A method for assessing injury risk in a collision process for child and elderly occupants

Abstract

This invention relates to the field of vehicle testing technology, and more particularly to a method for assessing the risk of injury during a collision for child and elderly occupants. First, collision data for child and elderly dummies are acquired separately. From the collision data, biomechanical data, human restraint data, and injury path correlation data are determined. Based on the biomechanical data, human restraint data, and injury path correlation data, biomechanical risk indicators, human restraint risk indicators, and injury path correlation risk indicators are determined respectively. Based on the biomechanical risk indicators, human restraint risk indicators, and injury path correlation risk indicators, risk values ​​are determined according to preset risk weights. Based on the risk values, the collision injury risk results for child and elderly occupants are determined. This solution changes the traditional assessment model of collision testing, which mainly relies on a single physical indicator or only targets standard adult dummies, and achieves a refined, multi-dimensional risk assessment of vulnerable road users.

Description

Technical Field

[0001] This manual relates to the field of vehicle testing technology, and in particular to a method for assessing the risk of injury during a collision for child and elderly occupants. Background Technology

[0002] With the widespread application of virtual simulation technology in vehicle safety assessment, simulation-based safety verification has become an important means of evaluating vehicle collision safety. Current mainstream occupant injury assessment methods typically use standardized adult dummy models and combine them with a single peak index or a fixed injury threshold to evaluate collision results.

[0003] However, children and elderly occupants differ significantly from ordinary adults in terms of body structure, physiological characteristics, and injury tolerance. Children's bones are not fully developed, and their head-neck ratio is larger, making them more sensitive to head and neck impacts. Elderly people have more brittle bones and decreased muscle control, making them more prone to postural instability and secondary impacts during a collision. Existing assessment methods are insufficient to accurately describe the injury risks of elderly and children in real-world accident scenarios.

[0004] Therefore, this manual provides a method for assessing the risk of injury during a collision for child and elderly occupants. Summary of the Invention

[0005] This specification provides a method for assessing the risk of injury during a collision for child and elderly occupants, in order to address the aforementioned problems existing in the prior art.

[0006] The following technical solution is adopted in this specification: This manual provides a method for assessing the risk of injury during a collision for child and elderly occupants, including: S1. Obtain collision data for the child dummy and the elderly dummy respectively; S2. From the collision data, determine the biomechanical data, human restraint data, and damage path correlation data; S3. Based on the biomechanical data, the human restraint data, and the injury path association data, determine the biomechanical risk index, the human restraint risk index, and the injury path association risk index, respectively. S4. Based on the biomechanical risk index, the human restraint risk index, and the damage path association risk index, determine the risk value according to the preset risk weight; S5. Based on the risk value, determine the risk outcome of injury to child and elderly occupants during the collision.

[0007] Based on the aforementioned technical methods, this solution changes the traditional crash test assessment model that mainly relies on a single physical indicator or only targets standard adult dummies, achieving a refined and multi-dimensional risk assessment for vulnerable road users (children and the elderly). It fills the gap in crash injury assessment methods for specific sensitive groups (children / the elderly). By introducing "damage path association" and "constraint data," it can reveal the coupling effect of improper constraints (such as improper seat adjustment) leading to aggravated biomechanical injuries. Multi-indicator weighted assessment more accurately reflects the complex physical collision process than the traditional single-indicator threshold method, helping to reduce the rate of serious injury to children and elderly occupants in traffic accidents.

[0008] Furthermore, the biomechanical data mentioned in S2 includes at least children's head-neck coupling data, children's trunk tilt data, elderly rib segmental strain data, and elderly pelvic support reaction force data; the human body constraint data includes at least children's seat fit data and elderly seat belt shoulder slippage data; the damage path association data includes at least key site damage response time sequence data and mechanical parameter mutation data on the damage path.

[0009] Furthermore, the biomechanical risk indicators mentioned in S3 include the head-neck coupling acceleration difference in children, the trunk tilt angular velocity in children, the rib segment strain difference in the elderly, and the pelvic support reaction force fluctuation coefficient in the elderly; the human restraint risk indicators include the human seat fit deviation and the seat belt slippage rate; and the damage path association risk indicators include the damage path response delay and the path mechanical change amplitude.

[0010] Furthermore, S3 also includes step S31: The following parameters are normalized: the head-neck coupling acceleration difference of the child, the tilt angular velocity of the child's trunk, the rib segment strain difference of the elderly, the pelvic support reaction force fluctuation coefficient of the elderly, the human seat fit deviation, the seat belt slip rate, the damage path response delay, and the path mechanical change amplitude, and the normalized values ​​are determined respectively. When all normalized values ​​under the biomechanical risk index reach the preset biomechanical risk threshold, the normalized values ​​under the biomechanical risk index are increased by 20%; when any normalized value under the human restraint risk index reaches the preset human restraint risk threshold, the normalized values ​​under the human restraint risk index are increased by 10%; when the normalized value of the damage path response delay reaches the preset damage path response delay exceedance threshold, the normalized values ​​under the damage path associated risk index are increased by 15%.

[0011] Furthermore, the time-series data of the injury response of the key parts includes the time-series data of the injury response of the head and neck injury path in children and the time-series data of the injury response of the rib and shoulder injury path in the elderly; the data of abrupt changes in the mechanical parameters along the injury path includes the data of abrupt changes in the mechanical parameters along the head and neck injury path in children and the data of abrupt changes in the mechanical parameters along the rib and shoulder injury path in the elderly.

[0012] Furthermore, the method further includes step S7: Determine the risk value for each millisecond during the collision process corresponding to the collision data; Based on the risk value for each millisecond, a risk evolution curve is plotted.

[0013] Furthermore, the collision data includes virtual simulation collision data and real dummy collision data.

[0014] This manual provides a collision injury risk assessment device for child and elderly occupants, including: The acquisition module is used to collect collision data for child and elderly dummies respectively; The first determining module is used to determine biomechanical data, human restraint data, and damage path association data from the collision data; The second determining module is used to determine the biomechanical risk index, the human restraint risk index, and the damage path association risk index based on the biomechanical data, the human restraint data, and the damage path association data, respectively. The third determining module is used to determine the risk value according to the biomechanical risk index, the human restraint risk index and the damage path association risk index, and according to the preset risk weight. The fourth determining module is used to determine the risk of injury to child and elderly occupants during a collision based on the risk value.

[0015] This specification provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for assessing collision injury risks for child and elderly occupants.

[0016] This specification provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a method for assessing the risk of injury during a collision for child and elderly occupants.

[0017] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This approach departs from traditional crash test assessments that rely primarily on single physical indicators or only on standard adult dummies, enabling refined, multi-dimensional risk assessments of vulnerable road users (children and the elderly). It fills the gap in crash injury assessment methods for specific sensitive groups (children / the elderly). By introducing "damage path correlation" and "constraint data," it reveals the coupling effect of improper constraints (such as improper seat adjustment) leading to aggravated biomechanical injuries. Multi-indicator weighted assessment more accurately reflects the complex physical collision process than traditional single-indicator threshold methods, helping to reduce the rate of serious injury to children and elderly occupants in traffic accidents. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a collision injury risk assessment method for child and elderly occupants provided in an embodiment of this specification. Figure 2 This is a schematic diagram of a collision injury risk assessment device for child and elderly occupants, provided in this manual. Figure 3 This specification provides a corresponding Figure 1 A schematic diagram of the structure of an electronic device. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0020] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0021] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 A flowchart illustrating a collision injury risk assessment method for child and elderly occupants provided in this specification, as illustrated in the embodiments, includes the following steps: S1: Obtain collision data for the child dummy and the elderly dummy respectively.

[0023] This specification describes the process of conducting a collision injury risk assessment for children and elderly occupants. In the embodiments described herein, this collision injury risk assessment for children and elderly occupants can be performed by a server. However, this specification does not limit the type of device or platform used to perform this collision injury risk assessment for children and elderly occupants; for example, personal computers, mobile terminals, and other devices or platforms can also be used. For ease of description, the following description uses a server as the executing entity.

[0024] In one or more embodiments of this specification, the server can acquire collision data for child dummies and elderly dummies respectively. The collision data includes virtual simulation collision data and real dummy collision data. Virtual simulation collision data can be collected through collision tests conducted using virtual simulation collision scenarios built with LS-DYNA / MADYMO software, while real dummy collision data can be collected through collision tests using standard dummies of the corresponding group, based on physical experiments.

[0025] S2: Determine biomechanical data, human restraint data, and damage path association data from the collision data.

[0026] In one or more embodiments of this specification, the server can determine biomechanical data, human restraint data, and damage path association data from collision data for child dummies and elderly dummies, respectively, at a sampling frequency of 1 kHz during the collision process.

[0027] Among them, biomechanical data include at least children's head-neck coupling data, children's trunk tilt data, elderly rib segment strain data, and elderly pelvic support reaction force data.

[0028] Human restraint data should include at least the following: data on the fit between the child seat and the human body; data on the shoulder slippage of the elderly seat belt; time-series data on the distributed pressure of the airbag in contact with the human body; pressure uniformity of the airbag in contact with the human body; changes in the lumbar fit of the elderly seat belt; and distribution of the seat back support force.

[0029] Damage path association data includes at least the time-series data of damage response at key locations and the data on abrupt changes in mechanical parameters along the damage path.

[0030] Human adaptation data can also be determined from collision data, including data such as seat vibration frequency during the collision, uniformity of airbag inflation rate, and changes in restraint system pretension force. Among them, the restraint system is a child safety seat or an elderly-adaptive seat belt, which is expressed as the real-time value of the pretension force every 1ms throughout the entire collision process, reflecting the complete force value change of the pretensioner from activation to depressurization during the collision.

[0031] S3: Based on the biomechanical data, the human restraint data, and the damage path association data, determine the biomechanical risk index, the human restraint risk index, and the damage path association risk index, respectively.

[0032] In one or more embodiments of this specification, the server can determine biomechanical risk indicators, human restraint risk indicators, and damage path correlation risk indicators based on biomechanical data, human restraint data, and damage path correlation data, respectively. It is worth noting that the data included in each indicator can be acquired through physical experiments by mounting dedicated sensors on key body parts of a dummy, and through virtual simulation by setting data monitoring points for the corresponding parts in the simulation model.

[0033] The biomechanical risk indicators include at least the following: head-neck coupling acceleration difference in children, trunk tilt angular velocity in children, rib segmental strain difference in the elderly, and pelvic support reaction force fluctuation coefficient in the elderly. The head-neck coupling acceleration difference in children quantifies the relative dynamic response difference between the head and neck of a child occupant during a car collision. It is the difference between the composite acceleration of the head's center of mass and the acceleration of the upper part of the neck (usually C1-T1, i.e., the first cervical vertebra to the first thoracic vertebra) throughout the collision, derived from children's head-neck coupling data. The trunk (dynamic) tilt angular velocity in children measures the rate of change of the tilt angle of the child's trunk per unit time, derived from children's trunk tilt data. The rib segmental strain difference in the elderly measures the difference in the degree of deformation of different parts of the ribs (e.g., anterior, middle, and posterior segments) during a collision. It is the maximum principal strain difference (dimensionless) in different regions of the thoracic cavity (e.g., anterior, lateral, and posterior ribs) during the collision, derived from rib segmental strain data in the elderly. The fluctuation coefficient of pelvic support reaction force in the elderly measures the dramatic fluctuation of the contact force between the pelvis and the seat (or restraint system) during a collision. It measures the amplitude of the fluctuation of the dynamic support reaction force on the pelvis (usually referring to the hip bone or ischial tuberosity) during the entire collision process. It is usually quantified by the fluctuation coefficient (standard deviation / mean) and is derived from the pelvic support reaction force data of the elderly.

[0034] Human restraint risk indicators include at least the deviation of the human seat fit and the seat belt slippage rate. The deviation of the human seat fit is derived from the fit data between the child seat and the human body. It is calculated by dividing the target fit by (preset target fit - real-time fit) / target fit × 100%, and then calculating the real-time relative deviation, i.e., the human seat fit deviation. The seat belt slippage rate is derived from the shoulder slippage data of the elderly seat belt.

[0035] The risk indicators associated with the injury path include at least the injury path response delay and the amplitude of path mechanical abrupt changes. The injury path response delay is derived from the time-series data of injury responses at key sites, including those related to head and neck injuries in children and rib and shoulder injuries in the elderly. The difference between the time when a mechanical response occurs at a subsequent site and the time when the response occurs at the initial site is the injury path response delay. The amplitude of path mechanical abrupt changes is derived from the data on abrupt changes in mechanical parameters along the injury path, including those related to head and neck injuries in children and rib and shoulder injuries in the elderly. It represents the magnitude of sudden changes in critical sites along the injury path within a short period. The path represents the transmission path of the impact load within the human body; for children, the path is extracted as head and neck → chest core injury; for the elderly, the path is extracted as rib → shoulder core injury.

[0036] S4: Determine the risk value according to the biomechanical risk index, the human restraint risk index, and the damage path association risk index, based on the preset risk weights.

[0037] In one or more embodiments of this specification, the server can determine the risk value based on biomechanical risk indicators, human restraint risk indicators, and damage path association risk indicators, according to preset risk weights.

[0038] Specifically, the server can first distinguish between the various indicators obtained from collision data of child dummies and collision data of elderly dummies. For collision data of child dummies, the indicators that can be obtained are the child's head-neck coupling acceleration difference, the child's trunk tilt angular velocity, the human body seat fit deviation, the damage path response delay, and the path mechanical change amplitude. Then, based on the collision data of child dummies, the pre-set risk weights for the child's head-neck coupling acceleration difference, the child's trunk tilt angular velocity, the human body seat fit deviation, the damage path response delay, and the path mechanical change amplitude can be determined. The risk value of the child dummies' collision data is determined by weighting each indicator with its corresponding risk weight and then summing them.

[0039] Similarly, for the collision data of elderly dummies, the indicators that can be obtained are the rib segment strain difference of the elderly, the pelvic support reaction force fluctuation coefficient of the elderly, the seat belt slip rate, the damage path response delay, and the path mechanical change amplitude. Based on the collision data of the elderly dummies, the preset risk weights of the rib segment strain difference of the elderly, the pelvic support reaction force fluctuation coefficient of the elderly, the seat belt slip rate, the damage path response delay, and the path mechanical change amplitude can be determined. The risk value of the collision data of the elderly dummies is determined by weighting each indicator with its corresponding risk weight and then summing them.

[0040] S5: Based on the risk value, determine the risk of injury to child and elderly occupants during a collision.

[0041] In one or more embodiments of this specification, the server can determine the collision injury risk outcome for child and elderly occupants based on a risk value. Specifically, the collision injury risk outcome for child and elderly occupants can be determined separately based on the collision data of child dummies and elderly dummies, respectively, according to the risk value. The risk value is directly proportional to the degree of danger of the collision injury risk outcome.

[0042] Furthermore, for each millisecond in the collision process, the risk value corresponding to the collision data for that millisecond is determined. Then, based on the risk value for each millisecond, a risk evolution curve is plotted to pinpoint the peak range of the risk value.

[0043] Furthermore, for the two core injury pathways—head and neck → chest in children and ribs → shoulder in the elderly—the risk value per millisecond for each core injury pathway can be determined. Further refining this, the two core injury pathways—head and neck → chest in children and ribs → shoulder in elderly occupants—are clearly marked, corresponding to high-risk body areas such as the head, cervical spine, upper chest (children), anterior ribs, clavicle, and shoulder joint (elderly). The risk values ​​for each area are simultaneously marked, and the risk contribution percentage over all time periods is statistically analyzed, accurately locating high-risk sites and time points. Next, based on the peak risk value, four risk levels can be defined: low, medium, high, and extremely high, corresponding to AIS level 1 and below, AIS level 2, AIS level 3, and AIS level 3 and above injury risks, respectively.

[0044] based on Figure 1The proposed collision injury risk assessment method for children and elderly occupants differs from traditional crash tests that rely primarily on single physical indicators or only on standard adult dummies. This method enables refined, multi-dimensional risk assessment of vulnerable road users (children and the elderly), filling a gap in collision injury assessment methods for specific sensitive groups (children / the elderly). By introducing "damage path association" and "constraint data," the method reveals the coupling effect of improper constraints (such as improper seat adjustment) leading to aggravated biomechanical injuries. The multi-indicator weighted assessment more accurately reflects the complex physical collision process than the traditional single-indicator threshold method, helping to reduce the rate of serious injury to children and elderly occupants in traffic accidents.

[0045] Furthermore, in one or more embodiments of this specification, the server can normalize the following parameters: the difference in head-neck coupling acceleration in children, the tilt angular velocity of children's trunk, the difference in rib segment strain in the elderly, the fluctuation coefficient of pelvic support reaction force in the elderly, the deviation of human seat fit, the slip rate of seat belt, the damage path response delay, and the amplitude of path mechanical change, and determine each normalized value.

[0046] For example, the head-neck coupling acceleration difference in children, the trunk tilt angular velocity in children, the rib segment strain difference in the elderly, and the fluctuation coefficient of pelvic support reaction force in the elderly can be standardized by (measured value - preset minimum value) / (preset threshold - preset minimum value), and then the standardized values ​​are normalized and mapped to the [0, 1] interval.

[0047] Subsequently, based on the normalized values, when all normalized values ​​under the biomechanical risk indicators reach the preset biomechanical risk threshold, the normalized values ​​under the biomechanical risk indicators are increased by 20%. This means that, based on the conclusions of collision biomechanical tests, when multiple indicators of the same category simultaneously approach or reach the threshold, the injury risk is non-linearly amplified, increasing by 20% compared to the baseline state. When any normalized value under the human restraint risk indicator reaches the preset human restraint risk threshold, the normalized values ​​under the human restraint risk indicator are increased by 10%. This means that, based on the statistical data of real-vehicle collision accident reconstruction, when a single indicator reaches the preset value, the occupant injury risk increases by 10% compared to the baseline state. When the normalized value of the injury path response delay reaches the preset injury path response delay exceeding threshold, the normalized values ​​under the injury path association risk indicator are increased by 15%. This means that, based on the correlation coefficients of head and neck → chest injury in children (1.2-1.3) and rib → shoulder injury in the elderly (1.1-1.2), a value of 1.15 is taken as a reference to the range of these coefficients.

[0048] In one or more embodiments of this specification, the server may also define the following for child and elderly passengers: Dynamic physiological (biomechanical) parameters: Six-degree-of-freedom coupling stiffness of the head and neck in children (adult baseline value (1800-2200N)) The coefficients for dynamic balance of the trunk (m / rad) are 0.7-0.8, for children (0.4-0.5), for elastic recovery coefficient of rib impact in the elderly (0.3-0.4), and for dynamic support coefficient of pelvic impact in the elderly (0.5-0.6).

[0049] Interactive characteristics (human restraint) parameters: child seat - human dynamic fit (≥85%), elderly seat belt - torso friction fit coefficient (0.35-0.45).

[0050] Injury path parameters: correlation coefficient of head and neck → chest injury in children (1.2-1.3), correlation coefficient of rib → shoulder injury in the elderly (1.1-1.2), path response delay (≤10ms).

[0051] Based on the parameters defined above, set the risk weights of the risk indicators corresponding to each parameter.

[0052] Furthermore, this specification divides the collision process into three stages, taking a collision process of 100ms as an example: the initial collision stage (0-30ms), the damage development stage (30-60ms), and the collision stabilization stage (60-100ms). In different stages, the weights of different risk indicators can be appropriately adjusted. For example, in the initial collision stage (0-30ms), the focus is on human restraint risk indicators, with a 25% increase in weight; in the damage development stage (30-60ms), the focus is on damage path association risk indicators, with a 30% increase in weight; and in the collision stabilization stage (60-100ms), the focus is on biomechanical risk indicators, with a 20% increase in weight. In addition, detection of fit <70%, pretension failure, and uneven airbag inflation can be used to determine restraint-human fit abnormalities, further increasing the weight of human restraint risk indicators by 25%.

[0053] Based on one or more embodiments of this specification, a collision injury risk assessment method for child and elderly occupants is provided. Following the same logic, this specification also provides a corresponding collision injury risk assessment device for child and elderly occupants, such as... Figure 2 As shown.

[0054] Figure 2 This instruction manual provides a schematic diagram of a collision injury risk assessment device for child and elderly occupants, specifically including: The acquisition module 200 is used to collect collision data for the child dummy and the elderly dummy respectively; The first determining module 202 is used to determine biomechanical data, human restraint data, and damage path association data from the collision data; The second determining module 204 is used to determine the biomechanical risk index, the human restraint risk index, and the damage path association risk index based on the biomechanical data, the human restraint data, and the damage path association data, respectively. The third determining module 206 is used to determine the risk value according to the biomechanical risk index, the human restraint risk index and the damage path association risk index, and according to the preset risk weight. The fourth determining module 208 is used to determine the collision injury risk outcome for child and elderly occupants based on the risk value.

[0055] Optionally, the biomechanical data in the first determining module 202 includes at least child head-neck coupling data, child trunk tilt data, elderly rib segment strain data, and elderly pelvic support reaction force data; the human body constraint data includes at least child seat fit data and elderly seat belt shoulder slippage data; the damage path association data includes at least key site damage response time sequence data and mechanical parameter mutation data on the damage path.

[0056] Optionally, the biomechanical risk indicators in the second determining module 204 include the head-neck coupling acceleration difference in children, the trunk tilt angular velocity in children, the rib segment strain difference in the elderly, and the pelvic support reaction force fluctuation coefficient in the elderly; the human restraint risk indicators include the human seat fit deviation and the seat belt slippage rate; and the damage path association risk indicators include the damage path response delay and the path mechanical change amplitude.

[0057] Optionally, the first determining module 202 is further configured to normalize the following parameters: the child's head-neck coupling acceleration difference, the child's trunk tilt angular velocity, the elderly's rib segment strain difference, the elderly's pelvic support reaction force fluctuation coefficient, the human seat fit deviation, the seat belt slippage rate, the damage path response delay, and the path mechanical abrupt change amplitude, and determine each normalized value; when each normalized value under the biomechanical risk index reaches a preset biomechanical risk threshold, the normalized value under the biomechanical risk index is increased by 20%; when any normalized value under the human restraint risk index reaches a preset human restraint risk threshold, the normalized value under the human restraint risk index is increased by 10%; when the normalized value of the damage path response delay reaches a preset damage path response delay exceedance threshold, the normalized value under the damage path associated risk index is increased by 15%.

[0058] Optionally, the time-series data of critical site injury response in the first determining module 202 includes time-series data of injury response along the head and neck injury path in children and time-series data of injury response along the rib and shoulder injury path in the elderly; the data of abrupt changes in mechanical parameters along the injury path includes data of abrupt changes in mechanical parameters along the head and neck injury path in children and data of abrupt changes in mechanical parameters along the rib and shoulder injury path in the elderly.

[0059] Optionally, the device further includes a plotting module 210, used to determine the risk value for each millisecond during the collision process corresponding to the collision data; and plot a risk evolution curve based on the risk value for each millisecond.

[0060] Optionally, the collision data in the acquisition module 200 includes virtual simulation collision data and real dummy collision data.

[0061] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 This paper presents a collision injury risk assessment method for child and elderly occupants.

[0062] This instruction manual also provides Figure 3 The diagram shows a schematic structural representation of the electronic device. Figure 3 As shown, at the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above. Figure 1 This paper presents a collision injury risk assessment method for child and elderly occupants.

[0063] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0064] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0065] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0066] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0067] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0068] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0070] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0071] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0072] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0074] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic or disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0075] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0076] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0078] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0079] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A method of assessing the risk of injury during a crash process for a child or elderly occupant, characterized by, include: S1. Obtain collision data for the child dummy and the elderly dummy respectively; S2. From the collision data, determine the biomechanical data, human restraint data, and damage path correlation data; S3. Based on the biomechanical data, the human restraint data, and the injury path association data, determine the biomechanical risk index, the human restraint risk index, and the injury path association risk index, respectively. S4. Based on the biomechanical risk index, the human restraint risk index, and the damage path association risk index, determine the risk value according to the preset risk weight; S5. Based on the risk value, determine the risk outcome of injury to child and elderly occupants during the collision.

2. A method of assessing the risk of injury to a child or elderly occupant during a crash process as defined in claim 1, characterized in that The biomechanical data mentioned in S2 includes at least the head-neck coupling data of children, the trunk tilt data of children, the segmental strain data of the ribs of the elderly, and the pelvic support reaction force data of the elderly; the human restraint data includes at least the fit data between the child seat and the human body, and the shoulder slippage data of the seat belt of the elderly; the damage path association data includes at least the damage response time sequence data of key parts and the abrupt change data of mechanical parameters on the damage path.

3. A method of assessing the risk of injury to a child or elderly occupant during a crash process as defined in claim 2, characterized in that The biomechanical risk indicators mentioned in S3 include the head-neck coupling acceleration difference in children, the trunk tilt angular velocity in children, the rib segment strain difference in the elderly, and the pelvic support reaction force fluctuation coefficient in the elderly; the human restraint risk indicators include the human seat fit deviation and the seat belt slippage rate; the damage path association risk indicators include the damage path response delay and the path mechanical change amplitude.

4. The collision injury risk assessment method for child and elderly occupants as described in claim 3, characterized in that, S3 also includes step S31: The following parameters are normalized: the head-neck coupling acceleration difference of the child, the tilt angular velocity of the child's trunk, the rib segment strain difference of the elderly, the pelvic support reaction force fluctuation coefficient of the elderly, the human seat fit deviation, the seat belt slip rate, the damage path response delay, and the path mechanical change amplitude, and the normalized values ​​are determined respectively. When all normalized values ​​under the biomechanical risk index reach the preset biomechanical risk threshold, the normalized values ​​under the biomechanical risk index will be increased by 20%. When any normalized value under the human restraint risk index reaches the preset human restraint risk threshold, the normalized values ​​under the human restraint risk index will be increased by 10%. When the normalized value of the damage path response delay reaches the preset damage path response delay threshold, the normalized values ​​of each of the damage path associated risk indicators are increased by 15%.

5. The collision injury risk assessment method for child and elderly occupants as described in claim 3, characterized in that, The time-series data of injury response in key areas includes time-series data of injury response along the head and neck injury path in children and time-series data of injury response along the rib and shoulder injury path in the elderly; the data of abrupt changes in mechanical parameters along the injury path includes data of abrupt changes in mechanical parameters along the head and neck injury path in children and data of abrupt changes in mechanical parameters along the rib and shoulder injury path in the elderly.

6. The collision injury risk assessment method for child and elderly occupants as described in claim 1, characterized in that, The method further includes step S7: Determine the risk value for each millisecond during the collision process corresponding to the collision data; Based on the risk value for each millisecond, a risk evolution curve is plotted.

7. The collision injury risk assessment method for child and elderly occupants as described in claim 1, characterized in that, The collision data includes virtual simulation collision data and real dummy collision data.

8. A collision injury risk assessment device for child and elderly occupants, characterized in that, include: The acquisition module is used to collect collision data for child and elderly dummies respectively; The first determining module is used to determine biomechanical data, human restraint data, and damage path association data from the collision data; The second determining module is used to determine the biomechanical risk index, the human restraint risk index, and the damage path association risk index based on the biomechanical data, the human restraint data, and the damage path association data, respectively. The third determining module is used to determine the risk value according to the biomechanical risk index, the human restraint risk index and the damage path association risk index, and according to the preset risk weight. The fourth determining module is used to determine the risk of injury to child and elderly occupants during a collision based on the risk value.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 7.

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