Material selection method for passenger car body parts and related equipment

Abstract

The invention discloses a passenger car body part material selection method and related equipment, and relates to the technical field of vehicle material selection, and the method comprises the steps: obtaining the performance attribute data of a candidate material and the function demand data of a target part; performing normalization processing on the performance attribute data to generate a first quantized data set; performing evaluation processing on the function demand data to generate a second quantitative data set; based on a matching relationship between the first quantized data set and the second quantized data set, screening candidate materials meeting the function demand data, and generating a candidate material set; based on user requirements, performing multi-attribute decision sorting on the candidate material set to generate a difference material selection sorting result; and determining a user material selection scheme according to the difference material selection sorting result. According to the method, a standardized matching mechanism of material performance and part function requirements is constructed, so that efficient and objective material selection decision is realized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle material selection, and in particular to a material selection method and related equipment for passenger vehicle body parts. Background Art

[0002] Traditionally, the selection of materials for passenger car body parts relies primarily on reverse engineering and engineers' experience. This involves disassembling benchmark vehicles to obtain component material data and making minor adjustments based on historical experience. This approach has significant drawbacks: First, benchmark vehicle testing consumes significant time and money, and the accuracy of test results is difficult to guarantee due to the impact of part size and processing conditions. Second, reverse material selection cannot trace back to the original design concept, leading to confusing material selection logic during new vehicle development and making it difficult for designers to systematically optimize solutions. Furthermore, this experience-driven material selection model relies heavily on individual ability, is highly subjective, and lacks replicability. This model hinders the widespread application of new materials and restricts improvements in vehicle body lightweighting and safety performance.

[0003] Existing technology lacks a scientific, standardized decision-making system for selecting materials for vehicle body parts. Because a vehicle body comprises hundreds of components with diverse functions, each with vastly different material performance requirements, traditional methods struggle to accurately match these multi-dimensional requirements. Empirical judgments are susceptible to subjective influences and are unable to quantitatively assess the overall merits of material selection solutions. This results in inefficient selection and lengthy decision-making cycles, making it difficult to meet the rapid development needs of automakers. Therefore, a material selection method for passenger vehicle body parts is urgently needed to address these technical challenges. Summary of the Invention

[0004] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, the present application provides a method for selecting materials for passenger vehicle body parts, the method comprising:

[0006] Obtain performance attribute data of candidate materials and functional requirement data of target components;

[0007] Normalizing the performance attribute data to generate a first quantitative data set;

[0008] Evaluating and processing the functional requirement data to generate a second quantitative data set;

[0009] Based on the matching relationship between the first quantitative data set and the second quantitative data set, screening candidate materials that meet the functional requirement data to generate a candidate material set;

[0010] Based on user needs, perform multi-attribute decision-making on the candidate material set to generate differential material selection ranking results;

[0011] Determine the user's material selection plan based on the differential material selection sorting results.

[0012] In some embodiments, the performance attribute data includes benefit-based indicator data and cost-based indicator data. Normalizing the performance attribute data to generate a first quantitative data set includes:

[0013] Normalizing the benefit-type indicator data to a first preset interval based on a first linear mapping formula to generate a quantitative value of the benefit-type indicator;

[0014] Normalizing the cost-type indicator data to a first preset interval based on a second linear mapping formula to generate a quantitative value of the cost-type indicator;

[0015] A first quantitative data set is generated based on the quantitative values ​​of the benefit-type indicators and the quantitative values ​​of the cost-type indicators.

[0016] In some embodiments, evaluating the functional requirement data to generate a second quantitative data set includes:

[0017] Expert scoring of functional requirement data;

[0018] The numerical range of the expert scores is constrained to a second preset interval to generate a second quantitative data set.

[0019] In some embodiments, based on the matching relationship between the first quantitative data set and the second quantitative data set, screening candidate materials that meet the functional requirement data to generate a candidate material set includes:

[0020] Based on preset rules, the performance attribute data in the first quantitative data set is matched one by one with the functional requirement data in the second quantitative data set, wherein the preset mapping rule is a correspondence between the classification dimensions of the performance attribute data and the classification dimensions of the functional requirement data;

[0021] Candidate materials whose performance attribute data in the first quantitative data set is greater than or equal to corresponding functional requirement data in the second quantitative data set are screened to generate a candidate material set.

[0022] In some embodiments, based on user needs, a multi-attribute decision-making ranking is performed on a set of candidate materials to generate a differential material selection ranking result, including:

[0023] Based on the analytic hierarchy process, a preference matrix is ​​constructed according to user needs and the consistency of the preference matrix is ​​checked. User needs include safety needs, format needs and cost needs.

[0024] Based on the preference matrix that passes the consistency test, the safety score, forming score and cost score are calculated for each candidate material in the candidate material set;

[0025] Generate a comprehensive ranking score for each candidate material based on safety score, forming score and cost score;

[0026] Arrange candidate materials in descending order according to comprehensive ranking scores to generate differential material selection ranking results;

[0027] Among them, the safety score is used to characterize the score of the candidate material in the safety performance dimension; the forming score is used to characterize the score of the candidate material in the forming process dimension; and the cost score is used to characterize the score of the candidate material in the economic dimension.

[0028] In some embodiments, further comprising:

[0029] Encode the target parts based on the preset coding rules and generate part identification codes;

[0030] The component identification code is used as an index code to obtain functional requirement data.

[0031] In some embodiments, determining a user material selection plan based on the differential material selection ranking results includes:

[0032] Based on the differential material selection ranking results, the candidate material with the highest comprehensive ranking score is selected as the user's material selection plan.

[0033] In a second aspect, the present application proposes a material selection device for passenger car body parts, the device comprising:

[0034] A data acquisition unit, used to acquire performance attribute data of candidate materials and functional requirement data of target components;

[0035] a performance processing unit, configured to perform normalization processing on the performance attribute data to generate a first quantitative data set;

[0036] a function processing unit, configured to evaluate and process the function requirement data to generate a second quantitative data set;

[0037] A preliminary material selection unit, based on the matching relationship between the first quantitative data set and the second quantitative data set, screens candidate materials that meet the functional requirement data and generates a candidate material set;

[0038] Optimize the material selection unit, perform multi-attribute decision-making and sorting on the candidate material set based on user needs, and generate differential material selection sorting results;

[0039] The material determination unit is used to determine the user's material selection plan based on the differential material selection sorting results.

[0040] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the material selection method for passenger vehicle body parts of any one of the first aspects when executing the computer program stored in the memory.

[0041] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the material selection method for passenger vehicle body parts according to any one of the first aspects.

[0042] In summary, this application transforms material performance parameters and component functional requirements into a quantitative data set with unified dimensions through data normalization. This method achieves precise matching of performance and requirements based on preset mapping rules, and employs a multi-attribute decision-making algorithm to generate differentiated ranking results. This method overcomes the limitations of traditional empirical material selection, improving both efficiency and objectivity. It can quickly recommend material solutions that meet differentiated safety, forming, and cost requirements, shortening decision cycles and reducing reliance on engineer experience. It also supports the efficient integration and promotion of new materials, providing a scientific basis for lightweighting and optimizing vehicle bodies.

[0043] The material selection method for passenger car body parts proposed in this application, and other advantages, objectives and features of this application will be reflected in part through the following description, and in part will also be understood by technical personnel in this field through research and practice of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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 description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0045] Figure 1 A schematic flow chart of a material selection method for passenger vehicle body parts provided in an embodiment of the present application;

[0046] Figure 2 A schematic structural diagram of a material selection device for passenger vehicle body parts provided in an embodiment of the present application;

[0047] Figure 3 A structural diagram of electronic equipment for selecting materials for passenger car body parts provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0049] See also Figure 1 , which is a flow chart of a material selection method for passenger car body parts provided in an embodiment of the present application, which may specifically include:

[0050] S110, obtaining performance attribute data of candidate materials and functional requirement data of target components;

[0051] For example, obtaining the performance attribute data of candidate materials is one of the core inputs of the material selection method. Its purpose is to provide an objective basis for subsequent matching and decision-making by quantifying the physical, mechanical and process properties of the material (such as critical fracture strain, tensile strength, drawing height, etc.). These data usually come from the material performance database or experimental test results, covering the full strength grade materials from mild steel to ultra-high strength steel, to ensure the comprehensiveness and scientificity of the material selection range. The collection of performance attribute data must cover multi-dimensional evaluation indicators, including both strength parameters that directly affect safety and key indicators that reflect the adaptability of the forming process (such as hole expansion rate, rebound angle, etc.), so as to fully characterize the comprehensive performance of the material.

[0052] The functional requirement data of the target parts are generated based on the actual application scenarios and design requirements of the body parts, such as energy absorption performance, load-bearing performance, and dent resistance. These requirement data are determined through expert evaluation combined with forming simulation, and quantify the performance requirements of the parts under different working conditions. The correspondence between functional requirement data and material performance data (such as energy absorption performance corresponding to critical fracture strain) is the logical basis for subsequent screening and matching, ensuring that the material selection scheme can accurately meet the functional objectives of the parts. By systematically collecting these two types of data, a data foundation is laid for subsequent normalization processing, multi-dimensional matching, and differentiated sorting, supporting the objectivity and efficiency of the material selection process.

[0053] S120, normalizing the performance attribute data to generate a first quantitative data set;

[0054] For example, the purpose of normalization is to eliminate the comparison barriers caused by differences in dimensions and value ranges of different performance attribute data, so that they can be quantitatively compared under the same standard. According to the physical characteristics of material performance, the attribute data are divided into benefit-type indicators (such as tensile strength and critical fracture strain) and cost-type indicators (such as material unit price and U-shaped rebound angle), and are converted into standardized values ​​​​from 0 to 9 through linear mapping formulas. The larger the value of the benefit-type indicator, the better the performance, and the smaller the value of the cost-type indicator, the better the economy. By unifying the dimensions, intuitive comparison and comprehensive evaluation of multi-dimensional data can be achieved.

[0055] The core of this step is to establish horizontal comparability of material properties. For example, the yield strength of ultra-high-strength steel and the formability of mild steel are originally incomparable. However, through normalization, the performance parameters of all materials are mapped to the same numerical range, forming a unified quantitative data set. This not only provides a data foundation for subsequent matching functional requirements but also ensures the objectivity of the comprehensive scoring of different materials in terms of safety, formability, and cost, supporting scientific and systematic material selection decisions.

[0056] S130, evaluating and processing the functional requirement data to generate a second quantitative data set;

[0057] For example, the evaluation and processing of functional requirement data aims to convert the multi-dimensional performance requirements of parts (such as energy absorption performance, forming process requirements, etc.) into a quantifiable and comparable numerical system. Through the expert scoring module combined with the forming simulation, the functional requirement sub-dimensions of the parts are scored. For example, the energy absorption performance requirement value is determined based on the collision safety simulation results, and the forming process requirement value is obtained through stamping forming simulation analysis. All scoring results are constrained to a preset range of 0 to 9 to form a unified second quantitative data set to ensure comparability with the normalized results of the material performance data in the same dimension.

[0058] The core significance of generating the second quantitative data set is to establish a benchmark linking component requirements and material properties. For example, the load-bearing performance requirement of a component is quantified as 7 points, and the corresponding material performance data must meet or exceed this score in terms of tensile strength. Through quantification, the complex functional requirements of the component are abstracted into standardized values, providing clear threshold conditions for subsequent screening and matching of candidate materials, supporting the objectivity and accuracy of the material selection process and avoiding bias caused by subjective experience.

[0059] S140 , based on the matching relationship between the first quantitative data set and the second quantitative data set, screening candidate materials that meet the functional requirement data to generate a candidate material set;

[0060] For example, the screening process uses preset mapping rules to precisely match material properties with component requirements, such as mapping critical fracture strain to energy absorption performance and tensile strength to load-bearing performance. Based on the standardized performance data of the materials in the first quantitative data set, the component requirement thresholds in the second quantitative data set are compared item by item. Only candidate materials with performance data greater than or equal to the corresponding requirement value are retained to form a preliminary set of material selection results. This step replaces traditional empirical judgment with objective numerical comparison, ensuring the scientific and repeatable nature of the screening results.

[0061] Generating a candidate material set is a core transitional step in the material selection process. Essentially, it narrows the selection range through a data-driven approach. For example, for a component requiring an energy absorption performance of 7, only materials with a normalized critical fracture strain greater than or equal to 7 are selected. The candidate materials in this set not only meet basic functional requirements but also provide optimization space for subsequent differentiated sorting based on safety, formability, and cost, supporting a systematic material selection process from coarse screening to fine sorting.

[0062] S150, based on user needs, performing multi-attribute decision-making sorting on the candidate material set to generate differential material selection sorting results;

[0063] For example, the core of multi-attribute decision-making ranking is to quantitatively score the comprehensive performance of candidate materials using algorithms such as the Analytic Hierarchy Process (AHP), combined with the user's differentiated demand weights for safety, forming, and cost. User demand weights reflect the priority of different dimensions (for example, when the safety demand ratio is high, the safety performance score of the material is increased). By constructing a preference matrix to calculate the safety score, forming score, and cost score of each candidate material, a comprehensive ranking score is finally generated, which enables the optimization solution that best meets the user's needs to be selected from the candidate material set.

[0064] Differentiated material selection ranking results address the problem of single-dimensional dominance or subjective judgment bias in traditional material selection by quantifying the match between user preferences and material properties. For example, if a user prioritizes lightweighting (high cost requirements), the material's unit price and weight reduction potential will be given greater weight in the ranking, and the system will recommend the material with the best overall score based on this. This process not only improves the objectivity of material selection decisions but also allows users to flexibly adjust demand weights based on actual scenarios, generating personalized material selection solutions.

[0065] S160. Determine the user's material selection plan based on the differential material selection sorting results.

[0066] For example, based on the differential material selection ranking results, the candidate material with the highest overall ranking score is automatically selected as the default user material selection plan. This plan directly reflects the user's weighted preferences for safety, forming, and cost. For example, the top-ranked material achieves the optimal balance between safety performance, forming process adaptability, and economic efficiency, ensuring that the material selection results meet both functional requirements and user priorities. If the user requires further adjustment, they can manually select other candidate materials with higher rankings, and the system will simultaneously update the recommended parameters to provide flexible decision support.

[0067] The key to determining a user's material selection solution lies in translating quantitative ranking results into actionable engineering decisions. By replacing traditional subjective judgments with objective ranking scores, the system significantly reduces uncertainty in the material selection process while enabling users to quickly identify the optimal solution based on specific scenarios (such as cost constraints or lightweighting goals). This step ultimately outputs key parameters such as material grade and recommended thickness, providing clear guidance for subsequent part manufacturing and performance verification, achieving closed-loop management of the material selection process.

[0068] In summary, the embodiments of this application improve the scientificity, efficiency, and scalability of material selection for passenger vehicle body parts by establishing a systematic material selection decision-making system. Its core benefits lie in the fact that, through a two-way standardization mechanism combining data normalization and quantitative functional requirement assessment, material performance parameters and component functional requirements are converted into comparable data with unified dimensions, resolving the mismatching problem caused by mixed dimensions and inconsistent subjective standards in traditional empirical material selection. A multi-attribute decision-making model constructed based on the analytic hierarchy process dynamically converts differentiated requirements such as safety, forming, and cost into a computable weight system, achieving coordinated optimization of multi-dimensional objectives while ensuring that basic functional thresholds are met. This effectively overcomes the decision-making bottleneck of traditional methods, where multi-objective conflicts are difficult to quantify and balance. Furthermore, through component coding rules and data association mechanisms, a standardized material selection knowledge base is constructed, forming a reusable decision-making logic framework, significantly shortening the material screening and solution verification cycle and reducing reliance on reverse engineering disassembly testing and manual experience accumulation. At the same time, this application opens up the connection channel between the material performance database and the component demand system, provides technical support for the rapid verification and promotion of new materials, promotes the efficient integration of advanced material research and development results of steel companies into the vehicle development process, and promotes the systematic improvement of vehicle body lightweighting, safety and economy, providing a feasible technical path for the automotive industry to achieve intelligent, data-driven forward design transformation.

[0069] In some examples, the performance attribute data includes benefit-based indicator data and cost-based indicator data. Normalizing the performance attribute data to generate a first quantitative data set includes:

[0070] Normalizing the benefit-type indicator data to a first preset interval based on a first linear mapping formula to generate a quantitative value of the benefit-type indicator;

[0071] Normalizing the cost-type indicator data to a first preset interval based on a second linear mapping formula to generate a quantitative value of the cost-type indicator;

[0072] A first quantitative data set is generated based on the quantitative values ​​of the benefit-type indicators and the quantitative values ​​of the cost-type indicators.

[0073] Exemplarily, in view of the characteristics of performance attribute data including benefit-type indicators and cost-type indicators, a differentiated linear mapping formula is used to implement normalization processing to construct a quantitative data set of unified dimension. For benefit-type indicator data (such as parameters that are positively correlated with material properties such as tensile strength, critical fracture strain, fatigue limit, etc.), normalization processing is performed based on the first linear mapping formula. Specifically, the maximum value of the benefit-type indicator is set to max(x), and the minimum value is set to min(x). The data conversion is achieved by the formula x'=(x-min(x)) / (max(x)-min(x))×9, and the original parameter value is mapped to a first preset interval of 0 to 9, where x is the original performance value and x' is the normalized quantitative value. This formula ensures that benefit-type indicators of different dimensions present relative advantages and disadvantages within a unified interval by scaling the extreme value difference ratio.

[0074] For cost-type indicator data (such as material unit price, U-shaped rebound angle, relative minimum bending radius and other parameters that are negatively correlated with material applicability), the second linear mapping formula is used to implement reverse normalization processing. The maximum value of the cost-type indicator is set to max(x), and the minimum value is min(x). The formula x'=(max(x)-x) / (max(x)-min(x))×9 is used for conversion, and it is also mapped to the range of 0 to 9. This formula uses reverse difference calculation to make the smaller the value of the cost-type indicator, the higher the score after normalization. This processing logic effectively distinguishes the difference in numerical significance between cost-type indicators and benefit-type indicators, and ensures that all parameters follow the unified evaluation criteria of "the larger the value, the better the performance" after normalization.

[0075] After completing the independent normalization of the two types of indicators, the quantitative values ​​of each indicator are integrated according to a preset data structure to generate the first quantitative data set. This data set is indexed by material brand and stores normalized values ​​categorized by performance attributes. For example, the data record for material HC340LAD+Z includes fields such as critical fracture strain → 8.1, tensile strength → 7.6, material unit price → 6.8, and U-shaped rebound angle → 7.9. The construction of the data set follows the principle of matrix storage. The row vector represents the full-dimensional performance of a single material, and the column vector represents the distribution of the same performance indicator across different materials, providing structured data input for the subsequent matching algorithm. In this way, the performance differences between different materials in dimensions such as safety, forming, and cost are converted into a quantitative matrix that can be horizontally compared.

[0076] It should be noted that in the embodiments of this application, a dual-channel linear mapping mechanism is used to solve the problem of inconsistent quantitative standards caused by the mixing of indicator types in traditional material selection. The mandatory constraints of the preset intervals eliminate the interference of dimensional differences on data analysis, making heterogeneous data such as tensile strength and hole expansion rate comparable. The normalization process retains the relative distribution pattern of the original data. For example, if a material is 10% better than other materials in tensile strength, the proportional relationship is still maintained after normalization, ensuring the objectivity of subsequent multi-attribute decision-making. This data processing mechanism provides a standardized and scalable data foundation for material screening and sorting, supporting the paradigm shift of the material selection process from empirical judgment to data-driven.

[0077] For example, as shown in Table 1, these are the benefit-type indicators and cost-type indicators of the embodiment of the present application.

[0078] Table 1 Benefit-based evaluation indicators and cost-based evaluation indicators

[0079] Serial number Benefit-based evaluation indicators Serial number Cost-based evaluation indicators 1 Critical fracture strain 8 U-shaped rebound angle 2 tensile strength 9 Relative minimum bending radius 3 Fatigue strength 10 Material unit cost 4 Yield strength 5 Drawing height 6 Bulging height 7 Hole expansion rate

[0080] Taking dent resistance and hole expansion rate as an example, the dent resistance is characterized by the yield strength of the material, and the flanging performance is characterized by the hole expansion rate of the material. The normalized results of dent resistance and flanging performance represented by some material grades are shown in Table 2.

[0081] Table 2 Normalization results of performance attribute data

[0082] Material grade Dent resistance Flanging performance Material grade Dent resistance Flanging performance DC51D+Z 0.68 5.92 HC340 / 590DPD+Z 2.35 0.29 DC53D+Z 0.42 6.92 HC330 / 590DHD+Z 2.42 0.21 DC54D+Z 0.34 7.36 HC420 / 780DPD+Z 3.02 0.17 DC56D+Z 0.31 8.17 HC440 / 780DHD+Z 3.40 0.17 170P1 0.81 4.32 HC550 / 980DPD+Z 4.19 0.45 210P1 1.09 4.18 HC550 / 980DHD+Z 3.55 0.40 250P1 2.13 4.42 HC700 / 980DPD+Z 5.26 0.51 HC180YD+Z 0.81 4.32 HC820 / 1180DPD+Z 7.61 0.24 HC220YD+Z 1.09 4.18 HC340LAD+Z 2.13 4.42 HC260YD+Z 2.13 4.42 HC420LAD+Z 2.98 4.14

[0083] In some examples, evaluating the functional requirement data to generate a second quantitative data set includes:

[0084] Expert scoring of functional requirement data;

[0085] The numerical range of the expert scores is constrained to a second preset interval to generate a second quantitative data set.

[0086] For example, for the evaluation and processing of functional requirement data, a quantitative mechanism combining expert experience and numerical constraints is used to construct a standardized second quantitative data set. Specifically, functional requirement data includes dimensions such as energy absorption performance, load-bearing performance, dent resistance, drawing forming requirements, flanging forming requirements, rebound control requirements and part cost requirements. Each requirement dimension is quantitatively evaluated through an expert scoring module. Experts perform initial scoring on the functional requirement sub-dimensions based on the actual working conditions of the parts and components, combined with the simulation analysis results of forming simulation software (such as AutoForm, PAM-STAMP). For example, for the energy absorption performance requirements of the front longitudinal beam components, the experts initially assessed it as 8.5 points in the range of 0-10 based on the energy absorption contribution of the part in the collision safety simulation; for the drawing forming requirements of the door inner panel, based on the analysis of the critical value of the fracture in the stamping forming simulation, it was initially assessed as 7.2 points. The scoring process integrates engineering simulation data and experience judgment to ensure the scientific nature and engineering applicability of the requirement scoring.

[0087] After completing the initial expert scoring, the system uses a preset linear mapping rule to constrain the original score to a second preset interval of 0-9 to achieve data standardization. In the specific processing, the original expert score range is set to [min_s, max_s], the target interval is [0, 9], and the formula s' = (s-min_s) / (max_s-min_s) × 9 is used for conversion, where s is the original expert score value and s' is the constrained quantified value. For example, the energy absorption performance of a component is originally scored 8.5 points (assuming the expert score range is 7.0-10.0), and the converted quantified value is (8.5-7.0) / (10.0-7.0) × 9 = 4.5; if the original score of a flanging forming requirement is 6.0 points (scoring range 5.0-8.0), the converted quantified value is (6.0-5.0) / (8.0-5.0) × 9 = 3.0. This constraint mechanism eliminates the scale differences caused by different experts' scoring habits and ensures that the quantitative values ​​of all demand dimensions are horizontally comparable.

[0088] The second quantitative data set generated uses the component identification code as an index and establishes structured data records according to the functional requirement dimension. The requirement data record for each component contains the following fields: component code, energy absorption performance → 7.5, load-bearing performance → 8.2, dent resistance → 6.8, drawing forming requirements → 5.4, flanging forming requirements → 6.1, rebound control requirements → 7.0, part cost requirements → 4.9, etc., where the values ​​are all quantitative results constrained to the range of 0-9. The data set is stored in a matrix format, with row vectors representing the full-dimensional functional requirements of a single component and column vectors representing the distribution of the same requirement dimension among different components. For example, the energy absorption requirement of the front longitudinal beam is 8.7, while the energy absorption requirement of the roof crossbeam is 5.3, reflecting the differentiated requirements of the two in terms of safety performance.

[0089] It should be noted that in the embodiment of the present application, through the fusion mechanism of expert scoring and simulation data, the functional requirements of parts are converted into quantifiable and traceable engineering parameters, eliminating the ambiguity in traditional empirical descriptions; the mandatory constraints of the preset intervals realize the comparability of data across parts and across demand dimensions, for example, the energy absorption performance of 8.5 and the drawing requirement of 7.2 can be directly compared in priority; the binding relationship between the structured data set and the part code supports the subsequent precise matching with the material performance data. For example, the energy absorption performance requirement value of 7.5 is automatically associated with the critical fracture strain index in the material performance through the preset mapping rules, providing a clear matching threshold for the screening algorithm. This quantification mechanism establishes an objective demand benchmark for the material selection process, effectively reducing the subjective interference of human experience.

[0090] In some examples, based on the matching relationship between the first quantitative data set and the second quantitative data set, screening candidate materials that meet the functional requirement data to generate a candidate material set includes:

[0091] Based on preset rules, the performance attribute data in the first quantitative data set is matched one by one with the functional requirement data in the second quantitative data set, wherein the preset mapping rule is a correspondence between the classification dimensions of the performance attribute data and the classification dimensions of the functional requirement data;

[0092] Candidate materials whose performance attribute data in the first quantitative data set is greater than or equal to corresponding functional requirement data in the second quantitative data set are screened to generate a candidate material set.

[0093] Exemplarily, the generation of candidate material sets is based on preset dimensional mapping rules and a rigid screening mechanism, enabling preliminary material selection by establishing a point-to-point matching relationship between material properties and component requirements. The preset mapping rules specifically represent a strict correspondence between the performance attribute data classification dimensions and the functional requirement data classification dimensions. For example, the following mapping relationships are formed: critical fracture strain → energy absorption performance, tensile strength → load-bearing performance, fatigue limit → durability performance, yield strength → dent resistance, drawing height → drawing requirements, bulging height → bulging requirements, hole expansion ratio → flanging requirements, relative minimum bend radius → bending requirements, U-shaped springback angle → springback control requirements, and material unit price → part cost requirements. These mapping relationships form 10 pairs of core mapping relationships. These rules are embedded in the system through structured data tables. For example, the material property field "HC340LAD+Z_Tensile Strength = 7.6" corresponds to the component requirement field "S02A01P001_Load-Bearing Performance = 7.5," establishing a logical association channel across data sets.

[0094] During the matching execution phase, the system traverses the first quantitative data set of candidate materials and compares their performance attribute data item by item with the corresponding required values ​​in the second quantitative data set of the target component. The specific screening logic is: for each candidate material, check whether its normalized performance values ​​in all mapping dimensions are greater than or equal to the required quantitative values ​​of the component. For example, if the energy absorption performance requirement quantitative value of a component is 7.5, then materials with a normalized critical fracture strain value greater than or equal to 7.5 will be screened out; if its flange forming requirement quantitative value is 6.0, then materials with a normalized hole expansion rate value greater than or equal to 6.0 will be further screened out. Only when the candidate material meets the threshold conditions in all 10 mapping dimensions will it be included in the candidate material set, forming a strict "and" logic screening condition to ensure that the selected materials meet the standards in terms of safety, forming and cost.

[0095] The generated set of candidate materials is stored in an indexed structure, with the component identification code as the primary key. This structure links all material grades that meet the requirements and records matching difference data for each dimension. For example, the critical fracture strain of candidate material HC340LAD+Z in the energy absorption dimension is quantified to 8.1, exceeding the required value of 7.5 by 0.6. The hole expansion ratio in the flange forming dimension is quantified to 7.2, exceeding the required value of 6.0 by 1.2. This difference data provides an optimization benchmark for subsequent multi-attribute decision-making, supporting the identification of performance redundancy distribution characteristics among qualified materials. For example, a material that significantly exceeds the safety dimension but barely reaches the cost threshold can form a differentiated solution compared to another material that has balanced excesses in all dimensions.

[0096] It should be noted that in the embodiments of the present application, complex engineering experience is converted into programmable data association rules through preset mapping rules. For example, the engineering cognition that "the flanging forming requirement depends on the material hole expansion performance" is solidified into a field mapping of hole expansion rate → flanging forming requirement to avoid misjudgment based on manual experience; full-dimensional rigid screening ensures that candidate materials meet the lower limit of functional requirements, eliminating the potential risk of traditional material selection where a single dimension is prominent while other dimensions do not meet the standards; the structured storage of candidate sets provides clean data input for subsequent sorting, eliminating invalid data interference. For example, although a certain material has a tensile strength of 8.5 that is significantly higher than the requirement of 7.0, it is automatically filtered by the system because the quantitative value of the U-shaped rebound angle is 5.8, which is lower than the required threshold of 6.0. This solves the problem of attention blind spots in manual screening and improves the reliability of the material selection plan.

[0097] In some instances, based on user needs, a multi-attribute decision-making ranking is performed on a set of candidate materials to generate differentiated material selection ranking results, including:

[0098] Based on the analytic hierarchy process, a preference matrix is ​​constructed according to user needs and the consistency of the preference matrix is ​​checked. User needs include safety needs, format needs and cost needs.

[0099] Based on the preference matrix that passes the consistency test, the safety score, forming score and cost score are calculated for each candidate material in the candidate material set;

[0100] Generate a comprehensive ranking score for each candidate material based on safety score, forming score and cost score;

[0101] Arrange candidate materials in descending order according to comprehensive ranking scores to generate differential material selection ranking results;

[0102] Among them, the safety score is used to characterize the score of the candidate material in the safety performance dimension; the forming score is used to characterize the score of the candidate material in the forming process dimension; and the cost score is used to characterize the score of the candidate material in the economic dimension.

[0103] For example, the generation of the differential material selection ranking results is based on a multi-attribute decision-making model constructed based on the hierarchical analysis method, which realizes a multi-dimensional comprehensive evaluation of candidate materials by converting the user's subjective preferences into an objective weight system. In specific implementation, first, according to the user's definition of the priority of safety requirements, forming requirements and cost requirements, a three-order preference matrix is ​​constructed: the relative importance scale values ​​of the three elements of safety, forming and cost are set, and the 1-9 scaling method is used to quantify the comparison results. For example, if the user defines the importance of safety requirements as 5 times that of forming requirements (scale value 5) and 3 times that of cost requirements (scale value 3), the corresponding elements of the initial preference matrix are a12=5 and a13=3, and the symmetric elements a21=1 / 5 and a31=1 / 3 are filled in according to the reciprocity principle, and the diagonal elements a11=a22=a33=1 to form a complete judgment matrix.

[0104] Then, a consistency check of the preference matrix is ​​performed to ensure the rationality of the user's logical judgment. The maximum eigenvalue λ_max of the matrix and the consistency index CI = (λ_max-n) / (n-1) are calculated, where n = 3 is the matrix order. Combined with the random consistency index RI value (RI = 0.58 when n = 3), the consistency ratio CR = CI / RI is calculated. When CR < 0.1, the matrix is ​​judged to have passed the test, otherwise the user is prompted to adjust the scale value. For example, an initial matrix is ​​calculated to have λ_max = 3.104, CI = 0.052, and CR = 0.089, which meets the CR < 0.1 condition and is considered a valid weight distribution; if CR = 0.12, the scale value needs to be readjusted until the consistency requirement is met.

[0105] The weight coefficients for each dimension are calculated using the eigenvector method using the tested preference matrix. The matrix is ​​normalized, and the geometric mean of each row is calculated and normalized again to obtain the weight vectors [w_s, w_f, w_c] for safety, forming, and cost. For example, for a valid matrix, the weight coefficients w_s = 0.637, w_f = 0.258, and w_c = 0.105 are calculated. For each material in the candidate material set, the safety score, forming score, and cost score are calculated separately: the safety score S is the weighted sum of the normalized values ​​of the critical fracture strain, tensile strength, fatigue limit, and yield strength, with the weights using the safety sub-dimension weights derived from the analytic hierarchy process; the forming score F is the weighted sum of the normalized values ​​of the drawing height, bulging height, hole expansion ratio, relative minimum bend radius, and U-shaped springback angle; and the cost score C is the normalized value of the material unit price. The comprehensive ranking score T = w_s×S+w_f×F+w_c×C realizes the linear weighted aggregation of multi-dimensional performance.

[0106] The resulting differential material selection ranking results are arranged in descending order by comprehensive score T, forming a material priority sequence. For example, material A scores 8.2 for safety, 7.5 for forming, and 6.8 for cost, with a comprehensive score of T = 0.637 × 8.2 + 0.258 × 7.5 + 0.105 × 6.8 = 7.89. Material B scores 7.6, 8.1, and 5.2, respectively, with a score of T = 0.637 × 7.6 + 0.258 × 8.1 + 0.105 × 5.2 = 7.45, placing material A ahead of material B. This ranking mechanism quantifies user requirements and preferences into calculable weight parameters. While ensuring that basic functionalities meet standards, it accurately reflects the differentiated priorities of safety, forming, and cost, resolves decision conflicts in multi-objective optimization, and outputs a sequence of optimal solutions that meets the actual project requirements.

[0107] In some instances, based on the results of the differential material selection ranking, a user material selection plan is determined, including:

[0108] Based on the differential material selection ranking results, the candidate material with the highest comprehensive ranking score is selected as the user's material selection plan.

[0109] Exemplarily, the user's material selection scheme is determined based on a top selection mechanism based on the results of differential material selection sorting, and the optimal solution screening is achieved through data-driven decision logic. Specifically, the system arranges the comprehensive ranking scores of the candidate materials calculated by the hierarchical analysis method in descending order, and automatically selects the material ranked first as the final material selection scheme. This mechanism follows the principle of "non-inferior solution first". On the premise of ensuring that all candidate materials have passed the basic functional threshold screening, the competitive relationship of multi-dimensional attributes such as safety, forming, and cost is converted into a linearly comparable comprehensive score, and the convergence of the multi-objective optimization problem is achieved by maximizing the weighted total score. For example, if the comprehensive score of material A is 7.89 and that of material B is 7.45, material A will be automatically selected and a material selection report will be generated. Its decision logic relies entirely on quantitative calculations to exclude priority misjudgments caused by human intervention.

[0110] The technical implementation of this step relies on the combination of a normalized data system and dynamic weight assignment to ensure the comparability and aggregation rationality of scores across different dimensions. The safety score, forming score, and cost score have been normalized to the same dimensional range in previous processing, and the weight coefficients obtained through the hierarchical analysis method meet the normalization constraint of Σw = 1, making the calculation of the comprehensive score conform to the mathematical norms of linear weighted aggregation. For example, with a weight of 0.637 for the safety dimension, 0.258 for the forming dimension, and 0.105 for the cost dimension, the weighted sum of the scores for each material dimension directly reflects the user's preferred value orientation, avoiding the ranking distortion problem caused by inconsistent dimensions or unbalanced weights in traditional methods.

[0111] When the system is executing the material selection plan, it will simultaneously generate a structured material selection record, including the brand of the selected material, detailed scores for each dimension, and weight configuration parameters. The record is stored in the material selection knowledge base with the component identification code as the index, forming a traceable decision-making chain. For example, the material selection result record for component S02A01P001 includes: selected material HC420LAD+Z, safety score 8.1 (weight 0.637), forming score 7.3 (weight 0.258), cost score 6.5 (weight 0.105), and comprehensive score 7.82. This mechanism not only supports the review and verification of material selection results, but also provides a data basis for subsequent solution optimization. For example, when the user adjusts the weight parameters, the system can quickly recalculate the sorting results based on historical records.

[0112] It should be noted that in the embodiments of this application, a fully automatic top-selection mechanism is used to transform the complex multi-attribute decision-making process into a deterministic output, eliminating the decision-making delays of traditional manual discussions; a data system based on normalization and weight constraints ensures that the ranking results conform to the principles of mathematical optimization and avoids the local optimal trap commonly seen in empirical material selection; a structured recording mechanism forms a closed-loop decision-making evidence chain, meeting the requirements of traceability and auditability of the material selection process in the quality management system of automobile companies. Compared to the inefficient model of traditional material selection methods that rely on engineers' personal experience to compare material parameter tables, this application implements decision responses based entirely on quantitative data and preset rules, improving the standardization of the material selection process and the consistency of results.

[0113] In some instances, this also includes:

[0114] Encode the target parts based on the preset coding rules and generate part identification codes;

[0115] The component identification code is used as an index code to obtain functional requirement data.

[0116] Exemplarily, the generation of component identification codes and the indexing mechanism for functional requirement data achieve precise positioning and rapid retrieval of component data through structured coding rules. The preset coding rules employ a hierarchical composite coding structure, comprising three parts: a system classification code, an assembly affiliation code, and a part serial code. The first letter "S" followed by two digits identifies the system classification (e.g., S01 for the opening and closing system, S02 for the body frame system, and S03 for the bumper system). The middle letter "A" followed by two digits identifies the assembly affiliation (e.g., A01 for the front compartment assembly, A02 for the front panel assembly, A03 for the side panel assembly, and A04 for the floor assembly). The last letter "P" followed by three digits identifies the specific part (e.g., P038 for the door hinge reinforcement plate), forming a complete identification code (e.g., S01A02P038). This coding rule uses a composite coding logic of letters and numbers to perform a structured mapping of the functional hierarchy of parts and components with their physical assembly relationships. For example, the identification code S02A03P015 is parsed as the vehicle body frame system-side panel assembly-part No. 15, thus achieving unique identification and machine-readable expression of the component identity information.

[0117] When using the part identification code as an index to retrieve functional requirement data, the system creates a functional requirement data table in the relational database with the identification code as the primary key. This data table stores fields including identification code, energy absorption performance quantification value, load-bearing performance quantification value, and forming requirement quantification value for 10 functional requirement dimensions. When the user enters the target part identification code (such as S02A01P001), a matching query based on the identification code is executed, and the full-dimensional functional requirement data for the part is retrieved using the SQL query statement "SELECT * FROM Functional_Requirements WHERE Part_ID = 'S02A01P001'". For example, the identification code S02A01P001 corresponds to field values ​​such as energy absorption performance 7.5, load-bearing performance 8.2, and draw forming requirement 5.4, providing a benchmark parameter set for subsequent material selection and matching.

[0118] It should be noted that in the embodiment of the present application, a strong association between component identity information and functional requirements is achieved through structured coding rules, avoiding data retrieval errors caused by non-standard naming in traditional methods; index code-driven data retrieval improves the efficiency of obtaining functional requirements to the millisecond level, and in the whole vehicle material selection scenario containing thousands of components, the query time complexity is optimized from O(n) to O(log n) through the B+ tree index structure; the scalability of the coding system supports the seamless integration of new component types. New components only need to generate unique identification codes according to the rules and write them into the data table, without modifying the core logic of the system, ensuring the continued applicability of the material selection system in vehicle model iterations.

[0119] See also Figure 2 , is a schematic structural diagram of a material selection device for passenger car body parts provided in an embodiment of the present application, comprising:

[0120] A data acquisition unit 21 is used to acquire performance attribute data of candidate materials and functional requirement data of target components;

[0121] The performance processing unit 22 is configured to perform normalization processing on the performance attribute data to generate a first quantitative data set;

[0122] A function processing unit 23 is configured to evaluate and process the function requirement data to generate a second quantitative data set;

[0123] A preliminary material selection unit 24, based on the matching relationship between the first quantitative data set and the second quantitative data set, screens candidate materials that meet the functional requirement data and generates a candidate material set;

[0124] The material selection optimization unit 25 performs multi-attribute decision-making and ranking of the candidate material set based on user needs, and generates differential material selection ranking results;

[0125] The material determination unit 26 is used to determine the user's material selection plan based on the difference material selection sorting results.

[0126] See also Figure 3 An embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any method for selecting materials for passenger car body parts.

[0127] Since the electronic device introduced in this embodiment is an apparatus used to implement a material selection device for passenger car body parts in the embodiment of this application, based on the method introduced in the embodiment of this application, technical personnel in this field can understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of this application will not be introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of this application falls within the scope of protection of this application.

[0128] During the specific implementation process, when the computer program 311 is executed by the processor, any implementation method of the embodiments corresponding to the first aspect can be implemented.

[0129] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those skilled in the art will appreciate that embodiments of the present application may provide methods, systems, or computer program products. Thus, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-readable storage media containing computer-readable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes Figure 1 The process of a material selection method for passenger car body parts in the corresponding embodiment.

[0135] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium can be a magnetic medium, an optical medium or a semiconductor medium, etc.

[0136] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0138] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, the functional units in the various embodiments of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The above-mentioned integrated units may be implemented in the form of hardware and / or software functional units.

[0140] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device to execute all or part of the steps of the various embodiments of the method of the present application.

[0141] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0142] Although the preferred embodiments of this specification have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of this specification.

[0143] Obviously, those skilled in the art may make various changes and modifications to this specification without departing from the spirit and scope of this specification. Thus, if such changes and modifications fall within the scope of the claims of this specification and their equivalents, this specification is intended to include such changes and modifications.

Claims

1. A method for selecting materials for passenger car body parts, characterized in that: The method comprises: Obtain performance attribute data of candidate materials and functional requirement data of target components; Normalizing the performance attribute data to generate a first quantitative data set; Evaluating and processing the functional requirement data to generate a second quantitative data set; Based on the matching relationship between the first quantitative data set and the second quantitative data set, screening candidate materials that meet the functional requirement data to generate a candidate material set; Based on user needs, the candidate material set is sorted by multi-attribute decision making to generate differential material selection sorting results; A user material selection plan is determined based on the differential material selection sorting results.

2. The method according to claim 1, characterized in that The performance attribute data includes benefit-type indicator data and cost-type indicator data. The normalization processing of the performance attribute data to generate a first quantitative data set includes: Normalizing the benefit-type indicator data to a first preset interval based on a first linear mapping formula to generate a quantitative value of the benefit-type indicator; Normalizing the cost-type indicator data to the first preset interval based on a second linear mapping formula to generate a quantitative value of the cost-type indicator; A first quantitative data set is generated based on the quantitative values ​​of the benefit-type indicators and the quantitative values ​​of the cost-type indicators.

3. The method according to claim 1, characterized in that The step of evaluating the functional requirement data to generate a second quantitative data set includes: Performing expert scoring on the functional requirement data; The numerical range of the expert scores is constrained to a second preset interval to generate a second quantitative data set.

4. The method according to claim 1, wherein The step of screening candidate materials that meet the functional requirement data based on the matching relationship between the first quantitative data set and the second quantitative data set to generate a candidate material set includes: Based on preset rules, the performance attribute data in the first quantitative data set is matched one by one with the functional requirement data in the second quantitative data set, wherein the preset mapping rule is a correspondence between the classification dimensions of the performance attribute data and the classification dimensions of the functional requirement data; Candidate materials whose performance attribute data in the first quantitative data set is greater than or equal to the corresponding functional requirement data in the second quantitative data set are screened to generate a candidate material set.

5. The method according to claim 1, wherein The method of performing multi-attribute decision-making sorting on the candidate material set based on user needs to generate differential material selection sorting results includes: Based on the hierarchical analysis method, a preference matrix is ​​constructed according to user needs and the consistency check is performed on the preference matrix, wherein the user needs include safety needs, forming needs and cost needs; Calculating a safety score, a forming score, and a cost score for each candidate material in the candidate material set based on the preference matrix that passes the consistency check; generating a comprehensive ranking score for each candidate material based on the safety score, the forming score, and the cost score; Arrange the candidate materials in descending order according to the comprehensive ranking scores to generate a differential material selection ranking result; Among them, the safety score is used to characterize the score of the candidate material in the safety performance dimension; the forming score is used to characterize the score of the candidate material in the forming process dimension; and the cost score is used to characterize the score of the candidate material in the economic dimension.

6. The method according to claim 1, wherein Also includes: Encode the target parts based on the preset coding rules and generate part identification codes; The component identification code is used as an index code to obtain the functional requirement data.

7. The method according to claim 1, characterized in that Determine the user's material selection plan based on the differential material selection sorting results, including: Based on the differential material selection ranking results, the candidate material with the highest comprehensive ranking score is selected as the user material selection plan.

8. A material selection device for passenger car body parts, characterized in that: The device comprises: A data acquisition unit, used to acquire performance attribute data of candidate materials and functional requirement data of target components; a performance processing unit, configured to perform normalization processing on the performance attribute data to generate a first quantitative data set; a function processing unit, configured to evaluate and process the function requirement data to generate a second quantitative data set; a preliminary material selection unit, screening candidate materials that meet the functional requirement data based on a matching relationship between the first quantitative data set and the second quantitative data set, and generating a candidate material set; The material selection unit is optimized to perform multi-attribute decision-making and sorting on the candidate material set based on user needs, and generate differential material selection sorting results; The material determination unit is used to determine the user's material selection plan based on the differential material selection sorting results.

9. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the method for selecting materials for passenger vehicle body parts as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

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 method for selecting materials for passenger vehicle body parts according to any one of claims 1 to 7 is implemented.

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