A method and system for constructing human biomechanical response channels in automobile collisions

Abstract

This application provides a method and system for constructing a biomechanical response channel for a human body in a car collision, specifically including: acquiring experimental data; aligning the biomechanical time-domain signals during the signal loading stage to obtain an aligned signal; performing principal component decomposition on the aligned signal to extract eigenvalues; constructing a prediction model between the eigenvalues ​​of the aligned signal and the human body parameters based on the human body parameters corresponding to the aligned signal; inputting the target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters; improving signal consistency by aligning the biomechanical time-domain signals during the signal loading stage, reducing computational load by performing principal component decomposition on the aligned signal, and achieving automated prediction of the biomechanical response channel corresponding to the target human body parameters by constructing a prediction model between the eigenvalues ​​of the aligned signal and the human body parameters, thereby improving prediction efficiency while ensuring prediction accuracy.

Description

Technical Field

[0001] This application relates to the field of automotive collision technology, specifically to a method and system for constructing a human biomechanical response channel in automotive collisions. Background Technology

[0002] In automotive crash safety research, biomechanical response channels constructed based on experimental data are an important standard for verifying the performance of human body substitutes. However, traditional methods have the following limitations: traditional global time normalization methods fail to fully consider individual differences in signal phase; commonly used channel construction methods based on the average response of all test samples lack statistical modeling of the explicit mapping relationship between anthropometric parameters (such as body size) and response characteristics, resulting in overly simplistic assumptions in normalization techniques and poor applicability in complex structures; alignment methods are subjective or global; the lack of statistical modeling of body size differences makes it impossible to generate accurate predictive channels for target body sizes; and channel construction is disconnected from the target body size, only representing the average level of test samples.

[0003] Therefore, there is an urgent need for a method that can automatically construct high-precision response channels for specific body shapes. 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 constructing a human biomechanical response channel during a vehicle collision.

[0005] According to one aspect of this application, a method for constructing a biomechanical response channel for a human body in a car collision is provided, comprising: acquiring experimental data; wherein the experimental data includes biomechanical time-domain signals and human body parameters; aligning the biomechanical time-domain signals during the alignment signal loading stage to obtain an alignment signal; performing principal component decomposition on the alignment signal to extract feature values ​​of the alignment signal; constructing a prediction model between the feature values ​​of the alignment signal and the human body parameters based on the human body parameters corresponding to the alignment signal; and inputting target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters.

[0006] In one embodiment, obtaining the alignment signal from the biomechanical time-domain signal during the alignment signal loading phase includes: calculating a reference signal of the biomechanical time-domain signal during the loading phase; and calculating the alignment signal based on the reference signal and the corresponding time offset.

[0007] In one embodiment, calculating the reference signal for the biomechanical time-domain signal during the loading phase includes: selecting one of a plurality of biomechanical time-domain signals during the loading phase as an initial reference signal; calculating the offset correlation function value between the other biomechanical time-domain signals and the initial reference signal; obtaining an optimal offset time based on the offset correlation function value; updating the other biomechanical time-domain signals based on the optimal offset time; calculating an updated reference signal based on the updated other biomechanical time-domain signals and the initial reference signal; calculating the difference between the initial reference signal and the updated reference signal; and determining the updated reference signal as the reference signal for the biomechanical time-domain signal during the loading phase if the difference is less than a preset difference threshold.

[0008] In one embodiment, the step of obtaining the optimal offset time based on the offset correlation function value includes: searching for the optimal offset time within a preset time offset range to maximize the offset correlation function value.

[0009] In one embodiment, the biomechanical time-domain signal includes: a force signal, a displacement signal, a deformation signal, and an acceleration signal; wherein, the method for determining the signal loading stage includes: the start point and end point of the signal loading stage for the force signal, the displacement signal, and the deformation signal are determined based on the signal amplitude of the corresponding signal; the start point and end point of the signal loading stage for the acceleration signal are determined based on the amplitude and integral value of the corresponding signal.

[0010] In one embodiment, performing principal component decomposition on the alignment signal to extract the feature values ​​of the alignment signal includes: calculating an average response curve based on the alignment signal; calculating the deviation between each alignment signal and the average response curve to obtain a deviation matrix; and performing principal component decomposition on the deviation matrix to obtain the feature values ​​of the alignment signal.

[0011] In one embodiment, constructing a prediction model between the feature values ​​of the alignment signal and the human body parameters based on the human body parameters corresponding to the alignment signal includes: constructing a prediction model between the feature values ​​of the alignment signal and the human body parameters using a linear regression model.

[0012] In one embodiment, the step of inputting the target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters includes: calculating the corresponding feature distribution based on the target human body parameters and the prediction model; generating multiple response curves of the target human body parameters based on the feature distribution; and predicting the biomechanical response channel parameters corresponding to the target human body parameters based on the multiple response curves of the target human body parameters.

[0013] In one embodiment, predicting the biomechanical response channel parameters corresponding to the target human body parameters based on multiple response curves of the target human body parameters includes: calculating the first quantile and the second quantile of the multiple response curves of the target human body parameters respectively; wherein the confidence levels corresponding to the first quantile and the second quantile are greater than a preset confidence threshold; and determining the lower limit and upper limit of the biomechanical response channel corresponding to the target human body parameters based on the first quantile and the second quantile.

[0014] According to another aspect of this application, a system for constructing a biomechanical response channel for a car collision is provided, comprising: a test data acquisition module for acquiring test data, wherein the test data includes biomechanical time-domain signals and human parameters; an alignment signal processing module for aligning the biomechanical time-domain signals during the signal loading phase to obtain an aligned signal; a signal feature extraction module for performing principal component decomposition on the aligned signal to extract feature values ​​of the aligned signal; a prediction model construction module for constructing a prediction model between the feature values ​​of the aligned signal and the human parameters based on the human parameters corresponding to the aligned signal; and a channel parameter determination module for inputting target human parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human parameters.

[0015] This application provides a method and system for constructing a biomechanical response channel for a human body in a car collision, specifically including: acquiring experimental data; aligning the biomechanical time-domain signals during the signal loading stage to obtain an aligned signal; performing principal component decomposition on the aligned signal to extract eigenvalues; constructing a prediction model between the eigenvalues ​​of the aligned signal and the human body parameters based on the human body parameters corresponding to the aligned signal; inputting the target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters; improving signal consistency by aligning the biomechanical time-domain signals during the signal loading stage, reducing computational load by performing principal component decomposition on the aligned signal, and achieving automated prediction of the biomechanical response channel corresponding to the target human body parameters by constructing a prediction model between the eigenvalues ​​of the aligned signal and the human body parameters, thereby improving prediction efficiency while ensuring prediction accuracy. Attached Figure Description

[0016] 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.

[0017] Figure 1This is a flowchart illustrating a method for constructing a human biomechanical response channel in a car collision, provided in an exemplary embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the structure of a vehicle collision human biomechanical response channel construction system provided in an exemplary embodiment of this application. Detailed Implementation

[0019] 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.

[0020] Figure 1 This is a flowchart illustrating a method for constructing a human biomechanical response channel in a car collision, provided in an exemplary embodiment of this application. Figure 1 As shown, the method for constructing the human biomechanical response channel in a car collision includes the following steps:

[0021] Step 110: Obtain experimental data.

[0022] The experimental data includes biomechanical time-domain signals and human body parameters. This application obtains the original biomechanical time-domain signals and the corresponding human body parameters for each test by referring to multiple valid PMHS crash tests. The biomechanical time-domain signals include, but are not limited to, contact force signals, acceleration signals, displacement or deformation signals, and the human body parameters include, but are not limited to, mass, chest depth, height, etc.

[0023] This application can filter test data to avoid data distortion, and ensure that the signal mean is zero before the impact by performing baseline correction (eliminating signal drift) and zero-time alignment (setting the external trigger time to zero).

[0024] Step 120: Align the biomechanical time-domain signal during the signal loading stage to obtain the alignment signal.

[0025] This application resamples all signals to a uniform sampling frequency (e.g., 10 kHz) using interpolation methods (such as linear or spline interpolation), aligns the initial time points of all signals to the moment of external triggering (e.g., impactor contact), and truncates all signals to the same total time length to ensure time vector consistency.

[0026] Step 130: Perform principal component decomposition on the aligned signal and extract the eigenvalues ​​of the aligned signal.

[0027] This application performs principal component decomposition on the aligned signal to extract its eigenvalues, thereby achieving dimensionality reduction of the aligned signal, reducing computational load, and minimizing interference from non-principal components.

[0028] Step 140: Based on the human body parameters corresponding to the alignment signal, construct a prediction model between the feature values ​​of the alignment signal and the human body parameters.

[0029] This application combines the alignment signal and the corresponding human body parameters to construct a prediction model between the feature values ​​of the alignment signal and the human body parameters, which has achieved automated prediction.

[0030] Step 150: Input the target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters.

[0031] In the actual prediction process, the target human body parameters are input into the prediction model, and the corresponding biomechanical response channel parameters are directly generated.

[0032] This application provides a method for constructing a biomechanical response channel for a human body in a car collision, specifically including: acquiring experimental data; aligning the biomechanical time-domain signals during the signal loading stage to obtain an aligned signal; performing principal component decomposition on the aligned signal to extract eigenvalues; constructing a prediction model between the eigenvalues ​​of the aligned signal and the human body parameters based on the human body parameters corresponding to the aligned signal; inputting the target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters; improving signal consistency by aligning the biomechanical time-domain signals during the signal loading stage, reducing computational load by performing principal component decomposition on the aligned signal, and achieving automated prediction of the biomechanical response channel corresponding to the target human body parameters by constructing a prediction model between the eigenvalues ​​of the aligned signal and the human body parameters, thereby improving prediction efficiency while ensuring prediction accuracy.

[0033] In one embodiment, step 120 can be implemented by: calculating a reference signal for the biomechanical time-domain signal during the loading phase; and calculating an alignment signal based on the reference signal and the corresponding time offset.

[0034] This application calculates a reference signal for the biomechanical time-domain signal during the loading phase, and calculates an alignment signal based on the reference signal and the corresponding time offset, so as to eliminate the relative phase delay between signals while preserving the differences in their amplitude characteristics. These differences reflect the real differences in biomechanical response between different individuals.

[0035] In one embodiment, step 120 can be implemented as follows: selecting one of the multiple biomechanical time-domain signals during the loading stage as the initial reference signal; calculating the offset correlation function value between the other biomechanical time-domain signals and the initial reference signal; obtaining the optimal offset time based on the offset correlation function value; updating the other biomechanical time-domain signals based on the optimal offset time; calculating the updated reference signal based on the updated other biomechanical time-domain signals and the initial reference signal; calculating the difference between the initial reference signal and the updated reference signal; if the difference is less than a preset difference threshold, then determining the updated reference signal as the reference signal of the biomechanical time-domain signals during the loading stage.

[0036] Specifically, let the set of original biomechanical time-domain signals be:

[0037] ,in, For the first i A biomechanical time-domain signal This represents the total number of biomechanical time-domain signals. From Choose one of the signals as the initial reference signal. For sets Each unaligned signal Within the preset time offset search range h ∈[ h min ,h max Within [the specified range], calculate its relationship with the current reference signal. The offset correlation function value between the two must be calculated in such cases that the denominator is not zero. For example, by setting a minimum threshold ε, it is required that the value of || be satisfied within the calculation interval. |>ε. Furthermore, by solving an optimization problem, the optimal offset time is found. , will signal The signal is aligned by translating according to the optimal offset time. ; align all signals Perform point-to-point averaging to generate a new reference signal. ; Calculate the difference between the old and new reference signals (e.g., root mean square error). If the difference is less than a set threshold (e.g., ... If the preset maximum number of iterations is reached, the loop will exit; otherwise, use... renew The loop continues until the condition for exiting the loop is met, using the current reference signal as the reference signal and returning to find the optimal offset time. Finally, the set of phase-aligned signals and their corresponding optimal offset times are obtained.

[0038] The formula for calculating the offset correlation function value is as follows:

[0039] ;in, It is the start time. It is the end time. This represents the offset time.

[0040] In one embodiment, step 120 can be implemented by searching for the optimal offset time within a preset time offset range to maximize the offset correlation function value.

[0041] Specifically, optimal offset time The formula for finding it is: ,in, Indicates in h ∈[ h min ,h max [Find one] h To maximize the offset correlation function value, the found h That is, the optimal offset time. .

[0042] In one embodiment, the biomechanical time-domain signal includes: force signal, displacement signal, deformation signal, and acceleration signal; wherein, the specific implementation of step 120 above may be: the start point and end point of the signal loading stage of the force signal, displacement signal, and deformation signal are determined according to the signal amplitude of the corresponding signal; the start point and end point of the signal loading stage of the acceleration signal are determined according to the amplitude and integral value of the corresponding signal.

[0043] For signals such as force, displacement, and deformation, the starting point can be determined as the moment when the signal amplitude first continuously exceeds the baseline noise threshold, where the baseline noise threshold is: Where y represents the biomechanical time-domain signal currently being processed. It corresponds to the maximum amplitude of the signal. It is the noise standard deviation of the signal segment before the impact; the end point can be determined as the moment when the signal reaches its global maximum value.

[0044] For acceleration signals, the starting point can be the same as that defined for force, displacement, deformation, and other signals, and the ending point can be determined as the velocity signal obtained by integrating it. The moment when the global maximum value is first reached, where, It is the start time. It is the end time. It is an acceleration signal.

[0045] In one embodiment, step 130 can be implemented as follows: based on the alignment signal, calculate the average response curve; calculate the deviation between each alignment signal and the average response curve to obtain the deviation matrix; perform principal component decomposition on the deviation matrix to obtain the eigenvalues ​​of the alignment signal.

[0046] Specifically, the average response curve of the aligned signal set is calculated, where the formula for calculating the average response curve is:

[0047] ;

[0048] in, For time The response signal value, For the first i Alignment signals in time The signal value, This represents the total number of biomechanical time-domain signals.

[0049] Then, the deviation between each alignment signal and the average response curve is calculated to form a deviation matrix. :

[0050] ;

[0051] in, .

[0052] Finally, the deviation matrix Perform principal component decomposition to find the eigenvalues ​​and eigenvectors of the covariance matrix. The eigenvalues ​​(variances) are arranged in descending order as follows: ,in, For the first 1 eigenvalue, The total number of eigenvalues; the response deviation of each aligned signal can be expressed as: ,in, For the first The eigenvectors corresponding to each eigenvalue. It is the first The alignment signal in the first The principal component scores on each eigenvector are obtained by solving for the minimum value. q This makes the cumulative variance contribution rate Greater than 99%.

[0053] In one embodiment, step 140 can be implemented by constructing a predictive model between the feature values ​​of the aligned signal and human body parameters using a linear regression model. The linear regression model includes, but is not limited to, stepwise regression, multiple linear regression, and other methods.

[0054] For each retained principal component (corresponding to the first) (a set of eigenvectors), with their principal component scores As the dependent variable, the corresponding anthropometry parameter vector Using stepwise linear regression as the independent variable, a multiple linear prediction model is constructed:

[0055] ;

[0056] in, The intercept is... These are the regression parameters, For quality, Chest depth, For height. The inclusion and exclusion significance levels for stepwise regression were set to α=0.05 and α=0.10, respectively, ultimately yielding... q An independent regression model explicitly establishes the mapping relationship from the human parameter space to the biomechanical response feature space.

[0057] In one embodiment, step 150 can be implemented as follows: based on the target human body parameters and the prediction model, calculate the corresponding feature value distribution; based on the feature value distribution, generate multiple response curves of the target human body parameters; based on the multiple response curves of the target human body parameters, predict the biomechanical response channel parameters corresponding to the target human body parameters.

[0058] This application specifies target human body parameters and inputs them into a prediction model (i.e., the regression model mentioned above) to obtain the predicted mean and variance of the prediction error of each principal component score, so as to obtain the normal distribution of each principal component score under the target body size. A value is randomly selected from the normal distribution, and multiple response curves corresponding to the target human body parameters are reconstructed. Based on the multiple response curves, the biomechanical response channel parameters corresponding to the target human body parameters are predicted.

[0059] In one embodiment, step 150 can be implemented as follows: calculating the first quantile and the second quantile of multiple response curves of the target human body parameters respectively; wherein the coverage probability corresponding to the first quantile and the second quantile is greater than a preset coverage probability threshold; and determining the lower limit and upper limit of the biomechanical response channel corresponding to the target human body parameters based on the first quantile and the second quantile.

[0060] Specifically, this application calculates the α / 2 quantile and 1-α / 2 quantile of the values ​​of multiple response curves at each time point (setting α=0.05, i.e., the coverage probability is 95%), and connects the α / 2 quantiles of all time points to form the lower limit of the biomechanical response channel, and connects the 1-α / 2 quantiles of all time points to form the upper limit of the biomechanical response channel.

[0061] Figure 2 This is a schematic diagram of the structure of a vehicle collision human biomechanical response channel construction system provided in an exemplary embodiment of this application. Figure 2 As shown, the automotive collision human biomechanical response channel construction system 20 includes: a test data acquisition module 21 for acquiring test data, including biomechanical time-domain signals and human parameters; an alignment signal processing module 22 for aligning the biomechanical time-domain signals during the signal loading stage to obtain an alignment signal; a signal feature extraction module 23 for performing principal component decomposition on the alignment signal to extract the feature values ​​of the alignment signal; a prediction model construction module 24 for constructing a prediction model between the feature values ​​of the alignment signal and the human parameters based on the human parameters corresponding to the alignment signal; and a channel parameter determination module 25 for inputting the target human parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human parameters.

[0062] This application provides a system for constructing a biomechanical response channel for a car collision. The system acquires experimental data through a test data acquisition module 21, which includes biomechanical time-domain signals and human parameters. An alignment signal processing module 22 aligns the biomechanical time-domain signals during the signal loading phase to obtain an aligned signal. A signal feature extraction module 23 performs principal component decomposition on the aligned signal to extract its eigenvalues. A prediction model construction module 24 constructs a prediction model between the eigenvalues ​​of the aligned signal and the human parameters based on the human parameters corresponding to the aligned signal. A channel parameter determination module 25 inputs the target human parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human parameters. By aligning the biomechanical time-domain signals during the signal loading phase to improve signal consistency, performing principal component decomposition on the aligned signal to reduce computational load, and constructing a prediction model between the eigenvalues ​​of the aligned signal and the human parameters to achieve automated prediction of the biomechanical response channel corresponding to the target human parameters, the system improves prediction efficiency while ensuring prediction accuracy.

[0063] In one embodiment, the alignment signal processing module 22 may be further configured to: calculate a reference signal of the biomechanical time-domain signal during the loading phase; and calculate an alignment signal based on the reference signal and the corresponding time offset.

[0064] In one embodiment, the alignment signal processing module 22 may be further configured to: select one of the multiple biomechanical time-domain signals during the loading stage as the initial reference signal; calculate the offset correlation function value between the other biomechanical time-domain signals and the initial reference signal; obtain the optimal offset time based on the offset correlation function value; update the other biomechanical time-domain signals based on the optimal offset time; calculate the updated reference signal based on the updated other biomechanical time-domain signals and the initial reference signal; calculate the difference between the initial reference signal and the updated reference signal; if the difference is less than a preset difference threshold, determine the updated reference signal as the reference signal of the biomechanical time-domain signals during the loading stage.

[0065] In one embodiment, the alignment signal processing module 22 can be further configured to: search for the optimal offset time within a preset time offset range so as to maximize the offset correlation function value.

[0066] In one embodiment, the biomechanical time-domain signal includes: force signal, displacement signal, deformation signal, and acceleration signal; wherein, the above-mentioned alignment signal processing module 22 can be further configured such that: the start point and end point of the signal loading stage of the force signal, displacement signal, and deformation signal are determined according to the signal amplitude of the corresponding signal; the start point and end point of the signal loading stage of the acceleration signal are determined according to the amplitude and integral value of the corresponding signal.

[0067] In one embodiment, the signal feature extraction module 23 can be further configured to: calculate the average response curve based on the alignment signal; calculate the deviation between each alignment signal and the average response curve in the alignment signal to obtain the deviation matrix; and perform principal component decomposition on the deviation matrix to obtain the eigenvalues ​​of the alignment signal.

[0068] In one embodiment, the prediction model building module 24 can be further configured to: construct a prediction model between the feature values ​​of the aligned signal and human body parameters using a linear regression model.

[0069] In one embodiment, the channel parameter determination module 25 can be further configured to: calculate the corresponding feature value distribution based on the target human body parameters and the prediction model; generate multiple response curves of the target human body parameters based on the feature value distribution; and predict the biomechanical response channel parameters corresponding to the target human body parameters based on the multiple response curves of the target human body parameters.

[0070] In one embodiment, the channel parameter determination module 25 can be further configured to: calculate the first quantile and the second quantile of multiple response curves of the target human body parameters respectively; wherein the confidence level corresponding to the first quantile and the second quantile is greater than a preset confidence threshold; and determine the lower limit and upper limit of the biomechanical response channel corresponding to the target human body parameters based on the first quantile and the second quantile.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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 constructing a human biomechanical response channel in a car collision, characterized in that, include: Acquire experimental data; wherein the experimental data includes biomechanical time-domain signals and human body parameters; The biomechanical time-domain signal during the alignment signal loading phase is used to obtain the alignment signal; Principal component decomposition is performed on the alignment signal to extract its feature values; Based on the human body parameters corresponding to the alignment signal, a prediction model is constructed between the feature values ​​of the alignment signal and the human body parameters. The target human body parameters are input into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters. The step of performing principal component decomposition on the aligned signal and extracting the feature values ​​of the aligned signal includes: Based on the alignment signal, the average response curve is calculated; The deviation between each alignment signal and the average response curve is calculated to obtain the deviation matrix; Principal component decomposition is performed on the deviation matrix to obtain the eigenvalues ​​of the alignment signal.

2. The method for constructing a human biomechanical response channel in a car collision according to claim 1, characterized in that, The biomechanical time-domain signal obtained during the alignment signal loading phase includes: A reference signal for calculating the biomechanical time-domain signal during the loading phase; The alignment signal is calculated based on the reference signal and the corresponding time offset.

3. The method for constructing a human biomechanical response channel in a car collision according to claim 2, characterized in that, The reference signal for calculating the biomechanical time-domain signal during the loading phase includes: One of the multiple biomechanical time-domain signals during the loading phase is selected as the initial reference signal; Calculate the offset correlation function values ​​between other biomechanical time-domain signals among the plurality of biomechanical time-domain signals and the initial reference signal; Based on the offset correlation function value, the optimal offset time is obtained; The other biomechanical time-domain signals are updated based on the optimal offset time; The updated reference signal is calculated based on the updated other biomechanical time-domain signals and the initial reference signal; Calculate the difference between the initial reference signal and the updated reference signal; If the difference is less than a preset difference threshold, then the updated reference signal is determined to be the reference signal of the biomechanical time domain signal in the loading stage.

4. The method for constructing a human biomechanical response channel in a car collision according to claim 3, characterized in that, The process of obtaining the optimal offset time based on the offset correlation function value includes: The optimal offset time is searched within a preset time offset range to maximize the value of the offset correlation function.

5. The method for constructing a human biomechanical response channel in a car collision according to claim 1, characterized in that, The biomechanical time-domain signals include: force signals, displacement signals, deformation signals, and acceleration signals; wherein, the method for determining the signal loading stage includes: The start and end points of the signal loading phase of the force signal, the displacement signal, and the deformation signal are determined according to the signal amplitude of the corresponding signals. The start and end points of the signal loading phase of the acceleration signal are determined based on the amplitude and integral value of the corresponding signal.

6. The method for constructing a human biomechanical response channel in a car collision according to claim 1, characterized in that, The step of constructing a prediction model between the feature values ​​of the alignment signal and the human body parameters based on the alignment signal includes: A linear regression model is used to construct a predictive model between the feature values ​​of the aligned signal and the human body parameters.

7. The method for constructing a human biomechanical response channel in a car collision according to claim 1, characterized in that, The step of inputting the target human body parameters into the prediction model to predict the biomechanical response channel parameters corresponding to the target human body parameters includes: Based on the target human body parameters and the prediction model, the corresponding feature value distribution is calculated; Based on the feature value distribution, multiple response curves for the target human body parameters are generated; Based on multiple response curves of the target human body parameters, the biomechanical response channel parameters corresponding to the target human body parameters are predicted.

8. The method for constructing a human biomechanical response channel in a car collision according to claim 7, characterized in that, The biomechanical response channel parameters corresponding to the target human body parameters, predicted based on multiple response curves of the target human body parameters, include: Calculate the first quantile and the second quantile of multiple response curves for the target human body parameters, respectively; wherein the confidence levels corresponding to the first quantile and the second quantile are greater than a preset confidence threshold. Based on the first quantile and the second quantile, the lower and upper limits of the biomechanical response channels corresponding to the target human body parameters are determined.

9. A system for constructing a human biomechanical response channel in a car collision, characterized in that, include: The experimental data acquisition module is used to acquire experimental data, which includes biomechanical time-domain signals and human body parameters. The alignment signal processing module is used to align the biomechanical time-domain signals during the signal loading phase to obtain the alignment signal. The signal feature extraction module is used to perform principal component decomposition on the aligned signal and extract the feature values ​​of the aligned signal. The prediction model building module is used to build a prediction model between the feature values ​​of the alignment signal and the human body parameters based on the human body parameters corresponding to the alignment signal. The channel parameter determination module is used to input the target human body parameters into the prediction model and predict the biomechanical response channel parameters corresponding to the target human body parameters. The signal feature extraction module is further configured as follows: Based on the alignment signal, the average response curve is calculated; The deviation between each alignment signal and the average response curve is calculated to obtain the deviation matrix; Principal component decomposition is performed on the deviation matrix to obtain the eigenvalues ​​of the alignment signal.

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