A method for reverse development of airbag performance using computer software

Abstract

The present invention discloses a method for reverse development of airbag performance by means of computer software, and relates to the field of automobile safety technology. The method comprises S1, establishing a dynamic model of airbag deployment based on sensor data at different collision angles, S2, determining the optimal deployment speed of the airbag at various collision angles according to the model output, S3, constructing an ignition timing control algorithm to match the above-mentioned optimal deployment speed, and S4, reprogramming the ignition controller of the airbag based on the above-mentioned algorithm; the method for reverse development of airbag performance by means of computer software improves the robustness of the system in a complex environment, and can still maintain efficient airbag deployment control even in the case of sensor signal fluctuation or noise interference, ensuring that the decision of airbag ignition and deployment is more scientific and accurate, and regulating the ignition timing of the airbag to solve the problem of inconsistent airbag deployment speed at different collision angles.

Description

Technical Field

[0001] The invention relates to the technical field of automobile safety, and in particular to a method for reverse development of airbag performance by means of computer software. Background Art

[0002] In modern automobile safety technology, the airbag system is one of the key devices to protect passengers from collision injuries. Traditional airbag systems usually ignite under preset trigger conditions based on collision sensor data, and quickly deploy airbags to protect the occupants in the car. However, with the increasing complexity of vehicle design and the diversity of driving environments, different collision angles and vehicle speeds may cause the airbag deployment speed to be inconsistent with actual needs. For example, at certain collision angles, the airbag may deploy too early or too late, thus affecting the occupant protection effect.

[0003] Existing airbag ignition control technologies usually use a predetermined ignition algorithm to respond to sensor signals through fixed timing logic. However, this method is difficult to achieve accurate response to different collision angles and speeds when facing complex collision scenarios. In addition, existing control methods usually rely on hardware optimization or sensor accuracy improvement, have a long R&D cycle, and are difficult to quickly iterate to adapt to new needs. Summary of the invention

[0004] The purpose of the present invention is to provide a method for reverse development of airbag performance by means of computer software, so as to adjust the ignition sequence of the airbag to solve the problem of inconsistent airbag deployment speed under different collision angles.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for reverse development of airbag performance by means of computer software, the method comprising:

[0006] S1. A dynamic model of airbag deployment is established based on sensor data at different collision angles, including obtaining collision acceleration and time data from the sensor and calculating instantaneous velocity. The specific formula is:

[0007] ;

[0008] Among them, v represents instantaneous velocity, a represents acceleration, and t represents time;

[0009] Combined with the vehicle mass, the momentum at different collision angles is calculated to analyze the impact of the collision force on the deployment of the airbag. The specific formula is: p = ma;

[0010] Among them, p represents momentum, m represents mass, and a represents acceleration;

[0011] The pressure curve of airbag deployment is fitted using experimental data, and a simple dynamic model is established to describe the relationship between pressure and deployment speed.

[0012] S2. Determine the optimal deployment speed of the airbag at various collision angles based on the model output, including analyzing the dynamic changes of the deployment speed at different collision angles, determining the growth rate of the airbag deployment speed and the maximum deployment speed of the airbag. The specific formula is:

[0013] ;

[0014] Wherein, N represents the deployment speed of the airbag, r represents the growth rate of the deployment speed of the airbag, K represents the maximum deployment speed of the airbag, and t represents time;

[0015] S3. Constructing an ignition timing control algorithm to match the above-mentioned optimal deployment speed, including using experimental data, collecting ignition timing and deployment effects under different collision conditions, and fitting the relationship between ignition time and collision angle and speed, specifically: y=dr+c;

[0016] Where y represents the ignition time, d represents the collision speed or angle, and r and c represent the regression coefficients;

[0017] S4. Reprogram the ignition controller of the airbag based on the above algorithm.

[0018] Preferably, the S1 comprises:

[0019] Using the collision angle data provided by the sensor and combined with the pressure distribution, a dynamic model of airbag deployment is established, specifically: ;

[0020] Among them, J represents the pressure transmission rate during the airbag deployment process, D represents the dynamic response coefficient of the airbag deployment speed, C represents the pressure value inside the airbag, and x represents the spatial coordinate inside the airbag.

[0021] Preferably, S3 includes:

[0022] The ignition signal is discretized in time sequence, and the time position of each ignition signal is calculated to form an initial ignition timing model. According to the maximum deployment speed of the airbag determined by S2, it is converted into the ignition frequency. The specific formula is: BPM = K × m / 1;

[0023] Where BPM represents the frequency of ignition, K represents the maximum deployment speed of the airbag, and m represents the frequency scaling factor;

[0024] The time interval between each ignition is calculated by the formula T=60 / BPM, and the ignition sequence is determined, where T represents the ignition time interval and BPM represents the ignition frequency.

[0025] The calculated ignition time interval is programmed into the controller to form a complete ignition timing table.

[0026] Preferably, S4 includes:

[0027] Encode the ignition control signal and convert the ignition time interval into a discrete control instruction. The specific formula is: H (x) = (x mod p);

[0028] Wherein, H(x) represents the code value of the ignition signal, x represents the time interval of the ignition signal, p represents a prime number, and mod represents a modulo operation;

[0029] Map the coded value of the ignition signal to the command value of the controller to optimize the transmission and execution of the ignition signal.

[0030] Preferably, the data collected by the sensor in S1 includes collision angle, collision acceleration and airbag deployment pressure.

[0031] Preferably, the specific calculation formula of the maximum deployment speed K of the airbag in S2 is:

[0032] ;

[0033] Where K represents the maximum deployment speed of the airbag, P max It represents the maximum pressure of the gas in the airbag, and C represents the elastic coefficient of the airbag material.

[0034] Preferably, the specific calculation formula of the dynamic response coefficient of the airbag deployment speed in S1 is: D=μ / ρ;

[0035] Wherein, D represents the dynamic response coefficient of the airbag deployment speed, μ represents the viscosity of the gas in the airbag, and ρ represents the density of the gas in the airbag.

[0036] Preferably, the pressure value C inside the airbag in S1 is obtained by interpolating and calculating the pressure data collected in real time by the sensor.

[0037] Preferably, the specific calculation formula of the time interval x of the ignition signal in S4 is:

[0038] x = T / Δt;

[0039] Wherein, x represents the time interval of the ignition signal, T represents the ignition time interval, and Δt represents the sampling interval of the sensor data.

[0040] Preferably, when the code value H(x) of the ignition signal in S4 is mapped to the controller instruction, the transmission speed is optimized by a lookup table, and the lookup table includes the corresponding relationship between the ignition signal time interval and the control instruction.

[0041] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0042] This method for reverse development of airbag performance with the aid of computer software establishes a dynamic model of airbag deployment based on sensor data at different collision angles, determines the optimal deployment speed of the airbag at various collision angles according to the model output, constructs an ignition timing control algorithm to match the above-mentioned optimal deployment speed, and reprograms the ignition controller of the airbag based on the above-mentioned algorithm, thereby achieving accurate response to different collision angles and speeds. Compared with traditional ignition control technology with fixed timing logic, the present invention can dynamically adjust the deployment timing and speed of the airbag according to real-time sensor data, significantly improving the adaptability and effectiveness of airbag deployment, and avoiding the problem of insufficient protection or excessive deployment caused by deploying the airbag too early or too late. The airbag deployment process is more in line with actual collision needs, thereby more effectively cushioning the impact force, reducing the risk of occupant injury, and reducing dependence on high-precision sensors and complex hardware, thereby reducing R&D costs. Compared with traditional methods, it has a shorter R&D cycle and higher technology iteration efficiency, and improves the robustness of the system in complex environments. Even in the case of sensor signal fluctuations or noise interference, it can still maintain efficient airbag deployment control, ensuring that the decision on airbag ignition and deployment is more scientific and accurate, adapting to complex and changeable vehicle collision scenarios, and adjusting the ignition timing of the airbag to solve the problem of inconsistent airbag deployment speed at different collision angles. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, the present invention provides a technical solution: a method for reverse development of airbag performance by means of computer software, the method comprising:

[0046] S1. A dynamic model of airbag deployment is established based on sensor data at different collision angles, including obtaining collision acceleration and time data from the sensor and calculating instantaneous velocity. The specific formula is:

[0047] ;

[0048] Among them, v represents instantaneous velocity, a represents acceleration, and t represents time;

[0049] Combined with the vehicle mass, the momentum at different collision angles is calculated to analyze the impact of the collision force on the deployment of the airbag. The specific formula is: p = ma;

[0050] Among them, p represents momentum, m represents mass, and a represents acceleration;

[0051] The pressure curve of airbag deployment is fitted using experimental data, and a simple dynamic model is established to describe the relationship between pressure and deployment speed.

[0052] S2. Determine the optimal deployment speed of the airbag at various collision angles based on the model output, including analyzing the dynamic changes of the deployment speed at different collision angles, determining the growth rate of the airbag deployment speed and the maximum deployment speed of the airbag. The specific formula is:

[0053] ;

[0054] Wherein, N represents the deployment speed of the airbag, r represents the growth rate of the deployment speed of the airbag, K represents the maximum deployment speed of the airbag, and t represents time;

[0055] S3. Constructing an ignition timing control algorithm to match the above-mentioned optimal deployment speed, including using experimental data, collecting ignition timing and deployment effects under different collision conditions, and fitting the relationship between ignition time and collision angle and speed, specifically: y=dr+c;

[0056] Where y represents the ignition time, d represents the collision speed or angle, and r and c represent the regression coefficients;

[0057] S4. Reprogram the ignition controller of the airbag based on the above algorithm.

[0058] In the above method, step S1 collects sensor data input at different collision angles and uses dynamic modeling technology to establish a dynamic model of airbag deployment, aiming to describe the deployment behavior characteristics of the airbag at different time points. Step S2 is based on model analysis and uses mathematical formulas to The dynamic change of the airbag deployment speed is calculated, and the growth rate r and the maximum deployment speed K of the airbag deployment speed are determined in combination with the different collision angles. This formula reflects the process in which the airbag deployment speed increases rapidly in the early stage and then gradually stabilizes, which is consistent with the actual airbag deployment behavior. Step S3 designs the ignition timing adjustment algorithm according to the above-mentioned optimal deployment speed to ensure that the ignition time matches the optimal dynamic characteristics of the airbag deployment, thereby optimizing the performance of the ignition controller. Step S4 loads the optimized algorithm into the airbag ignition controller through software reprogramming technology to ensure that the response speed and deployment accuracy of the system in actual applications meet expectations. This method makes the dynamic characteristics of the airbag deployment process more accurate by modeling sensor data based on the collision angle, thereby improving the applicability and reliability of the model. Using the mathematical model formula to calculate the optimal deployment speed and its rate of change can achieve dynamic adaptability to different collision angles, thereby effectively improving the airbag deployment performance. The introduction of the ignition timing adjustment algorithm optimizes the time control during the airbag deployment process, enabling the airbag to be deployed more quickly and safely at critical moments, thereby improving the vehicle safety performance. By reprogramming the ignition controller, the actual performance of the system is ensured to be consistent with the optimized design, the development cost is reduced, and the performance upgrade of the existing airbag system is achieved.

[0059] S1 includes using the collision angle data provided by the sensor and combining it with the pressure distribution to establish a dynamic model for airbag deployment, specifically: ;

[0060] Among them, J represents the pressure transmission rate during the airbag deployment process, D represents the dynamic response coefficient of the airbag deployment speed, C represents the pressure value inside the airbag, and x represents the spatial coordinate inside the airbag.

[0061] In the above method, the collision angle data collected by the sensor provides the basic input for dynamic modeling, and combined with the pressure distribution, it can more accurately describe the physical changes during the airbag deployment process. The dynamic model uses the formula Characterize the transmission process of pressure in the airbag, where J represents the pressure transmission rate, reflecting the relationship between the pressure in the airbag and time and space; D is the dynamic response coefficient of the airbag deployment speed, which is used to characterize the response degree of the airbag material characteristics to pressure changes; C represents the pressure value inside the airbag, reflecting the gas flow characteristics generated by the deployment of the airbag; x is the spatial coordinate inside the airbag, which is used to represent the spatial position of the pressure distribution. By combining the pressure distribution and the collision angle data, the dynamic model can accurately reflect the pressure distribution of the airbag at different deployment stages, thereby providing theoretical support for the subsequent deployment speed optimization and ignition control. By combining the collision angle data and the dynamic modeling of the pressure distribution, the accuracy of the model is further improved, providing a more accurate theoretical basis for the optimization of the airbag performance. The dynamic model introduces the pressure transmission rate J and the response coefficient D, which can accurately describe the temporal and spatial changes of the pressure distribution in the airbag, which helps to optimize the deployment performance and reliability of the airbag. By modeling the distribution of the spatial coordinate x inside the airbag, the dynamic optimization of the spatial and temporal parameters in the airbag deployment process can be achieved, ensuring that the airbag can respond quickly under different collision conditions. Improve the simulation accuracy of physical phenomena during airbag deployment, thereby enhancing the scientificity and efficiency of reverse development.

[0062] S3 includes discretizing the ignition signal in time sequence, calculating the time position of each ignition signal, forming an initial ignition timing model, and converting it into an ignition frequency according to the maximum deployment speed of the airbag determined by S2. The specific formula is: BPM = K × m / 1;

[0063] Where BPM represents the frequency of ignition, K represents the maximum deployment speed of the airbag, and m represents the frequency scaling factor;

[0064] The time interval between each ignition is calculated by the formula T=60 / BPM, and the ignition sequence is determined, where T represents the ignition time interval and BPM represents the ignition frequency.

[0065] The calculated ignition time interval is programmed into the controller to form a complete ignition timing table.

[0066] In the above method, the time sequence of the ignition signal is divided into multiple time nodes by discretization technology, and each node corresponds to the time position of an ignition signal, thereby forming an initial ignition timing model. Combined with the determined maximum deployment speed K of the airbag, it is converted into the ignition frequency BPM, which is specifically calculated by the formula BPM = K × m / 1. Among them, m is the frequency ratio, which is used to adjust the ignition frequency to adapt to the optimization requirements under different collision conditions. Subsequently, the time interval T of each ignition signal is calculated according to the formula T = 60 / BPM to determine the specific time point of the ignition timing. All calculated ignition time points are input into the ignition controller to generate a complete ignition timing table to ensure that the ignition signal can accurately match the dynamic characteristics of airbag deployment. The ignition signal discretization technology provides an accurate time position calculation method, which lays the foundation for the precise control of airbag deployment. The ignition frequency BPM is determined according to the maximum deployment speed K and the frequency ratio m, which can dynamically adapt to the airbag deployment requirements under different collision angles, and improve the flexibility and applicability of the ignition timing model. The ignition time interval is determined by the formula T=60 / BPM, which can ensure the accuracy of ignition control, thereby improving the response speed and safety performance of the airbag system. The complete ignition timing table is programmed into the controller to realize the automatic control of the ignition signal, optimize the system operation efficiency and reduce the development cost.

[0067] S4 includes encoding the ignition control signal and converting the ignition time interval into a discrete control instruction. The specific formula is: H (x) = (x mod p);

[0068] Wherein, H(x) represents the code value of the ignition signal, x represents the time interval of the ignition signal, p represents a prime number, and mod represents a modulo operation;

[0069] Map the coded value of the ignition signal to the command value of the controller to optimize the transmission and execution of the ignition signal.

[0070] In this method, the time interval x of the ignition control signal is discretized and converted into a code value H(x). Specifically, the formula H(x)=(x modp) is used to calculate the code value of each ignition signal with a prime number p as the modulus. The advantage of this encoding method is that it can effectively convert continuous time interval values ​​into discrete instruction values, thereby reducing the complexity in signal processing and improving transmission efficiency. Subsequently, the calculated code value H(x) is mapped to the instruction set of the ignition controller to ensure that the controller can quickly identify and execute the ignition command. Through this method, the transmission process and execution accuracy of the ignition signal are optimized, providing a more efficient signal processing mechanism for airbag deployment control. Discretizing the ignition time interval x into the code value H(x) simplifies the processing flow of the ignition signal and improves the real-time and efficiency of signal processing. Using the prime number p as the modulus effectively reduces the possibility of coding conflicts and ensures the uniqueness and stability of the ignition signal. Mapping the code value H(x) to the instruction value of the controller realizes the efficient conversion of the ignition signal from the time interval to the instruction set, improving the response speed and reliability of the system. By discretizing and optimizing the ignition signal, the risk of interference in signal transmission is reduced, the accuracy of ignition control is improved, and support is provided for the rapid and safe deployment of the airbag.

[0071] The data collected by the sensor in S1 include collision angle, collision acceleration and airbag deployment pressure. In this embodiment, step S1 collects multi-dimensional data through sensors, providing accurate input for establishing a dynamic model of airbag deployment. The collected data include: collision angle: obtained by the angle sensor, used to describe the directional information of the vehicle collision, as the initial condition for model analysis. Collision acceleration: collected by the acceleration sensor, used to reflect the speed change rate of the vehicle during collision, directly affecting the triggering condition and deployment speed calculation of the airbag deployment. Airbag deployment pressure: obtained by the pressure sensor, used to monitor the pressure distribution inside the airbag in real time, reflecting the dynamic characteristics of the airbag deployment process. The above data are transmitted to the computer software system in real time, and input into the dynamic model of airbag deployment after preprocessing. The model uses these data as input variables to perform multi-dimensional analysis and optimization calculation, thereby generating an accurate dynamic characteristic curve of airbag deployment. The diversity and real-time nature of the data set can effectively improve the accuracy and adaptability of the model. By adding collision angle, collision acceleration and airbag deployment pressure data, a more comprehensive initial condition is provided for the airbag dynamic model, thereby improving the accuracy of the model. By using multi-dimensional sensor data, we can adapt to collision scenarios at different angles, speeds and environments, and improve the reliability of the airbag system. The data collected by the sensor can be input into the system in real time, providing a fast response capability for the airbag dynamic model, effectively shortening the calculation and reaction time. Through real-time monitoring and analysis of the airbag deployment pressure, the airbag deployment process can be dynamically adjusted, the deployment speed and pressure distribution can be optimized, and the vehicle safety performance can be enhanced.

[0072] The specific calculation formula for the maximum deployment speed K of the airbag in S2 is: ;

[0073] Where K represents the maximum deployment speed of the airbag, P max It represents the maximum pressure of the gas in the airbag, and C represents the elastic coefficient of the airbag material.

[0074] The maximum pressure in the airbag is P max It is the peak pressure reached during the gas expansion process, which directly determines the thrust during the airbag deployment process. This pressure is determined by the gas expansion rate, the initial inflation volume, and the environmental conditions inside the airbag, such as temperature and humidity. The elastic coefficient C of the airbag material is a key parameter in material mechanics, reflecting the ability of the airbag material to deform during force and the reaction force to gas expansion. The more elastic the material, the higher the airbag deployment speed. Through the formula , combining the maximum gas pressure with the elastic properties of the material, the maximum deployment speed K of the airbag is obtained. This calculation method provides a theoretical basis for the optimization of the airbag deployment speed, and can be calibrated and adjusted through experiments to meet specific performance requirements. , which can accurately calculate the maximum deployment speed of the airbag, providing a scientific basis for system design and optimization. The formula contains two dynamic variables, pressure and elastic coefficient, which can adapt to the optimization needs of different vehicle models, airbag types and environmental conditions. It improves the understanding and control of the airbag deployment process and enhances the safety and reliability of the airbag. This calculation method can be applied to various types of airbag systems, such as front, side or knee airbags, and has good universality.

[0075] The specific calculation formula of the dynamic response coefficient of the airbag deployment speed in S1 is: D = μ / ρ;

[0076] Wherein, D represents the dynamic response coefficient of the airbag deployment speed, μ represents the viscosity of the gas in the airbag, and ρ represents the density of the gas in the airbag.

[0077] In this embodiment, the dynamic response coefficient D is a key parameter for describing the gas dynamic characteristics during the airbag deployment process, and is used to quantify the effect of the gas flow performance on the airbag deployment speed. Viscosity (μ): indicates the magnitude of the internal friction of the gas during the flow process, which directly affects the resistance of the gas flow. The higher the viscosity, the greater the resistance of the gas flow, thereby reducing the airbag deployment speed. Density (ρ): indicates the unit volume mass of the gas, which affects the inertia of the gas flow. The higher the density, the greater the inertia of the gas, which increases the driving force for the airbag deployment. Calculation formula (D=μ / ρ): By defining the ratio of gas viscosity to density as the dynamic response coefficient DDD, the effect of the gas flow performance on the airbag deployment speed is comprehensively described. The smaller the ratio of viscosity to density, the higher the responsiveness of the airbag deployment speed. This formula provides a physical basis for calculating the optimization of the airbag deployment speed, and can accurately determine the viscosity and density through experimental measurement or theoretical calculation, thereby improving the accuracy of the model calculation. The formula D=μ / ρ quantitatively describes the effect of the gas flow performance on the airbag deployment speed, providing theoretical support for the precise control of the airbag deployment process. By adjusting the gas viscosity and density parameters, the airbag deployment speed can be dynamically optimized to improve the response efficiency and reliability of the airbag. This formula is applicable to airbag systems with different gas types, such as air, nitrogen or mixed gases, and has a wide range of applicability. By introducing the dynamic response coefficient D, the flow characteristics of the gas can be better simulated, providing data support for further optimizing the airbag deployment performance.

[0078] The pressure value C inside the airbag in S1 is obtained by interpolation calculation of the pressure data collected by the sensor in real time. In this embodiment, in order to obtain the pressure value C inside the airbag, the pressure data collected by the sensor in real time is used, and more accurate pressure distribution information is generated by the interpolation calculation method: the pressure sensor is installed inside the airbag or at its key position, and the pressure data inside the airbag is collected by real-time monitoring. These data can reflect the dynamic pressure changes of the airbag during the deployment process. Due to the limited number of sensors, the pressure data collected directly may be unevenly distributed and have gaps. Through interpolation algorithms, such as linear interpolation, segmented interpolation or high-order polynomial interpolation, the collected discrete data are calculated to generate continuous pressure values ​​C at each spatial point inside the airbag. The pressure value C after interpolation calculation can be used to construct a pressure distribution model inside the airbag, providing accurate initial conditions for dynamic modeling and optimization calculation. Through this method, high-resolution pressure distribution data can be obtained at a low cost when the number of sensors is limited, thereby improving the accuracy of the dynamic model. Using the interpolation algorithm, the limited sensor data is expanded into a continuous pressure distribution, which significantly improves the accuracy of the pressure value C. Through real-time acquisition and calculation, the pressure changes during airbag deployment can be dynamically tracked, improving the system's adaptability to complex collision conditions. The interpolation method reduces the reliance on the number of sensors, thereby reducing costs without sacrificing data accuracy. The generated high-resolution pressure distribution provides a reliable foundation for subsequent airbag dynamic modeling, optimizing deployment speed and deployment characteristics.

[0079] The specific calculation formula of the time interval x of the ignition signal in S4 is: x=T / Δt;

[0080] Wherein, x represents the time interval of the ignition signal, T represents the ignition time interval, and Δt represents the sampling interval of the sensor data.

[0081] In this embodiment, in order to accurately determine the time interval x of the ignition signal, the formula x=T / Δt is used for calculation: Ignition time interval T: represents the total time between two consecutive ignition signals, is the core parameter of ignition control, and determines the timing of airbag deployment. Sensor sampling interval Δt: represents the time interval for sensor data collection, is the reciprocal of the sensor collection frequency, and directly affects the data resolution. Time interval calculation: By dividing the ignition time interval T by the sampling interval Δt, the continuous time interval can be accurately discretized into several sampling points so that it is synchronized with the data collected by the sensor. This method can ensure the accuracy of the ignition signal time interval and keep it consistent with the sensor data, thereby achieving accurate execution of ignition control. Through this calculation method, the timing control accuracy and response speed of the airbag system can be effectively improved. Discretizing the ignition time interval T into the sampling point interval x ensures that the ignition signal is synchronized with the sensor data collected, and improves the coordination and accuracy of the control system. Through the calculation based on the sampling interval, the timing of the ignition signal can be adjusted in real time to meet the needs of rapid response under different collision conditions. This calculation method ensures the time accuracy of the ignition signal at each sampling point and improves the stability and reliability of the ignition control. The sensor system is suitable for different sampling frequencies and sampling accuracies, and can dynamically adjust the calculation parameters to adapt to a variety of application scenarios.

[0082] When the encoding value H(x) of the ignition signal in S4 is mapped to the controller instruction, the transmission speed is optimized through a lookup table, and the lookup table includes the corresponding relationship between the ignition signal time interval and the control instruction.

[0083] In this embodiment, in order to improve the speed and accuracy of mapping the ignition signal H(x) to the controller instruction, a lookup table is introduced to optimize the transmission process: in the initialization stage, a static lookup table is generated according to the relationship between the ignition signal time interval x and the controller instruction value. The structure of the lookup table is a key-value pair, in which the key is a different ignition signal time interval x and the value is the corresponding controller instruction. When the system is running, after calculating the encoding value H(x) of the ignition signal, the system directly queries the corresponding control instruction through the lookup table without performing complex real-time calculations, thereby significantly reducing signal transmission and processing time. The control instruction obtained by the lookup table query is immediately transmitted to the ignition controller to trigger the corresponding ignition behavior, ensuring the real-time and reliability of the ignition process. Through the lookup table method, the complex calculation process is converted into a simple search operation, which optimizes the transmission efficiency of the ignition signal and the system response speed. By replacing the real-time calculation with the lookup table, the mapping speed of the ignition signal encoding value H(x) to the controller instruction is greatly improved, and the system delay is reduced. The use of the lookup table avoids runtime calculation errors and improves the accuracy and stability of signal transmission. The fast lookup operation ensures that the ignition signal can be transmitted to the controller with the lowest latency, adapting to the real-time response requirements in highly dynamic collision environments. The content of the lookup table can be flexibly adjusted according to different vehicle models or airbag types to adapt to a variety of application scenarios.

[0084] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for reverse development of airbag performance by means of computer software, characterized in that: The method comprises: S1. A dynamic model of airbag deployment is established based on sensor data at different collision angles, including obtaining collision acceleration and time data from the sensor and calculating instantaneous velocity. The specific formula is: ； Among them, v represents instantaneous velocity, a represents acceleration, and t represents time; Combined with the vehicle mass, the momentum at different collision angles is calculated to analyze the impact of the collision force on the deployment of the airbag. The specific formula is: p = ma; Among them, p represents momentum, m represents mass, and a represents acceleration; The pressure curve of airbag deployment is fitted using experimental data, and a simple dynamic model is established to describe the relationship between pressure and deployment speed. S2. Determine the optimal deployment speed of the airbag at various collision angles based on the model output, including analyzing the dynamic changes of the deployment speed at different collision angles, determining the growth rate of the airbag deployment speed and the maximum deployment speed of the airbag. The specific formula is: ； Wherein, N represents the deployment speed of the airbag, r represents the growth rate of the deployment speed of the airbag, K represents the maximum deployment speed of the airbag, and t represents time; S3. Constructing an ignition timing control algorithm to match the above-mentioned optimal deployment speed, including using experimental data, collecting ignition timing and deployment effects under different collision conditions, and fitting the relationship between ignition time and collision angle and speed, specifically: y=dr+c; Where y represents the ignition time, d represents the collision speed or angle, and r and c represent the regression coefficients; S4. Reprogram the ignition controller of the airbag based on the above algorithm.

2. The method for reverse development of airbag performance by computer software according to claim 1, characterized in that: The S1 includes: Using the collision angle data provided by the sensor and combined with the pressure distribution, a dynamic model of airbag deployment is established, specifically: ; Wherein, J represents the pressure transmission rate during the airbag deployment process, D represents the dynamic response coefficient of the airbag deployment speed, C represents the pressure value inside the airbag, and x represents the spatial coordinate inside the airbag.

3. The method for reverse development of airbag performance by computer software according to claim 1, characterized in that: The S3 includes: The ignition signal is discretized in time sequence, and the time position of each ignition signal is calculated to form an initial ignition timing model. According to the maximum deployment speed of the airbag determined by S2, it is converted into the ignition frequency. The specific formula is: BPM = K × m / 1; Where BPM represents the frequency of ignition, K represents the maximum deployment speed of the airbag, and m represents the frequency scaling factor; The time interval between each ignition is calculated by the formula T=60 / BPM, and the ignition sequence is determined, where T represents the ignition time interval and BPM represents the ignition frequency. The calculated ignition time interval is programmed into the controller to form a complete ignition timing table.

4. The method for reverse development of airbag performance by computer software according to claim 1, characterized in that: The S4 includes: Encode the ignition control signal and convert the ignition time interval into a discrete control instruction. The specific formula is: H (x) = (x mod p); Wherein, H(x) represents the code value of the ignition signal, x represents the time interval of the ignition signal, p represents a prime number, and mod represents a modulo operation; Map the coded value of the ignition signal to the command value of the controller to optimize the transmission and execution of the ignition signal.

5. The method for reverse development of airbag performance by computer software according to claim 1, characterized in that: The data collected by the sensors in S1 include collision angle, collision acceleration and airbag deployment pressure.

6. The method for reverse development of airbag performance by computer software according to claim 1, characterized in that: The specific calculation formula for the maximum deployment speed K of the airbag in S2 is: ； Where K represents the maximum deployment speed of the airbag, P max It represents the maximum pressure of the gas in the airbag, and C represents the elastic coefficient of the airbag material.

7. The method for reverse development of airbag performance by computer software according to claim 2, characterized in that: The specific calculation formula of the dynamic response coefficient of the airbag deployment speed in S1 is: D = μ / ρ; Wherein, D represents the dynamic response coefficient of the airbag deployment speed, μ represents the viscosity of the gas in the airbag, and ρ represents the density of the gas in the airbag.

8. The method for reverse development of airbag performance by computer software according to claim 2, characterized in that: The pressure value C inside the airbag in S1 is obtained by interpolating the pressure data collected by the sensor in real time.

9. The method for reverse development of airbag performance by computer software according to claim 4, characterized in that: The specific calculation formula of the time interval x of the ignition signal in S4 is: x = T / Δt; Wherein, x represents the time interval of the ignition signal, T represents the ignition time interval, and Δt represents the sampling interval of the sensor data.

10. The method for reverse development of airbag performance by computer software according to claim 4, characterized in that: When the encoding value H(x) of the ignition signal in S4 is mapped to the controller instruction, the transmission speed is optimized by a lookup table, and the lookup table includes the corresponding relationship between the ignition signal time interval and the control instruction.

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