Reverse development method for airbag performance by means of computer software

Abstract

The invention discloses an airbag performance reverse development method by means of computer software, and relates to the technical field of automobile safety, the method comprises the following steps: S1, establishing an airbag unfolding dynamic model based on sensor data at different collision angles, S2, determining the optimal unfolding speed of the airbag at various collision angles according to the model output, and S3, determining the optimal unfolding speed of the airbag at various collision angles according to the optimal unfolding speed. S3, an ignition time sequence regulation and control algorithm is constructed to be matched with the optimal unfolding speed; S4, an ignition controller of the safety air bag is reprogrammed based on the algorithm; according to the safety airbag performance reverse development method by means of the computer software, the robustness of a system in a complex environment is improved, efficient airbag unfolding control can still be kept even under the condition of sensor signal fluctuation or noise interference, it is ensured that airbag ignition and unfolding decisions are more scientific and accurate, and the safety airbag performance reverse development method is suitable for popularization and application. And the ignition time sequence of the safety air bag is regulated and controlled to solve the problem that the unfolding speeds of the air bag are inconsistent at different collision angles.

Description

Technical Field

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

[0002] In modern automotive safety technology, airbag systems are a key component in protecting occupants from collisions. Traditional airbag systems typically rely on collision sensor data to trigger the system under pre-set conditions, rapidly deploying the airbag to protect occupants. However, with the increasing complexity of vehicle designs and the diversity of driving environments, varying collision angles and vehicle speeds can lead to airbag deployment speeds that are inconsistent with actual requirements. For example, at certain collision angles, the airbag may deploy too early or too late, compromising occupant protection.

[0003] Existing airbag firing control technologies typically use a predetermined firing algorithm, responding to sensor signals through fixed sequential logic. However, this approach struggles to accurately respond to varying collision angles and speeds in complex collision scenarios. Furthermore, existing control methods often rely on hardware optimization or sensor accuracy enhancement, resulting in lengthy development cycles and difficulty in rapidly iterating to adapt to new requirements. 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, which regulates the firing sequence of the airbag to solve the problem of inconsistent airbag deployment speed under different collision angles.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a method for reverse engineering airbag performance using computer software, the method comprising: S1. Build a dynamic model for airbag deployment based on sensor data at different collision angles. This involves obtaining collision acceleration and time data from the sensor and calculating the 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. This includes analyzing the dynamic changes in the deployment speed at different collision angles, determining the growth rate of the airbag deployment speed, and determining 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. Build an ignition timing control algorithm to match the above-mentioned optimal deployment speed, including using experimental data to collect 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.

[0006] Preferably, 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.

[0007] Preferably, the S3 includes: Discretize the ignition signal in time sequence and calculate the time position of each ignition signal to form an initial ignition timing model. According to the maximum airbag deployment speed determined by S2, it is converted into the ignition frequency. The specific formula is: BPM = K × m / 1; Where BPM represents the firing frequency, K represents the maximum deployment speed of the airbag, and m represents the frequency scaling factor; The time interval between each ignition is calculated using the formula T = 60 / BPM to determine the ignition sequence, 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.

[0008] Preferably, 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.

[0009] Preferably, the data collected by the sensor in S1 includes collision angle, collision acceleration and airbag deployment pressure.

[0010] Preferably, 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.

[0011] Preferably, the specific calculation formula of the dynamic response coefficient of the airbag deployment speed in S1 is: D=μ / ρ; Where 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.

[0012] 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.

[0013] Preferably, the specific calculation formula of the time interval x of the ignition signal in S4 is: x = T / Δt; Where 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.

[0014] 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 a correspondence between the ignition signal time interval and the control instruction.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: 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 based on the model output, constructs an ignition timing control algorithm to match the above-mentioned optimal deployment speed, and reprograms the airbag ignition controller based on the above-mentioned algorithm to achieve precise 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 problems of insufficient protection or excessive deployment caused by premature or late deployment of the airbag. The airbag deployment process is more in line with actual collision requirements, 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 of airbag ignition and deployment is more scientific and accurate, adapting to complex and changeable vehicle collision scenarios, and adjusting the airbag ignition timing to solve the problem of inconsistent airbag deployment speed at different collision angles. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a method for reverse development of airbag performance using computer software, the method comprising: S1. Build a dynamic model for airbag deployment based on sensor data at different collision angles. This involves obtaining collision acceleration and time data from the sensor and calculating the 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. This includes analyzing the dynamic changes in the deployment speed at different collision angles, determining the growth rate of the airbag deployment speed, and determining 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. Build an ignition timing control algorithm to match the above-mentioned optimal deployment speed, including using experimental data to collect 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.

[0019] 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 changes in the airbag deployment speed are calculated, and the growth rate r and maximum deployment speed K are determined based on the impact angle. This formula reflects the initial rapid growth of the airbag deployment speed followed by a gradual stabilization, which is consistent with actual airbag deployment behavior. In step S3, an ignition timing adjustment algorithm is designed based on the optimal deployment speed to ensure that the ignition timing matches the optimal dynamic characteristics of airbag deployment, thereby optimizing the performance of the ignition controller. In step S4, the optimized algorithm is loaded into the airbag ignition controller through software reprogramming technology to ensure that the system's response speed and deployment accuracy meet expectations in actual applications. This method, by modeling sensor data based on the impact angle, provides a more accurate representation of the dynamic characteristics of the airbag deployment process, improving the model's applicability and reliability. Using a mathematical model formula to calculate the optimal deployment speed and its rate of change enables dynamic adaptability to different impact angles, effectively improving airbag deployment performance. The introduction of the ignition timing adjustment algorithm optimizes the timing control during the airbag deployment process, enabling faster and safer deployment at critical moments, thereby enhancing vehicle safety. By reprogramming the ignition controller, the actual performance of the system is ensured to be consistent with the optimized design, development costs are reduced, and the performance of the existing airbag system is upgraded.

[0020] S1 involves using the collision angle data provided by the sensor and combining it with the pressure distribution to build a dynamic model for airbag deployment. 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.

[0021] 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 Characterizes the pressure transmission process within the airbag, where J represents the pressure transmission rate, reflecting the temporal and spatial variations of the pressure within the airbag; D is the dynamic response coefficient of the airbag deployment velocity, characterizing the responsiveness of the airbag material properties to pressure changes; C represents the pressure value within the airbag, reflecting the gas flow characteristics generated by airbag deployment; and x is the spatial coordinate within the airbag, representing the spatial location of the pressure distribution. By combining pressure distribution with collision angle data, a dynamic model is constructed that accurately reflects the pressure distribution at different stages of airbag deployment, providing theoretical support for subsequent deployment velocity optimization and ignition control. By combining collision angle data with dynamic modeling of pressure distribution, the model's accuracy is further improved, providing a more accurate theoretical basis for optimizing airbag performance. The dynamic model, incorporating the pressure transmission rate J and the response coefficient D, accurately describes the temporal and spatial variations of the pressure distribution within the airbag, helping to optimize airbag deployment performance and reliability. By modeling the distribution of the spatial coordinate x within the airbag, dynamic optimization of spatial and temporal parameters during airbag deployment is achieved, ensuring rapid response under varying collision conditions. Improve the simulation accuracy of physical phenomena during airbag deployment, thereby enhancing the scientific nature and efficiency of reverse development.

[0022] S3 involves discretizing the ignition signals in time sequence, calculating the time position of each ignition signal, forming an initial ignition timing model, and converting it into an ignition frequency based on the maximum airbag deployment speed determined in S2. The specific formula is: BPM = K × m / 1; Where BPM represents the firing frequency, K represents the maximum deployment speed of the airbag, and m represents the frequency scaling factor; The time interval between each ignition is calculated using the formula T = 60 / BPM to determine the ignition sequence, 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.

[0023] In this method, the temporal sequence of ignition signals is divided into multiple time nodes using discretization technology, with each node corresponding to a specific ignition signal's temporal position, thus forming an initial ignition timing model. Combined with the determined maximum airbag deployment velocity K, this is converted into an ignition frequency (BPM) using the formula BPM = K × m / 1. Here, m is the frequency ratio, which is used to adjust the ignition frequency to meet optimization requirements under different collision conditions. Subsequently, the interval T between each ignition signal is calculated using the formula T = 60 / BPM to determine the specific timing points of the ignition sequence. All calculated ignition timing points are input into the ignition controller to generate a complete ignition timing table, ensuring that the ignition signals precisely match the dynamic characteristics of airbag deployment. Ignition signal discretization technology provides a precise method for calculating temporal positions, laying the foundation for precise control of airbag deployment. Determining the ignition frequency (BPM) based on the maximum deployment velocity K and the frequency ratio m dynamically adapts to airbag deployment requirements under different collision angles, improving the flexibility and applicability of the ignition timing model. Determining the ignition interval using the formula T = 60 / BPM ensures ignition control accuracy, thereby improving the airbag system's responsiveness and safety. Programming a complete ignition timing table into the controller enables automated control of the ignition signal, optimizing system efficiency and reducing development costs.

[0024] 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); 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.

[0025] In this method, the ignition control signal's time interval x is discretized and converted into a code value H(x). Specifically, the code value for each ignition signal is calculated using the formula H(x) = (x mod p), with the prime number p as the modulus. The advantage of this encoding method is that it effectively converts continuous time interval values ​​into discrete command values, thereby reducing signal processing complexity and improving transmission efficiency. The calculated code value H(x) is then mapped to the ignition controller's instruction set, ensuring that the controller can quickly identify and execute the ignition command. This method optimizes the ignition signal transmission process and execution accuracy, 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 ignition signal processing flow and improves the real-time and efficiency of signal processing. Using the prime number p as the modulus effectively reduces the possibility of code conflicts and ensures the uniqueness and stability of the ignition signal. Mapping the code value H(x) to the controller's instruction value achieves efficient conversion from ignition signal time interval to instruction set, improving the system's response speed and reliability. 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.

[0026] The data collected by the sensors in step S1 includes collision angle, collision acceleration, and airbag deployment pressure. In this embodiment, step S1 collects multidimensional data through sensors, providing precise input for building the dynamic model of airbag deployment. The collected data includes: collision angle, acquired by the angle sensor, which describes the directional information of the vehicle collision and serves as the initial condition for model analysis; collision acceleration, acquired by the acceleration sensor, which reflects the rate of change of vehicle velocity during a collision and directly influences the triggering conditions and deployment speed calculation of the airbag deployment; and airbag deployment pressure, acquired by the pressure sensor, which monitors the pressure distribution inside the airbag in real time and reflects the dynamic characteristics of the airbag deployment process. These data are transmitted in real time to a computer software system and, after preprocessing, are input into the dynamic model of airbag deployment. The model uses these data as input variables to perform multidimensional analysis and optimization calculations, thereby generating a precise dynamic characteristic curve for airbag deployment. The diversity and real-time nature of this data set effectively improve the accuracy and adaptability of the model. By incorporating collision angle, collision acceleration, and airbag deployment pressure data, the airbag dynamic model provides more comprehensive initial conditions, thereby improving the model's accuracy. Leveraging multi-dimensional sensor data, the system can adapt to collision scenarios at varying angles, speeds, and environments, improving the reliability of the airbag system. Sensor data can be fed into the system in real time, providing rapid response capabilities for the airbag dynamic model and effectively shortening calculation and reaction times. Real-time monitoring and analysis of airbag deployment pressure allows for dynamic adjustments to the deployment process, optimizing deployment speed and pressure distribution, and enhancing vehicle safety.

[0027] 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.

[0028] Maximum pressure in the airbag 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 the force process and the reaction force to the gas expansion. The more elastic the material, the higher the airbag deployment speed. Through the formula , combining the maximum pressure of the gas 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 the parameter values ​​can be calibrated and adjusted through experiments to meet specific performance requirements. , can accurately calculate the maximum airbag deployment velocity, providing a scientific basis for system design and optimization. The formula incorporates two dynamic variables, pressure and spring coefficient, and can adapt to the optimization needs of different vehicle models, airbag types, and environmental conditions. This improves the understanding and control of the airbag deployment process, enhancing the safety and reliability of airbags. This calculation method can be applied to various types of airbag systems, such as front, side, and knee airbags, showing good universality.

[0029] The specific calculation formula of the dynamic response coefficient of the airbag deployment speed in S1 is: D = μ / ρ; Where 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.

[0030] In this embodiment, the dynamic response coefficient D is a key parameter describing the dynamic characteristics of gas during airbag deployment, quantifying the impact of gas flow properties on the airbag deployment speed. Viscosity (μ): Indicates the magnitude of internal friction during gas flow and directly affects the resistance to gas flow. Higher viscosity increases gas flow resistance, thereby reducing the airbag deployment speed. Density (ρ): Indicates the mass per unit volume of gas and affects its inertia. Higher density increases gas inertia, increasing the driving force for airbag deployment. The calculation formula (D = μ / ρ): Defined as the ratio of gas viscosity to density, the dynamic response coefficient DDD comprehensively describes the impact of gas flow properties on the airbag deployment speed. The smaller the viscosity-to-density ratio, the higher the responsiveness of the airbag deployment speed. This formula provides a physical basis for optimizing the calculation of airbag deployment speed and allows for precise determination of viscosity and density through experimental measurements or theoretical calculations, thereby improving the accuracy of model calculations. The formula D = μ / ρ quantitatively describes the impact of gas flow properties on the airbag deployment speed, providing theoretical support for precise control of the airbag deployment process. By adjusting gas viscosity and density parameters, airbag deployment speed can be dynamically optimized, improving airbag response efficiency and reliability. This formula is applicable to airbag systems with different gas types, such as air, nitrogen, or mixed gases, and has broad applicability. By introducing the dynamic response coefficient D, gas flow characteristics can be better simulated, providing data support for further optimizing airbag deployment performance.

[0031] The pressure value C inside the airbag in S1 is obtained by interpolating the pressure data collected in real time by sensors. In this embodiment, to obtain the pressure value C inside the airbag, real-time pressure data collected by sensors is used, and more accurate pressure distribution information is generated through interpolation. Pressure sensors are installed inside the airbag or at key locations, and real-time monitoring and acquisition of internal pressure data are performed. This data can reflect the dynamic pressure changes during the airbag deployment process. Due to the limited number of sensors, directly collected pressure data may be unevenly distributed and contain gaps. Interpolation algorithms, such as linear interpolation, piecewise interpolation, or high-order polynomial interpolation, are used to calculate the collected discrete data and generate continuous pressure values ​​C at each spatial point inside the airbag. The interpolated pressure values ​​C are used to construct a pressure distribution model inside the airbag, providing accurate initial conditions for dynamic modeling and optimization calculations. This method can obtain high-resolution pressure distribution data at a low cost when sensors are limited, thereby improving the accuracy of the dynamic model. The interpolation algorithm expands the limited sensor data into a continuous pressure distribution, significantly improving the accuracy of the pressure value C. Through real-time data acquisition and calculation, pressure changes during airbag deployment can be dynamically tracked, improving the system's adaptability to complex collision conditions. Interpolation methods reduce reliance on the number of sensors, thereby lowering costs without sacrificing data accuracy. The resulting high-resolution pressure distribution provides a reliable foundation for subsequent airbag dynamic modeling, optimizing deployment speed and characteristics.

[0032] The specific calculation formula of the time interval x of the ignition signal in S4 is: x=T / Δt; Where 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.

[0033] In this embodiment, to accurately determine the ignition signal interval x, the formula x = T / Δt is used: The ignition interval T represents the total time between two consecutive ignition signals and is a core parameter for ignition control, determining the timing of airbag deployment. The sensor sampling interval Δt represents the time interval between sensor data acquisition and is the inverse of the sensor acquisition frequency, directly affecting data resolution. Time interval calculation: By dividing the ignition interval T by the sampling interval Δt, the continuous time interval can be accurately discretized into a number of sampling points, which are synchronized with the sensor data. This method ensures the accuracy of the ignition signal interval and its consistency with the sensor data, thereby achieving precise ignition control. This calculation method effectively improves the timing control accuracy and response speed of the airbag system. Discretizing the ignition interval T into the sampling point interval x ensures synchronization between the ignition signal and the sensor data, enhancing the coordination and accuracy of the control system. This sampling interval-based calculation allows real-time adjustment of the ignition signal timing to meet the requirements for rapid response under different collision conditions. This calculation method ensures the timing accuracy of the ignition signal at each sampling point, improving the stability and reliability of ignition control. The sensor system is suitable for different sampling frequencies and sampling accuracy, and can dynamically adjust calculation parameters to adapt to various application scenarios.

[0034] 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, which includes the correspondence between the ignition signal time interval and the control instruction.

[0035] In this embodiment, to improve the speed and accuracy of mapping the ignition signal H(x) to the controller command, a lookup table is introduced to optimize the transmission process. During the initialization phase, a static lookup table is generated based on the relationship between the ignition signal time interval x and the controller command value. The lookup table is structured as key-value pairs, where the key represents the different ignition signal time intervals x and the value represents the corresponding controller command. During system operation, after calculating the ignition signal code value H(x), the system directly searches the lookup table for the corresponding control command, eliminating the need for complex real-time calculations. This significantly reduces signal transmission and processing time. The control command obtained from the lookup table is immediately transmitted to the ignition controller, triggering the corresponding ignition action and ensuring the real-time and reliability of the ignition process. The lookup table method transforms the complex calculation process into a simple lookup operation, optimizing the transmission efficiency of the ignition signal and the system's response speed. By replacing real-time calculations with a lookup table, the mapping speed of the ignition signal code value H(x) to the controller command is significantly improved, reducing system latency. The use of a lookup table avoids runtime calculation errors and improves the accuracy and stability of signal transmission. Fast lookup operations ensure that ignition signals are transmitted to the controller with minimal latency, meeting the real-time response requirements in highly dynamic crash environments. The lookup table content can be flexibly adjusted based on different vehicle models or airbag types, adapting to various application scenarios.

[0036] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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.

Images