Automobile column structure optimization method and device, computer equipment and storage medium

Abstract

The invention discloses an automobile column structure optimization method and device, computer equipment and a storage medium, relates to the technical field of automobile structures, and can realize combined adjustment of geometric parameters of a column A, a column B and a column C and automatic traversal of geometric parameter combinations by an algorithm to finally find target geometric parameters of which performance indexes meet corresponding index requirements. The method comprises the following steps: constructing an environmental parameter model according to a vehicle driving speed and rainfall intensity; establishing a correlation function between the geometric parameter of each automobile column and the corresponding performance index; integrating the correlation function of each automobile column into a comprehensive objective function according to a weight coefficient, and defining constraint conditions; and iteratively optimizing the target function by adopting a simulated annealing algorithm until an optimization stopping condition is met, outputting a target geometric parameter of the automobile column, obtaining a new geometric parameter through a random disturbance parameter vector by adopting the simulated annealing algorithm, and accepting the new geometric parameter based on a temperature attenuation strategy until the optimization stopping condition is met.

Description

Technical Field

[0001] The present application relates to the technical field of automobile structures, and in particular to a method, device, computer equipment, and storage medium for optimizing automobile pillar structures. Background Art

[0002] The design of the A-, B-, and C-pillars plays a crucial role in a vehicle's structural safety, aerodynamics, and driving comfort. As the front support structure, the A-pillar directly impacts the driver's field of view and airflow distribution. As a force-bearing structure, the B-pillar directly influences collision loads and structural strength. As the rear support structure, the C-pillar influences rearward air turbulence and wake distribution.

[0003] Prior art optimization of automotive pillars is often based on constant environmental parameters (such as steady airflow and rain-free conditions), without considering the negative impact of dynamic disturbances on performance in high-speed, rainy environments. This results in a significant conflict between safety, comfort, and aerodynamic performance in automotive pillar design. Therefore, a structural optimization method for automotive pillars that can systematically address these deficiencies is urgently needed. Summary of the Invention

[0004] In light of this, this application provides a method, apparatus, computer device, and storage medium for optimizing the structure of an automotive pillar. The primary purpose is to address the problem that the optimization design of automotive pillars is often based on constant environmental parameters (such as steady airflow and rain-free conditions), without considering the negative impact of dynamic disturbances on performance in high-speed rainy environments. This results in a significant conflict between safety, comfort, and aerodynamic performance in automotive pillar design.

[0005] According to a first aspect of the present application, a method for optimizing an automobile pillar structure is provided, the method comprising:

[0006] Constructing an environmental parameter model based on vehicle speed and rainfall intensity, the environmental parameter model including a separate marginal distribution function of airflow velocity, a separate marginal distribution function of rain density, and a joint probability density function of airflow velocity and rain density;

[0007] Establishing a correlation function between the geometric parameters of each vehicle pillar and corresponding performance indicators, the vehicle pillars including A-pillars, B-pillars, and C-pillars, the geometric parameters including inclination angle, curvature, and cross-sectional area, the performance indicators corresponding to the A-pillar being driving visibility, wind noise level, and airflow turbulence, and the performance indicators corresponding to the B-pillars and C-pillars being the wind noise level, airflow turbulence, and structural strength;

[0008] The correlation function of each vehicle pillar is integrated into a comprehensive objective function according to the weight coefficient, and the constraint conditions are defined, wherein the constraint conditions include the range of geometric parameters, the lower limit of structural strength, and the upper limit of wind noise;

[0009] A simulated annealing algorithm is used to iteratively optimize the objective function until a stop optimization condition is reached, and target geometric parameters of the automobile pillar are output. The simulated annealing algorithm obtains new geometric parameters by randomly perturbing a parameter vector and accepts the new geometric parameters based on a temperature decay strategy until the stop optimization condition is reached, and the currently accepted geometric parameters are output as the target geometric parameters.

[0010] Optionally, according to the vehicle driving speed, the airflow velocity at a specified point during the vehicle driving process is determined, and the airflow velocity individual marginal distribution function f(v) is constructed according to the airflow velocity, the mean of the airflow velocity, and the variance of the airflow velocity;

[0011]

[0012] Determine the number of raindrops per unit volume or the mass density of rainwater according to the rainfall intensity to obtain a shower density, and construct a separate marginal distribution function f(ρ) of the shower density according to the airflow velocity, the mean of the shower density, and the variance of the shower density;

[0013]

[0014] Constructing a covariance matrix and a deviation transposed vector of the airflow velocity and the rain density, wherein the covariance matrix is ​​used to describe the correlation between the airflow velocity and the rain density;

[0015] Constructing a joint probability density function f(v,ρ) based on the covariance matrix and the deviation transposed vector;

[0016]

[0017] Wherein, v is the air flow velocity; μ v is the mean value of the airflow velocity; σ v is the variance of the airflow velocity; μ ρ is the mean value of the rain density; σ ρ is the variance of the rain density; σ vρ is the covariance of air velocity and rain density; -1 is the covariance matrix; the Z T is the deviation transposed vector.

[0018] Optionally, establishing a correlation function between the geometric parameters of each automobile pillar and the corresponding performance index includes:

[0019] For the A-pillar, a correlation function V between the A-pillar and the driving visibility is constructed according to the inclination angle of the A-pillar, the curvature of the A-pillar, and the cross-sectional area of ​​the A-pillar. A, and constructing a correlation function N between the A-pillar and the wind noise level based on the airflow velocity individual marginal distribution function, the curvature of the A-pillar and the cross-sectional area of ​​the A-pillar A , and constructing a correlation function D between the A-pillar and the airflow turbulence according to the inclination angle of the A-pillar, the curvature of the A-pillar, the cross-sectional area of ​​the A-pillar, the airflow velocity individual marginal distribution function, the rain density individual marginal distribution function and the joint probability density function. A ;

[0020] For the B-pillar, the correlation function D between the B-pillar and the airflow turbulence is constructed based on the pressure difference between the inside and outside of the vehicle, the airflow velocity independent marginal distribution function, and the rain density independent marginal distribution function. B , and calculating the gap area between the B-pillar and the door according to the inclination angle of the B-pillar and the curvature of the B-pillar, and constructing the correlation function N between the B-pillar and the wind noise level according to the gap area and the airflow velocity independent edge distribution function. B , and constructing a correlation function S between the B-pillar and the structural strength according to the yield strength of the B-pillar material, the collision force of the B-pillar material and the cross-sectional area of ​​the B-pillar. B ;

[0021] For the C-pillar, the correlation function D between the C-pillar and the airflow turbulence is constructed based on the inclination angle of the C-pillar, the curvature of the C-pillar, the airflow velocity independent marginal distribution function, and the rain density independent marginal distribution function. C , and constructing a correlation function N between the C-pillar and the wind noise level based on the curvature of the C-pillar and the airflow velocity individual edge distribution function C , and constructing a correlation function S between the C-pillar and the structural strength according to the yield strength of the C-pillar material, the curvature of the C-pillar and the cross-sectional area of ​​the C-pillar C .

[0022] Optionally, the step of integrating the correlation function of each automobile pillar into a comprehensive objective function according to a weight coefficient includes:

[0023] According to the correlation function V between the A-pillar and the driving visibility A , the correlation function N between the A-pillar and the wind noise level A and the correlation function D between the A-pillar and the airflow turbulence A , construct the sub-objective function J of the A column A (θ A ,C A ,A A );

[0024] According to the correlation function D between the B-pillar and the airflow turbulenceB , the correlation function N between the B-pillar and the wind noise level B and the correlation function S between the B-pillar and the structural strength B , construct the sub-objective function J of the B column B (θ B ,C B ,A B );

[0025] According to the correlation function D between the C-pillar and the airflow turbulence C , the correlation function N between the C-pillar and the wind noise level C And the correlation function S between the C-pillar and the structural strength C , construct the sub-objective function J of the C column C (θ C ,C C ,A C );

[0026] The first objective function, the second objective function and the third objective function are integrated into a specified objective function J(θ, C, A)=ω1J according to the weight coefficients. A (θ A ,C A ,A A )+ω2J B (θ B ,C B ,A B )+ω3J C (θ C ,C C ,A C ), wherein ω1, ω2, ω3 are the weight coefficients;

[0027] The geometric parameters of the A-pillar, the B-pillar, and the C-pillar are integrated into a high-dimensional parameter vector, and the nonlinear terms of the sub-objective functions in the specified objective function are adjusted to quadratic forms or linear combinations based on the high-dimensional parameter vector to obtain the comprehensive objective function J(x), where x is the high-dimensional parameter vector.

[0028] Optionally, the simulated annealing algorithm is used to iteratively optimize the objective function until a stop optimization condition is reached, and the target geometric parameters of the automobile pillar are output, including:

[0029] Randomly generating an initial high-dimensional parameter vector, and calculating an initial target value according to the initial high-dimensional parameter vector and the comprehensive target function;

[0030] The initial high-dimensional parameter vector is perturbed by a Gaussian perturbation matrix, and the high-dimensional parameter vector to be accepted is constrained by a defined constraint condition;

[0031] Calculating a target value to be accepted according to the high-dimensional parameter vector to be accepted and the comprehensive objective function, and selecting a target high-dimensional parameter vector corresponding to the current iteration round from the initial high-dimensional parameter vector and the high-dimensional parameter vector to be accepted according to the initial target value, the target value to be accepted, and the simulation temperature corresponding to the current iteration round;

[0032] Determine whether the optimization stop condition is met;

[0033] If the optimization stopping condition is met, the geometric parameters corresponding to the target high-dimensional parameter vector are output as the target geometric parameters. The optimization stopping condition is that the number of iterations reaches a preset number threshold or the simulation temperature of the current iteration round reaches a preset temperature threshold.

[0034] Optionally, after determining whether the optimization stopping condition is met, the method further includes:

[0035] If the optimization stop condition is not met, the cooling rate is determined according to the simulation temperature corresponding to the previous iteration round, and the simulation temperature is reduced according to the cooling rate to obtain the simulation temperature corresponding to the current iteration round;

[0036] Perturbing the target high-dimensional parameter vector by a Gaussian perturbation matrix to obtain a high-dimensional parameter vector to be accepted, and calculating a target value to be accepted based on the high-dimensional parameter vector to be accepted and the comprehensive objective function;

[0037] According to the target value corresponding to the target high-dimensional parameter vector, the target value to be accepted and the simulation temperature corresponding to the current iteration round, the target high-dimensional parameter vector corresponding to the current iteration round is selected from the target high-dimensional parameter vector and the high-dimensional parameter vector to be accepted, and it is judged whether the optimization stopping condition is met, and the iterative optimization is continued or the target geometric parameters are output according to the judgment result.

[0038] Optionally, selecting a target high-dimensional parameter vector corresponding to the current iteration round from the initial high-dimensional parameter vector and the high-dimensional parameter vector to be accepted according to the initial target value, the target value to be accepted, and the simulation temperature corresponding to the current iteration round includes:

[0039] Comparing the initial target value with the target value to be accepted;

[0040] If the comparison result indicates that the target value to be accepted is smaller than the initial target value, the high-dimensional parameter vector corresponding to the target value to be accepted is accepted as the target high-dimensional parameter vector;

[0041] If the comparison result indicates that the target value to be accepted is greater than or equal to the initial target value, the acceptance probability is calculated based on the initial target value, the target value to be accepted and the simulation temperature corresponding to the current iteration round, and the high-dimensional parameter vector corresponding to the target value to be accepted is selected as the target high-dimensional parameter vector according to the acceptance probability. If the high-dimensional parameter vector corresponding to the target value to be accepted is not selected as the target high-dimensional parameter vector, the high-dimensional parameter vector corresponding to the initial target value is selected as the target high-dimensional parameter vector.

[0042] According to a second aspect of the present application, a vehicle pillar structure optimization device is provided, the device comprising:

[0043] A first construction module is used to construct an environmental parameter model based on the vehicle speed and rainfall intensity, wherein the environmental parameter model includes a separate marginal distribution function of airflow velocity, a separate marginal distribution function of rain density, and a joint probability density function of airflow velocity and rain density;

[0044] A second construction module is configured to establish a correlation function between geometric parameters of each vehicle pillar and corresponding performance indicators, wherein the vehicle pillars include A-pillars, B-pillars, and C-pillars, the geometric parameters including inclination angle, curvature, and cross-sectional area, the performance indicators corresponding to the A-pillars are driving visibility, wind noise level, and airflow turbulence, and the performance indicators corresponding to the B-pillars and C-pillars are the wind noise level, the airflow turbulence, and structural strength;

[0045] a constraint module, for integrating the correlation function of each vehicle pillar into a comprehensive objective function according to a weight coefficient, and defining constraint conditions, wherein the constraint conditions include a geometric parameter range, a lower limit of structural strength, and an upper limit of wind noise;

[0046] The optimization module is configured to iteratively optimize the objective function using a simulated annealing algorithm until a stop optimization condition is reached, and output target geometric parameters of the automobile pillar. The simulated annealing algorithm obtains new geometric parameters by randomly perturbing a parameter vector and accepts the new geometric parameters based on a temperature decay strategy until the stop optimization condition is reached, and outputs the currently accepted geometric parameters as the target geometric parameters.

[0047] According to a third aspect of the present application, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0048] According to a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0049] By means of the above technical solution, the present application provides a method, device, computer equipment and storage medium for optimizing the structure of an automobile pillar. The embodiment of the present application first constructs an environmental parameter model based on the vehicle speed and rainfall intensity. Furthermore, a correlation function is established between the geometric parameters of each automobile pillar of the A-pillar, B-pillar and C-pillar, including the inclination angle, curvature and cross-sectional area and the corresponding performance indicators. Next, the correlation function of each automobile pillar is integrated into a comprehensive objective function according to the weight coefficient, and the constraint conditions are defined. Finally, the simulated annealing algorithm is used to iteratively optimize the objective function until the optimization stop condition is reached, and the target geometric parameters of the automobile pillar are output. The embodiment of the present application accurately quantifies the dynamic disturbance effect under high-speed rain environment by constructing a joint Gaussian distribution model of air flow velocity and rain density, thereby realizing accurate simulation of complex working conditions, improving environmental adaptability, and adapting design parameters to different rainfall intensities. Furthermore, the embodiments of the present application use the random perturbation and temperature attenuation strategy of the simulated annealing algorithm to globally search for the optimal solution in the high-dimensional parameter space, breaking through the limitations of traditional single-objective optimization. Through probabilistic modeling and intelligent algorithms, it solves the collaborative contradictions of multi-dimensional indicators such as field of view, intensity, and noise, and achieves a systematic improvement in safety, comfort, and economy.

[0050] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present application. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0052] Figure 1 A schematic flow chart of a method for optimizing an automobile pillar structure provided by an embodiment of the present application is shown;

[0053] Figure 2 A schematic diagram of a convergence curve of an objective function of an automobile pillar structure optimization method provided by an embodiment of the present application is shown;

[0054] Figure 3 A schematic diagram of a convergence curve of an objective function of an automobile pillar structure optimization method provided by an embodiment of the present application is shown;

[0055] Figure 4 A schematic structural diagram of an automobile pillar structure optimization device provided by an embodiment of the present application is shown;

[0056] Figure 5A schematic diagram of the device structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0057] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0058] Those skilled in the art will understand that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of this application refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0059] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0060] Those skilled in the art will appreciate that the term "terminal" as used herein includes both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices with single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service) devices that may combine voice, data processing, fax, and / or data communication capabilities; PDAs (Personal Digital Assistants) that may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices that have and / or include a radio frequency receiver. As used herein, a "terminal" may be portable, transportable, installed in a vehicle (air, sea, and / or land), or adapted and / or configured to operate locally, and / or in a distributed manner, at any other location on Earth and / or in space. As used herein, a "terminal" may also be a communication terminal, an Internet access terminal, or a music / video playback terminal, such as a PDA, an MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or a device such as a smart TV or a set-top box.

[0061] The design of the A-pillar, B-pillar, and C-pillar plays an important role in the structural safety, aerodynamics, and driving comfort of the car. The A-pillar, as the front support structure of the car, directly affects the driver's field of view and the airflow distribution of the vehicle. As a force-bearing structure, the B-pillar directly affects the collision load and structural strength. As the rear support structure of the car, the C-pillar affects the rear air disturbance and wake distribution. In related technologies, the optimization design of automobile pillars is mostly based on constant environmental parameters (such as steady-state airflow and rain-free conditions), and the negative impact of dynamic disturbances on performance in high-speed rain environments is not considered. As a result, there is a significant contradiction between safety, comfort, and aerodynamic performance in the design of automobile pillars. Therefore, there is an urgent need for an automobile pillar structure optimization method that can systematically solve the above-mentioned defects.

[0062] The embodiment of the present application provides a method for optimizing the structure of an automobile pillar, which can be specifically applied to the automobile pillar structure design system. The system combines the simulated annealing algorithm to optimize the inclination, curvature, and cross-sectional area of ​​the A-pillar, B-pillar, and C-pillar, and solve the multi-objective coupling conflict, such as Figure 1As shown, the method includes:

[0063] S1. Construct an environmental parameter model based on vehicle speed and rainfall intensity. The environmental parameter model includes a separate marginal distribution function of airflow velocity, a separate marginal distribution function of rain density, and a joint probability density function of airflow velocity and rain density.

[0064] In the embodiment of the present application, the system dynamically analyzes the airflow velocity at a specified observation point during driving based on the real-time driving speed of the vehicle, and constructs a separate marginal distribution function f(v) of the airflow velocity according to the airflow velocity, the mean of the airflow velocity, and the variance of the airflow velocity. The separate marginal distribution function of the airflow velocity is expressed as the following formula 1:

[0065] Formula 1:

[0066] Where v is the air velocity; μ v is the mean air velocity; σ v is the variance of air flow velocity.

[0067] It can be understood that the airflow velocity v refers to the airflow velocity at a certain point around the vehicle when it is moving, and its mean value μ v It is mainly controlled by the time-varying characteristics of vehicle speed and vehicle body geometry. The two establish a nonlinear mapping relationship through computational fluid dynamics, and its variance σ v Affected by turbulence intensity.

[0068] Furthermore, the system determines the number of raindrops per unit volume or the mass density of rainwater based on rainfall intensity to obtain the shower density. Furthermore, the shower density's individual marginal distribution function f(ρ) is constructed based on the airflow velocity, the mean of the shower density, and the variance of the shower density. The shower density's individual marginal distribution function is shown in the following formula 2:

[0069] Formula 2:

[0070] Where ρ is the rainfall density; μ ρ is the mean value of the rainfall density; σ ρ is the variance of the rainfall density.

[0071] It is understandable that the mean value of the rain density μ ρ Affected by rainfall intensity, its variance σ ρ Affected by the environment and vehicle motion, airflow velocity and rain density usually follow a normal distribution.

[0072] When the vehicle is traveling at high speed, the increase in air velocity may enhance the lateral deviation of raindrops, thereby changing the local rain density. Therefore, the covariance matrix is ​​introduced to describe the correlation between the two. For a continuous random variable X, the mathematical expectation is defined as where fX (x) is the probability density function of X. The covariance of air velocity and rain density is σ vρ , σ vρ =E[(v-μ v )(ρ-μ ρ )]. The covariance matrix is ​​shown in the following formula 3, its inverse matrix is ​​shown in the following formula 4, and the deviation transposed vector is Z T , Z T =[v-μ v ρ-μ ρ ].

[0073] Formula 3:

[0074] Formula 4:

[0075] The joint probability density function f(v,ρ) is constructed based on the covariance matrix and the deviation transposed vector, as shown in the following formula 5:

[0076] Formula 5:

[0077] In a specific application scenario, the probability distribution of air flow velocity and rain density at different vehicle speeds is shown in Table 1 below:

[0078] Table 1 Probability distribution of air velocity and rain density at different vehicle speeds

[0079]

[0080] By bringing the obtained data into the vehicle environment simulation chamber, the median airflow velocity and rain density under each working condition can be obtained, thus providing a reliable theoretical data basis for subsequent tests. The airflow velocity and rain density under each working condition are shown in Table 2 below:

[0081] Table 2 Examples under four environmental conditions

[0082] Rainfall conditions Air flow velocity v (m / s) Rainfall density ρ(mm / min) fog and rain <5 <15 light rain 10 50 heavy rain 12 135 rainstorm >30 >200

[0083] S2. Establish a correlation function between the geometric parameters of each vehicle pillar and the corresponding performance indicators. The vehicle pillars include the A-pillar, B-pillar, and C-pillar. The geometric parameters include inclination angle, curvature, and cross-sectional area. The performance indicators corresponding to the A-pillar are driving visibility, wind noise level, and airflow turbulence. The performance indicators corresponding to the B-pillar and C-pillar are wind noise level, airflow turbulence, and structural strength.

[0084] In the embodiment of the present application, each automobile pillar has three geometric parameters: inclination, curvature, and cross-sectional area. Each automobile pillar corresponds to a different performance index, and each performance index is also affected by different geometric parameters. It should be noted that for the various associated functions involved in this model architecture, although there is a phenomenon of reuse of parameter symbols, each parameter system follows a strict namespace isolation principle. The parameters defined in each function (including but not limited to Greek letter parameters such as α, β, γ, δ) are only valid within the function domain to which they belong, and the numerical solution processes of the same symbolic parameters in different functions are independent of each other.

[0085] Specifically, for the A-pillar, the performance indicators corresponding to the A-pillar include driving visibility, wind noise level and airflow disturbance. Among them, driving visibility is affected by the comprehensive influence of the inclination angle, curvature and cross-sectional area of ​​the A-pillar. Specifically, the larger the inclination angle (approximately approaching the vertical direction), the more obvious the physical obstruction of the driver's line of sight. The increase in curvature will aggravate the interference of the A-pillar surface shape on the refraction of light and the field of view (approximate modeling is performed by simplifying the influence of high-order terms). The cross-sectional area has a two-way effect-an excessively large cross-sectional area will directly block the incident light, while an excessively small cross-sectional area may cause secondary safety hazards due to insufficient structural strength, and the relationship between the two needs to be balanced through compensation effects. Therefore, based on the above parameter characteristics, the system establishes a quantitative correlation function V between the geometric parameters of the A-pillar and driving visibility through Formula 6 A , realizing multi-variable collaborative optimization.

[0086] Formula 6:

[0087] Based on the airflow velocity individual edge distribution function, the curvature of the A-pillar, and the cross-sectional area of ​​the A-pillar, the system constructs the correlation function N between the A-pillar and the wind noise level through Formula 7. A .

[0088] Formula 7:

[0089] For the wind noise level N, the square term of the airflow velocity β1f 2 (v) is the dominant factor affecting the wind noise level, which conforms to the Bernoulli principle of fluid mechanics, where β1>0. The coupling term β2C of curvature and airflow velocity A f(v) represents the degree of disturbance of the airflow caused by the increase in curvature, which is consistent with the turbulence generation mechanism. It means that the cross-sectional area is inversely proportional to the local wind speed, and the local velocity concentration effect will cause a significant increase in wind noise.

[0090] According to the inclination angle of the A-pillar, the curvature of the A-pillar, the cross-sectional area of ​​the A-pillar, the airflow velocity individual marginal distribution function, the rain density individual marginal distribution function and the joint probability density function, the system constructs the correlation function D between the A-pillar and the airflow disturbance through formula 8 and formula 9. A .

[0091] Formula 8: D A =γ1∫ v,ρ f(v,ρ)*g(θ A ,C A ,A A )dvdρ

[0092] Formula 9: g(θ A ,C A ,A A )=δ1[θ A f(v)]+δ2[C A f(ρ)]-δ3[A A f(v)f(ρ)]

[0093] Perturbation contribution function g(θ A ,C A ,A A ) is the result of empirical modeling, which is used to quantify the direct effect of A-pillar geometric parameters on turbulence, where the product term δ1[θ A f(v)] indicates that the contribution of the inclination angle to the disturbance increases with the increase of airflow velocity, and the line bundle is amplified, δ1>0; the coupling term C of curvature and rain density A f(ρ) indicates that the disturbance to the rain distribution increases when the curvature increases, δ2>0; A A f(v)f(ρ) represents the suppression effect of cross-sectional area on disturbance. The larger the cross-sectional area, the smaller the disturbance effect of air flow velocity and rain density, and δ3>0.

[0094] For the B-pillar, based on the pressure difference between the inside and outside of the vehicle, the airflow velocity independent marginal distribution function, and the rain density independent marginal distribution function, the system constructs the correlation function D between the B-pillar and the airflow disturbance through formula 10. B .

[0095] Formula 10: D B =α1f 2 (v)Δp+α2f 2 (ρ)g(Δp)

[0096] Where Δp is the pressure difference between the inside and outside of the car, reflecting the sealing performance of the car; g(Δp) is the nonlinear response function of the pressure difference to rain disturbance.

[0097] The gap area between the B-pillar and the door is calculated based on the inclination angle and curvature of the B-pillar. Based on the individual edge distribution functions of the gap area and airflow velocity, the system constructs the correlation function N between the B-pillar and the wind noise level through formula 11. B .

[0098] Formula 11: N B =β1f2 (v)+β2(β3C B +β4C B ) 2

[0099] The wind noise level is mainly determined by the gap area between the B-pillar and the door, that is, β3C B +β4C B , reducing the gap area (optimizing the curvature and inclination of the B-pillar) is the key to reducing wind noise.

[0100] According to the yield strength of the B-pillar material, the collision force of the B-pillar material and the cross-sectional area of ​​the B-pillar, the system constructs the correlation function S between the B-pillar and the structural strength through formula 12 B .

[0101] Formula 12:

[0102] The structural strength constraint S needs to meet both impact resistance and rigidity requirements and is determined by the cross-sectional area, where σ yield is the yield strength of the B-pillar material, F impact is the collision force, which is obtained from actual data testing, and the data is shown in Table 3 below.

[0103] Table 3 Structural parameters of different materials

[0104] Material <![CDATA[Yield strength σ yield (MPa)]]> <![CDATA[Collision force F impact (N)]]> Bending moment M (N*M) steel 350 5000 1200 aluminum alloy 250 4000 1000 carbon fiber 500 6000 1500

[0105] For the C-pillar, based on the inclination angle of the C-pillar, the curvature of the C-pillar, the airflow velocity independent marginal distribution function, and the rain density independent marginal distribution function, the system constructs the correlation function D between C and airflow disturbance through formula 13. C .

[0106] Formula 13: D C =α1(C C +θ C )f 2 (v)+α2f(ρ)C C 2

[0107] The turbulence of airflow is affected by curvature and inclination. Specifically, curvature and inclination combine to influence the strength of the wake vortex. The square factor of the airflow velocity reflects the dependence of the wake vortex strength on the kinetic energy of the airflow. When the rain density is high, the contribution of curvature to the turbulence of the airflow becomes more significant.

[0108] Based on the curvature of the C-pillar and the individual edge distribution function of the airflow velocity, the system constructs the correlation function N between the C-pillar and the wind noise level through formula 14 C .

[0109] Formula 14: N C =β1f2 (v)+β2C C 2

[0110] The wind noise level measures the noise of air flowing through the C-pillar. High air flow speed leads to stronger wind shear and pressure fluctuations, that is, wind noise is proportional to the square of the air flow speed. At the same time, the greater the curvature, the easier it is for the air flow to separate, which may cause vortex shedding, thereby increasing noise.

[0111] According to the yield strength of the C-pillar material, the curvature of the C-pillar and the cross-sectional area of ​​the C-pillar, the system constructs the correlation function S between the C-pillar and the structural strength through formula 15 C .

[0112] Formula 15: S C =δ1σ yield -δ2stress(C C ,A C )

[0113] The goal of structural strength optimization is to reduce geometric parameters on the basis of meeting strength requirements to ensure that the structural strength is not lower than the allowable value of the material. yield It is the yield strength of the C-pillar material, which needs to be high enough. Where I is the interface inertia moment (m 4 ), depends on the size of the cross-sectional area A, h is the height of the section (for rectangular sections); y is the distance of the cross-sectional area from the neutral axis; M is the C-column bending moment, which is a measure of the rotational effect of a force on a point or axis, and is composed of the product of the distance from the point of application of the collision force, load, and wind force to the bottom support of the C-column and the corresponding force; stress (C C ,A C ) represents the actual stress, which must be ensured to be lower than the yield strength of the material, i.e. S C >0.

[0114] In a specific application scenario, the initial structural parameter range of the automobile column is shown in Table 4 below.

[0115] Table 4 Initial parameters of automobile pillars

[0116] parameter A-pillar B-pillar C-pillar Inclination angle θ(°) 45-60 5-15 135-155 <![CDATA[Curvature C (m -1 )]]> 0.1-0.3 0.05-0.2 0.05-0.15 <![CDATA[Cross-sectional area A (cm 2 )]]> 20-40 30-50 25-45

[0117] The structural optimization of automobile pillars is mainly reflected in the structural optimization of the A-pillar, B-pillar, and C-pillar, and the emphasis is different, as shown in Table 5:

[0118] Table 5 Focus on vehicle pillar structure optimization

[0119]

[0120] S3. Integrate the correlation function of each vehicle pillar into a comprehensive objective function according to the weight coefficient, and define the constraint conditions, which include the range of geometric parameters, the lower limit of structural strength, and the upper limit of wind noise.

[0121] In the embodiment of the present application, according to the correlation function V between the A-pillar and driving visibility A , the correlation function N between the A-pillar and the wind noise level A and the correlation function D between the A-pillar and airflow turbulence A , construct the sub-objective function J of column A A (θ A ,C A ,A A )=ω1V A +ω2N A +ω3D A . According to the correlation function D between the B-pillar and airflow turbulence B , the correlation function N between the B-pillar and the wind noise level B and the correlation function S between the B-pillar and the structural strength B , construct the sub-objective function J of column B B (θ B ,C B ,A B )=ω1D B +ω2N B -ω3S B . According to the correlation function D between the C-pillar and airflow turbulence C , the correlation function N between the C-pillar and the wind noise level C and the correlation function S between the C-pillar and the structural strength C , construct the sub-objective function J of column C C (θ C ,C C ,A C )=ω1D C +ω2N C -ω3S C .

[0122] Furthermore, the first objective function, the second objective function and the third objective function are integrated into a specified objective function J(θ, C, A)=λ1J according to the weight coefficients. A (θ A ,C A ,A A )+λ2J B (θ B ,C B ,A B )+λ3J C (θ C ,C C ,A C), where λ1, λ2, and λ3 are weight coefficients;

[0123] The system-defined constraint parameters are:

[0124] θ min ≤θ A ,θ B ,θ C ≤θ max

[0125] A min ≤A A , A B , A C ≤A max

[0126] C min ≤C A , C B , C C ≤C max

[0127] S B (θ B ,C B ,A B )>0

[0128] S C (θ C ,C C ,A C )>0

[0129] N A (θ A ,C A ,A A ),N B (θ B ,C B ,A B ),N C (θ C ,C C ,A C ) <N max

[0130] The comprehensive objective function J(θ, C, A) for the A-, B-, and C-pillars is derived from the above formula. For the A-pillar, wind noise is the primary optimization objective, so ω2 needs to be increased. Visibility affects driving behavior, so ω1 should be appropriately increased. For the B-pillar, structural strength is the primary objective, so ω3 needs to be increased. Aerodynamic performance is a secondary concern, so ω1 should be appropriately increased. For the C-pillar, safety is the primary objective, so ω3 needs to be increased. Aerodynamic performance is a secondary concern, so ω1 should be appropriately increased.

[0131] Furthermore, in the comprehensive objective function J(θ, C, A), each sub-objective function J A (θA ,C A ,A A ), J B (θ B ,C B ,A B ), J C (θ C ,C C ,A C ) consists of multiple nonlinear terms (quadratic terms, integral terms, etc.). The system unified variable vector integrates the geometric parameters of the A-pillar, B-pillar and C-pillar into a high-dimensional parameter vector x, x = [θ A ,θ B ,θ C ,C A ,C B ,C C ,A A ,A B ,A C ] T , and adjust the nonlinear terms of the sub-objective functions in the specified objective function to quadratic forms or linear combinations according to the high-dimensional parameter vector Among them, Q i is a symmetric weight matrix, is the linear coefficient vector, d i is a constant term, and then the comprehensive objective function J(x) is obtained. The comprehensive objective function is shown in the following formula 16.

[0132] Formula 16:

[0133] Where Q = ∑λ i Q i , c=∑λ i c i , d=∑λ i d i The original constraints can be integrated into BX≤b, where B is the inequality matrix and b is the boundary vector.

[0134] S4. Use the simulated annealing algorithm to iteratively optimize the objective function until the optimization stop condition is reached, and output the target geometric parameters of the automobile column. The simulated annealing algorithm obtains new geometric parameters by randomly perturbing the parameter vector and accepts the new geometric parameters based on the temperature attenuation strategy until the optimization stop condition is reached, and outputs the currently accepted geometric parameters as the target geometric parameters.

[0135] In the embodiment of the present application, the optimization process of the simulated annealing algorithm is as follows: Figure 2 As shown in the figure, the system first randomly generates initial geometric parameters and integrates them into an initial high-dimensional parameter vector x0. The initial target value J(x0) is calculated based on the initial high-dimensional parameter vector and the comprehensive objective function. The results are shown in Table 6 below.

[0136] Table 6 Initial geometric parameters and objective function values

[0137] parameter A-pillar B-pillar C-pillar Inclination angle θ(°) 61.57 14.82 149.34 <![CDATA[Curvature C (m -1 )]]> 0.208 0.125 0.106 <![CDATA[Cross-sectional area A (cm 2 )]]> 36.94 44.12 41.21 Objective function value 189.45 128.37 78.12

[0138] Furthermore, the system perturbs the initial high-dimensional parameter vector through the Gaussian perturbation matrix Δ, that is, adds the disturbance (Δθ, ΔC, ΔA), and uses the defined constraint conditions to constrain the high-dimensional parameter vector to be accepted after the perturbation, that is, the high-dimensional parameter vector to be accepted x1=x0+Δ.

[0139] Next, the system calculates the target value to be accepted based on the high-dimensional parameter vector to be accepted and the comprehensive objective function, and selects the target high-dimensional parameter vector corresponding to the current iteration round from the initial high-dimensional parameter vector and the high-dimensional parameter vector to be accepted based on the initial target value, the target value to be accepted and the simulation temperature T corresponding to the current iteration round. Specifically, the initial target value is compared with the target value to be accepted. If the comparison result indicates that the target value to be accepted J(x1) is less than the initial target value J(x0), it means that the new objective function value is better, and the high-dimensional parameter vector corresponding to the target value to be accepted is accepted as the target high-dimensional parameter vector. On the contrary, if the comparison result indicates that the target value to be accepted J(x1) is greater than or equal to the initial target value J(x0), then based on the initial target value J(x0), the target value to be accepted J(x1) and the simulation temperature T corresponding to the current iteration round, the following formula 17 is used to calculate the acceptance probability P accept , and according to the acceptance probability P accept The high-dimensional parameter vector corresponding to the target value to be accepted is selected as the target high-dimensional parameter vector. If the high-dimensional parameter vector corresponding to the target value to be accepted is not selected as the target high-dimensional parameter vector, the high-dimensional parameter vector corresponding to the initial target value is selected as the target high-dimensional parameter vector.

[0140] Formula 17:

[0141] Furthermore, it is determined whether the optimization stopping condition is met.

[0142] In a specific application scenario, if the optimization stopping condition is reached, the geometric parameters corresponding to the target high-dimensional parameter vector are output as the target geometric parameters. The optimization stopping condition is that the number of iterations reaches a preset number threshold or the simulation temperature of the current iteration round reaches a preset temperature threshold.

[0143] In a specific application scenario, if the optimization stop condition is not met, the cooling rate is determined according to the simulation temperature corresponding to the previous iteration, and the simulation temperature is reduced according to the cooling rate to obtain the simulation temperature corresponding to the current iteration. It should be noted that when the simulation temperature is at a high temperature, large temperature jumps are allowed, that is, a faster cooling rate can be selected to prevent early convergence to the local optimal solution. When the simulation temperature is at a low temperature, a slower cooling rate needs to be selected to narrow the exploration range and ensure that the global optimal solution is approached. Then, the target high-dimensional parameter vector is perturbed by the Gaussian perturbation matrix to obtain the high-dimensional parameter vector to be accepted, and the target value to be accepted is calculated based on the high-dimensional parameter vector to be accepted and the comprehensive objective function. Based on the target value corresponding to the target high-dimensional parameter vector, the target value to be accepted, and the simulation temperature corresponding to the current iteration, the target high-dimensional parameter vector corresponding to the current iteration is selected from the target high-dimensional parameter vector and the high-dimensional parameter vector to be accepted, and it is determined whether the optimization stop condition has been met. Based on the judgment result, iterative optimization continues or the target geometric parameters are output.

[0144] In a specific application scenario, the algorithm will stop after reaching the maximum number of iterations or T drops to a certain threshold. After 136 optimizations, the final convergence results are shown in Table 7 below:

[0145] Table 7 Optimized geometric parameters and objective function values

[0146] parameter A-pillar B-pillar C-pillar Inclination angle θ(°) 65.23 10.16 148.79 <![CDATA[Curvature C (m -1 )]]> 0.103 0.0054 0.050 <![CDATA[Cross-sectional area A (cm 2 )]]> 32.04 38.43 35.92 Objective function value 149.33 98.37 44.94

[0147] Draw the curve of the objective function changing with the number of iterations as shown in Figure 3 As shown, the optimized A-pillar's field of view obstruction rate is reduced by 15%, and wind noise is reduced by 8dB. The optimized B-pillar's collision resistance is increased by 20%, and door gap wind noise is reduced by 12%. The optimized C-pillar's tail vortex disturbance is reduced by 18%, and the uniformity of material stress distribution is improved. The vehicle's drag coefficient is reduced by 5%, and the range is increased by 3% (for new energy vehicles).

[0148] The method provided in the embodiments of this application accurately quantifies the dynamic disturbance effects of high-speed rain showers by constructing a joint Gaussian distribution model of airflow velocity and rain density. This allows for precise simulation of complex operating conditions, improves environmental adaptability, and adapts design parameters to varying rainfall intensities. Furthermore, the embodiments of this application utilize the random perturbation and temperature decay strategies of a simulated annealing algorithm to globally search for optimal solutions in a high-dimensional parameter space, breaking through the limitations of traditional single-objective optimization. Through probabilistic modeling and intelligent algorithms, they address the synergistic conflicts among multi-dimensional indicators such as field of view, intensity, and noise, achieving systematic improvements in safety, comfort, and economy.

[0149] Further, as Figure 1 In the specific implementation of the method, the embodiment of the present application provides a vehicle column structure optimization device, such as Figure 4As shown, the system includes: a first construction module 401 , a second construction module 402 , a constraint module 403 , and an optimization module 404 .

[0150] The first construction module 401 is used to construct an environmental parameter model based on the vehicle speed and rainfall intensity. The environmental parameter model includes a separate marginal distribution function of airflow velocity, a separate marginal distribution function of rain density, and a joint probability density function of airflow velocity and rain density.

[0151] The second building module 402 is configured to establish a correlation function between geometric parameters of each vehicle pillar and corresponding performance indicators, wherein the vehicle pillars include A-pillars, B-pillars, and C-pillars, the geometric parameters include inclination angle, curvature, and cross-sectional area, the performance indicators corresponding to the A-pillar are driving visibility, wind noise level, and airflow turbulence, and the performance indicators corresponding to the B-pillars and C-pillars are the wind noise level, airflow turbulence, and structural strength;

[0152] The constraint module 403 is used to integrate the correlation function of each vehicle pillar into a comprehensive objective function according to the weight coefficient, and define constraint conditions, which include the range of geometric parameters, the lower limit of structural strength, and the upper limit of wind noise;

[0153] The optimization module 404 is configured to iteratively optimize the objective function using a simulated annealing algorithm until a stop optimization condition is reached, and output target geometric parameters of the vehicle pillar. The simulated annealing algorithm obtains new geometric parameters by randomly perturbing a parameter vector and accepts the new geometric parameters based on a temperature decay strategy until the stop optimization condition is reached, at which point the currently accepted geometric parameters are output as the target geometric parameters.

[0154] It should be noted that for other corresponding descriptions of the functional units involved in the power distribution network power restoration device provided in the embodiment of the present application, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0155] To solve the above technical problems, the embodiment of the present invention also provides a computer device. Figure 5 , Figure 5 This is a basic structural block diagram of the computer device in this embodiment.

[0156] like Figure 5As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a non-volatile storage medium, a memory and a network interface connected via a system bus. Among them, the non-volatile storage medium of the computer device stores an operating system, a database and computer-readable instructions, and the database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor can implement a data relationship reconstruction method. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute a data relationship reconstruction method. The network interface of the computer device is used to connect and communicate with the terminal. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0157] In this embodiment, the processor is used to execute Figure 4 The memory stores the program code and various data required to execute the specific functions of the first construction module 401, the second construction module 402, the constraint module 403, and the optimization module 404. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all submodules in the data relationship reconstruction device. The server can call the server's program code and data to execute the functions of all submodules.

[0158] The present invention also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the data relationship reconstruction method in any of the above embodiments.

[0159] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0160] The present invention also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the data relationship reconstruction method in any of the above embodiments.

[0161] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0162] Those skilled in the art will appreciate that the steps, measures, and schemes in the various operations, methods, and processes discussed in this application may be interchanged, modified, combined, or deleted. Furthermore, other steps, measures, and schemes in the various operations, methods, and processes discussed in this application may also be interchanged, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and schemes in the prior art that are similar to those disclosed in this application may also be interchanged, modified, rearranged, decomposed, combined, or deleted.

[0163] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing automobile pillar structure, characterized in that: include: Constructing an environmental parameter model based on vehicle speed and rainfall intensity, the environmental parameter model including a separate marginal distribution function of airflow velocity, a separate marginal distribution function of rain density, and a joint probability density function of airflow velocity and rain density; Establishing a correlation function between the geometric parameters of each vehicle pillar and corresponding performance indicators, the vehicle pillars including A-pillars, B-pillars, and C-pillars, the geometric parameters including inclination angle, curvature, and cross-sectional area, the performance indicators corresponding to the A-pillar being driving visibility, wind noise level, and airflow turbulence, and the performance indicators corresponding to the B-pillars and C-pillars being the wind noise level, airflow turbulence, and structural strength; The correlation function of each vehicle pillar is integrated into a comprehensive objective function according to the weight coefficient, and the constraint conditions are defined, wherein the constraint conditions include the range of geometric parameters, the lower limit of structural strength, and the upper limit of wind noise; A simulated annealing algorithm is used to iteratively optimize the objective function until a stop optimization condition is reached, and target geometric parameters of the automobile pillar are output. The simulated annealing algorithm obtains new geometric parameters by randomly perturbing a parameter vector and accepts the new geometric parameters based on a temperature decay strategy until the stop optimization condition is reached, and the currently accepted geometric parameters are output as the target geometric parameters.

2. The method according to claim 1, characterized in that The environmental parameter model is constructed according to the vehicle speed and rainfall intensity, including: Determining the airflow velocity at a specified point during the vehicle's travel according to the vehicle's travel speed, and constructing the airflow velocity individual marginal distribution function f(v) according to the airflow velocity, the mean of the airflow velocity, and the variance of the airflow velocity; Determine the number of raindrops per unit volume or the mass density of rainwater according to the rainfall intensity to obtain a shower density, and construct a separate marginal distribution function f(ρ) of the shower density according to the airflow velocity, the mean of the shower density, and the variance of the shower density; Constructing a covariance matrix and a deviation transposed vector of the airflow velocity and the rain density, wherein the covariance matrix is ​​used to describe the correlation between the airflow velocity and the rain density; Constructing a joint probability density function f(v,ρ) based on the covariance matrix and the deviation transposed vector; Wherein, v is the air flow velocity; μ v is the mean value of the airflow velocity; σ v is the variance of the airflow velocity; μ ρ is the mean value of the rain density; σ ρ is the variance of the rain density; σ vρ is the covariance of air velocity and rain density; -1 is the covariance matrix; the Z T is the deviation transposed vector.

3. The method according to claim 1, characterized in that The establishment of the correlation function between the geometric parameters of each automobile pillar and the corresponding performance index includes: For the A-pillar, a correlation function V between the A-pillar and the driving visibility is constructed according to the inclination angle of the A-pillar, the curvature of the A-pillar, and the cross-sectional area of ​​the A-pillar. A , and constructing a correlation function N between the A-pillar and the wind noise level based on the airflow velocity individual marginal distribution function, the curvature of the A-pillar and the cross-sectional area of ​​the A-pillar A , and constructing a correlation function D between the A-pillar and the airflow turbulence according to the inclination angle of the A-pillar, the curvature of the A-pillar, the cross-sectional area of ​​the A-pillar, the airflow velocity individual marginal distribution function, the rain density individual marginal distribution function and the joint probability density function. A ; For the B-pillar, the correlation function D between the B-pillar and the airflow turbulence is constructed based on the pressure difference between the inside and outside of the vehicle, the airflow velocity independent marginal distribution function, and the rain density independent marginal distribution function. B , and calculating the gap area between the B-pillar and the door according to the inclination angle of the B-pillar and the curvature of the B-pillar, and constructing the correlation function N between the B-pillar and the wind noise level according to the gap area and the airflow velocity independent edge distribution function. B , and constructing a correlation function S between the B-pillar and the structural strength according to the yield strength of the B-pillar material, the collision force of the B-pillar material and the cross-sectional area of ​​the B-pillar. B ; For the C-pillar, the correlation function D between the C-pillar and the airflow turbulence is constructed based on the inclination angle of the C-pillar, the curvature of the C-pillar, the airflow velocity independent marginal distribution function, and the rain density independent marginal distribution function. C , and constructing a correlation function N between the C-pillar and the wind noise level based on the curvature of the C-pillar and the airflow velocity individual edge distribution function C , and constructing a correlation function S between the C-pillar and the structural strength according to the yield strength of the C-pillar material, the curvature of the C-pillar and the cross-sectional area of ​​the C-pillar C .

4. The method according to claim 3, characterized in that The correlation function of each car column is integrated into a comprehensive objective function according to the weight coefficient, including: According to the correlation function V between the A-pillar and the driving visibility A , the correlation function N between the A-pillar and the wind noise level A and the correlation function D between the A-pillar and the airflow turbulence A , construct the sub-objective function J of the A column A (θ A ,C A ,A A ); According to the correlation function D between the B-pillar and the airflow turbulence B , the correlation function N between the B-pillar and the wind noise level B and the correlation function S between the B-pillar and the structural strength B , construct the sub-objective function J of the B column B (θ B ,C B ,A B ); According to the correlation function D between the C-pillar and the airflow turbulence C , the correlation function N between the C-pillar and the wind noise level C And the correlation function S between the C-pillar and the structural strength C , construct the sub-objective function J of the C column C (θ C ,C C ,A C ); The first objective function, the second objective function and the third objective function are integrated into a specified objective function J(θ, C, A)=ω1J according to the weight coefficients. A (θ A ,C A ,A A )+ω2J B (θ B ,C B ,A B )+ω3J C (θ C ,C C ,A C ), wherein ω1, ω2, ω3 are the weight coefficients; The geometric parameters of the A-pillar, the B-pillar, and the C-pillar are integrated into a high-dimensional parameter vector, and the nonlinear terms of the sub-objective functions in the specified objective function are adjusted to quadratic forms or linear combinations based on the high-dimensional parameter vector to obtain the comprehensive objective function J(x), where x is the high-dimensional parameter vector.

5. The method according to claim 4, characterized in that The simulated annealing algorithm is used to iteratively optimize the objective function until the optimization stop condition is reached, and the target geometric parameters of the automobile pillar are output, including: Randomly generating an initial high-dimensional parameter vector, and calculating an initial target value based on the initial high-dimensional parameter vector and the comprehensive target function; The initial high-dimensional parameter vector is perturbed by a Gaussian perturbation matrix, and the high-dimensional parameter vector to be accepted is constrained by a defined constraint condition; Calculating a target value to be accepted according to the high-dimensional parameter vector to be accepted and the comprehensive objective function, and selecting a target high-dimensional parameter vector corresponding to the current iteration round from the initial high-dimensional parameter vector and the high-dimensional parameter vector to be accepted according to the initial target value, the target value to be accepted, and the simulation temperature corresponding to the current iteration round; Determine whether the optimization stop condition is met; If the optimization stopping condition is met, the geometric parameters corresponding to the target high-dimensional parameter vector are output as the target geometric parameters. The optimization stopping condition is that the number of iterations reaches a preset number threshold or the simulation temperature of the current iteration round reaches a preset temperature threshold.

6. The method according to claim 4, characterized in that After determining whether the optimization stopping condition is met, the method further includes: If the optimization stop condition is not met, the cooling rate is determined according to the simulation temperature corresponding to the previous iteration round, and the simulation temperature is reduced according to the cooling rate to obtain the simulation temperature corresponding to the current iteration round; Perturbing the target high-dimensional parameter vector by a Gaussian perturbation matrix to obtain a high-dimensional parameter vector to be accepted, and calculating a target value to be accepted based on the high-dimensional parameter vector to be accepted and the comprehensive objective function; According to the target value corresponding to the target high-dimensional parameter vector, the target value to be accepted and the simulation temperature corresponding to the current iteration round, the target high-dimensional parameter vector corresponding to the current iteration round is selected from the target high-dimensional parameter vector and the high-dimensional parameter vector to be accepted, and it is judged whether the optimization stopping condition is met, and the iterative optimization is continued or the target geometric parameters are output according to the judgment result.

7. The method according to claim 5, characterized in that The selecting, according to the initial target value, the target value to be accepted, and the simulation temperature corresponding to the current iteration round, a target high-dimensional parameter vector corresponding to the current iteration round from the initial high-dimensional parameter vector and the high-dimensional parameter vector to be accepted, includes: Comparing the initial target value with the target value to be accepted; If the comparison result indicates that the target value to be accepted is smaller than the initial target value, the high-dimensional parameter vector corresponding to the target value to be accepted is accepted as the target high-dimensional parameter vector; If the comparison result indicates that the target value to be accepted is greater than or equal to the initial target value, the acceptance probability is calculated based on the initial target value, the target value to be accepted and the simulation temperature corresponding to the current iteration round, and the high-dimensional parameter vector corresponding to the target value to be accepted is selected as the target high-dimensional parameter vector according to the acceptance probability. If the high-dimensional parameter vector corresponding to the target value to be accepted is not selected as the target high-dimensional parameter vector, the high-dimensional parameter vector corresponding to the initial target value is selected as the target high-dimensional parameter vector.

8. An automobile pillar structure optimization device, characterized in that: include: A first construction module is used to construct an environmental parameter model based on the vehicle speed and rainfall intensity, wherein the environmental parameter model includes a separate marginal distribution function of airflow velocity, a separate marginal distribution function of rain density, and a joint probability density function of airflow velocity and rain density; A second construction module is configured to establish a correlation function between geometric parameters of each vehicle pillar and corresponding performance indicators, wherein the vehicle pillars include A-pillars, B-pillars, and C-pillars, the geometric parameters including inclination angle, curvature, and cross-sectional area, the performance indicators corresponding to the A-pillars are driving visibility, wind noise level, and airflow turbulence, and the performance indicators corresponding to the B-pillars and C-pillars are the wind noise level, the airflow turbulence, and structural strength; a constraint module, for integrating the correlation function of each vehicle pillar into a comprehensive objective function according to a weight coefficient, and defining constraint conditions, wherein the constraint conditions include a geometric parameter range, a lower limit of structural strength, and an upper limit of wind noise; The optimization module is configured to iteratively optimize the objective function using a simulated annealing algorithm until a stop optimization condition is reached, and output target geometric parameters of the automobile pillar. The simulated annealing algorithm obtains new geometric parameters by randomly perturbing a parameter vector and accepts the new geometric parameters based on a temperature decay strategy until the stop optimization condition is reached, and outputs the currently accepted geometric parameters as the target geometric parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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