Method for Predicting Trauma Risk of Vehicle Occupants for Rapid Rescue in Traffic Accidents

Abstract

The present invention belongs to the technical field of traffic accident rescue, and specifically relates to a method for predicting the trauma risk of vehicle occupants for rapid traffic accident rescue, including: S0, based on preset initial parameters, building a basic model for occupant injury analysis, where the input parameters of the basic model include dynamic boundary data, restraint system execution data, and occupant basic data, and the output parameters include human injury simulation data; S1, obtaining accident data within a preset time before and after vehicle collision through an in-vehicle electronic system, and sending the accident data to the background end; wherein, the accident data includes dynamic boundary data, restraint system execution data, and occupant basic data; S2, replacing the initial parameters in the basic model with the accident data, performing vehicle collision simulation analysis, and obtaining human injury simulation data; S3, performing injury analysis based on the human injury simulation data to obtain an injury analysis result. This method can provide accurate rescue guidance for the rescue center.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic accident rescue, and particularly relates to a method for predicting the trauma risk of vehicle occupants for rapid traffic accident rescue. Background Art

[0002] After a traffic accident occurs, in order to enable the traffic law enforcement department to quickly deploy a rescue plan and provide guidance for the precise allocation of medical first aid supplies, an in-vehicle emergency call system has emerged.

[0003] The in-vehicle emergency call system can automatically detect important information such as whether an accident has occurred, the accident location, the accident time, the impact force, the number of occupants, and the injury conditions of the personnel through a built-in algorithm and send it to the call center, thereby greatly improving the response rate of emergency rescue. Currently, for the injury estimation of occupants in the in-vehicle emergency call system, it is all achieved by calculating the change in vehicle collision speed (i.e., Delta v) and the corresponding injury risk probability. However, using such a method, the assessment of the occupants' injuries is not accurate because it does not consider the morphological differences during vehicle collisions. When the vehicle morphology during collisions is different, the injury probability and the degree of injury of the vehicle and its internal occupants are different; in addition, even when the vehicle morphology is the same during collisions, the states of the vehicle occupants are different, such as the sitting postures of the occupants and whether the occupants are wearing seat belts, which will also affect the degree of their injuries.

[0004] Therefore, although using the existing in-vehicle emergency call system can provide certain guidance for the allocation of medical first aid supplies and improve the response speed of emergency rescue, due to its low accuracy in analyzing the injury conditions of occupants, when hospital personnel arrive at the scene, there will often be a situation where the prepared rescue plan does not match the actual injury conditions of the occupants, resulting in the effectiveness of the emergency treatment received by the injured occupants at the scene not being guaranteed, and they still need to go to the emergency center to receive targeted treatment, resulting in the aggravation of the injuries of some wounded due to missing the precious first aid time or the treatment effect being affected. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for predicting the trauma risk of vehicle occupants for rapid traffic accident rescue, which can provide accurate rescue guidance for the rescue center and ensure the effectiveness of first aid for the injured occupants.

[0006] The basic solution provided by the present invention is as follows:

[0007] A method for predicting the trauma risk of vehicle occupants for rapid traffic accident rescue includes:

[0008] S0, based on preset initial parameters, build a basic model for occupant injury analysis. The input parameters of the basic model include dynamic boundary data, restraint system execution data, and occupant basic data, and the output parameters include human injury simulation data;

[0009] S1, obtaining accident data within a preset time before and after the vehicle collision through the vehicle electronic system, and sending the accident data to the backend; wherein the accident data includes dynamic boundary data, restraint system execution data and occupant basic data;

[0010] S2, replace the initial parameters in the basic model with the accident data, conduct vehicle collision simulation analysis, and obtain human body injury simulation data;

[0011] S3, performing injury analysis based on the human body injury simulation data to obtain an injury analysis result.

[0012] Basic solution working principle and beneficial effects:

[0013] First, use the preset initial parameters to build a basic model for occupant injury analysis. After a traffic accident occurs, the vehicle electronic system can first obtain the accident data within a preset time before and after the vehicle collision, and send the accident data to the backend.

[0014] Afterwards, the initial parameters in the basic damage analysis model are replaced with accident data, and vehicle collision simulation analysis is performed to obtain human body injury simulation data; then, injury analysis is performed based on the human body injury simulation data to obtain damage analysis results. By performing simulation analysis with real data, the situation of the occupants in the traffic accident can be restored. Moreover, this processing method fully considers the body shape of the occupants at the time of the accident, the protective performance of the restraint system at the time of the collision, and the shape of the vehicle at the time of the collision, so that the situation inside the car at the time of the accident can be simulated as close to the real situation as possible. In this way, it is possible to understand in detail which parts of the occupants will be damaged and what the probability of damage to these parts is.

[0015] According to the injury analysis results, the medical staff of the rescue center can prepare emergency resources in a targeted manner and provide first aid to the occupants immediately when they arrive at the accident scene. Compared with the prior art, the present application can avoid the situation where the rescue center's prepared plan is inconsistent with the actual first aid plan due to the high accuracy of the injury analysis, thereby ensuring that the occupants can receive targeted treatment as soon as the rescuers arrive.

[0016] In summary, this method can provide accurate rescue guidance for the rescue center and ensure the effectiveness of its first aid for injured occupants.

[0017] Furthermore, in S3, the damage analysis results include damage location, damage level and damage probability.

[0018] Beneficial effects: Such damage analysis results can allow rescuers to fully understand the conditions of the occupants and ensure the accuracy and adequacy of their rescue plans.

[0019] Furthermore, in S3, when the injury level exceeds the preset level, an urgent signal is also generated.

[0020] Beneficial effect: If the injury level exceeds the preset level, it indicates that the injury of the occupant is particularly serious and needs to be dealt with as soon as possible. Therefore, an urgent signal is generated to let the rescue center know the situation.

[0021] Furthermore, the basic occupant data includes the sitting position, seat occupancy rate, and weight.

[0022] Beneficial effect: Through these basic occupant data, the injury of the occupant at the time of the accident can be more accurately restored and analyzed.

[0023] Furthermore, the dynamic boundary data includes the three-axis velocity and acceleration curves of the vehicle.

[0024] Beneficial effect: The collision of the vehicle can be accurately restored.

[0025] Furthermore, the acceleration curve is the acceleration curve within 200 ms after the collision.

[0026] Beneficial effect: While controlling the transmitted data, the accuracy of the analysis results can be ensured.

[0027] Furthermore, the restraint system data includes the airbag deployment situation, deployment time, gas flow rate, and seat belt usage situation.

[0028] Beneficial effect: The effect of the restraint system in the accident can be accurately restored, so as to more accurately understand the injury situation of the occupant.

[0029] Furthermore, the initial parameters and accident data also respectively include the basic vehicle data.

[0030] Beneficial effect: The safety levels and anti-collision performances of different vehicles are different. Such a setting can more accurately analyze and predict the injury conditions of the occupants in the vehicle.

[0031] Furthermore, the acquisition device of the in-vehicle electronic system includes a pressure sensor set on the seat and an infrared sensor under the seat; in S1, when obtaining the basic occupant data, if there is pressure on a certain seat and the seat belt is not used, the infrared sensor under the corresponding seat is activated to analyze the number of legs of the occupant on that seat touching the ground; if both feet are touching the ground, it is recorded as a normal sitting posture, if one foot is touching the ground, it is recorded as crossing legs; if neither foot is touching the ground, the number and position of the corresponding seat are analyzed. If it is the three rear seats, it is recorded as sleeping, otherwise the pressure data on the seat is analyzed. If the pressure data is greater than the preset value, it is recorded as sitting cross-legged, and if the pressure data is not greater than the preset value, it is recorded as a child.

[0032] Beneficial effects: In the event of an accident, the posture of the occupant and the form of the vehicle during the accident are different. For example, the collision location and the direction of the force are different, and the injured parts and the probability of injury of the occupant are also different. Since the driver's seat requires vehicle operation, the posture is usually relatively fixed. However, the occupants in the remaining positions may have various postures.

[0033] In the prior art, the method of analyzing the occupant's posture is to set pressure sensors on the seat to understand the weight of the occupant and the occupancy of the seat. Using such an analysis method, it is assumed by default that the passenger posture of the occupant is sitting with both feet on the ground. However, some occupants have improper postures when riding in a vehicle. If the occupant uses a seat belt in the event of an accident, the injury situation will not differ much due to the fixation of the seat belt. However, if the seat belt is not used, the injured part and the degree of injury will be highly correlated with the posture. For example, for two unbelted occupants sitting with normal legs on the ground and cross-legged, even if the vehicle collides head-on, due to different sitting postures and different degrees of mobility of each part of the body, the injured parts and the degrees of injury will be different.

[0034] In order to ensure accurate restoration analysis of the occupant's injury, in this application, when there is pressure on a certain seat and the seat belt is not used, the infrared sensor under the seat will be activated to analyze the number of legs of the occupant on the seat that are touching the ground. If both feet are on the ground, it means that the sitting posture of the occupant is normal, so it is recorded as a normal sitting posture; if only one foot is on the ground, there may be two situations: cross-legged or one leg stepping on the seat. However, during driving, stepping on the seat with one leg will make the body very unbalanced, so it can be excluded. Therefore, the passenger's posture is recorded as cross-legged, that is, sitting cross-legged. If neither foot is on the ground, it is necessary to understand the situation of the corresponding occupant based on the number and position of the seats with the above-mentioned situation. If the seats with the above-mentioned situation are the three rear seats, it means that someone is lying in the back row, so it is recorded as sleeping. If it is not the case that all three rear seats have the above-mentioned situation, it may be that someone is sitting cross-legged. However, in addition to sitting cross-legged, there is another exceptional situation, that is, the occupant is a child whose feet cannot touch the ground. Therefore, by analyzing the pressure data on the seat, if the pressure data is greater than the preset value, it means that the occupant's feet can touch the ground, so it is recorded as sitting cross-legged. If the pressure data is not greater than the preset value, it means that the occupant is a child whose feet cannot touch the ground, so it is recorded as a child.

[0035] In this way, when the occupant does not use a seat belt, the current posture of the occupant can be analyzed more accurately, so as to ensure that when the subsequent injury restoration analysis of the occupant is carried out, the injury situation of the occupant can be accurately restored and analyzed.

[0036] Furthermore, there are multiple pressure sensors for each seat, and the multiple pressure sensors are evenly arranged on the seat. In S1, when obtaining the basic data of the occupant, if a certain occupant is recorded as sleeping, the pressure data of the corresponding seat is also combined to analyze the sleeping posture of the occupant, and the sleeping posture includes the head orientation and the face orientation.

[0037] Beneficial effects: In the event of an accident, if someone is lying and sleeping, even with the same collision position and force, the orientation of the head and the face orientation are different, and the injured parts and degrees will also be different. Therefore, in this application, when a certain occupant is recorded as sleeping, the pressure data of the corresponding seat is also combined to analyze the sleeping posture of the occupant. Since there are multiple evenly arranged pressure sensors on each seat, through the pressure data of these seats, the gravity distribution of the sleeping occupant on the seat can be known, so that the sleeping posture of the occupant including the head orientation and the face orientation can be obtained. Together with the dynamic boundary data and the restraint system execution data, the injury situation of the passenger in the accident can be accurately restored. Further ensure the accuracy of providing accurate rescue guidance for the rescue center. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flowchart of Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following is a more detailed description through specific embodiments:

[0040] Embodiment 1

[0041] It should be noted that the implementation of this method relies on the background end and the vehicle end installed on the vehicle; in this embodiment, the background end is a server; the vehicle end is used to collect and send accident data. As Figure 1 shown, the method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents includes:

[0042] S0. Based on preset initial parameters, a basic model for occupant injury analysis is built. The input parameters of the basic model include vehicle basic data, dynamic boundary data, restraint system execution data, and occupant basic data, and the output parameters include human injury simulation data. Specifically, when building the basic model, the MADYMO software is used to build a modular simulation model of "occupant compartment - occupant - restraint system" with parametric editing features for the simulation model. MADYMO (MAthematical DYnamic MOdel), a multi-rigid body dynamics analysis software, was initially completed by the TNO Road Vehicle Research Institute in the Netherlands in 1975. It successfully integrates finite elements into the analysis of multi-rigid body systems and has become a mathematical simulation software combining multi-rigid body and finite elements, mainly used for automotive crash safety research and capable of performing occupant restraint system analysis. Moreover, MADYMO software has the best mathematical models of mechanical dummies in the world and the mathematical model of HUMO2 newly developed by the European Human Model Project. The specific content of the injury simulation data can be specifically set by those skilled in the art according to their injury determination methods. In this embodiment, the injury simulation data includes output variables of injury indicators for the head, neck, chest, pelvis, and lower limbs. For example, for the head: HIC, linear acceleration of the center of mass (m / s²), angular acceleration (rad / s²); for the chest: compression amount (mm), viscous index (VC); for the lower limbs: thigh compression force (kN), calf compression force (kN).

[0043] S1. Obtain accident data within a preset time before and after vehicle collision through the vehicle-mounted electronic system, and send the accident data to the backend; the accident data includes vehicle basic data, dynamic boundary data, restraint system execution data, and occupant basic data;

[0044] S2. Replace the initial parameters in the basic model with the accident data, conduct vehicle collision simulation analysis, and obtain human injury simulation data;

[0045] S3. Conduct injury analysis based on the human injury simulation data to obtain injury analysis results; the injury analysis results include the injury location, injury level, and injury probability; when the injury level exceeds the preset level, an urgent signal is also generated.

[0046] Among them, the basic data of the occupants include the sitting position, seat occupancy rate and weight, the dynamic boundary data include the vehicle's x / y / z three-axis speed and the acceleration curve within 600ms after the collision, and the restraint system data include the airbag detonation status, detonation time, gas flow rate and seat belt usage. Through these data, the injuries of the occupants in the accident can be analyzed comprehensively and accurately. The basic data of the occupants can be obtained using existing acquisition methods, such as setting multiple sensors on the seat. According to the feedback data of the sensor, the seat position of the occupant, the occupancy of the seat and the weight of the occupant can be understood. In other embodiments, the personal information entered by the driver after buying the car can also be used as the driver's own basic data of the occupants. These are all existing technologies and will not be repeated here.

[0047] The specific implementation process is as follows:

[0048] When using this method, the staff can first build a basic model for occupant injury analysis with preset initial parameters and store it in the background.

[0049] After a traffic accident occurs, the vehicle electronic system automatically obtains accident data within a preset time before and after the vehicle collision, and sends the accident data to the backend. After that, the backend automatically replaces the initial parameters in the basic damage analysis model with the accident data, performs vehicle collision simulation analysis, and obtains human body injury simulation data; then, the injury analysis is performed based on the human body injury simulation data to obtain the injury analysis results.

[0050] Through simulation analysis of real data, the situation of the occupants in the traffic accident can be restored. In addition, this processing method fully considers the body shape of the occupants at the time of the accident, the protection performance of the restraint system at the time of the collision, and the shape of the vehicle at the time of the collision, so that the situation inside the car at the time of the accident can be simulated as close to the real situation as possible. In this way, it is possible to understand in detail which parts of the occupants will be damaged in what way, and the probability of these parts being damaged.

[0051] According to the injury analysis results, the medical staff of the rescue center can prepare emergency resources in a targeted manner and provide first aid to the occupants immediately when they arrive at the accident scene. Compared with the prior art, the present application can avoid the situation where the rescue center's prepared plan is inconsistent with the actual first aid plan due to the high accuracy of the injury analysis, thereby ensuring that the occupants can receive targeted treatment as soon as the rescuers arrive.

[0052] In summary, this solution uses a method that combines vehicle - end data extraction and network - end simulation modeling to predict the occupant injury risk in road traffic accidents. It fully exploits and utilizes the value of in - vehicle data and applies the advantages of modeling and simulation in a new analysis dimension, giving full play to the strengths of different analysis methods. Innovative methods such as considering multi - physical - field parameters, using modular modeling, and distinguishing the injury conditions of different parts of the human body can effectively improve the rationality of emergency rescue plan deployment and medical supply allocation, and enhance the rescue accuracy and effectiveness.

[0053] Embodiment 2

[0054] Different from Embodiment 1, in S0 of this embodiment, a verification library is also set up and stored in the background end. The verification library stores the damage - level ranges corresponding to each acceleration curve.

[0055] In S3, after obtaining the damage analysis result, the corresponding damage - level range is also matched according to the acceleration curve. If the damage level in the damage analysis result is not within the damage - level range, a re - collection signal is sent to the in - vehicle electronic system. After receiving the re - collection signal, the in - vehicle electronic system re - collects the accident data and sends it to the background end.

[0056] If the damage level in the damage analysis result is not within the damage - level range, it indicates that there is a problem in the analysis. For example, a relatively slow acceleration curve shows a very high - severity damage level. Since the analysis of the background end is based on model analysis and the stability of the analysis process can be guaranteed, the link where the problem occurs can be basically determined to be due to the accident data. Therefore, re - collecting the accident data and sending it to the background end can ensure the credibility of the damage analysis.

[0057] Embodiment 3

[0058] Different from Embodiment 1, in this embodiment, the acquisition device of the in - vehicle electronic system includes a pressure sensor set on the seat and an infrared sensor under the seat; among them, there are multiple pressure sensors for each seat, and the multiple pressure sensors are evenly arranged on the seat.

[0059] In S1, when obtaining the basic data of the occupant, if there is pressure on a certain seat and the seat belt is not used, the infrared sensor under the corresponding seat is activated to analyze the number of legs of the occupant on that seat touching the ground; if both feet are on the ground, it is recorded as a normal sitting posture; if one foot is on the ground, it is recorded as crossing legs; if neither foot is on the ground, the number and position of the corresponding seat are analyzed. If it is the three rear seats, it is recorded as sleeping. Otherwise, the pressure data on the seat is analyzed. If the pressure data is greater than the preset value, it is recorded as sitting cross - legged; if the pressure data is not greater than the preset value, it is recorded as a child. Among them, if a certain occupant is recorded as sleeping, the sleeping posture of the occupant is also analyzed in combination with the pressure data of the corresponding seat. The sleeping posture includes the head orientation and the face orientation.

[0060] The specific implementation process is as follows:

[0061] In the event of an accident, the postures of the occupants are different, and the forms of the vehicle during the accident are also different. For example, the collision position and the force direction are different, and the injured parts and the probability of injury of the occupants are also different. Since the driver's seat requires vehicle operation, its posture is usually relatively fixed. However, the occupants in the remaining positions may have various postures.

[0062] In the prior art, the way to analyze the occupant's posture is to set pressure sensors on the seat to understand the weight of the occupant and the occupancy of the seat. Using such an analysis method, it is defaulted that the passenger posture of the occupant is sitting with both feet on the ground. However, some occupants have improper postures when sitting in the car. If the occupant uses a seat belt during an accident, due to the fixation of the seat belt, the injury situation will not vary much. But if the seat belt is not used, the injured part and degree will be highly correlated with their posture. For example, for two unbelted occupants sitting with normal legs on the ground and cross-legged respectively, even if the vehicle collides head-on, due to different sitting postures and different degrees of mobility of each part of the body, the injured parts and degrees will be different.

[0063] To ensure accurate reduction analysis of the injuries of the occupants, in this application, when there is pressure on a certain seat and the seat belt is not used, the infrared sensor under the seat will be activated to analyze the number of legs of the occupant on the seat touching the ground. If both feet are on the ground, it means the occupant's sitting posture is normal, so it is recorded as a normal sitting posture; if only one foot is on the ground, there may be two situations: cross-legged or one leg stepping on the seat. However, during driving, if one leg steps on the seat, the body will be very unbalanced, so this can be excluded. Therefore, the passenger's posture is recorded as cross-legged, that is, sitting cross-legged. If neither foot is on the ground, it is necessary to understand the situation of the corresponding occupant based on the number and position of the seats with the above-mentioned situation. If the seats with the above-mentioned situation are the three rear seats, it means someone is lying in the back row, so it is recorded as sleeping. If it is not the case that all three rear seats have the above-mentioned situation, it may be that someone is sitting cross-legged. However, in addition to sitting cross-legged, there is another exceptional situation, that is, the occupant is a child whose feet cannot touch the ground. Therefore, by analyzing the pressure data on the seat, if the pressure data is greater than the preset value, it means the occupant's feet can touch the ground, so it is recorded as sitting cross-legged. If the pressure data is not greater than the preset value, it means the occupant is a child whose feet cannot touch the ground, so it is recorded as child.

[0064] It should be noted that if a person is lying down and sleeping, even with the same collision location and force, the orientation of the head and the face will be different, and the injured parts and degrees will also be different. Therefore, in this application, when a certain occupant is recorded as sleeping, the sleeping posture of the occupant is also analyzed in combination with the pressure data of the corresponding seat. Since there are multiple uniformly arranged pressure sensors on each seat, through the pressure data of these seats, the gravity distribution of the sleeping occupant on the seat can be known, so that the sleeping posture of the occupant including the head orientation and the face orientation can be obtained. Then, in cooperation with the dynamic boundary data and the restraint system execution data, the injury situation of the passenger in the accident can be accurately restored. Further ensure the accuracy of providing accurate rescue guidance for the rescue center.

[0065] In this way, when the occupant does not use the seat belt, the current posture of the occupant can be analyzed more accurately, so as to ensure that when the injury restoration analysis of the occupant is carried out subsequently, the injury situation of the occupant can be accurately restored and analyzed.

[0066] The above are only the embodiments of the present invention. Common knowledge such as the specific structure and characteristics known in the art are not described in detail here. Those of ordinary skill in the art know all the common technical knowledge in the technical field to which the invention belongs before the application date or the priority date, can know all the existing technologies in this field, and have the ability to apply the conventional experimental means before this date. Those of ordinary skill in the art can, under the inspiration given in this application and in combination with their own abilities, improve and implement this solution. Some typical well-known structures or well-known methods should not become an obstacle for those of ordinary skill in the art to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can still be made, and these should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be based on the content of its claims, and the specific implementation manners described in the specification can be used to explain the content of the claims.

Claims

1. A method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents, characterized in that, it includes: S0, based on preset initial parameters, build a basic model for occupant injury analysis. The input parameters of the basic model include dynamic boundary data, restraint system execution data, and occupant basic data, and the output parameters include human injury simulation data; S1, obtain accident data within a preset time before and after vehicle collision through an in-vehicle electronic system, and send the accident data to the background end; among them, the accident data includes dynamic boundary data, restraint system execution data, and occupant basic data; The acquisition device of the in-vehicle electronic system includes a pressure sensor set on the seat and an infrared sensor under the seat; when obtaining occupant basic data, if there is pressure on a certain seat and the seat belt is not used, the infrared sensor under the corresponding seat is activated to analyze the number of legs of the occupant on that seat touching the ground; if both feet are on the ground, it is recorded as a normal sitting posture, if one foot is on the ground, it is recorded as crossing legs; if neither foot is on the ground, analyze the number and position of the corresponding seat. If it is three rear seats, it is recorded as sleeping. Otherwise, analyze the pressure data on the seat. If the pressure data is greater than the preset value, it is recorded as sitting cross-legged, if the pressure data is not greater than the preset value, it is recorded as a child; if a certain occupant is recorded as sleeping, also analyze the sleeping posture of the occupant in combination with the pressure data of the corresponding seat. The sleeping posture includes the head orientation and the face orientation; S2, replace the initial parameters in the basic model with the accident data, perform vehicle collision simulation analysis, and obtain human injury simulation data; S3, perform injury analysis based on the human injury simulation data to obtain an injury analysis result.

2. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 1, characterized in that: In S3, the injury analysis result includes the injury site, injury level, and injury probability.

3. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 2, characterized in that: When the injury level exceeds the preset level in S3, an urgent signal is also generated.

4. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 1, characterized in that: The occupant basic data includes the sitting position, seat occupancy rate, and weight.

5. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 1, characterized in that: The dynamic boundary data includes the three-axis velocity and acceleration curves of the vehicle.

6. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 5, characterized in that: The acceleration curve is the acceleration curve within 200 ms after collision.

7. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 1, characterized in that: The restraint system data includes airbag deployment situation, deployment time, gas flow rate, and seat belt usage situation.

8. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 7, characterized in that: The initial parameters and the accident data also respectively include vehicle basic data.

9. The method for predicting the trauma risk of vehicle occupants for rapid rescue in traffic accidents according to claim 1, characterized in that: There are multiple pressure sensors for each seat, and the multiple pressure sensors are evenly arranged on the seat.

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