Method and apparatus for user injury prediction

Abstract

The application discloses a user injury prediction method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: in response to an injury prediction instruction, a target user and a prediction part are acquired, and an associated part of the prediction part is queried; a set of movement parameters of the target user in each preset direction corresponding to the prediction part and the associated part is collected; the quality of the associated part and the length of the associated part are acquired, so as to call a preset prediction model, combine the set of movement parameters corresponding to each preset direction, and determine the bearing force and the bending degree of the prediction part corresponding to each preset direction; and the injury result of the prediction part is determined according to the bearing force and the bending degree of the prediction part corresponding to each preset direction. The embodiment can solve the problem that the user injury caused by the collision process cannot be accurately determined, the targeted rescue and treatment cannot be timely implemented for the user, and the survival and injury recovery probability of the user is reduced.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method and apparatus for predicting user damage. Background Technology

[0002] During vehicle operation, accidents such as collisions can easily lead to injuries to vehicle occupants. For example, the neck is a particularly vulnerable area. Because neck injuries are often not easily detected at the scene, and passengers are not typically wearing specialized measuring equipment, the extent of damage during a collision cannot be accurately assessed. This hinders timely and targeted rescue and treatment, reducing the chances of survival and recovery. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method and apparatus for predicting user injury, which can solve the problem that user injury caused during a collision cannot be accurately judged, and that timely and targeted rescue and treatment of the user cannot be implemented, thereby reducing the chances of user survival and injury recovery.

[0004] To achieve the above objectives, according to one aspect of the embodiments of this application, a method for predicting user damage is provided.

[0005] An embodiment of this application provides a method for predicting user injury, comprising: responding to an injury prediction instruction, acquiring a corresponding target user and a predicted location, and querying associated locations of the predicted location; collecting a set of movement parameters of the target user in each preset direction corresponding to the predicted location and the associated location; acquiring the mass and length of the associated location, and calling a preset prediction model, and combining the set of movement parameters corresponding to each preset direction to determine the bearing capacity and curvature of the predicted location in each preset direction; and determining the injury result of the predicted location based on the bearing capacity and curvature of the predicted location in each preset direction.

[0006] Optionally, the step of calling a preset prediction model and combining it with the set of movement parameters corresponding to each preset direction to determine the bearing force of the predicted part in each preset direction includes:

[0007] Based on the set of movement parameters corresponding to each preset direction, calculate the movement acceleration of the associated part in each preset direction;

[0008] Multiply the acceleration of the associated part in each of the preset directions by the mass of the associated part to obtain the force that the predicted part bears in each of the preset directions.

[0009] Optionally, based on the set of movement parameters corresponding to each preset direction, the movement acceleration of the associated part in each preset direction is calculated, including:

[0010] Query the mobile connection parts corresponding to the associated parts, and obtain the length of the associated parts and each of the mobile connection parts;

[0011] For each preset direction, based on the set of movement parameters corresponding to each preset direction, calculate the movement speed parameters and rotation angle parameters corresponding to each moving connection part, as well as the rotation angle parameters corresponding to the associated part, to construct the movement acceleration matrix of the associated part; call the preset calculation model to calculate the weight matrix corresponding to the movement acceleration matrix;

[0012] Multiply the acceleration matrix and weight matrix of each preset direction to obtain the acceleration of the associated part in each preset direction.

[0013] Optionally, the preset direction includes a first preset direction; constructing the motion acceleration matrix of the associated part includes:

[0014] Based on the moving speed, moving acceleration, rotational angular velocity, and rotational angular acceleration of each of the moving connection parts, and the rotational angular velocity and rotational angular acceleration of the associated parts, a moving acceleration matrix of the associated parts in the first preset direction is constructed.

[0015] Optionally, the preset direction includes a first preset direction;

[0016] The preset calculation model is invoked to calculate the weight matrix corresponding to the motion acceleration matrix, including:

[0017] Based on the lengths of the associated parts and each of the movable connection parts, and in conjunction with the rotation angle parameters of the associated parts and each of the movable connection parts, the weight matrix corresponding to the motion acceleration matrix is ​​calculated.

[0018] Optionally, the preset direction includes a second preset direction;

[0019] Constructing the motion acceleration matrix of the associated part includes:

[0020] A first mobile connection portion and a second mobile connection portion are determined from each of the mobile connection portions, wherein the first mobile connection portion is closer to the associated device than the second mobile connection portion;

[0021] Based on the moving acceleration of the first movable connection part and the rotational angular acceleration of the associated part, a moving acceleration matrix of the associated part is constructed, wherein the moving acceleration of the first movable connection part is calculated based on the rotational angular acceleration, length of the first movable connection part, and moving acceleration of the second movable connection part.

[0022] Optionally, a preset calculation model is invoked to calculate the weight matrix corresponding to the motion acceleration matrix, including:

[0023] The weight matrix corresponding to the motion acceleration matrix is ​​calculated based on the preset value and the length of the associated part.

[0024] Optionally, a preset prediction model is invoked, and combined with the set of movement parameters corresponding to each preset direction, the curvature of the predicted part in each preset direction is determined, including:

[0025] The movement acceleration and rotational angular acceleration of the associated part in each preset direction are calculated based on the set of movement parameters to construct an acceleration matrix corresponding to each preset direction.

[0026] Obtain the rotational inertia corresponding to the target user, and construct an attribute matrix of the associated part based on the rotational inertia, the mass of the associated part, and the length of the associated part relative to each preset direction;

[0027] Multiplying the acceleration matrix by the attribute matrix yields the curvature of the predicted part in each of the preset directions.

[0028] Optionally, the damage result of the predicted part is determined based on the bearing force and bending degree corresponding to the predicted part in each of the preset directions, including:

[0029] Obtain the bearing capacity level range corresponding to each of the preset directions, and match it with the bearing capacity of the predicted part in each of the preset directions to obtain the first damage level of the predicted part in each of the preset directions;

[0030] Obtain the curvature level range corresponding to each of the preset directions, and match it with the curvature of the predicted part in each of the preset directions to obtain the second damage level of the predicted part in each of the preset directions;

[0031] The damage result of the predicted site is determined based on the first damage level and the second damage level.

[0032] To achieve the above objectives, according to another aspect of the embodiments of this application, an apparatus for predicting user damage is provided.

[0033] An apparatus for predicting user injury according to an embodiment of this application includes: an acquisition unit configured to, in response to an injury prediction command, acquire a corresponding target user and a predicted body part, and query associated body parts of the predicted body part; a collection unit configured to collect a set of movement parameters of the target user corresponding to the predicted body part and the associated body parts in each preset direction; a determination unit configured to acquire the mass and length of the associated body parts, and to invoke a preset prediction model, and, in combination with the set of movement parameters corresponding to each preset direction, determine the bearing force and curvature of the predicted body part in each preset direction; the determination unit is further configured to determine the degree of injury to the target user's neck based on the bearing force and curvature of the target user's head.

[0034] Optionally, the defined unit can also be configured as follows:

[0035] Based on the set of movement parameters corresponding to each preset direction, calculate the movement acceleration of the associated part in each preset direction;

[0036] Multiply the acceleration of the associated part in each of the preset directions by the mass of the associated part to obtain the force that the predicted part bears in each of the preset directions.

[0037] Optionally, the defined unit can also be configured as follows:

[0038] Query the mobile connection parts corresponding to the associated parts, and obtain the length of the associated parts and each of the mobile connection parts;

[0039] For each preset direction, based on the set of movement parameters corresponding to each preset direction, calculate the movement speed parameters and rotation angle parameters corresponding to each moving connection part, as well as the rotation angle parameters corresponding to the associated part, to construct the movement acceleration matrix of the associated part; call the preset calculation model to calculate the weight matrix corresponding to the movement acceleration matrix;

[0040] Multiply the acceleration matrix and weight matrix of each preset direction to obtain the acceleration of the associated part in each preset direction.

[0041] Optionally, the preset direction includes a first preset direction; the determining unit can also be configured to:

[0042] Based on the moving speed, moving acceleration, rotational angular velocity, and rotational angular acceleration of each of the moving connection parts, and the rotational angular velocity and rotational angular acceleration of the associated parts, a moving acceleration matrix of the associated parts in the first preset direction is constructed.

[0043] Optionally, the preset direction includes a first preset direction;

[0044] The defined unit can also be configured as follows:

[0045] Based on the lengths of the associated parts and each of the movable connection parts, and in conjunction with the rotation angle parameters of the associated parts and each of the movable connection parts, the weight matrix corresponding to the motion acceleration matrix is ​​calculated.

[0046] Optionally, the preset direction includes a second preset direction; the determining unit can also be configured to:

[0047] A first mobile connection portion and a second mobile connection portion are determined from each of the mobile connection portions, wherein the first mobile connection portion is closer to the associated device than the second mobile connection portion;

[0048] Based on the moving acceleration of the first movable connection part and the rotational angular acceleration of the associated part, a moving acceleration matrix of the associated part is constructed, wherein the moving acceleration of the first movable connection part is calculated based on the rotational angular acceleration, length of the first movable connection part, and moving acceleration of the second movable connection part.

[0049] Optionally, the defined unit can also be configured as follows:

[0050] The weight matrix corresponding to the motion acceleration matrix is ​​calculated based on the preset value and the length of the associated part.

[0051] Optionally, the defined unit can also be configured as follows:

[0052] The movement acceleration and rotational angular acceleration of the associated part in each preset direction are calculated based on the set of movement parameters to construct an acceleration matrix corresponding to each preset direction.

[0053] Obtain the rotational inertia corresponding to the target user, and construct an attribute matrix of the associated part based on the rotational inertia, the mass of the associated part, and the length of the associated part relative to each preset direction;

[0054] Multiplying the acceleration matrix by the attribute matrix yields the curvature of the predicted part in each of the preset directions.

[0055] Optionally, the defined unit can also be configured as follows:

[0056] Obtain the bearing capacity level range corresponding to each of the preset directions, and match it with the bearing capacity of the predicted part in each of the preset directions to obtain the first damage level of the predicted part in each of the preset directions;

[0057] Obtain the curvature level range corresponding to each of the preset directions, and match it with the curvature of the predicted part in each of the preset directions to obtain the second damage level of the predicted part in each of the preset directions;

[0058] The damage result of the predicted site is determined based on the first damage level and the second damage level.

[0059] Additionally, this application provides a vehicle including a processor, a memory, and a display, wherein the processor is configured to acquire and execute code in the memory to perform the user damage prediction method described above.

[0060] To achieve the above objectives, according to another aspect of the embodiments of this application, an electronic device is provided.

[0061] An electronic device according to an embodiment of this application includes: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the user injury prediction method provided in the embodiment of this application.

[0062] To achieve the above objectives, according to another aspect of the embodiments of this application, a computer-readable medium is provided.

[0063] This application provides a computer-readable medium storing a computer program that, when executed by a processor, implements the user injury prediction method provided in this application.

[0064] To achieve the above objectives, according to another aspect of the embodiments of this application, a computer program product is provided.

[0065] A computer program product according to an embodiment of this application includes a computer program that, when executed by a processor, implements the user injury prediction method provided in an embodiment of this application.

[0066] One embodiment of the above invention has the following advantages or beneficial effects: In this application, in response to the damage prediction command, the corresponding target user and the predicted part can be obtained first, and the associated parts of the predicted part can be queried. Then, the set of movement parameters of the target user in each preset direction corresponding to the predicted part and the associated parts can be collected. Then, the mass and length of the associated parts can be combined to determine the bearing force and curvature of the predicted part in each preset direction, so as to determine the damage result of the predicted part. Thus, in this embodiment of the invention, the damage prediction of the user's predicted part can be performed by using the movement data of the predicted part and the associated parts, so as to determine the degree of damage to the user in a timely and accurate manner, and thus to implement targeted rescue and treatment for the user, thereby improving the user's survival and damage recovery probability.

[0067] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0068] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0069] Figure 1 This is a schematic flowchart of a user injury prediction method provided according to an embodiment of this application;

[0070] Figure 2 This is a schematic diagram of a shooting device configuration according to an embodiment of this application;

[0071] Figure 3 This is a schematic diagram of a user moving along the xz axis according to an embodiment of this application;

[0072] Figure 4 This is a schematic diagram of a user moving along the yz axis according to an embodiment of this application;

[0073] Figure 5 This is a schematic diagram of the centroid of various parts of a user according to an embodiment of this application;

[0074] Figure 6 This is a schematic flowchart of a user injury prediction method provided according to an embodiment of this application;

[0075] Figure 7 This is a schematic diagram of the force variation curve of the upper neck according to an embodiment of this application;

[0076] Figure 8 This is a schematic diagram of a neck curvature variation curve provided according to an embodiment of this application;

[0077] Figure 9 This is a schematic diagram of a head movement distance variation curve provided according to an embodiment of this application;

[0078] Figure 10 This is a schematic diagram of a head movement speed variation curve provided according to an embodiment of this application;

[0079] Figure 11 This is a schematic diagram of a head movement acceleration variation curve provided according to an embodiment of this application;

[0080] Figure 12 This is a schematic diagram of a curve showing the change in torso movement distance according to an embodiment of this application;

[0081] Figure 13 This is a schematic diagram of a torso movement speed variation curve provided according to an embodiment of this application;

[0082] Figure 14 This is a schematic diagram of a trunk movement acceleration variation curve provided according to an embodiment of this application;

[0083] Figure 15 This is a schematic diagram of the main units of a user injury prediction device according to an embodiment of this application;

[0084] Figure 16 This is a schematic diagram of the structure of a vehicle according to an embodiment of this application;

[0085] Figure 17 This is an exemplary vehicle system architecture diagram to which embodiments of this application can be applied;

[0086] Figure 18 This is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application. Detailed Implementation

[0087] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with relevant national laws and regulations.

[0088] It should be noted that, unless otherwise specified, the embodiments of this application and the technical features thereof can be combined with each other.

[0089] Furthermore, the terms "first," "second," and "third," etc., included in the terminology of this application's embodiments are used to distinguish similar objects and are not necessarily used to describe a specific number or order. It should be understood that such terms can be used interchangeably where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application.

[0090] Furthermore, the vehicles involved in the embodiments of this application may be internal combustion engine vehicles that use an engine as a power source, hybrid vehicles that use an engine and an electric motor as power sources, electric vehicles that use an electric motor as a power source, etc.

[0091] Vehicles are susceptible to accidents in various situations, such as collisions. Many accidents can cause movement of the user inside the vehicle, resulting in injuries to various parts of the user's body. The user injury prediction method provided in this application can be used to predict user injuries, specifically to predict neck injuries.

[0092] Figure 1 This is a schematic diagram of the main process for predicting neck injury according to an embodiment of this application, as shown below. Figure 1 As shown, it includes:

[0093] Step S101: In response to the damage prediction command, obtain the corresponding target user and prediction site, and query the related sites of the prediction site.

[0094] The damage prediction command can be automatically triggered by the prediction system or received from an external system. Since user injuries typically occur during vehicle accidents, this application allows for the automatic triggering of the damage prediction command upon detecting a vehicle accident. The prediction system can be installed within the vehicle.

[0095] The damage prediction instruction indicates that damage prediction should be performed on the predicted location of the target user. The damage prediction instruction can include information about the user corresponding to this damage prediction and the predicted location, so as to identify the prediction object, i.e., the target user, and the location of the predicted damage. If the damage prediction instruction does not include information about the user corresponding to the damage prediction, it can be preset to predict damage for the driver in the vehicle, or it can be preset to predict damage for all users in the vehicle.

[0096] Damage prediction for various parts of a user's body typically requires prediction based on parameters of related parts. Therefore, this application allows for the preset of related parts for damage prediction, enabling accurate prediction. For example, damage prediction for the neck can be performed using parameters associated with the head. The related part of the neck can be set to the head, so when the predicted part is the neck, the related part can be found to be the head.

[0097] It should be noted that the associated sites for different predicted sites may include one or more, and this application does not limit them. For example, for the neck, the head and trunk can both be identified as associated sites for damage prediction.

[0098] Step S102: Collect the set of movement parameters of the target user in each preset direction corresponding to the predicted part and the associated part.

[0099] Since users may move in various directions during a vehicle accident, this application can preset the direction of user movement analysis, i.e., each preset direction, so as to collect and analyze data from each preset direction.

[0100] Specifically, this application can obtain a motion data set by acquiring images of the target user. For example, camera devices can be set up at different locations on the vehicle to acquire user image data from various preset directions. Figure 2 The diagram shown is a schematic representation of a shooting device setup in this application. Figure 2 As shown in the figure, establish the x, y, z coordinate system. Figure 2 In this diagram, z and x represent coordinate axes. The imaging device can be set at positions A, B, and C respectively. The imaging device at position A can capture images in the xy plane, the imaging device at position B can capture images in the xz plane, and the imaging device at position C can capture images in the zy plane.

[0101] After acquiring images of the target user, this application can perform image processing through a preset image processor, such as image preprocessing, image feature point extraction, target recognition in the image, etc., and then analyze and obtain the set of movement parameters of the target user corresponding to the predicted part and the associated part in each preset direction.

[0102] Since a vehicle accident will cause the target user to move for a period of time, this application can collect movement parameters in real time during this period to obtain a movement data set. Specifically, the time period can represent the period from when the damage prediction command is triggered to when the target user stops moving.

[0103] It should be noted that the parameters to be collected in this application can be set based on requirements, and then a motion data set can be obtained through the collection operation. Taking the predicted part as the neck and the associated part as the head as an example, the motion data set can include the displacement, time spent on movement, rotation angle, etc. of the predicted part and the associated part in each preset direction.

[0104] In this application, since user movement during a vehicle accident is typically forward / backward and left / right, to more accurately predict neck injuries, the user's movement can be analyzed in two directions: a first preset direction and a second preset direction. Figure 2 Taking the established coordinate axes as an example, the plane containing the xz axis can be used as a preset direction, i.e., the first preset direction, which is the direction for analyzing the user's frontal movement. The plane containing the yz axis can also be used as a preset direction, i.e., the second preset direction, which is the direction for analyzing the user's lateral movement. Figure 3The image shows a schematic diagram illustrating the user's movement along the xz axis in the event of a vehicle accident. Figure 4 The diagram shown illustrates a user's movement along the yz axis during a vehicle accident. 41 represents the seat, 421 represents the user in the initial state before movement, and 422 represents the user after movement along the yz axis.

[0105] It should be noted that in this application, movement data is collected in the first preset direction and the second preset direction, so the movement parameter set may include the first movement parameter set corresponding to the first preset direction and the second movement parameter set corresponding to the second preset direction.

[0106] Step S103: Obtain the mass and length of the associated part, call the preset prediction model, and combine it with the set of corresponding movement parameters in each preset direction to determine the bearing force and bending degree of the predicted part in each preset direction.

[0107] Since the damage to the predicted part is usually affected by the force and curvature of the associated part, the damage prediction of the predicted part can be performed by predicting the force and curvature of the associated part. Therefore, in this step, the mass and length of the associated part can be obtained, and the force and curvature of the predicted part can be determined by combining the set of movement parameters.

[0108] Taking the neck as the predicted location as an example, the user's head and neck are connected. Neck injury can include damage caused by the force exerted on the user's head and the angle of head rotation. Therefore, neck injury prediction can be based on the force exerted by the neck on the head and the torque (bending angle) exerted by the neck on the head. In this application, the prediction model is pre-set and trained, and can be implemented based on a preset calculation principle. The force exerted on the target user's head can include the force exerted by the neck on the head, which can be a tensile force; the bending angle of the target user's head can include the torque exerted by the neck on the head, such as a rotational torque (bending torque). The force and bending angle of the neck in each preset direction can represent the force and torque exerted by the neck on the head in each preset direction.

[0109] In one embodiment of this application, the bearing capacity corresponding to the predicted part can be obtained by multiplying the moving acceleration of the associated part by the mass of the associated part. That is, the step of determining the bearing capacity of the predicted part in each of the preset directions can be specifically performed as follows: calculate the moving acceleration of the associated part in each preset direction according to the set of moving parameters corresponding to each preset direction; multiply the moving acceleration of the associated part in each preset direction by the mass of the associated part to obtain the bearing capacity of the predicted part in each preset direction.

[0110] For each part, its movement is usually driven by the connection of other parts. For example, for the head, its movement can be driven by the torso and neck. Therefore, in this application, the movement connection parts of each part can be preset, that is, the parts that drive the movement. In this step, the calculation of the movement acceleration of the related parts can be done by first querying the movement connection parts corresponding to the related parts, and then combining the related parts and the movement connection parts to calculate the movement acceleration of the related parts.

[0111] Therefore, the calculation of the movement acceleration of the associated part in each preset direction in this step can be specifically performed as follows: query each moving connection part corresponding to the associated part, and obtain the length of the associated part and each moving connection part; for each preset direction, calculate the movement speed parameter and rotation angle parameter corresponding to each moving connection part, as well as the rotation angle parameter of the associated part, according to the movement parameter set, to construct the movement acceleration matrix of the associated part; call the preset calculation model to calculate the weight matrix corresponding to the movement acceleration matrix; multiply the movement acceleration matrix and weight matrix of each preset direction to obtain the movement acceleration of the associated part in each preset direction.

[0112] For each preset direction, the movement velocity parameters and rotation angle parameters of each moving connection part in that preset direction, as well as the rotation angle parameters of the associated part in that preset direction, can all be calculated based on the collected set of movement parameters. The method for constructing the movement acceleration matrix of the associated part can be based on a preset construction method, and the calculation model can be pre-configured.

[0113] For a first preset direction, this application can obtain the moving speed, moving acceleration, rotational angular velocity, and rotational angular acceleration corresponding to each moving connecting part, as well as the rotational angular velocity and rotational angular acceleration of the associated part, to construct a moving acceleration matrix of the associated part in the first preset direction; and based on the length of the associated part and each moving connecting part, and in combination with the rotational angle parameters of the associated part and each moving connecting part, calculate the weight matrix corresponding to the moving acceleration matrix. The moving speed, moving acceleration, rotational angular velocity, and rotational angular acceleration corresponding to each moving connecting part, the rotational angular velocity and rotational angular acceleration of the associated part, and the rotational angle parameters of the associated part and each moving connecting part can all be calculated based on the first set of moving parameters.

[0114] For example, taking the neck as the predicted location and the head as the associated location, the acceleration of the head in each preset direction can be calculated based on the set of movement parameters corresponding to each preset direction. Then, the acceleration is multiplied by the mass of the head to obtain the force that the neck bears in each preset direction.

[0115] by Figure 2Taking the coordinate axis shown as an example, in the first preset direction, the force on the head can be divided into the force on the x-bearing and the force on the z-bearing, so the calculation method can be as shown in Formula 1.

[0116]

[0117] In Formula 1, F x F represents the force exerted on the target user's head along the x-axis in the first preset direction. z1 This represents the force exerted on the user's head along the z-axis in the first preset direction, m h Indicates the quality of the target user header. This represents the acceleration of the target user's head along the x-axis in the first preset direction. The target user's head movement acceleration along the z-axis in a first preset direction. Wherein, It can be calculated using Formula 2. It can be calculated using Formula 3.

[0118]

[0119]

[0120] In formulas 2 and 3, This represents the acceleration matrix of the head along the x-axis. This represents the acceleration matrix of the head along the z-axis. This represents the acceleration of the torso along the x-axis in the first preset direction. This represents the acceleration of the torso along the z-axis in the first preset direction. This represents the rotational angular acceleration of the torso in the first preset direction. This indicates the rotational angular velocity of the torso in the first preset direction. This indicates the angular acceleration of the neck in the first preset direction. f(a) represents the angular velocity of the neck in the first preset direction. n，h，t )express The weight matrix, that is, the weight matrix that represents The weight corresponding to each element in the matrix can be calculated as shown in Formula 4, g(a n，h，t )express The weight matrix, that is, the weight matrix that represents The weight corresponding to each element in the matrix can be calculated as shown in Formula 5.

[0121]

[0122]

[0123] In formulas 4 and 5, an1 Indicates the angle of neck rotation, a h1 Indicates the angle of head rotation, a t1 d represents the rotation angle of the torso. t-n1 d represents the length from the center of mass of the torso to the lower part of the neck. l-u d represents the length of the neck. u_h It indicates the distance from the upper part of the neck to the center of mass of the head.

[0124] It should be noted that, for each part, the movement trajectory of that part from its initial position to its current position (the user's position at the moment when the predicted force and curvature of the part are determined) can be collected. This allows for the calculation of the curve corresponding to the displacement and time of that part. Thus, the movement velocity can be obtained by calculating the first derivative of the displacement with respect to time, and the movement acceleration can be obtained by calculating the second derivative of the displacement with respect to time. Therefore, in this step, the movement velocity and movement acceleration of the associated part in each preset direction can be calculated using the movement data set. For each part, the rotation angle and time of its movement from its initial position to its current position can be collected. This allows for the calculation of the curve corresponding to the rotation angle and time of that part. Thus, the rotational angular velocity can be obtained by calculating the first derivative of the rotation angle with respect to time, and the rotational angular acceleration can be obtained by calculating the second derivative of the rotation angle with respect to time. In this application, the center of mass of each part can be predetermined, and the movement trajectory of the center of mass can be used as the movement trajectory of that part. Specifically, it can be as follows... Figure 5 As shown, P represents the center of mass of the user's pelvis, H is the center of mass of the head, T is the center of mass of the torso, and the center of mass of the neck is not shown; the rotation angle of each part in a preset direction can be calculated in different ways depending on the part, such as... Figure 5 As shown, the rotation angle of the torso in the first direction can be the rotation angle of the line connecting the centers of mass between the torso and the pelvis relative to the x-axis of the first preset direction, and the rotation angle of the neck in the first direction can be the rotation angle of the lower part of the neck (e.g., Figure 5 (N1) and upper neck (such as Figure 5 The rotation angle of the line connecting N2 and N2 relative to the x-axis of the first preset direction, where the rotation angle of the head in the first direction can be the angle between the center of mass of the head and the upper part of the neck (e.g., Figure 5 The rotation angle of the line connecting (N2) with respect to the x-axis of the first preset direction. The upper part of the neck represents the area where the neck and head meet, such as the occipital bone of the user.

[0125] For the second preset direction, the construction of the motion acceleration matrix of the associated part in this application can be performed as follows: First and second mobile connecting parts are determined from the various mobile connecting parts, wherein the first mobile connecting part is closer to the associated device than the second mobile connecting part; a motion acceleration matrix of the associated part is constructed based on the motion acceleration of the first mobile connecting part and the rotational angular acceleration of the associated part, wherein the motion acceleration of the first mobile connecting part is calculated based on the rotational angular acceleration, length, and motion acceleration of the second mobile connecting part. The calculation of the weight matrix corresponding to the motion acceleration matrix can be performed as follows: the weight matrix corresponding to the motion acceleration matrix is ​​calculated based on preset values ​​and the length of the associated part.

[0126] The connection order of each movable connection part can be determined based on the order in which the parts are connected to each other. For example, the movable connection parts of the head include the neck and the torso, so the connection order is head, neck and torso, the associated part is the head, the first movable connection part is the neck, and the second movable connection part is the torso.

[0127] Since the acceleration matrix of the preceding part in the connection sequence is constructed based on the rotational angular acceleration of that preceding part and the acceleration of the following part it connects to, we can first determine the first moving connection part adjacent to the associated part from among the moving connection parts based on the connection sequence. Then, based on the acceleration of the first moving connection part and the rotational angular acceleration of the associated part, we can construct the acceleration matrix of the associated part. Correspondingly, the weight matrix of the preceding part in the connection sequence is calculated based on a preset value and the length of that preceding part.

[0128] For example, taking the neck as the predicted location and the head as the associated location. Figure 2 Taking the coordinate axis shown as an example, in the second preset direction, the force on the head can be divided into the force on the y-bearing and the force on the z-bearing, so the calculation method can be as shown in Formula 6.

[0129]

[0130] In Formula 1, F y F represents the force exerted by the head along the y-axis in the second preset direction. z2 This represents the force exerted by the head along the z-axis in the second preset direction, m. h Indicates head mass. This represents the acceleration of the head along the y-axis in the second preset direction. This represents the acceleration of the head along the z-axis in the second preset direction. It can be calculated using Formula 7. It can be calculated using formula 8.

[0131]

[0132]

[0133] In formulas 7 and 8, This represents the acceleration matrix of the head along the y-axis in the second preset direction. This represents the acceleration matrix of the head along the z-axis in the second preset direction. This represents the acceleration of the neck along the y-axis in the second preset direction. This represents the acceleration of the neck along the z-axis in the second preset direction. This indicates the angular acceleration of the head in the second preset direction. L represents the weight matrix of the head in the second preset direction. h This indicates the length of the head. Among them, It can be calculated based on Formula 9. It can be calculated based on Formula 10.

[0134]

[0135]

[0136] In formulas 9 and 10, This represents the acceleration matrix of the neck along the y-axis in the second preset direction. This represents the acceleration matrix of the neck along the z-axis in the second preset direction. This represents the acceleration of the torso along the y-axis in the second preset direction. This represents the acceleration of the torso along the z-axis in the second preset direction. L represents the weight matrix of the torso in the second preset direction. c Indicates the length of the torso.

[0137] In another embodiment of this application, the force corresponding to the predicted part can be specifically executed as follows: calculate the moving acceleration and rotational angular acceleration of the associated part in each preset direction according to the set of moving parameters, so as to construct the acceleration matrix corresponding to each preset direction; obtain the rotational inertia corresponding to the target user, so as to construct the attribute matrix of the associated part according to the rotational inertia, the mass of the associated part, and the length of the associated part relative to each preset direction; multiply the acceleration matrix and the attribute matrix to obtain the curvature of the predicted part.

[0138] Specifically, taking the neck as the predicted location and the head as the associated location, the curvature of the head can be calculated based on the angular acceleration and motion acceleration of the head movement, as well as the attributes of the head.

[0139] The preset directions include a first preset direction and a second preset direction. Figure 2Taking the coordinate axis shown as an example, the curvature of the head in the first preset direction can be calculated as shown in Formula 11.

[0140]

[0141] In Formula 11, M ocy1 J represents the curvature of the head about the y-axis in the first direction. h The moment of inertia of the head, m h Indicates head mass, d x This represents the length of the head along the x-axis in the first preset direction, which can be the length from the upper part of the neck to the head along the x-axis in the first preset direction, d. z1 This represents the length of the head along the z-axis in the first preset direction, which can be the length from the upper part of the neck to the head along the z-axis in the first preset direction.

[0142] It should be noted that since the rotation of the head along the z-axis in the first direction is small, it can be ignored, so M is used. ocy1 This indicates the degree of curvature of the head in the first direction.

[0143] by Figure 2 Taking the coordinate axis shown as an example, in the second preset direction, the curvature of the head includes the curvature on the x-axis and the curvature on the y-axis, and the calculation method can be as shown in Formula 12 and Formula 13.

[0144]

[0145]

[0146] In formulas 12 and 13, M ocy2 M represents the curvature of the head about the y-axis in the second direction. ocx d represents the curvature of the head about the x-axis in the second direction. y This represents the length of the head along the y-axis in the second preset direction, which can be the length from the upper part of the neck to the head along the y-axis in the second preset direction, d. z2 This represents the length of the head along the z-axis in the second preset direction, which can be the length from the upper part of the neck to the head along the z-axis in the second preset direction. This represents the angular acceleration of the head along the y-axis in the second preset direction.

[0147] Step S104: Determine the damage result of the predicted part based on the bearing force and bending degree corresponding to the predicted part in each preset direction.

[0148] In this application, the bearing capacity level range and curvature level range corresponding to each preset direction can be preset. Therefore, in this step, the following steps can be taken: obtain the bearing capacity level range corresponding to each preset direction, and match it with the bearing capacity of the predicted part in each preset direction to obtain the first damage level of the predicted part in each preset direction; obtain the curvature level range corresponding to each preset direction, and match it with the curvature of the predicted part in each preset direction to obtain the second damage level of the predicted part in each preset direction; and determine the injury result of the target user's neck based on the first damage level and the second damage level.

[0149] It should be noted that in this application, the degree of damage can also be expressed in other ways, such as damage percentage, etc.

[0150] In this embodiment, in response to a damage prediction command, the corresponding target user and predicted location can be obtained first, and the associated locations of the predicted location can be queried. Then, the set of movement parameters of the target user in each preset direction corresponding to the predicted location and associated locations can be collected. Furthermore, the mass and length of the associated locations can be combined to determine the bearing capacity and curvature of the predicted location in each preset direction, thereby determining the damage result of the predicted location. Thus, in this embodiment, damage prediction of the user's predicted location can be performed through the movement data of the predicted location and associated locations, thereby timely and accurately determining the degree of damage to the user, and enabling targeted rescue and treatment of the user, improving the user's survival and recovery rate.

[0151] Figure 6 This is a schematic diagram of the main flow of a user injury prediction method provided according to an embodiment of this application, as shown below. Figure 6 As shown, it includes:

[0152] Step S601: In response to the damage prediction command, obtain the corresponding target user and prediction site, and query the related sites of the prediction site.

[0153] Step S602: Collect the set of movement parameters of the target user in each preset direction corresponding to the predicted part and the associated part.

[0154] Step S603: Calculate the movement acceleration of the associated part in each preset direction based on the set of movement parameters corresponding to each preset direction, and multiply the movement acceleration of the associated part in each preset direction by the mass of the associated part to obtain the bearing force of the predicted part in each preset direction.

[0155] Step S604: Calculate the movement acceleration and rotational angular acceleration of the associated part in each preset direction based on the set of movement parameters to construct the acceleration matrix corresponding to each preset direction; obtain the rotational inertia corresponding to the target user to construct the attribute matrix of the associated part based on the rotational inertia, the mass of the associated part, and the length of the associated part relative to each preset direction; multiply the acceleration matrix and the attribute matrix to obtain the curvature of the predicted part in each preset direction.

[0156] Step S605: Obtain the bearing capacity level range corresponding to each preset direction, and match it with the bearing capacity of the predicted part in each preset direction to obtain the first damage level of the predicted part in each preset direction; obtain the curvature level range corresponding to each preset direction, and match it with the curvature of the predicted part in each preset direction to obtain the second damage level of the predicted part in each preset direction.

[0157] Step S606: Determine the damage result of the predicted site based on the first damage level and the second damage level.

[0158] It should be noted that the data processing principle in the embodiments of this application is the same as... Figure 1 The data processing principles in the illustrated embodiments are the same, and will not be repeated here.

[0159] It should be noted that, in this embodiment of the invention, when performing step S103 (or steps S603-604) to calculate the bearing capacity and curvature of the predicted part in each preset direction, the parameters required in the process described in step S103 can be calculated first using a preset method. For example, taking the neck of the predicted part described in step S103 as an example, Figure 2 Under the reference of the coordinate axes shown, the required parameters for calculation in the first preset direction can be as follows: Figures 7-14 As shown.

[0160] Figure 7 The diagram shows a schematic of the force (force on the head) variation curve of the upper neck. The horizontal axis represents time, the vertical axis represents the force, line 701 is the force variation curve of the z-axis direction with time, line 702 is the force variation curve of the y-axis direction with time, and line 703 is the force variation curve of the x-axis direction with time.

[0161] Figure 8 The diagram shows a schematic representation of the curves showing the change in neck curvature. The horizontal axis represents time, and the vertical axis represents the curvature. Line 801 is the curve showing the change in curvature over time in the x-axis direction, line 802 is the curve showing the change in curvature over time in the x-axis direction, and line 803 is the curve showing the change in curvature over time in the z-axis direction.

[0162] Figure 9 The diagram shows a schematic of the head movement distance variation curve, where the horizontal axis represents time and the vertical axis represents the force. Line 901 is the curve of head movement distance variation with time in the z-axis direction, line 902 is the curve of head movement distance variation with time in the y-axis direction, and line 903 is the curve of head movement distance variation with time in the x-axis direction. Figure 10 The diagram shows a schematic of the head movement speed variation curve, where the horizontal axis represents time and the vertical axis represents speed. Line 1001 is the curve of head movement speed in the z-axis direction as a function of time, line 1002 is the curve of head movement speed in the y-axis direction as a function of time, and line 1003 is the curve of head movement speed in the x-axis direction as a function of time. Figure 11 The diagram shows a schematic of the head movement acceleration variation curve, where the horizontal axis represents time and the vertical axis represents acceleration. Line 1101 is the curve of head movement acceleration in the z-axis direction as a function of time, line 1102 is the curve of head movement acceleration in the y-axis direction as a function of time, and line 1103 is the curve of head movement acceleration in the x-axis direction as a function of time.

[0163] Figure 12 The diagram shows a schematic of the curve of the change in trunk movement distance, where the horizontal axis represents time and the vertical axis represents the force. Line 1201 is the curve of the change in trunk movement distance with time in the y-axis direction, line 1202 is the curve of the change in trunk movement distance with time in the z-axis direction, and line 1203 is the curve of the change in trunk movement distance with time in the x-axis direction. Figure 13 The diagram shows a schematic of the trunk movement speed variation curve, where the horizontal axis represents time and the vertical axis represents speed. Line 1301 is the trunk movement speed variation curve in the z-axis direction with time, line 1302 is the trunk movement speed variation curve in the y-axis direction with time, and line 1303 is the trunk movement speed variation curve in the x-axis direction with time. Figure 14 The diagram shows a schematic of the trunk movement acceleration variation curve, where the horizontal axis represents time and the vertical axis represents acceleration. Line 1401 is the trunk movement acceleration variation curve in the z-axis direction with time, line 1402 is the trunk movement acceleration variation curve in the y-axis direction with time, and line 1403 is the trunk movement acceleration variation curve in the x-axis direction with time.

[0164] Figure 15 This is a schematic diagram of the main units of a user injury prediction device according to an embodiment of this application. Figure 15 As shown, the user damage prediction device 1500 includes an acquisition unit 1501, a collection unit 1502, and a determination unit 1504.

[0165] The acquisition unit 1501 is configured to, in response to a damage prediction instruction, acquire the corresponding target user and the predicted location, and query the associated locations of the predicted location;

[0166] The acquisition unit 1502 is configured to acquire a set of movement parameters of the target user in each preset direction corresponding to the predicted part and the associated part.

[0167] The determining unit 1503 is configured to obtain the mass and length of the associated part, so as to call a preset prediction model and combine it with the set of movement parameters corresponding to each preset direction to determine the bearing force and curvature of the predicted part in each preset direction.

[0168] The determining unit 1503 is further configured to determine the degree of injury to the neck of the target user based on the bearing force and curvature of the target user's head.

[0169] Optionally, the determining unit 1503 can also be configured as follows:

[0170] Based on the set of movement parameters corresponding to each preset direction, calculate the movement acceleration of the associated part in each preset direction;

[0171] Multiply the acceleration of the associated part in each of the preset directions by the mass of the associated part to obtain the force that the predicted part bears in each of the preset directions.

[0172] Optionally, the determining unit 1503 can also be configured as follows:

[0173] Query the mobile connection parts corresponding to the associated parts, and obtain the length of the associated parts and each of the mobile connection parts;

[0174] For each preset direction, the set of movement parameters corresponding to each preset direction is used to calculate the movement speed parameters and rotation angle parameters corresponding to each moving connection part, as well as the rotation angle parameters corresponding to the associated part, so as to construct the movement acceleration matrix of the associated part; a preset calculation model is called to calculate the weight matrix corresponding to the movement acceleration matrix;

[0175] Multiply the acceleration matrix and weight matrix of each preset direction to obtain the acceleration of the associated part in each preset direction.

[0176] Optionally, the preset direction includes a first preset direction;

[0177] Unit 1503 can also be configured as follows:

[0178] Based on the moving speed, moving acceleration, rotational angular velocity, and rotational angular acceleration of each of the moving connection parts, and the rotational angular velocity and rotational angular acceleration of the associated parts, a moving acceleration matrix of the associated parts in the first preset direction is constructed.

[0179] Optionally, the preset direction includes a first preset direction;

[0180] The determining unit 1503 can also be configured to: calculate the weight matrix corresponding to the motion acceleration matrix based on the length of the associated part and each of the moving connection parts, in combination with the rotation angle parameters of the associated part and each of the moving connection parts.

[0181] Optionally, the preset direction includes a second preset direction;

[0182] The determining unit 1503 can also be configured to: determine a first mobile connection portion and a second mobile connection portion from each of the mobile connection portions, wherein the first mobile connection portion is closer to the associated device than the second mobile connection portion;

[0183] Based on the moving acceleration of the first movable connection part and the rotational angular acceleration of the associated part, a moving acceleration matrix of the associated part is constructed, wherein the moving acceleration of the first movable connection part is calculated based on the rotational angular acceleration, length of the first movable connection part, and moving acceleration of the second movable connection part.

[0184] Optionally, the determining unit 1503 can also be configured to: calculate the weight matrix corresponding to the moving acceleration matrix based on a preset value and the length of the associated part.

[0185] Optionally, the determining unit 1503 can also be configured as follows:

[0186] The movement acceleration and rotational angular acceleration of the associated part in each preset direction are calculated based on the set of movement parameters to construct an acceleration matrix corresponding to each preset direction.

[0187] Obtain the rotational inertia corresponding to the target user, and construct an attribute matrix of the associated part based on the rotational inertia, the mass of the associated part, and the length of the associated part relative to each preset direction;

[0188] Multiplying the acceleration matrix by the attribute matrix yields the curvature of the predicted part in each of the preset directions.

[0189] Optionally, the determining unit 1503 can also be configured as follows:

[0190] Obtain the bearing capacity level range corresponding to each of the preset directions, and match it with the bearing capacity of the predicted part in each of the preset directions to obtain the first damage level of the predicted part in each of the preset directions;

[0191] Obtain the curvature level range corresponding to each of the preset directions, and match it with the curvature of the predicted part in each of the preset directions to obtain the second damage level of the predicted part in each of the preset directions;

[0192] The damage result of the predicted site is determined based on the first damage level and the second damage level.

[0193] It should be noted that the user damage prediction method and the user damage prediction device in this application are related in terms of specific implementation, so the repeated content will not be described again.

[0194] like Figure 16 As shown, this application embodiment provides a vehicle 1600, which may include the user injury prediction device 1500 provided in the above embodiments.

[0195] Figure 17 An exemplary vehicle system architecture 1700 is shown, to which the method or apparatus for user damage prediction, according to embodiments of this application, can be applied.

[0196] like Figure 17 As shown, the vehicle system architecture 1700 may include various systems, such as a driving system 1701, a powertrain system 1702, a sensor system 1703, a control system 1704, one or more peripheral devices 1705, a power supply 1706, a computer system 1707, and a user interface 1708. Optionally, the vehicle system architecture 1700 may include more or fewer systems, and each system may include multiple components. Furthermore, each system and component of the vehicle system architecture 1700 may be interconnected via wired or wireless means.

[0197] The vehicle system architecture 1700 includes a driving system 1701, which can be in a fully or partially automated driving mode. For example, the driving system 1701 can automatically control the vehicle's movement without human interaction; the driving system 1701 can also control the vehicle's autonomous driving while interacting with a human to adjust its driving behavior. Specifically, the driving system 1701 can respond to damage prediction commands, obtain the corresponding target user and predicted location, query the associated locations of the predicted location; collect the set of movement parameters of the target user in each preset direction corresponding to the predicted location and associated locations; obtain the mass and length of the associated locations, call a preset prediction model, and combine it with the set of movement parameters corresponding to each preset direction to determine the bearing capacity and bending degree of the predicted location in each preset direction; and determine the damage result of the predicted location based on the bearing capacity and bending degree of the predicted location in each preset direction. This allows for timely and accurate determination of the user's degree of injury, enabling targeted rescue and treatment, and improving the user's survival and recovery rate.

[0198] The powertrain 1702 may include components that provide power to the vehicle. For example, the powertrain 1702 may include an engine, an energy source, a transmission, wheels, tires, etc. The engine may be an internal combustion engine, an electric motor, an air-compressed engine, or other combinations of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air-compressed engine. The engine converts the energy source into mechanical energy to supply the transmission. Examples of energy sources may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other electrical sources. The energy source may also provide energy to other systems of the vehicle. Furthermore, the transmission may include a gearbox, a differential, a drive shaft, and a clutch, etc.

[0199] Sensor system 1703 may include sensors for sensing the vehicle's surrounding environment. Examples include a positioning system (which may be a Global Positioning System (GPS) system, a BeiDou system, or another positioning system), radar, a laser rangefinder, an inertial measurement unit (IMU), and a camera. The positioning system can be used to determine the vehicle's geographical location. The IMU is used to sense changes in the vehicle's position and orientation based on inertial acceleration. In one embodiment, the IMU may be a combination of an accelerometer and a gyroscope. Radar can use radio signals to sense objects in the vehicle's surrounding environment. In some embodiments, in addition to sensing objects, radar can also be used to sense the speed and / or direction of travel of objects.

[0200] To detect environmental information and objects located in front of, behind, or to the sides of the vehicle, radar, cameras, and other devices can be configured at appropriate locations on the exterior of the vehicle. For example, to acquire an image of the front of the vehicle, a camera can be configured inside the vehicle and close to the windshield. Alternatively, the camera can be configured around the front bumper or radiator grille. Similarly, to acquire an image of the rear of the vehicle, a camera can be configured inside the vehicle and close to the rear window. Alternatively, the camera can be configured around the rear bumper, trunk, or tailgate. To acquire images of the sides of the vehicle, a camera can be configured inside the vehicle and close to at least one of the side windows. Alternatively, the camera can be configured around the side mirrors, fenders, or doors.

[0201] Laser rangefinders use lasers to sense objects in the environment in which a vehicle is located.

[0202] A camera can be used to capture multiple images of the vehicle's surroundings. The camera can be a still or video camera.

[0203] The control system 1704 may include software systems for predicting user injury to enable safe driving, such as route planning systems, obstacle avoidance systems, vision systems for image analysis, light detection systems, and environmental information acquisition systems. The control system 1704 may also include hardware systems such as throttle and steering wheel systems. Furthermore, the control system 1704 may add or replace components other than those shown and described. Alternatively, some of the components shown above may be omitted.

[0204] The control system 1704 interacts with external sensors, other user damage prediction devices, other computer systems, or users via peripheral devices 1705. Peripheral devices 1705 may include wireless communication systems, on-board computers, microphones, and / or speakers.

[0205] In some embodiments, peripheral device 1705 provides a means for user interaction with the control system 1704 via a user interface. For example, an onboard computer may provide information to a user of the vehicle. The user interface may also operate the onboard computer to receive user input. The onboard computer may be operated via a touchscreen. In other cases, peripheral device may provide a means for communicating with other devices located within the vehicle. For example, a microphone may receive audio (e.g., voice commands or other audio input) from a user of the control system 1704. Similarly, a speaker may output audio to a user of the control system 1704.

[0206] Wireless communication systems can communicate wirelessly with one or more devices, either directly or via a communication network. For example, wireless communication systems can use networks such as cellular networks, WiFi, and wireless local area networks (WLANs), or they can use infrared links, Bluetooth, or ZigBee to communicate directly with devices. Other wireless protocols include communication systems related to user injury prediction and safe driving in various vehicles.

[0207] The power source 1706 can provide power to various components of the vehicle. The power source 1706 can be a rechargeable lithium-ion or lead-acid battery.

[0208] The computer system 1707 controls some or all of the functions of predicting user injury in the vehicle to meet user needs and ensure safe driving. The computer system 1707 may include at least one processor that executes instructions stored in a non-transitory computer-readable medium such as memory. The computer system 1707 provides the aforementioned driving system with execution code that implements user injury prediction in the vehicle to meet user needs.

[0209] The processor can be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor can be a special-purpose device such as an application-specific integrated circuit (ASIC) or other hardware-based processor. Those skilled in the art will understand that the processor, computer, or memory can actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing. For example, memory can be a hard disk drive or other storage media located in a housing different from that of a computer. Therefore, references to processors or computers will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Unlike using a single processor to perform the steps described herein, some components, such as steering and deceleration components, may each have their own processor, which performs calculations only related to the component's specific function.

[0210] User interface 1708 is used to provide information to or receive information from a user of the vehicle. Optionally, user interface 1708 may include one or more input / output devices within a set of peripheral devices 1705, such as wireless communication systems, on-board computers, microphones, and speakers.

[0211] It should be understood that the components described above are merely an example. In actual applications, components in the various modules or systems mentioned above may be added or removed as needed. Figure 17 This should not be construed as a limitation on the embodiments of this application.

[0212] The following is for reference. Figure 18 It shows a schematic diagram of the structure of a computer system 1800 suitable for implementing embodiments of the present application. Figure 18 The computer system shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0213] like Figure 18 As shown, the computer system 1800 includes a central processing unit (CPU) 1801, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1802 or programs loaded from storage section 1808 into random access memory (RAM) 1803. The RAM 1803 also stores various programs and data required for the operation of the system 1800. The CPU 1801, ROM 1802, and RAM 1803 are interconnected via bus 1804. An input / output (I / O) interface 1805 is also connected to bus 1804.

[0214] The following components are connected to I / O interface 1805: an input section 1806; an output section 1807 including devices such as cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; a storage section 1808 including devices such as hard disks; and a communication section 1809 including network interface cards such as LAN cards and modems. The communication section 1809 performs communication processing via a network such as the Internet. A drive 1810 is also connected to I / O interface 1805 as needed. Removable media 1811, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 1810 as needed so that computer programs read from them can be installed into storage section 1808 as required.

[0215] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1809, and / or installed from removable medium 1811. When the computer program is executed by central processing unit (CPU) 1801, it performs the functions defined in the system of this application.

[0216] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer 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. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0217] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0218] The modules described in the embodiments of this application can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor can be described as including an acquisition unit, a prediction unit, a color value determination unit, and a color adjustment execution unit. The names of these modules do not necessarily limit the module itself; for example, the acquisition unit can also be described as "a module or unit that acquires angle data and intensity data of unknown light."

[0219] In another aspect, this application also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to, in response to detecting unknown light, acquire angle data and intensity data of the unknown light, determine the light type of the unknown light based on the angle data and intensity data; acquire environmental information of the target vehicle, predict the light trajectory based on the environmental information and the light type; acquire driver information in the target vehicle, determine the color temperature value of the windshield of the target vehicle based on the light trajectory and the driver information; and invoke a polarization design component to perform a process of adjusting the color of the windshield according to the color temperature value.

[0220] According to the technical solution of the embodiments of this application, the problem that drivers cannot react to sudden bright light in time and that sudden bright light may disrupt the driver's operation, resulting in poor driving safety can be solved. The solution can flexibly block light that interferes with the driver, reduce direct light to the driver's eyes, and improve driving safety.

[0221] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for predicting user damage, characterized in that, include: In response to a damage prediction command, the corresponding target user and predicted location are obtained, and the associated locations of the predicted location are queried. Collect the set of movement parameters of the target user in each preset direction corresponding to the predicted part and the associated part; The mass and length of the associated part are obtained, and a preset prediction model is invoked. Combined with the set of movement parameters corresponding to each preset direction, the bearing force and bending degree of the predicted part in each preset direction are determined respectively. The damage result of the predicted part is determined based on the bearing force and bending degree corresponding to the predicted part in each of the preset directions.

2. The method according to claim 1, characterized in that, The step of calling a preset prediction model and combining it with the set of movement parameters corresponding to each preset direction to determine the bearing capacity of the predicted part in each preset direction includes: Based on the set of movement parameters corresponding to each preset direction, calculate the movement acceleration of the associated part in each preset direction; Multiply the acceleration of the associated part in each of the preset directions by the mass of the associated part to obtain the force that the predicted part bears in each of the preset directions.

3. The method according to claim 2, characterized in that, Based on the set of movement parameters corresponding to each preset direction, the movement acceleration of the associated part in each preset direction is calculated, including: Query the mobile connection parts corresponding to the associated parts, and obtain the length of the associated parts and each of the mobile connection parts; For each preset direction, based on the set of movement parameters corresponding to each preset direction, calculate the movement speed parameters and rotation angle parameters corresponding to each moving connection part, as well as the rotation angle parameters corresponding to the associated part, to construct the movement acceleration matrix of the associated part; call the preset calculation model to calculate the weight matrix corresponding to the movement acceleration matrix; Multiply the acceleration matrix and weight matrix of each preset direction to obtain the acceleration of the associated part in each preset direction.

4. The method according to claim 3, characterized in that, The preset direction includes a first preset direction; Constructing the motion acceleration matrix of the associated part includes: Based on the moving speed, moving acceleration, rotational angular velocity, and rotational angular acceleration of each of the moving connection parts, and the rotational angular velocity and rotational angular acceleration of the associated parts, a moving acceleration matrix of the associated parts in the first preset direction is constructed.

5. The method according to claim 3, characterized in that, The preset direction includes a first preset direction; The preset calculation model is invoked to calculate the weight matrix corresponding to the motion acceleration matrix, including: Based on the lengths of the associated parts and each of the movable connection parts, and in conjunction with the rotation angle parameters of the associated parts and each of the movable connection parts, the weight matrix corresponding to the motion acceleration matrix is ​​calculated.

6. The method according to claim 3, characterized in that, The preset direction includes a second preset direction; Constructing the motion acceleration matrix of the associated part includes: A first mobile connection portion and a second mobile connection portion are determined from each of the mobile connection portions, wherein the first mobile connection portion is closer to the associated portion than the second mobile connection portion; Based on the moving acceleration of the first movable connection part and the rotational angular acceleration of the associated part, a moving acceleration matrix of the associated part is constructed, wherein the moving acceleration of the first movable connection part is calculated based on the rotational angular acceleration, length, and moving acceleration of the second movable connection part.

7. The method according to claim 6, characterized in that, The step of calling a preset calculation model to calculate the weight matrix corresponding to the motion acceleration matrix includes: The weight matrix corresponding to the motion acceleration matrix is ​​calculated based on the preset value and the length of the associated part.

8. The method according to claim 1, characterized in that, By invoking a preset prediction model and combining it with the set of movement parameters corresponding to each preset direction, the curvature of the predicted part in each preset direction is determined, including: The movement acceleration and rotational angular acceleration of the associated part in each preset direction are calculated based on the set of movement parameters to construct an acceleration matrix corresponding to each preset direction. Obtain the rotational inertia corresponding to the target user, and construct an attribute matrix of the associated part based on the rotational inertia, the mass of the associated part, and the length of the associated part relative to each preset direction; Multiplying the acceleration matrix by the attribute matrix yields the curvature of the predicted part in each of the preset directions.

9. The method according to claim 1, characterized in that, Based on the stress and curvature corresponding to the predicted location in each of the preset directions, the damage result of the predicted location is determined, including: Obtain the bearing capacity level range corresponding to each of the preset directions, and match it with the bearing capacity of the predicted part in each of the preset directions to obtain the first damage level of the predicted part in each of the preset directions; Obtain the curvature level range corresponding to each of the preset directions, and match it with the curvature of the predicted part in each of the preset directions to obtain the second damage level of the predicted part in each of the preset directions; The damage result of the predicted site is determined based on the first damage level and the second damage level.

10. A user injury prediction device, characterized in that, include: The acquisition unit is configured to, in response to a damage prediction command, acquire the corresponding target user and the predicted site, and query the associated sites of the predicted site; The acquisition unit is configured to acquire a set of movement parameters of the target user in each preset direction corresponding to the predicted part and the associated part, respectively; The determining unit is configured to acquire the mass and length of the associated part, and then call a preset prediction model to determine the bearing force and curvature of the predicted part in each preset direction by combining the set of movement parameters corresponding to each preset direction. The determining unit is further configured to determine the degree of injury to the neck of the target user based on the bearing force and curvature of the target user's head.

11. A vehicle, characterized in that, Includes the user injury prediction device as described in claim 10.

12. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-9.

13. A computer-readable medium having a computer program thereon storing a user injury prediction method, characterized in that, include: When the computer program is executed by the vehicle-mounted processor, it implements the method as described in any one of claims 1-9.

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