Automobile A column design and evaluation method and device considering side window rainwater pollution, terminal and storage medium

Abstract

The invention belongs to the technical field of automobiles, and particularly relates to an automobile A column design and evaluation method and device considering side window rainwater pollution, a terminal and a storage medium. Comprising the following steps of 1, collecting and analyzing design requirements of the automobile A column; step 2, carrying out parametric modeling; step 3, constructing a multi-dimensional performance mapping model; 4, evaluating the dynamic performance of the rainwater; 5, performing optimization design on multiple targets; step 6, carrying out trade-off decision making on multiple targets; step 7, verifying and optimizing the real vehicle; and 8, confirming the design and freezing the data. According to the method, the rainwater management requirement is systematically considered in the early stage of A column design for the first time and is synchronously carried out with the traditional requirements of view, safety, arrangement and the like, and comprehensive integration of design requirements is realized. The method is favorable for reducing later design changes and improving the design efficiency.

Description

Technical Field

[0001] The invention belongs to the field of automobile technology, and in particular to a method, device, terminal and storage medium for designing and evaluating an automobile A-pillar taking into account rainwater pollution on side windows. Background Art

[0002] When driving in the rain, the rain on the side windows of the car will interfere with the driver's vision, especially when the rain flows through the A-pillar area, it may form a water film or water droplets, further affecting the driver's observation of the front and sides of the vehicle. The current design of the A-pillar of the car mainly focuses on structural strength, aerodynamic performance, field of vision requirements and layout requirements, and rarely considers the problem of rain contamination on the side windows in the early stages of design. This leads to the need to redesign the A-pillar when it is found that the side window rain management performance is insufficient in the later stage, which increases development costs and time.

[0003] In the existing technology, some methods improve the flow of rainwater by changing the shape of the A-pillar or adding a guide device, but these methods are often considered in the late stage of design and are difficult to organically combine with other design requirements. Other methods use hydrophobic coatings or special surface treatments to reduce water droplet residue, but these methods mainly focus on solutions at the material level and lack systematic considerations from the design process and method level. Summary of the invention

[0004] The present invention provides a method, device, terminal and storage medium for designing and evaluating an automobile A-pillar taking into account rainwater pollution on side windows, which helps to reduce later design changes, improve design efficiency and solve the problem in the prior art that rainwater management needs are not effectively considered during the A-pillar design process.

[0005] The technical solution of the present invention is described as follows in conjunction with the accompanying drawings:

[0006] In a first aspect, an embodiment of the present invention provides a method for designing and evaluating an automobile A-pillar taking into account rain water contamination of side windows, comprising the following steps:

[0007] Step 1: Collect and analyze the design requirements of the car A-pillar;

[0008] Step 2: Perform parametric modeling;

[0009] Step 3: Construct a multi-dimensional performance mapping model;

[0010] Step 4: Evaluate the rainwater dynamics performance;

[0011] Step 5: Optimize the design of multiple objectives;

[0012] Step 6: Make trade-off decisions on multiple objectives;

[0013] Step 7: Verify and optimize the actual vehicle;

[0014] Step 8: Confirm the design and freeze the data.

[0015] Furthermore, the specific method of step one is as follows:

[0016] 11) Collect requirements related to A-pillar design;

[0017] 12) Use the analytic hierarchy process (AHP) to determine the weight of each requirement.

[0018] Further, the specific method of step 11) is as follows:

[0019] a) Vision requirements:

[0020] Driver's forward vision requirements: Comply with the provisions of GB / T 15089-2001 "Motor vehicle external vision area and wiper performance";

[0021] A-pillar shielding angle: ≤6°;

[0022] The minimum distance from the driver's eye point to the surface of the A-pillar is ≥550mm;

[0023] b) Layout requirements:

[0024] The minimum gap between the A-pillar and the instrument panel is ≥20mm;

[0025] The clearance between the A-pillar and the door = 4 ± 1 mm;

[0026] A-pillar internal wiring harness layout space ≥ 400mm 2 ;

[0027] c) Security requirements:

[0028] Side impact performance: in compliance with GB 20071-2006 “Occupant protection in motor vehicle side impact”;

[0029] Roof strength: in compliance with GB 26134-2010 "Compressive strength of passenger car roof";

[0030] A-pillar material tensile strength ≥ 980MPa;

[0031] d) Side window rainwater management performance requirements:

[0032] At a speed of 80 km / h and a rainfall intensity of 30 mm / h, the water droplet coverage rate of the driver's side view area is ≤15%;

[0033] Within 10 seconds after the rain stops, the diameter of the remaining water droplets is ≤2mm;

[0034] The maximum water film thickness on the A-pillar surface is ≤ 0.3mm;

[0035] e) Other constraints:

[0036] A-pillar weight: meets the requirements of vehicle lightweighting;

[0037] Cost increase ≤ 5%;

[0038] Wind noise control: in compliance with GB / T 18697-2002 "Automobile interior noise measurement method";

[0039] The specific method of step 12) is as follows:

[0040] a) Establish a judgment matrix and use the 1-9 scaling method;

[0041] b) Calculate the eigenvector and the maximum eigenroot;

[0042] c) Conduct consistency test, and the consistency ratio CR<0.1;

[0043] d) Get the weight of each requirement:

[0044] Security needs: 40%

[0045] Vision requirement: 25%

[0046] Side window rainwater management performance: 20%;

[0047] Layout requirements: 10%;

[0048] Other constraints: 5%.

[0049] Furthermore, the specific method of step 2 is as follows:

[0050] 21) Define key design parameters;

[0051] 22) Create a CAD model.

[0052] Further, the specific method of step 21) is as follows:

[0053] Use CATIA V5 R21 software to perform parametric modeling of the A-pillar and define key design parameters:

[0054] a) A-pillar cross-section shape parameters:

[0055] Ellipse major axis a: range: 60-80mm, step length: 1mm;

[0056] Short axis b: range: 40-60 mm, step length: 1 mm;

[0057] Ellipse eccentricity e: range: 0.5-0.8, step size: 0.05;

[0058] b) A-pillar tilt angle θ: range: 60°-75°, step length: 0.5°;

[0059] c) A-pillar surface curvature κ: range: 0.01-0.05mm^-1, step: 0.001mm^-1; d) A-pillar and windshield connection angle α: range: 135°-150°, step: 1°; e) A-pillar and side window connection angle β: range: 135°-150°, step: 1°;

[0060] f) Chamfer radius R at the top of A-pillar: range: 5-15mm, step length: 0.5mm;

[0061] The specific method of step 22) is as follows:

[0062] Establish parametric CAD models to ensure model stability and flexibility:

[0063] a) Use feature tree structure to ensure that the model does not crash after parameter modification;

[0064] b) Set the constraint relationship between parameters: a>b, α+β<300°;

[0065] c) Write VBA macros to quickly modify parameters and update models.

[0066] Furthermore, the specific method of step three is as follows:

[0067] 31) Establish multi-dimensional performance indicator mapping relationship;

[0068] 32) Response surface methodology (RSM) was used to construct the mapping model.

[0069] Further, the specific method of 31) is as follows:

[0070] Establish the mapping relationship between A-pillar geometric parameters and multi-dimensional performance indicators:

[0071] P=f(G,E)

[0072] Where: P is the performance index vector, including: P1: field of view obstruction angle, P2: structural strength index, P3: rainwater coverage, P4: A-pillar weight;

[0073] G is the geometric parameter vector, including a, b, e, θ, κ, α, β, and R;

[0074] E is the environmental parameter vector, including vehicle speed V and rainfall intensity I;

[0075] The specific method of 32) is as follows:

[0076] a) Use Box-Behnken design for experimental design and generate simulation experimental plan:

[0077] Number of factors: 8, i.e., geometric parameters + 2, i.e., environmental parameters = 10;

[0078] Number of trials: 170, based on Box-Behnken design;

[0079] b) Aerodynamics and rain flow simulation using ANSYS Fluent 2021R1:

[0080] Computational mesh: tetrahedral mesh, with about 5 million cells;

[0081] Flow model: k-ωSST;

[0082] The discrete phase model, or DPM, simulates the motion of raindrops;

[0083] Boundary conditions: inlet velocity 40-120km / h, raindrop diameter 0.1-5mm;

[0084] c) Structural strength simulation using ANSYS Mechanical 2021R1:

[0085] Unit type: tetrahedral unit, the number of units is about 200,000;

[0086] Material model: Isotropic elastoplastic;

[0087] Loading conditions: side impact force 18kN, roof crushing force 40kN;

[0088] Use radial basis function, RBF neural network, to fit the response surface:

[0089] Number of hidden layer nodes: 50, activation function: Gaussian function, training algorithm: orthogonal least squares (OLS), 10-fold cross validation method is used to evaluate model accuracy to ensure R 2 >0.95.

[0090] Furthermore, the specific method of step 4 is as follows:

[0091] 41) Define the Comprehensive Stormwater Management Index CRMI;

[0092] 42) Establish evaluation criteria for rainwater dynamics performance.

[0093] Furthermore, the specific method of step 41) is as follows:

[0094] Definition of Comprehensive Stormwater Management Index CRMI:

[0095] CMRI=ω1·CR+ω2·DSD+ω3·WFT+ω4·VCI

[0096] in:

[0097] CR: Rainwater coverage, CR = A ω / A t ×100, A ω is the rainwater coverage area, A t is the total observation area;

[0098] DSD: droplet size distribution, DSD = DSD = n i ·∑(n i ·d i ) / N,n i The diameter is d i The number of water droplets, N is the total number of water droplets;

[0099] WFT: Water film thickness, WFT = V ω / A ω , V ω is the water film volume, V ω is the water film coverage area;

[0100] VCI: Visibility Clarity Index, calculated based on the image clarity evaluation algorithm, VCI = 1-(∑ΔI / ∑I0), ΔI is the image gradient change in the raindrop area, and I0 is the original image gradient;

[0101] ω1, ω2, ω3, ω4 are weight coefficients;

[0102] Further, the specific method of step 42) is as follows:

[0103] The evaluation criteria for rainwater dynamics performance are defined as follows:

[0104] Excellent: CRMI < 0.2;

[0105] Good: 0.2≤CRMI<0.4;

[0106] General: 0.4≤CRMI<0.6;

[0107] Poor: 0.6≤CRMI<0.8;

[0108] Fail: CRMI ≥ 0.8.

[0109] Furthermore, the specific method of step five is as follows:

[0110] 51) Establish a multi-objective optimization model;

[0111] 52) Use NSGA-II, i.e. non-dominated sorting genetic algorithm II, to solve multi-objective optimization problems;

[0112] 53) Adopt an adaptive strategy.

[0113] Furthermore, the specific method of step 51) is as follows:

[0114] minF(x)=[f1(X), f2(X), f3(X), f4(X)]

[0115] in:

[0116] X is the design variable vector, X = [a, b, e, θ, κ, α, β, R];

[0117] f1(X) is CRMI (minimization);

[0118] f2(X) is the field of view occlusion angle (minimized);

[0119] f3(X) is the structural strength index (maximized), defined as 1 / σ max , σ max is the maximum von Mises stress;

[0120] f4(X) is the weight (minimized);

[0121] Constraints:

[0122] g1(X): 20mm≤Clearance_min≤30mm (clearance between A-pillar and surrounding parts);

[0123] g2(X):σ max <σ yield (maximum stress is less than yield strength);

[0124] g3(X):δ max <100mm (maximum deformation);

[0125] g4(X):F roof ≥4× vehicle weight (bearing capacity in roof crushing test);

[0126] g5(X):-g 12 (X): the value range constraints of each design variable (a, b, e, θ, κ, α, β, R);

[0127] The specific method of step 52) is as follows:

[0128] Use NSGA-II to solve multi-objective optimization, and set the parameters as follows:

[0129] Population size: 200;

[0130] Iterations: 1000

[0131] Crossover probability: 0.9;

[0132] Mutation probability: 1 / n, where n is the number of decision variables;

[0133] Crossover operator: simulated binary crossover (SBX), distribution index η_c = 20; mutation operator: polynomial mutation, distribution index η_m = 20;

[0134] Using crowding distance for diversity maintenance;

[0135] Elite strategy: retain the best 10% of individuals in each generation;

[0136] The specific method of step 53) is as follows:

[0137] Dynamically adjust crossover and mutation probabilities:

[0138] p c =p c1 -(p c1 -p c2 )×(f′-f min ) / (f avg -f min )

[0139] p m =p m1 -(p m1 -p m2 )×(f max -f) / (f max -f avg )

[0140] in:

[0141] f′ is the fitness value of the better individual in the parent generation;

[0142] f is the fitness value of the current individual;

[0143] p c1 =1.0, p c2 =0.5, p m1 =0.1, p m2 =0.01;

[0144] b) Perform a local search every 100 generations using a pattern search algorithm.

[0145] Furthermore, the specific method of step six is ​​as follows: 61) establishing a decision model based on TOPSIS, i.e., the method of ranking by approximating to an ideal solution; 62) using fuzzy analytic hierarchy process, i.e., Fuzzy AHP, to determine the weight of each indicator.

[0146] Furthermore, the specific method of step 61) is as follows:

[0147] Construct a decision matrix D, where rows represent candidate solutions and columns represent evaluation indicators;

[0148] Normalize the decision matrix to obtain the standardized matrix R;

[0149] Calculate the weighted normalized decision matrix V = W·R, where W is the weight matrix;

[0150] Determine the ideal solution A + and negative ideal solution A - ;

[0151] Calculate the distance from each solution to the ideal solution The Euclidean distance to the negative ideal solution

[0152] Calculate the relative closeness of each solution

[0153] Sort the solutions by value. The larger the value, the better the solution.

[0154] The specific method of step 62) is as follows:

[0155] Fuzzy AHP is a hierarchical analysis method combined with fuzzy logic to deal with uncertainty and subjectivity in decision-making;

[0156] Fuzzy judgment matrix A: Each element a ij Indicates the importance of index i relative to index j, using fuzzy numbers;

[0157] Triangular fuzzy number: expressed as (l ij , m ij ,u ij ), where l, m, and u are the lower bound, median, and upper bound of the fuzzy number respectively;

[0158] Fuzzy weight vector W: The fuzzy weight of each indicator is calculated using the geometric mean method;

[0159] Defuzzification: Convert fuzzy weights into clear weights, the formula is W i =(l ij +4m ij +u ij ) / 6, and then normalize it to get the final weight vector.

[0160] Furthermore, the specific method of step seven is as follows:

[0161] 71) Prototype production;

[0162] 72) Conduct wind tunnel tests;

[0163] 73) Conduct road tests;

[0164] 74) Conduct data analysis and optimization.

[0165] Furthermore, the specific method of step 71) is as follows:

[0166] Based on the optimized design results, three sets of A-pillar samples with different parameters were produced using rapid prototyping technology;

[0167] Material: ABS engineering plastic;

[0168] Printing accuracy: ±0.1mm;

[0169] Surface treatment: polished to Ra≤1.6μm;

[0170] Install the sample on a mid-sized car, ensuring the installation accuracy is ≤0.5mm;

[0171] The specific method of step 72) is as follows:

[0172] Using 1:1 full-scale wind tunnel, wind speed range: 0-180km / h, accuracy ±0.1km / h;

[0173] Rainfall simulation system: Rainfall intensity 0-200mm / h, can adjust raindrop size, size 0.5-5mm;

[0174] The specific method of step 73) is as follows:

[0175] Test route: including urban roads, highways and mountain roads, with a total mileage of 500km;

[0176] Test conditions: natural rainfall, rainfall intensity 5-50mm / h.

[0177] Test cycle: covers 4 seasons, with at least 5 tests per season;

[0178] Data collection:

[0179] On-board HD camera: 4K resolution, 60fps frame rate;

[0180] Vehicle-mounted rain sensor: accuracy 0.1mm / h;

[0181] Vehicle status data such as vehicle speed and steering wheel angle: sampling frequency 100Hz;

[0182] Objective evaluation: Use a standardized scoring table with unified standards, 1-5 points

[0183] The specific method of step 74) is as follows:

[0184] a) Use computer vision algorithms to process video data and calculate the actual CRMI index:

[0185] Use OpenCV library for image preprocessing and segmentation;

[0186] Use deep learning models for water droplet recognition and classification;

[0187] Calculate CR, DSD, WFT and VCI indicators, accuracy requirements: CR±1%, DSD±0.1mm, WFT±0.01mm;

[0188] b) Compare the virtual simulation results with the actual vehicle test data and calculate the error rate:

[0189] The maximum acceptable error rate: CRMI index ±10%, other performance indicators ±5%;

[0190] Based on the actual vehicle test results, the performance mapping model is calibrated using the Kriging interpolation method, the model parameters are determined using the maximum likelihood estimation method, and the weight parameters of the RBF neural network are updated;

[0191] Perform sensitivity analysis to identify the design parameters that have the greatest impact on CRMI:

[0192] Using the Sobol global sensitivity analysis method, the first-order sensitivity index S is calculated. i and total sensitivity index ST i ,According to the sensitivity results, adjust the optimization strategy;

[0193] Furthermore, the specific method of step eight is as follows:

[0194] 81) Select the final solution;

[0195] 82) Freeze design data;

[0196] 83) Archive design documents.

[0197] Further, the specific method of step 81) is as follows:

[0198] a) Based on the TOPSIS decision model, select the optimal A-pillar design solution from the optimization results;

[0199] b) Conduct FMEA to ensure the reliability of the design:

[0200] Severity (S), occurrence (O), and detection (D) all use a 1-10 point system;

[0201] Calculate the risk priority number RPN = S × O × D, and set the threshold RPN < 100;

[0202] c) Final confirmation of the design plan;

[0203] The specific method of step 82) is as follows:

[0204] a) Output and freeze the following design data:

[0205] A-pillar geometric parameters: cross-sectional dimensions, inclination angle, surface curvature, connection angle;

[0206] Material specifications: ultra-high strength steel, yield strength ≥980MPa;

[0207] Surface treatment requirements: such as roughness Ra≤0.8μm;

[0208] b) Generate detailed engineering drawings, including 2D drawings and 3D models;

[0209] c) Prepare design specifications, detailing design parameters, performance indicators and manufacturing requirements;

[0210] The specific method of step 83) is as follows:

[0211] a) Prepare a detailed design report including the design process, key decision points and performance evaluation results;

[0212] b) Organize all simulation data, test data and analysis reports;

[0213] c) Use PLM system for document management and version control.

[0214] In a second aspect, an embodiment of the present invention further provides a vehicle A-pillar design and evaluation device taking into account rain water contamination of side windows, comprising:

[0215] The collection module is used to collect and analyze the design requirements of the automobile A-pillar;

[0216] Modeling module, used for parametric modeling;

[0217] A construction module, used to construct a multi-dimensional performance mapping model;

[0218] Evaluation module, used to evaluate the rainwater dynamics performance;

[0219] Design module, used for optimizing design for multiple objectives;

[0220] Decision-making module, used to make trade-off decisions on multiple objectives;

[0221] Optimization module, used to verify and optimize the actual vehicle;

[0222] The confirmation module is used to confirm the design and freeze the data.

[0223] In a third aspect, a terminal is provided, including:

[0224] one or more processors;

[0225] a memory for storing the one or more processor-executable instructions;

[0226] Wherein, the one or more processors are configured to:

[0227] Execute the method described in the first aspect of the embodiment of the present invention.

[0228] In a fourth aspect, a non-temporary computer-readable storage medium is provided. When the instructions in the storage medium are executed by a processor of a terminal, the terminal is enabled to execute the method described in the first aspect of an embodiment of the present invention.

[0229] In a fifth aspect, an application product is provided. When the application product is running on a terminal, the terminal executes the method described in the first aspect of the embodiment of the present invention.

[0230] The beneficial effects of the present invention are:

[0231] 1) This invention is the first to systematically consider rainwater management requirements in the early stages of A-pillar design, in parallel with traditional requirements such as vision, safety and layout, thus achieving comprehensive integration of design requirements. This approach helps reduce late-stage design changes and improves design efficiency;

[0232] 2) The present invention proposes a comprehensive rainwater management index (CRMI) and its evaluation criteria, which provides an objective and quantitative method for evaluating the rainwater management performance of A-pillars. The introduction of this index allows rainwater management performance to participate in the optimization process together with other performance indicators;

[0233] 3) The present invention establishes a mapping relationship between the geometric parameters of the A-pillar and the multi-dimensional performance indicators, and uses the response surface method to build a model. This method improves the accuracy of performance prediction and provides a reliable basis for subsequent optimization;

[0234] 4) The present invention adopts NSGA-II algorithm for multi-objective optimization and combines it with TOPSIS decision-making method to achieve the balance of A-pillar design in terms of field of view, safety, rainwater management and weight. This method can find the best compromise solution while meeting multiple design requirements.

[0235] 5) The present invention improves the flexibility and efficiency of the design process through parametric modeling and adaptive optimization strategy; the adaptive strategy can dynamically adjust parameters according to the optimization process, which is conducive to finding the global optimal solution;

[0236] 6) The present invention combines wind tunnel tests and road tests to conduct actual vehicle verification, and uses measured data to calibrate the performance mapping model; this closed-loop optimization method improves the reliability and practicality of the final design solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0237] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0238] Figure 1 It is a flow chart of a method for designing and evaluating an automobile A-pillar taking into account rainwater contamination on side windows according to the present invention;

[0239] Figure 2 This is a schematic structural diagram of a vehicle A-pillar design and evaluation device considering rainwater contamination on side windows according to the present invention;

[0240] Figure 3 The present invention is a schematic block diagram of a terminal structure. DETAILED DESCRIPTION

[0241] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0242] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0243] Embodiment 1

[0244] This embodiment provides a method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows. The method can be performed by a device for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows in an embodiment of the present invention. The device can be implemented in software and / or hardware, as follows:

[0245] Step 1: Collect and analyze the design requirements of the car A-pillar, as follows:

[0246] 11) Collect requirements related to A-pillar design;

[0247] a) Vision requirements:

[0248] Driver's forward vision requirements: Comply with the provisions of GB / T 15089-2001 "Motor vehicle external vision area and wiper performance";

[0249] A-pillar shielding angle: ≤6° (refer to QC / T 740-2014 "Passenger car A-pillar shielding angle measurement method");

[0250] The minimum distance from the driver's eye point to the surface of the A-pillar is ≥550mm;

[0251] b) Layout requirements:

[0252] The minimum gap between the A-pillar and the instrument panel is ≥20mm;

[0253] The clearance between the A-pillar and the door = 4±1mm (refer to QC / T 504-1999 "Automobile Body Dimension Tolerance");

[0254] A-pillar internal wiring harness layout space ≥ 400mm 2 ;

[0255] c) Security requirements:

[0256] Side impact performance: in compliance with GB 20071-2006 “Occupant protection in motor vehicle side impact”;

[0257] Roof strength: in compliance with GB 26134-2010 "Compressive strength of passenger car roof";

[0258] A-pillar material tensile strength ≥ 980MPa (refer to QC / T 729-2005 "Cold-rolled high-strength steel plates and strips for automobiles");

[0259] d) Side window rainwater management performance requirements:

[0260] At a speed of 80 km / h and a rainfall intensity of 30 mm / h, the water droplet coverage rate of the driver's side view area is ≤15%;

[0261] Within 10 seconds after the rain stops, the diameter of the remaining water droplets is ≤2mm;

[0262] The maximum water film thickness on the A-pillar surface is ≤ 0.3mm;

[0263] e) Other constraints:

[0264] A-pillar weight: meets the requirements of vehicle lightweighting;

[0265] Cost increase ≤5% (compared to traditional design);

[0266] Wind noise control: in accordance with GB / T 18697-2002 "Automotive interior noise measurement method"; 12) Use the analytic hierarchy process (AHP) to determine the weight of each requirement;

[0267] a) Establish a judgment matrix and use the 1-9 scaling method;

[0268] b) Calculate the eigenvector and the maximum eigenroot;

[0269] c) Conduct consistency test, and the consistency ratio CR<0.1;

[0270] d) Get the weight of each requirement:

[0271] Security needs: 40%

[0272] Vision requirement: 25%

[0273] Side window rainwater management performance: 20%;

[0274] Layout requirements: 10%;

[0275] Other constraints: 5%.

[0276] Step 2: Perform parametric modeling, as follows:

[0277] 21) Define key design parameters;

[0278] Use CATIA V5 R21 software to perform parametric modeling of the A-pillar and define key design parameters:

[0279] a) A-pillar section shape parameters:

[0280] Ellipse major axis a: range: 60-80mm, step length: 1mm;

[0281] Short axis b: range: 40-60 mm, step length: 1 mm;

[0282] Ellipse eccentricity e: range: 0.5-0.8, step size: 0.05;

[0283] b) A-pillar tilt angle θ: range: 60°-75°, step length: 0.5°;

[0284] c) A-pillar surface curvature κ: range: 0.01-0.05mm^-1, step size: 0.001mm^-1;

[0285] d) The connection angle α between the A-pillar and the windshield: range: 135°-150°, step length: 1°;

[0286] e) Connection angle β between A-pillar and side window: range: 135°-150°, step length: 1°;

[0287] f) Chamfer radius R at the top of the A-pillar: range: 5-15mm, step length: 0.5mm;

[0288] 22) Establish CAD model;

[0289] Establish parametric CAD models to ensure model stability and flexibility:

[0290] a) Use feature tree structure to ensure that the model does not crash after parameter modification;

[0291] b) Set the constraint relationship between parameters: a>b, α+β<300°;

[0292] c) Write VBA macros to quickly modify parameters and update models.

[0293] Step 3: Construct a multi-dimensional performance mapping model, as follows:

[0294] 31) Establish multi-dimensional performance indicator mapping relationship;

[0295] Establish the mapping relationship between A-pillar geometric parameters and multi-dimensional performance indicators:

[0296] P=f(G,E)

[0297] in:

[0298] P is a performance index vector, including: P1: field of view obstruction angle, P2: structural strength index, P3: rainwater coverage, P4: A-pillar weight;

[0299] G is a geometric parameter vector, including a, b, e, θ, κ, α, β, R, and is defined in the same way as in step 21);

[0300] E is the environmental parameter vector, including vehicle speed V and rainfall intensity I;

[0301] 32) Use response surface methodology (RSM) to construct a mapping model;

[0302] a) Use Box-Behnken design for experimental design and generate simulation experimental plan:

[0303] Number of factors: 8 (geometric parameters) + 2 (environmental parameters) = 10;

[0304] Number of trials: 170 (based on Box-Behnken design);

[0305] b) Aerodynamics and rain flow simulation using ANSYS Fluent 2021R1:

[0306] Computational mesh: tetrahedral mesh, with about 5 million cells;

[0307] Flow model: k-ωSST;

[0308] The discrete phase model (DPM) simulates raindrop motion;

[0309] Boundary conditions: inlet velocity 40-120km / h, raindrop diameter 0.1-5mm;

[0310] c) Structural strength simulation using ANSYS Mechanical 2021R1:

[0311] Unit type: tetrahedral unit, the number of units is about 200,000;

[0312] Material model: Isotropic elastoplastic;

[0313] Loading conditions: side impact force 18kN, roof crushing force 40kN;

[0314] Fitting the response surface using a radial basis function (RBF) neural network:

[0315] Number of hidden layer nodes: 50, activation function: Gaussian function, training algorithm: orthogonal least squares (OLS), 10-fold cross validation method is used to evaluate model accuracy to ensure R 2 >0.95.

[0316] Step 4: Evaluate the rainwater dynamics performance as follows:

[0317] 41) Define Comprehensive Stormwater Management Index (CRMI);

[0318] CMRI=ω1·CR+ω2·DSD+ω3·WFT+ω4·VCI

[0319] in:

[0320] CR: Rainwater coverage, CR = A ω / A t ×100, A ω is the rainwater coverage area, A t is the total observation area;

[0321] DSD: droplet size distribution, DSD = DSD = n i ·∑(n i ·d i ) / N,n i The diameter is d i The number of water droplets, N is the total number of water droplets;

[0322] WFT: Water film thickness, WFT = V ω / A ω , V ω is the water film volume, V ω is the water film coverage area;

[0323] VCI: Visibility Clarity Index, calculated based on the image clarity evaluation algorithm, VCI = 1-(∑ΔI / ∑I0), ΔI is the image gradient change in the raindrop area, and I0 is the original image gradient;

[0324] ω1, ω2, ω3, and ω4 are weight coefficients, which are determined by a unified scoring method.

[0325] 42) Establish evaluation criteria for rainwater dynamics performance;

[0326] The evaluation criteria for rainwater dynamics performance are defined as follows:

[0327] Excellent: CRMI < 0.2;

[0328] Good: 0.2≤CRMI<0.4;

[0329] General: 0.4≤CRMI<0.6;

[0330] Poor: 0.6≤CRMI<0.8;

[0331] Fail: CRMI ≥ 0.8.

[0332] Step 5: Optimize the design of multiple objectives, as follows:

[0333] 51) Establish a multi-objective optimization model;

[0334] minF(x)=[f1(X), f2(X), f3(X), f4(X)]

[0335] in:

[0336] X is the design variable vector, X = [a, b, e, θ, κ, α, β, R];

[0337] f1(X) is CRMI (minimization);

[0338] f2(X) is the field of view occlusion angle (minimized);

[0339] f3(X) is the structural strength index (maximized), defined as 1 / σ max , σ max is the maximum von Mises stress;

[0340] f4(X) is the weight (minimized);

[0341] Constraints:

[0342] g1(X): 20mm≤Clearance_min≤30mm (clearance between A-pillar and surrounding parts);

[0343] g2(X):σ max<σ yield (maximum stress is less than yield strength);

[0344] g3(X):δ max <100mm (maximum deformation);

[0345] g4(X):F roof ≥4× vehicle weight (bearing capacity in roof crushing test);

[0346] g5(X):-g 12 (X): the value range constraints of each design variable (a, b, e, θ, κ, α, β, R);

[0347] 52) Use NSGA-II (Non-dominated Sorting Genetic Algorithm II) to solve multi-objective optimization problems. Use NSGA-II to solve multi-objective optimization problems, and set the parameters as follows:

[0348] Population size: 200;

[0349] Iterations: 1000

[0350] Crossover probability: 0.9;

[0351] Mutation probability: 1 / n (n is the number of decision variables);

[0352] Crossover operator: simulated binary crossover (SBX), distribution index η_c = 20;

[0353] Mutation operator: polynomial mutation, distribution index η_m = 20;

[0354] Using crowding distance for diversity maintenance;

[0355] Elite strategy: retain the best 10% of individuals in each generation;

[0356] 53) Adopt adaptive strategies;

[0357] a) Dynamically adjust crossover and mutation probabilities:

[0358] p c =p c1 -(p c1 -p c2 )×(f′-f min ) / (f avg -f min )

[0359] p m =p m1 -(p m1 -p m2 )×(f max -f) / (f max -favg )

[0360] in:

[0361] f′ is the fitness value of the better individual in the parent generation;

[0362] f is the fitness value of the current individual;

[0363] p c1 =1.0, p c2 =0.5, p m1 =0.1, p m2 =0.01;

[0364] b) Perform a local search every 100 generations using a pattern search algorithm.

[0365] Step 6: Make trade-off decisions on multiple objectives, as follows:

[0366] 61) Establish a decision model based on TOPSIS (top-up to ideal solution ranking method);

[0367] Construct a decision matrix D, where rows represent candidate solutions and columns represent evaluation indicators;

[0368] Normalize the decision matrix to obtain the standardized matrix R;

[0369] Calculate the weighted normalized decision matrix V = W·R, where W is the weight matrix;

[0370] Determine the ideal solution A + and negative ideal solution A - ;

[0371] Calculate the distance from each solution to the ideal solution The Euclidean distance to the negative ideal solution

[0372] Calculate the relative closeness of each solution

[0373] Sort the solutions by value. The larger the value, the better the solution.

[0374] 62) Use fuzzy AHP to determine the weight of each indicator;

[0375] Fuzzy AHP is a hierarchical analysis method combined with fuzzy logic to deal with uncertainty and subjectivity in decision-making.

[0376] Fuzzy judgment matrix A: each element a ij It indicates the importance of index i relative to index j, and is represented by fuzzy numbers (such as triangular fuzzy numbers).

[0377] Triangular fuzzy number: expressed as (l ij , m ij ,u ij ), where l, m, and u are the lower bound, median, and upper bound of the fuzzy number, respectively.

[0378] Fuzzy weight vector W: The fuzzy weight of each indicator is calculated using the geometric mean method.

[0379] Defuzzification: Convert fuzzy weights into clear weights, the formula is W i =(l ij +4m ij +u ij ) / 6, and then normalize it to get the final weight vector.

[0380] Step 7: Verify and optimize the actual vehicle. The specific methods are as follows:

[0381] 71) Prototype production;

[0382] Based on the optimized design results, three sets of A-pillar samples with different parameters are produced using rapid prototyping technology (such as 3D printing);

[0383] Material: ABS engineering plastic;

[0384] Printing accuracy: ±0.1mm;

[0385] Surface treatment: polished to Ra≤1.6μm;

[0386] Install the sample on a mid-sized car, ensuring the installation accuracy is ≤0.5mm;

[0387] 72) Wind tunnel test;

[0388] Using 1:1 full-scale wind tunnel, wind speed range: 0-180km / h, accuracy ±0.1km / h. Rainfall simulation system: Rain intensity 0-200mm / h, adjustable raindrop size (0.5-5mm).

[0389] Test conditions:

[0390] Steady state: 80km / h, 30mm / h, for 5 minutes.

[0391] Variable speed: 40-120km / h cycle (20km / h step, each level lasts 60s), 30mm / h. Variable rain intensity: 80km / h, 10-50mm / h cycle (10mm / h step, each level lasts 60s). Measuring equipment:

[0392] High-speed camera: frame rate 1000fps, resolution 1920x1080.

[0393] Laser tomography system: used to measure water film thickness with an accuracy of ±0.01mm.

[0394] Acoustic camera: used to measure wind noise, frequency range 20Hz-20kHz;

[0395] 73) Road test:

[0396] Test route: including urban roads, highways and mountain roads, with a total mileage of 500km.

[0397] Test conditions: natural rainfall, rainfall intensity 5-50mm / h.

[0398] Test cycle: Covering 4 seasons, with at least 5 tests per season.

[0399] Data collection:

[0400] On-board HD camera: 4K resolution, 60fps frame rate.

[0401] Vehicle-mounted rain sensor: accuracy 0.1mm / h.

[0402] Vehicle status data such as vehicle speed and steering wheel angle: sampling frequency 100Hz.

[0403] Driver subjective evaluation: using a standardized rating scale, 1-5 points;

[0404] 74) Data analysis and optimization:

[0405] A) Use computer vision algorithms to process video data and calculate the actual CRMI index:

[0406] OpenCV library is used for image preprocessing and segmentation.

[0407] Use deep learning models such as U-Net for water droplet recognition and classification.

[0408] Calculate CR, DSD, WFT and VCI indicators, accuracy requirements: CR±1%, DSD±0.1mm, WFT±0.01mm;

[0409] B) Compare the virtual simulation results with the actual vehicle test data and calculate the error rate:

[0410] The maximum acceptable error rate is: CRMI index ±10%, other performance indicators ±5%.

[0411] Based on the actual vehicle test results, the performance mapping model is calibrated using the Kriging interpolation method, the model parameters are determined using the maximum likelihood estimation method, and the weight parameters of the RBF neural network are updated.

[0412] Perform sensitivity analysis to identify the design parameters that have the greatest impact on CRMI:

[0413] Using the Sobol global sensitivity analysis method, the first-order sensitivity index S is calculated. i and total sensitivity index ST i , adjust the optimization strategy based on the sensitivity results.

[0414] Step 8: Confirm the design and freeze the data, as follows:

[0415] 81) Final solution selection:

[0416] a) Based on the TOPSIS decision model, select the optimal A-pillar design solution from the optimization results;

[0417] b) Conduct FMEA (Failure Mode and Effect Analysis) to ensure the reliability of the design

[0418] Severity (S), occurrence (O), and detection (D) all use a 1-10 point system;

[0419] Calculate the risk priority number RPN = S × O × D, and set the threshold RPN < 100;

[0420] c) Organize a review meeting to finally confirm the design plan;

[0421] 82) Design data freeze:

[0422] a) Output and freeze the following design data:

[0423] A-pillar geometric parameters: cross-sectional dimensions, inclination angle, surface curvature, connection angle, etc.;

[0424] Material specifications: such as ultra-high strength steel (DP980), yield strength ≥980MPa;

[0425] Surface treatment requirements: such as roughness Ra≤0.8μm;

[0426] b) Generate detailed engineering drawings, including 2D drawings and 3D models;

[0427] c) Prepare design specifications, detailing design parameters, performance indicators and manufacturing requirements;

[0428] 83) Design document archiving:

[0429] a) Prepare a detailed design report including the design process, key decision points and performance evaluation results;

[0430] b) Organize all simulation data, test data and analysis reports;

[0431] c) Use PLM (Product Lifecycle Management) system for document management and version control.

[0432] In summary, this invention is the first to systematically consider rainwater management requirements in the early stages of A-pillar design, in parallel with traditional requirements such as vision, safety and layout, thus achieving comprehensive integration of design requirements. This approach helps reduce late-stage design changes and improves design efficiency.

[0433] Embodiment 2

[0434] See also Figure 2 This embodiment provides a vehicle A-pillar design and evaluation device considering side window rainwater contamination, including:

[0435] The collection module is used to collect and analyze the design requirements of the automobile A-pillar;

[0436] Modeling module, used for parametric modeling;

[0437] A construction module, used to construct a multi-dimensional performance mapping model;

[0438] Evaluation module, used to evaluate the rainwater dynamics performance;

[0439] Design module, used for optimizing design for multiple objectives;

[0440] Decision-making module, used to make trade-off decisions on multiple objectives;

[0441] Optimization module, used to verify and optimize the actual vehicle;

[0442] The confirmation module is used to confirm the design and freeze the data.

[0443] Embodiment 3

[0444] Figure 3 1 is a block diagram of a terminal provided in an embodiment of the present application, and the terminal may be a terminal in the above embodiment. The terminal may be a portable mobile terminal, such as a smart phone or a tablet computer. The terminal may also be referred to as a user device, a portable terminal, or other names.

[0445] Typically, the terminal includes: a processor 301 and a memory 302 .

[0446] The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 301 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0447] The memory 302 may include one or more computer-readable storage media, which may be tangible and non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement a vehicle A-pillar design and evaluation method considering side window rain water contamination provided in this application.

[0448] In some embodiments, the terminal may further include: a peripheral device interface 303 and at least one peripheral device. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304 , a touch display screen 305 , a camera 306 , an audio circuit 307 , a positioning component 308 and a power supply 309 .

[0449] The peripheral device interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the peripheral device interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the peripheral device interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0450] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with the communication network and other communication devices through electromagnetic signals. The radio frequency circuit 304 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 304 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 304 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0451] The touch display screen 305 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos and any combination thereof. The touch display screen 305 also has the ability to collect touch signals on the surface or above the surface of the touch display screen 305. The touch signal can be input to the processor 301 as a control signal for processing. The touch display screen 305 is used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the touch display screen 305 can be one, and the front panel of the terminal is set; in other embodiments, the touch display screen 305 can be at least two, which are respectively set on different surfaces of the terminal or are folded; in some other embodiments, the touch display screen 305 can be a flexible display screen, which is set on the curved surface or folded surface of the terminal. Even, the touch display screen 305 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The touch display screen 305 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0452] The camera assembly 306 is used to capture images or videos. Optionally, the camera assembly 306 includes a front camera and a rear camera. Typically, the front camera is used to realize video calls or selfies, and the rear camera is used to realize the shooting of photos or videos. In some embodiments, there are at least two rear cameras, which are any one of a main camera, a depth of field camera, and a wide-angle camera, so as to realize the fusion of the main camera and the depth of field camera to realize the background blur function, and the fusion of the main camera and the wide-angle camera to realize panoramic shooting and VR (Virtual Reality) shooting functions. In some embodiments, the camera assembly 306 may also include a flash. The flash can be a monochrome temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cold light flash, which can be used for light compensation at different color temperatures.

[0453] The audio circuit 307 is used to provide an audio interface between the user and the terminal. The audio circuit 307 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 301 for processing, or input them into the radio frequency circuit 304 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there may be multiple microphones, which are respectively arranged at different parts of the terminal. The microphone may also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 301 or the radio frequency circuit 304 into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 307 may also include a headphone jack.

[0454] The positioning component 308 is used to locate the current geographical location of the terminal to implement navigation or LBS (Location Based Service). The positioning component 308 can be a positioning component based on the US GPS (Global Positioning System) or China's Beidou system.

[0455] The power supply 309 is used to power various components in the terminal. The power supply 309 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 309 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery charged through a wired line, and a wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0456] Those skilled in the art will understand that Figure 3The structure shown in the figure does not constitute a limitation on the terminal, and may include more or less components than those shown in the figure, or combine certain components, or adopt a different component arrangement.

[0457] Embodiment 4

[0458] In an exemplary embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the program is executed by a processor, a method for designing and evaluating an automobile A-pillar taking into account rain water contamination on the side windows as provided in all the inventive embodiments of the present application is implemented.

[0459] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0460] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0461] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0462] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0463] Embodiment 5

[0464] In an exemplary embodiment, an application product is also provided, including one or more instructions, which can be executed by the processor 301 of the above-mentioned device to complete the above-mentioned automobile A-pillar design and evaluation method considering rain water contamination on the side windows.

[0465] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and implementation mode. It can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily realized. Therefore, without departing from the general concept defined by the claims and equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.

Claims

1. A design and evaluation method for an automobile A-pillar considering rainwater pollution on side windows, characterized in that: The following steps are involved: Step 1: Collect and analyze the design requirements of the car A-pillar; Step 2: Perform parametric modeling; Step 3: Construct a multi-dimensional performance mapping model; Step 4: Evaluate the rainwater dynamics performance; Step 5: Optimize the design of multiple objectives; Step 6: Make trade-off decisions on multiple objectives; Step 7: Verify and optimize the actual vehicle; Step 8: Confirm the design and freeze the data.

2. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step one is as follows: 11) Collect requirements related to A-pillar design; 12) Use the analytic hierarchy process (AHP) to determine the weight of each requirement.

3. A method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 2, characterized in that: The specific method of step 11) is as follows: a) Vision requirements: Driver's forward vision requirements: Comply with the provisions of GB / T 15089-2001 "Motor vehicle external vision area and wiper performance"; A-pillar shielding angle: ≤6°; The minimum distance from the driver's eye point to the surface of the A-pillar is ≥550mm; b) Layout requirements: The minimum gap between the A-pillar and the instrument panel is ≥20mm; The clearance between the A-pillar and the door = 4 ± 1 mm; A-pillar internal wiring harness layout space ≥ 400mm 2 ; c) Security requirements: Side impact performance: in compliance with GB 20071-2006 “Occupant protection in motor vehicle side impact”; Roof strength: in compliance with GB 26134-2010 "Compressive strength of passenger car roof"; A-pillar material tensile strength ≥ 980MPa; d) Side window rainwater management performance requirements: At a speed of 80 km / h and a rainfall intensity of 30 mm / h, the water droplet coverage rate of the driver's side view area is ≤15%; Within 10 seconds after the rain stops, the diameter of the remaining water droplets is ≤2mm; The maximum water film thickness on the A-pillar surface is ≤ 0.3mm; e) Other constraints: A-pillar weight: meets the requirements of vehicle lightweighting; Cost increase ≤ 5%; Wind noise control: in compliance with GB / T 18697-2002 "Automobile interior noise measurement method"; The specific method of step 12) is as follows: a) Establish a judgment matrix and use the 1-9 scaling method; b) Calculate the eigenvector and the maximum eigenroot; c) Conduct consistency test, and the consistency ratio CR<0.1; d) Get the weight of each requirement: Security needs: 40% Vision requirement: 25% Side window rainwater management performance: 20%; Layout requirements: 10%; Other constraints: 5%.

4. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step 2 is as follows: 21) Define key design parameters; 22) Create a CAD model.

5. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 4, characterized in that: The specific method of step 21) is as follows: Use CATIA V5 R21 software to perform parametric modeling of the A-pillar and define key design parameters: a) A-pillar cross-section shape parameters: Ellipse major axis a: range: 60-80mm, step length: 1mm; Short axis b: range: 40-60 mm, step length: 1 mm; Ellipse eccentricity e: range: 0.5-0.8, step size: 0.05; b) A-pillar tilt angle θ: range: 60°-75°, step length: 0.5°; c) A-pillar surface curvature κ: range: 0.01-0.05mm^-1, step size: 0.001mm^-1; d) The connection angle α between the A-pillar and the windshield: range: 135°-150°, step length: 1°; e) Connection angle β between A-pillar and side window: range: 135°-150°, step length: 1°; f) Chamfer radius R at the top of the A-pillar: range: 5-15mm, step length: 0.5mm; The specific method of step 22) is as follows: Establish parametric CAD models to ensure model stability and flexibility: a) Use feature tree structure to ensure that the model does not crash after parameter modification; b) Set the constraint relationship between parameters: a>b, α+β<300°; c) Write VBA macros to quickly modify parameters and update models.

6. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step three is as follows: 31) Establish multi-dimensional performance indicator mapping relationship; 32) Response surface methodology (RSM) was used to construct the mapping model.

7. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 6, characterized in that: The specific method of 31) is as follows: Establish the mapping relationship between A-pillar geometric parameters and multi-dimensional performance indicators: P=f(G,E) Where: P is the performance index vector, including: P1: field of view obstruction angle, P2: structural strength index, P3: rainwater coverage, P4: A-pillar weight; G is the geometric parameter vector, including a, b, e, θ, κ, α, β, and R; E is the environmental parameter vector, including vehicle speed V and rainfall intensity I; The specific method of 32) is as follows: a) Use Box-Behnken design for experimental design and generate simulation experimental plan: Number of factors: 8, i.e., geometric parameters + 2, i.e., environmental parameters = 10; Number of tests: 170, based on Box-Behnken design; b) Aerodynamics and rain flow simulation using ANSYS Fluent 2021 R1: Computational mesh: tetrahedral mesh, with about 5 million cells; Flow model: k-ωSST; The discrete phase model, or DPM, simulates the motion of raindrops; Boundary conditions: inlet velocity 40-120km / h, raindrop diameter 0.1-5mm; c) Structural strength simulation using ANSYS Mechanical 2021 R1: Unit type: tetrahedral unit, the number of units is about 200,000; Material model: Isotropic elastoplastic; Loading conditions: side impact force 18kN, roof crushing force 40kN; Use radial basis function, RBF neural network, to fit the response surface: Number of hidden layer nodes: 50, activation function: Gaussian function, training algorithm: orthogonal least squares (OLS), 10-fold cross validation method is used to evaluate model accuracy to ensure R 2 >0.

95.

8. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step 4 is as follows: 41) Define the Comprehensive Stormwater Management Index CRMI; 42) Establish evaluation criteria for rainwater dynamics performance.

9. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 8, characterized in that: The specific method of step 41) is as follows: Definition of Comprehensive Stormwater Management Index CRMI: CMRI=ω1·CR+ω2·DSD+ω3·WFT+ω4·VCI Where: CR is the rainwater coverage rate, CR = A ω / A t ×100, A ω is the rainwater coverage area, A t is the total observation area; DSD is the droplet size distribution, DSD = DSD = n i ·∑(n i ·d i ) / N,n i The diameter is d i The number of water droplets, N is the total number of water droplets; WFR is the water film thickness, WFT=V ω / A ω , V ω is the water film volume, V ω is the water film coverage area; VCI is the visibility clarity index, which is calculated based on the image clarity evaluation algorithm. VCI = 1-(∑ΔI / ∑I0), ΔI is the image gradient change in the raindrop area, and I0 is the original image gradient; ω1, ω2, ω3, ω4 are weight coefficients; The specific method of step 42) is as follows: The evaluation criteria for rainwater dynamics performance are defined as follows: Excellent: CRMI < 0.2; Good: 0.2≤CRMI<0.4; General: 0.4≤CRMI<0.6; Poor: 0.6≤CRMI<0.8; Fail: CRMI ≥ 0.

8.

10. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step five is as follows: 51) Establish a multi-objective optimization model; 52) Use NSGA-II, i.e. non-dominated sorting genetic algorithm II, to solve multi-objective optimization problems; 53) Adopt an adaptive strategy.

11. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 10, characterized in that: The specific method of step 51) is as follows: minF(x)=[f1(X), f2(X), f3(X), f4(X)] in: X is the design variable vector, X = [a, b, e, θ, κ, α, β, R]; f1(X) is CRMI, i.e. minimization; f2(X) is the visual field occlusion angle, which is minimized; f3(X) is the structural strength index, which is maximized and is defined as 1 / σ max , σ max is the maximum von Mises stress; f4(X) is the weight, i.e. minimized; The specific method of step 52) is as follows: Use NSGA-II to solve multi-objective optimization, and set the parameters as follows: Population size: 200; Iterations: 1000 Crossover probability: 0.9; Mutation probability: 1 / n, where n is the number of decision variables; Crossover operator: simulate binary crossover (SBX), distribution index η_c = 20; Mutation operator: polynomial mutation, distribution index η_m = 20; Using crowding distance for diversity maintenance; Elite strategy: retain the best 10% of individuals in each generation; The specific method of step 53) is as follows: Dynamically adjust crossover and mutation probabilities: p c =p c1 -(p c1 -p c2 )×(f′-f min ) / (f avg -f min ) p m =p m1 -(p m1 -p m2 )×(f max -f) / (f max -f avg ) in: f′ is the fitness value of the better individual in the parent generation; f is the fitness value of the current individual; p c1 =1.0,p c2 =0.5,p m1 =0.1,p m2 =0.01; b) Perform a local search every 100 generations using a pattern search algorithm.

12. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step six is ​​as follows: 61) Establish a decision model based on TOPSIS, i.e., the method of ranking by approximating to the ideal solution; 62) Use fuzzy AHP to determine the weight of each indicator.

13. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 12, characterized in that: The specific method of step 61) is as follows: Construct a decision matrix D, where rows represent candidate solutions and columns represent evaluation indicators; Normalize the decision matrix to obtain the standardized matrix R; Calculate the weighted normalized decision matrix V = W·R, where W is the weight matrix; Determine the ideal solution A + and negative ideal solution A - ; Calculate the distance from each solution to the ideal solution The Euclidean distance to the negative ideal solution Calculate the relative closeness C of each solution i : Sort the solutions by value. The larger the value, the better the solution. The specific method of step 62) is as follows: Fuzzy AHP is a hierarchical analysis method combined with fuzzy logic to deal with uncertainty and subjectivity in decision-making; Fuzzy judgment matrix A: Each element a ij Indicates the importance of index i relative to index j, using fuzzy numbers; Triangular fuzzy number: expressed as (l ij , m ij ,u ij ), where l, m, and u are the lower bound, median, and upper bound of the fuzzy number respectively; Fuzzy weight vector W: The fuzzy weight of each indicator is calculated using the geometric mean method; Defuzzification: Convert fuzzy weights into clear weights, the formula is W i =(l ij +4m ij +u ij ) / 6, and then normalize it to get the final weight vector.

14. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step seven is as follows: 71) Prototype production; 72) Conduct wind tunnel tests; 73) Conduct road tests; 74) Conduct data analysis and optimization.

15. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 14, characterized in that: The specific method of step 71) is as follows: Based on the optimized design results, three sets of A-pillar samples with different parameters were produced using rapid prototyping technology; Material: ABS engineering plastic; Printing accuracy: ±0.1mm; Surface treatment: polished to Ra≤1.6μm; Install the sample on a mid-sized car, ensuring the installation accuracy is ≤0.5mm; The specific method of step 72) is as follows: Using 1:1 full-scale wind tunnel, wind speed range: 0-180km / h, accuracy ±0.1km / h; Rainfall simulation system: Rainfall intensity 0-200mm / h, can adjust raindrop size, size 0.5-5mm; The specific method of step 73) is as follows: Test route: including urban roads, highways and mountain roads, with a total mileage of 500km; Test conditions: natural rainfall, rainfall intensity 5-50mm / h. Test cycle: covers 4 seasons, with at least 5 tests per season; Data collection: On-board HD camera: 4K resolution, 60fps frame rate; Vehicle-mounted rain sensor: accuracy 0.1mm / h; Vehicle status data such as vehicle speed and steering wheel angle: sampling frequency 100Hz; Objective evaluation: Use a standardized scoring table with uniform standards, 1-5 points; The specific method of step 74) is as follows: a) Use computer vision algorithms to process video data and calculate the actual CRMI index: Use OpenCV library for image preprocessing and segmentation; Use deep learning models for water droplet recognition and classification; Calculate CR, DSD, WFT and VCI indicators, accuracy requirements: CR±1%, DSD±0.1mm, WFT ± 0.01mm; b) Compare the virtual simulation results with the actual vehicle test data and calculate the error rate: The maximum acceptable error rate: CRMI index ±10%, other performance indicators ±5%; Based on the actual vehicle test results, the performance mapping model is calibrated using the Kriging interpolation method, the model parameters are determined using the maximum likelihood estimation method, and the weight parameters of the RBF neural network are updated; Perform sensitivity analysis to identify the design parameters that have the greatest impact on CRMI: Using the Sobol global sensitivity analysis method, the first-order sensitivity index S is calculated. i and total sensitivity index ST i , adjust the optimization strategy based on the sensitivity results.

16. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 1, characterized in that: The specific method of step eight is as follows: 81) Select the final solution; 82) Freeze design data; 83) Archive design documents.

17. The method for designing and evaluating an automobile A-pillar considering rainwater pollution on side windows according to claim 16, characterized in that: The specific method of step 81) is as follows: a) Based on the TOPSIS decision model, select the optimal A-pillar design solution from the optimization results; b) Conduct FMEA to ensure the reliability of the design: Severity (S), occurrence (O), and detection (D) all use a 1-10 point system; Calculate the risk priority number RPN = S × O × D, and set the threshold RPN < 100; c) Final confirmation of the design plan; The specific method of step 82) is as follows: a) Output and freeze the following design data: A-pillar geometric parameters: cross-sectional dimensions, inclination angle, surface curvature, connection angle; Material specifications: ultra-high strength steel, yield strength ≥980MPa; Surface treatment requirements: such as roughness Ra≤0.8μm; b) Generate detailed engineering drawings, including 2D drawings and 3D models; c) Prepare design specifications, detailing design parameters, performance indicators and manufacturing requirements; The specific method of step 83) is as follows: a) Prepare a detailed design report including the design process, key decision points and performance evaluation results; b) Organize all simulation data, test data and analysis reports; c) Use PLM system for document management and version control.

18. A vehicle A-pillar design and evaluation device considering side window rainwater contamination, characterized in that: include: The collection module is used to collect and analyze the design requirements of the automobile A-pillar; Modeling module, used for parametric modeling; A construction module, used to construct a multi-dimensional performance mapping model; Evaluation module, used to evaluate the rainwater dynamics performance; Design module, used for optimizing design for multiple objectives; Decision-making module, used to make trade-off decisions on multiple objectives; Optimization module, used to verify and optimize the actual vehicle; The confirmation module is used to confirm the design and freeze the data.

19. A terminal, characterized in that: include: one or more processors; a memory for storing the one or more processor-executable instructions; Wherein, the one or more processors are configured to: A method for designing and evaluating an automobile A-pillar taking into account rainwater contamination on side windows as described in any one of claims 1 to 17 is implemented.

20. A non-transitory computer-readable storage medium, characterized in that When the instructions in the storage medium are executed by the processor of the terminal, the terminal is enabled to execute the automobile A-pillar design and evaluation method considering side window rain water contamination as described in any one of claims 1 to 17.

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