damage risk function is optimized to obtain an optimized damage risk function; Based on the optimized damage risk function, the rib fracture risk probability of the human body model under different representative strain values is predicted; The method comprises the following steps: Calculating the representative strain value of each rib in the finite element human body model; Based on the representative strain values of all ribs in the finite element human body model, the strain response matrix is obtained; The method comprises the following steps: Identifying the shell elements of the cortical bone of each rib of the finite element human body model; For each rib, the absolute peak strain time of each shell element is calculated; wherein the absolute peak strain time is calculated by calculating the maximum principal strain time history and the minimum principal strain time history of the integral points of the shell element, and selecting the maximum value in the maximum principal strain time history and the minimum principal strain time history as the absolute peak strain time history; Based on the absolute peak strain time of all shell elements of each rib, the representative strain value of the corresponding rib is calculated; wherein the representative strain value is calculated by collecting the peak values of all shell elements of the rib on the absolute value peak strain time history to form a peak strain set, and taking the 95% quantile of the peak strain set of all shell elements of the rib to obtain the representative strain value of the rib; The method comprises the following steps: The damage risk function is: ； wherein, is a representative strain value, AGE is a covariate of representative age, is a scale parameter, k is a shape parameter, is a parameter vector, , is an intercept term, representing the log scale parameter at a reference age, is a coefficient of representative age.

2. The rib fracture risk prediction method of the finite element human body model according to claim 1, characterized by, The method comprises the following steps: Based on the measured data, the inertial load or displacement load of the finite element human body model is calculated.

3. The rib fracture risk prediction method of the finite element human body model according to claim 1, characterized by, The method comprises the following steps: Based on the strain response matrix, the overall risk probability of each experiment is calculated; Based on the overall risk probability of all experiments, the parameter vector of the damage risk function is optimized to obtain the optimized damage risk function.

4. The rib fracture risk prediction method of the finite element human body model according to claim 3, characterized in that, The method comprises the following steps: Based on the strain response matrix corresponding to each experiment and the damage risk function, the fracture probability of a single rib in each experiment is calculated; Based on the fracture probability of a single rib, the fracture probability of multiple ribs is calculated to obtain the overall risk probability.

5. The rib fracture risk prediction method of the finite element human body model according to claim 3, characterized in that, The parameter vector of the damage risk function is optimized based on the overall risk probability of all experiments, to obtain the optimized damage risk function, which comprises: An optimal parameter vector of the damage risk function is obtained by solving a maximum likelihood estimation optimization problem based on the overall risk probability of all experiments; wherein the maximum likelihood estimation optimization problem is: ； wherein, is the optimal parameter vector, is the parameter vector, is the maximum likelihood function, , is the overall risk probability of the i th experiment under the current parameter vector, is the damage result of the i th experiment, is the number of experiments.

6. The rib fracture risk prediction method of the finite element human body model according to claim 1, characterized by, The rib fracture risk probability of the human body model with different representative strain values is predicted based on the optimized damage risk function, which comprises: The representative strain value of the human body model and the representative age of the covariate are input into the optimized damage risk function to obtain the rib fracture risk probability.

7. A rib fracture risk prediction system for a finite element human body model, characterized by, Comprise: The measured data acquisition module is configured to acquire measured data in a real collision experiment; wherein the measured data comprises a load vector, a dummy characteristic vector, and a dummy damage vector; The human body model construction module is configured to construct a finite element human body model based on the measured data; The strain matrix determination module is configured to obtain a strain response matrix representing the representative strain value of the human rib based on the finite element human body model; The damage function construction module is configured to construct a damage risk function representing the statistical relationship between the representative strain value and the rib fracture risk probability based on the strain response matrix; The damage function optimization module is configured to optimize the parameter vector of the damage risk function based on the strain response matrix to obtain an optimized damage risk function; The failure probability prediction module is configured to predict the rib fracture risk probability of the human body model with different representative strain values based on the optimized damage risk function; The strain matrix determination module is further configured to: Calculate the representative strain value of each rib in the finite element human body model; Obtain the strain response matrix based on the representative strain value of all ribs in the finite element human body model; The strain matrix determination module is further configured to: Identify the shell elements of the cortical bone of each rib of the finite element human body model; For each rib, calculate the absolute peak strain time of each shell element; wherein the absolute peak strain time is calculated by calculating the maximum principal strain time history and the minimum principal strain time history at the integration points of the shell elements, and selecting the maximum value in the maximum principal strain time history and the minimum principal strain time history as the absolute peak strain time history; Based on the absolute peak strain time of all shell elements of each rib, the representative strain value of the corresponding rib is calculated; wherein the representative strain value is calculated by collecting the peak values of all shell elements of the rib on the absolute value peak strain time history to form a peak strain set, and taking the 95% quantile of the peak strain set of all shell elements of the rib to obtain the representative strain value of the rib; The damage function construction module is further configured to: The damage risk function is: ； wherein, is a representative strain value, AGE is a representative age covariate, is a scale parameter, k is a shape parameter, is a parameter vector, , is an intercept term, representing the log scale parameter at a reference age, is a coefficient of representative age.

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