A method and system for determining the segmental mass of a car crash dummy

Abstract

This application provides a method and system for determining the segmented mass of a car crash dummy. The method involves acquiring multiple human body scan data to construct corresponding three-dimensional human body models; dividing each three-dimensional human body model into multiple independent sub-regions and calculating the volume, density, mass, and centroid position of each sub-region; determining a segmented mass prediction model for the car crash dummy based on the mass and centroid position of each sub-region; constructing a segmentation plane equation between adjacent sub-regions within the same three-dimensional human body model based on the landmark points of each sub-region; determining the segmented mass of the car crash dummy based on the segmented mass prediction model and the segmentation plane equation between adjacent sub-regions; and determining the prediction model after region segmentation based on real human body data, thereby improving the prediction accuracy of the prediction model. Furthermore, optimizing the prediction model by combining the parameters of each region further improves the accuracy of the prediction model.

Description

Technical Field

[0001] This application relates to the field of automotive crash test dummies, specifically to a method and system for determining the segmental mass of an automotive crash test dummy. Background Technology

[0002] With the increasing awareness of active and passive safety in automobiles, automotive crash testing, as one of the important means of detecting passive safety, has become a crucial reference for vehicle development and customer use. As a key testing device, the crash dummy reflects the potential injuries to the human body during a collision through sensor measurements distributed in key areas, directly determining the results of passive safety tests. The mass of each component and the overall mass of the crash dummy affect its inertia, thus influencing its motion during a collision and potentially impacting the injury values ​​measured by the sensors. However, current research lacks information on obtaining the mass of each component of the crash dummy, leading to inaccuracies in crash measurements. Therefore, there is an urgent need to develop a method for determining the segmental mass of a crash dummy to provide methodological support for future crash dummy development. 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 determining the segmented mass of a car crash dummy.

[0004] According to one aspect of this application, a method for determining the segmented mass of a car crash dummy is provided, comprising: acquiring multiple human body scan data; constructing multiple corresponding three-dimensional human body models based on the multiple human body scan data; dividing each of the three-dimensional human body models into multiple independent sub-regions; calculating the volume and density of each sub-region; calculating the mass and centroid position of the corresponding sub-region based on the volume and density of each sub-region; determining a segmented mass prediction model of the car crash dummy based on the mass and centroid position of each sub-region; constructing a segmentation plane equation between adjacent sub-regions in the same three-dimensional human body model based on the marker points of each sub-region; and determining the segmented mass of the car crash dummy based on the segmented mass prediction model and the segmentation plane equation between adjacent sub-regions.

[0005] In one embodiment, dividing each of the three-dimensional human body models into multiple independent sub-regions includes: dividing each of the three-dimensional human body models into multiple independent sub-regions based on a grayscale threshold.

[0006] In one embodiment, calculating the volume and density of each of the sub-regions includes: calculating the volume based on the shape of each sub-region, and correcting its density according to the characteristics of the organs contained in the sub-region.

[0007] In one embodiment, constructing the segmentation plane equation between adjacent sub-regions in the same human body 3D model based on the marker points of each sub-region includes: calculating the normal vector of the axis formed by the marker points based on the marker points of each sub-region of the same human body 3D model; and constructing the segmentation plane equation between adjacent sub-regions based on the marker points and the normal vector.

[0008] In one embodiment, determining the segmented mass prediction model of the car crash dummy based on the mass and centroid position of each of the sub-regions includes: constructing a data matrix based on the mass, centroid position, size, and density of each of the sub-regions of the plurality of human three-dimensional models; calculating the principal component score matrix based on the data matrix; and solving the segmented mass prediction model based on the principal component score matrix.

[0009] In one embodiment, calculating the principal component score matrix based on the data matrix includes: calculating the covariance matrix of the data matrix; solving for the eigenvalues ​​and eigenvectors of the covariance matrix; and calculating the principal component score matrix based on the eigenvalues ​​and eigenvectors of the covariance matrix and the data matrix.

[0010] In one embodiment, the step of obtaining the segmented quality prediction model based on the principal component score matrix includes: solving a linear regression model based on the principal component score matrix and the data matrix to obtain the segmented quality prediction model; wherein, the segmented quality prediction model represents the correspondence between the segmented quality parameters of the car crash dummy and each principal component.

[0011] According to another aspect of this application, a system for determining the segmented mass of a car crash dummy is provided, comprising: a scanning data acquisition module for acquiring multiple human body scanning data; a three-dimensional model construction module for constructing multiple corresponding three-dimensional human body models based on the multiple human body scanning data; a three-dimensional model segmentation module for segmenting each of the three-dimensional human body models into multiple independent sub-regions; a segmentation parameter calculation module for calculating the volume and density of each of the sub-regions; a segmented mass calculation module for calculating the mass and centroid position of the corresponding sub-region based on the volume and density of each sub-region; a prediction model determination module for determining a segmented mass prediction model of the car crash dummy based on the mass and centroid position of each of the sub-regions; a segmentation plane construction module for constructing a segmentation plane equation between adjacent sub-regions in the same three-dimensional human body model based on the marker points of each sub-region; and a segmented mass determination module for determining the segmented mass of the car crash dummy based on the segmented mass prediction model and the segmentation plane equation between adjacent sub-regions.

[0012] This application provides a method and system for determining the segmented mass of a car collision dummy. The method involves acquiring multiple human body scan data; constructing multiple corresponding three-dimensional human body models based on the scan data; dividing each three-dimensional human body model into multiple independent sub-regions; calculating the volume and density of each sub-region; calculating the mass and centroid position of the corresponding sub-region based on its volume and density; determining a segmented mass prediction model for the car collision dummy based on the mass and centroid position of each sub-region; constructing a segmentation plane equation between adjacent sub-regions within the same three-dimensional human body model based on the marker points of each sub-region; and determining the segmented mass prediction model and the adjacent sub-regions based on the segmented mass prediction model. The segmentation plane equations between the segments are used to determine the segmental mass of the car crash dummy. Specifically, after obtaining basic data by scanning the human body, a 3D model is constructed. This model is then divided into sub-regions, and parameters such as volume, density, mass, and center of mass position are calculated for each sub-region. Based on these parameters, a prediction model for the segmental mass is determined. Simultaneously, segmentation plane equations are constructed based on the landmark points of each sub-region. Combining the prediction model and the segmentation plane equations comprehensively determines the segmental mass of the car crash dummy. By segmenting regions based on real human body data, the prediction model can be determined, thereby improving its accuracy. Furthermore, by optimizing the prediction model using parameters from each region, the accuracy of the prediction model can be further improved. Attached Figure Description

[0013] 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 the application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0014] Figure 1 This is a flowchart illustrating a method for determining the segmental mass of a car collision dummy provided in an exemplary embodiment of this application.

[0015] Figure 2 This is a schematic diagram of the structure of a vehicle collision dummy segment mass determination system provided in an exemplary embodiment of this application. Detailed Implementation

[0016] 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.

[0017] Figure 1 This is a flowchart illustrating a method for determining the segmental mass of a car collision dummy according to an exemplary embodiment of this application. Figure 1As shown, the method for determining the segmental mass of the car crash dummy includes the following steps:

[0018] Step 110: Acquire multiple human body scan data.

[0019] This application utilizes medical imaging equipment (such as CT, MRI, etc.) or a 3D human scanner to acquire tomographic scan data or surface point cloud data of the target human body, and performs data format standardization processing. Furthermore, it performs noise filtering, artifact correction, and coordinate system unification on the raw data to ensure the accurate extraction of anatomical landmarks defined in each segmented plane. The scan data output is a 3D voxel matrix or a high-precision mesh model containing grayscale information (corresponding to tissue density), providing an input basis for subsequent modeling.

[0020] Step 120: Construct multiple corresponding 3D human body models based on multiple human body scan data.

[0021] Based on human body scan data, a three-dimensional geometric model of the human body is constructed using reverse engineering software (such as Mimics, 3D Slicer, etc.). A triangular mesh model is then generated using surface reconstruction algorithms (such as Marching Cubes).

[0022] Step 130: Divide each human body 3D model into multiple independent sub-regions.

[0023] Based on predefined segmentation plane rules, the 3D model is cut into independent sub-regions. Specifically, each human 3D model is divided into skeletal, soft tissue, and cavity regions. Topology optimization is performed on the model to repair holes and irregular edges, ensuring the geometric continuity of each anatomical region (such as head and neck, limbs, and trunk).

[0024] Step 140: Calculate the volume and density of each sub-region.

[0025] Because the human body is complex, comprising bones (cortical and cancellous bone), muscles, internal organs, fat, and water, the distribution and proportion of these components vary in different parts, resulting in different average densities. Therefore, this application uses the volume ratio of different segments of the human body to replace the mass ratio of different body parts, assuming a homogeneous density coefficient of 1 for each part. However, the upper and lower torsos are particularly unique. The upper torso's thoracic cavity contains organs such as the heart and lungs, with the lungs containing gas; the lower torso includes the abdomen, which is relatively soft and also contains organs such as the intestines. If we still assume a density coefficient of 1 for these two segments, we would overestimate their mass. Therefore, this application uses a correction factor to adjust the density of the thoracic cavity (similarly, when calculating the volume proportion of the lower torso, it needs to be further divided into the abdomen and pelvis to use different density coefficients). Specifically, the volume of the segmented sub-regions is calculated and the density is corrected. The homogeneous density assumption is adopted, and the density coefficient of the head, neck and limbs is set to 1 by default. The density coefficient of the thoracic cavity is assigned based on the proportion of gas in the lungs and the density characteristics of the heart. The density coefficient of the abdomen is modified considering the contents of the intestines and the flexibility of soft tissue.

[0026] Step 150: Calculate the mass and centroid position of the corresponding sub-region based on the volume and density of each sub-region.

[0027] Using the volume and corrected density data of each sub-region, the mass of each sub-region is calculated, and the centroid position is determined based on this. The calculation of the centroid position includes both traditional geometric centroid and optimization using density weighting methods.

[0028] Step 160: Based on the mass and centroid location of each sub-region, determine the segmented mass prediction model for the car crash dummy.

[0029] The calculated centroid position is then transformed to the global coordinate system using a rigid body transformation matrix. During this process, rotation and translation matrices are constructed by registering key anatomical landmarks (such as the acromion and lateral femoral condyle) to achieve a precise mapping between local and global coordinates. Finally, the parameter information from all sub-regions is fused to determine the segmented mass prediction model for the car crash dummy.

[0030] Step 170: Based on the landmark points of each sub-region, construct the segmentation plane equation between adjacent sub-regions in the same 3D human body model.

[0031] By taking the sacral vertex as the origin of the global coordinate system, the coordinates of key anatomical landmarks are automatically read, the vectors between adjacent anatomical points are calculated and standardized to obtain the normal vector of the cutting plane, and then the equation of the cutting plane is constructed.

[0032] Step 180: Determine the segment mass of the car collision dummy based on the segmented mass prediction model and the segmentation plane equation between adjacent sub-regions.

[0033] This application obtains the segmentation plane equation between the segmented quality prediction model and adjacent sub-regions, and combines the segmented quality prediction model with the segmentation plane equation between adjacent sub-regions to comprehensively determine the segmented quality of the car crash dummy, thereby obtaining the segmented quality of the car crash dummy after integrating multiple human body scan data, which improves the accuracy of the segmented quality of the car crash dummy while taking into account the characteristics of different groups of people.

[0034] This application provides a method for determining the segmented mass of a car crash dummy. The method involves acquiring multiple human body scan data; constructing multiple corresponding 3D human body models based on the scan data; dividing each 3D human body model into multiple independent sub-regions; calculating the volume and density of each sub-region; calculating the mass and centroid position of each sub-region based on its volume and density; determining a segmented mass prediction model for the car crash dummy based on the mass and centroid position of each sub-region; constructing a segmentation plane equation between adjacent sub-regions within the same 3D human body model based on the marker points of each sub-region; and determining the segmented mass prediction model and the equation between adjacent sub-regions. The segmentation plane equation is used to determine the segmental mass of a car crash dummy. This involves constructing a 3D model by scanning the human body to obtain basic data, dividing the 3D model into sub-regions, calculating parameters such as volume, density, mass, and center of mass position for each sub-region, and determining a prediction model for segmental mass based on these parameters. Simultaneously, a segmentation plane equation is constructed based on the landmark points of each sub-region. Combining the prediction model and the segmentation plane equation comprehensively determines the segmental mass of the car crash dummy. By segmenting regions based on real human body data, the prediction model can be determined, thereby improving its accuracy. Furthermore, optimizing the prediction model by incorporating parameters from each region further enhances its accuracy.

[0035] In one embodiment, step 130 can be implemented by dividing each human body 3D model into multiple independent sub-regions based on a grayscale threshold.

[0036] This application segments the human 3D model into multiple independent sub-regions based on the grayscale threshold. The specific segmentation results are shown in the table below:

[0037] Table 1. Segmentation results of the 3D human body model

[0038]

[0039] The head-neck segmentation is performed using three defined planes: a horizontal plane passing through the occipital condyle, a coronal plane anterior to the occipital condyle, and a plane passing through the left and right mandibular angles and forming a specific angle with the horizontal plane. The neck-upper trunk segmentation plane is defined as a plane passing through the boundary between the seventh cervical vertebra and the first thoracic vertebra, perpendicular to the line connecting the occipital condyle and the boundary between these two vertebrae. A cutting plane with a normal vector perpendicular to the connecting line is generated by calculating the direction vector of the line connecting these two points. The upper trunk-lower trunk segmentation plane is defined as an inclined plane passing through the upper lumbar vertebral joints (upper edge of the vertebra) and forming a set angle with the horizontal plane. This plane slopes along the physiological curvature of the lumbar spine, segmenting the thoracic cavity and lower trunk (abdomen + pelvis). After segmentation, the lower trunk needs to be further divided into the abdomen and pelvis. The lower trunk-thigh segmentation plane is dynamically adapted. In the upright sitting posture, the segmentation plane is a vertical plane from the posterior edge of the buttocks. In the standing posture, it is equivalent to a horizontal plane passing above the perineum. The sitting model uses the vertical plane for cutting, while the standing model uses the horizontal plane. The thigh-lower leg segmentation plane is strictly defined as a horizontal plane passing through the lateral femoral condyle, perpendicular to the long axis of the femur, and generated by locating anatomical landmarks of the lateral femoral condyle. The lower leg-foot segmentation plane is a horizontal plane passing through the medial malleolus and aligned with the anatomical axis of the ankle joint. The upper trunk-upper arm segmentation plane passes through the acromion and follows the anterior and posterior creases of the axilla, segmenting the trunk and upper arm. The plane direction is determined by the line connecting the acromion to the midpoint of the axillary crease, ensuring clear boundaries between the deltoid and pectoralis major muscles. The upper arm-forearm segmentation plane is a fitted plane passing through the olecranon of the ulna and the medial and lateral epicondyles of the humerus. The forearm-hand segmentation plane passes through the styloid processes of the ulna and radius and is perpendicular to the long axis of the forearm.

[0040] In one embodiment, step 140 can be implemented by calculating the volume based on the shape of each sub-region and adjusting its density according to the characteristics of the organs contained in the sub-region.

[0041] This application calculates the shape and size of each segmented sub-region, and then calculates its volume. Furthermore, it adjusts the density based on the characteristics of the organs contained within each sub-region. Because the thoracic cavity contains many hollow organs, its average mass is significantly lower than the overall mass; therefore, a density correction factor is applied. (Less than 1). The lower torso of the human body is further subdivided into the pelvic region and the lower abdominal region. The abdominal region, due to its hollow nature, also possesses a density correction factor. (less than 1), while the pelvic region does not require density correction.

[0042] In one embodiment, step 170 can be implemented as follows: based on the marker points of each sub-region of the same human three-dimensional model, calculate the normal vector of the axis formed by the marker points; based on the marker points and the normal vector, construct the segmentation plane equation between adjacent sub-regions.

[0043] This application automatically calculates the composition parameters of each anatomical segment plane, using the sacral apex as the origin of the global coordinate system. Specifically, by inputting key points into the system, the system calculates the vectors between adjacent anatomical landmarks, thereby obtaining the local axis direction vector. V Next, the vector is standardized.

[0044]

[0045] This determines that the dividing plane should cut along the direction of this local axis. Based on the selected reference point and the above normal vector, the equation of the dividing plane is constructed, and its mathematical expression is:

[0046]

[0047] in, The coordinates of the preset reference points for each segment of the model, This is the unit normal vector of the plane. The spatial relationships of each segmentation plane are verified using a homogeneous coordinate transformation matrix, ensuring that each plane is consistent with the expected anatomical boundary. This not only significantly improves the accuracy of anatomical segmentation but also provides an accurate geometric basis for subsequent calculations and simulations based on physical parameters such as the mass and inertia of each part.

[0048] In one embodiment, step 160 can be implemented as follows: a data matrix is ​​constructed based on the mass, centroid position, size, and density of each sub-region of multiple human three-dimensional models; a principal component score matrix is ​​calculated based on the data matrix; and a segmented quality prediction model is obtained based on the principal component score matrix.

[0049] This application uses principal component analysis to reduce the dimensionality of high-dimensional biomechanical data from numerous acquired 3D human models in order to extract key variables (principal components) that can fully reflect the main differences in the original data, thereby establishing accurate and stable predictive variables for subsequent regression analysis.

[0050] Specifically, 3D human body model data collected from different individuals are organized into a unified data matrix, where each row corresponds to a single human body model, and each column represents specific measurement parameters related to the quality, size, and density characteristics of each anatomical segment of the human body. This data matrix is ​​then standardized to eliminate dimensional differences and reduce the influence of outliers, ensuring a more balanced contribution of each variable to data variability. After standardization, the principal component score matrix is ​​calculated, and a segmental quality prediction model is obtained by solving the regression model formed by the principal component score matrix.

[0051] The data standardization formula is as follows:

[0052] ;

[0053] in, The first element in the original data matrix i Line 1 j Column elements, The first j The mean and standard deviation of the data. For the standardized first i Line 1 j The elements of the column.

[0054] In one embodiment, step 160 can be implemented as follows: calculate the covariance matrix of the data matrix; solve for the eigenvalues ​​and eigenvectors of the covariance matrix; and calculate the principal component score matrix based on the eigenvalues ​​and eigenvectors of the covariance matrix and the data matrix.

[0055] The preprocessed data matrix is ​​further used to calculate the covariance matrix. The covariance matrix is ​​obtained through eigenvalue decomposition or singular value decomposition to obtain eigenvalues ​​and corresponding eigenvectors. The resulting eigenvectors represent the principal component directions of the data, while the eigenvalues ​​indicate the explanatory power of the corresponding principal component for the overall variability of the data. The principal components are sorted from largest to smallest according to their eigenvalues, and the top few principal components with a cumulative contribution rate reaching a predetermined threshold (e.g., 85%) are selected as the final predictor variables. The original standardized data is projected onto the selected principal component space to obtain the principal component score matrix. The score of each sample represents the coordinates of the human body model data in the corresponding principal component direction. These principal component scores are then used as input variables for a regression model to establish the mathematical relationship between biomechanical parameters such as mass, moment of inertia, and centroid coordinates of different human body segments and the selected principal components, thereby achieving accurate prediction of the physical characteristics of different segments of different human body models.

[0056] The formula for calculating the covariance matrix is ​​as follows:

[0057] ,

[0058] The eigenvalue decomposition formula is:

[0059] ,

[0060] The formula for calculating principal component scores is:

[0061] ,

[0062] in, C Let covariance matrix be the variance matrix. For the standardized data matrix, for The transpose of the matrix, N The number of samples used to calculate the principal component contribution rate. Indicates the firsti 1 eigenvalue, The characteristic matrix represents the first... i A vector, T Principal component score matrix, g This is a parameter matrix composed of shape and pose parameters of a 3D human body model. U k For the selected first k The feature matrix is ​​composed of the directions of the principal components.

[0063] In one embodiment, step 160 can be implemented as follows: based on the principal component score matrix and the data matrix, a linear regression model is solved to obtain a piecewise quality prediction model; wherein, the piecewise quality prediction model represents the correspondence between the piecewise quality parameters of the car crash dummy and each principal component.

[0064] This application, while retaining the main information of the original data, achieves a significant reduction in data dimensionality and effective extraction of key variables. The principal component regression model (i.e., the segmented quality prediction model) provided in this application is shown below, used to establish the mathematical relationship between the quality of each segment of the human body and the principal components:

[0065] ;

[0066] in, This represents the quality of each segment (i.e., the quality of the sub-region). Represents the moment of inertia of the center of mass. Let be the coordinates of the centroid. PC i For the extracted first i Principal components (as defined in the above formula) i (e.g., =3) , , , , , , , These are the parameters of the principal component regression model, obtained by solving the model.

[0067] Figure 2 This is a schematic diagram of the structure of a vehicle collision dummy segment mass determination system provided in an exemplary embodiment of this application. Figure 2As shown, the vehicle collision dummy segment mass determination system 20 includes: a scan data acquisition module 21 for acquiring multiple human body scan data; a 3D model construction module 22 for constructing multiple corresponding human body 3D models based on the multiple human body scan data; a 3D model segmentation module 23 for segmenting each human body 3D model into multiple independent sub-regions; a segmentation parameter calculation module 24 for calculating the volume and density of each sub-region; a segment mass calculation module 25 for calculating the mass and centroid position of the corresponding sub-region based on the volume and density of each sub-region; a prediction model determination module 26 for determining the segment mass prediction model of the vehicle collision dummy based on the mass and centroid position of each sub-region; a segmentation plane construction module 27 for constructing the segmentation plane equation between adjacent sub-regions in the same human body 3D model based on the marker points of each sub-region; and a segment mass determination module 28 for determining the segment mass of the vehicle collision dummy based on the segment mass prediction model and the segmentation plane equation between adjacent sub-regions.

[0068] This application provides a segmented mass determination system for a car collision dummy. The system comprises: a scanning data acquisition module 21 acquiring multiple human body scan data; a 3D model construction module 22 constructing multiple corresponding 3D human body models based on the scan data; a 3D model segmentation module 23 segmenting each 3D human body model into multiple independent sub-regions; a segmentation parameter calculation module 24 calculating the volume and density of each sub-region; a segmented mass calculation module 25 calculating the mass and centroid position of each sub-region based on its volume and density; a prediction model determination module 26 determining the segmented mass prediction model of the car collision dummy based on the mass and centroid position of each sub-region; and a segmentation plane construction module 27 constructing adjacent sub-regions within the same 3D human body model based on the landmark points of each sub-region. The segmentation plane equations between regions; the segment mass determination module 28 determines the segment mass of the car collision dummy based on the segment mass prediction model and the segmentation plane equations between adjacent sub-regions; that is, after obtaining basic data by scanning the human body, a three-dimensional model is constructed, the three-dimensional model is divided into various sub-regions, and parameters such as volume, density, mass and centroid position of each sub-region are calculated. Based on the parameters, the segment mass prediction model is determined, and the segmentation plane equations are constructed according to the landmark points of the sub-regions. The segment mass of the car collision dummy is determined by combining the prediction model and the segmentation plane equations. The prediction model is determined after region segmentation based on real human body data, which can improve the prediction accuracy of the prediction model. At the same time, the prediction model is optimized by combining the parameters of each region, thereby further improving the accuracy of the prediction model.

[0069] In one embodiment, the above-mentioned three-dimensional model segmentation module 23 can be further configured to: segment each human three-dimensional model into multiple independent sub-regions based on a grayscale threshold.

[0070] In one embodiment, the segmentation parameter calculation module 24 can be further configured to: calculate the volume based on the shape of each sub-region, and correct its density according to the characteristics of the organs contained in the sub-region.

[0071] In one embodiment, the segmentation plane construction module 27 can be further configured to: calculate the normal vector of the axis formed by the marker points of each sub-region of the same human three-dimensional model; and construct the segmentation plane equation between adjacent sub-regions based on the marker points and the normal vector.

[0072] In one embodiment, the prediction model determination module 26 can be further configured to: construct a data matrix based on the mass, centroid position, size and density of each sub-region of multiple human three-dimensional models; calculate the principal component score matrix based on the data matrix; and solve for the segmented quality prediction model based on the principal component score matrix.

[0073] In one embodiment, the prediction model determination module 26 can be further configured to: calculate the covariance matrix of the data matrix; solve for the eigenvalues ​​and eigenvectors of the covariance matrix; and calculate the principal component score matrix based on the eigenvalues ​​and eigenvectors of the covariance matrix and the data matrix.

[0074] In one embodiment, the prediction model determination module 26 can be further configured to: solve the linear regression model based on the principal component score matrix and the data matrix to obtain the piecewise quality prediction model; wherein, the piecewise quality prediction model represents the correspondence between the piecewise quality parameters of the car crash dummy and each principal component.

[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 determining the segmental mass of a car crash dummy, characterized in that, include: Acquire multiple human body scan data; Based on the multiple human body scan data, construct multiple corresponding three-dimensional human body models; Each of the aforementioned three-dimensional human body models is divided into multiple independent sub-regions; Calculate the volume and density of each of the sub-regions respectively; Based on the volume and density of each sub-region, the mass and centroid position of the corresponding sub-region are calculated; Based on the mass and centroid location of each sub-region, a segmented mass prediction model for the car crash dummy is determined. Based on the landmark points of each sub-region, construct the segmentation plane equation between adjacent sub-regions in the same human body 3D model; Based on the segmented mass prediction model and the segmentation plane equations between adjacent sub-regions, the segmented mass of the car collision dummy is determined.

2. The method for determining the segmented mass of a car collision dummy according to claim 1, characterized in that, The step of dividing each of the three-dimensional human body models into multiple independent sub-regions includes: Each of the three-dimensional human body models is segmented into multiple independent sub-regions based on a grayscale threshold.

3. The method for determining the segmented mass of a car collision dummy according to claim 1, characterized in that, The calculation of the volume and density of each of the sub-regions includes: The volume is calculated based on the shape of each sub-region, and its density is adjusted according to the characteristics of the organs contained in the sub-region.

4. The method for determining the segmented mass of a car collision dummy according to claim 1, characterized in that, The step of constructing the segmentation plane equation between adjacent sub-regions in the same 3D human body model based on the marker points of each sub-region includes: Based on the marker points of each sub-region of the same three-dimensional human body model, calculate the normal vector of the axis formed by the marker points; Based on the marker points and the normal vectors, the segmentation plane equations between adjacent sub-regions are constructed.

5. The method for determining the segmented mass of a car collision dummy according to claim 1, characterized in that, The segmented mass prediction model for determining the car crash dummy based on the mass and centroid position of each of the sub-regions includes: A data matrix is ​​constructed based on the mass, centroid position, size, and density of each sub-region of the multiple human 3D models; Based on the data matrix, the principal component score matrix is ​​calculated; Based on the principal component score matrix, the segmented quality prediction model is obtained.

6. The method for determining the segmented mass of a car collision dummy according to claim 5, characterized in that, The principal component score matrix calculated based on the data matrix includes: Calculate the covariance matrix of the data matrix; Solve for the eigenvalues ​​and eigenvectors of the covariance matrix; The principal component score matrix is ​​calculated based on the eigenvalues ​​and eigenvectors of the covariance matrix and the data matrix.

7. The method for determining the segmented mass of a car collision dummy according to claim 5, characterized in that, The step of solving the segmented quality prediction model based on the principal component score matrix includes: Based on the principal component score matrix and the data matrix, the piecewise quality prediction model is obtained by solving the linear regression model; wherein, the piecewise quality prediction model represents the correspondence between the piecewise quality parameters of the car crash dummy and each principal component.

8. A system for determining the segmented mass of a car crash dummy, characterized in that, include: The scan data acquisition module is used to acquire scan data from multiple human bodies; A 3D model building module is used to build multiple corresponding 3D human body models based on the multiple human body scan data. The 3D model segmentation module is used to segment each of the human 3D models into multiple independent sub-regions; The segmented parameter calculation module is used to calculate the volume and density of each sub-region separately; The segmented mass calculation module is used to calculate the mass and centroid position of the corresponding sub-region based on the volume and density of each sub-region; The prediction model determination module is used to determine the segmented mass prediction model of the car crash dummy based on the mass and centroid position of each of the sub-regions. A segmentation plane construction module is used to construct segmentation plane equations between adjacent sub-regions in the same human body 3D model based on the landmark points of each sub-region; The segmented quality determination module is used to determine the segmented quality of the car collision dummy based on the segmented quality prediction model and the segmentation plane equation between adjacent sub-regions.

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