Vehicle interior material performance evaluation method, device and equipment and storage medium

Abstract

The invention discloses a vehicle interior material performance evaluation method, device and equipment and a storage medium, and the method comprises the steps: building a hierarchical structure model which comprises a criterion layer, a control layer and a scheme layer; calculating the weight of each criterion layer and the weight of each index of a control layer in the model to determine the comprehensive weight of each index; determining a grey correlation coefficient of each scheme and each index in a scheme layer in the model based on a grey correlation method; and constructing a weighting standard matrix through the comprehensive weight and the grey correlation coefficient, and determining an optimal evaluation scheme by using the weighting standard matrix so as to evaluate the performance of the vehicle interior material. According to the method, the performance of the automotive interior material can be accurately evaluated, the performance characteristics of different materials can be effectively evaluated, the design and selection of the interior material are optimized, the development and application of a new material are promoted, and the quality of the automotive interior and the quality of an automobile are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile performance evaluation, and in particular to a vehicle interior material performance evaluation method, device, equipment and storage medium. Background Art

[0002] Automotive interior materials play an important role in automobile manufacturing. They directly affect the comfort, safety, environmental protection, aesthetics and riding experience of the car. Good interior materials can improve the comfort of passengers, improve the in-car environment, and increase the satisfaction of drivers and passengers. Interior materials can also provide necessary support and protection. For example, seat materials should have good support and durability to ensure the safety and stability of drivers and passengers. Therefore, automotive interior materials are crucial to the overall quality and value of the car. The range of automotive interior materials is wide, and there are many parameters that affect performance. There is a relatively independent but interrelated relationship between the parameters. The factors that affect the performance of interior materials are complex, and the interaction between the various influencing factors has great research value.

[0003] At present, there is still a blank in the field of automobile interior material evaluation system. In the field of automobile evaluation, subjective evaluation method is mainly used, and weights are matched artificially, which is one-sided and cannot accurately evaluate the performance of automobile interior materials.

[0004] Therefore, how to accurately evaluate the performance of automotive interior materials is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The main purpose of the present invention is to provide a vehicle interior material performance evaluation method, device, equipment and storage medium, which can accurately evaluate the performance of automobile interior materials, and can also effectively evaluate the performance characteristics of different materials, optimize the design and selection of interior materials, promote the development and application of new materials, and improve the quality of automobile interiors and automobile quality.

[0006] In a first aspect, the present application provides a method for evaluating the performance of a vehicle interior material, wherein the method comprises the steps of:

[0007] Constructing a hierarchical structure model, the model comprising a criterion layer, a control layer and a solution layer;

[0008] Calculate the weight of each criterion layer and the weight of each indicator of the control layer in the model to determine the comprehensive weight of each indicator;

[0009] Based on the grey correlation method, the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model is determined;

[0010] A weighted standard matrix is ​​constructed by using the comprehensive weight and the grey correlation coefficient, and the weighted standard matrix is ​​used to determine the best evaluation scheme to evaluate the performance of vehicle interior materials.

[0011] In combination with the first aspect above, as an optional implementation method, the evaluation indicators in the criterion layer are compared and assigned values ​​in pairs, and the judgment matrix element a is obtained after taking the average value. ij , where the judgment matrix expression is: A = (a ij ) k×k (i,j=1,2,3,...,k);

[0012] According to the formula: Calculate the weight of the i-th criterion layer indicator, where is the square root of the product of the judgment matrix, and its expression is:

[0013] In combination with the first aspect, as an optional implementation method, an evaluation matrix X is constructed, and the evaluation matrix is ​​standardized to obtain a standardized matrix Y, where X=(x ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), Y=(y ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), where x ij is the collected data corresponding to the jth evaluation index of the i-th scheme, yi j The data obtained after dimensionless normalization of the jth evaluation index of the i-th scheme;

[0014] According to the formula: Calculate the information entropy of each indicator, where It is the proportion of the jth indicator in the ith scheme, M is the number of evaluation objects at the scheme level, and n is the number of evaluation indicators at the control level;

[0015] According to the information entropy of each indicator, the weight w of each indicator in the control layer is determined Bj ,in, n is the number of evaluation indicators in the control layer.

[0016] In combination with the first aspect above, as an optional implementation method, according to the formula: Calculate the comprehensive weight of each indicator, where wij is the comprehensive weight of the jth indicator under the i-th criterion layer, and w Ai is the weight of the index of the ith criterion layer, W Bjis the information weight of the j-th indicator in the control layer, and C(i) is the number of indicators contained in the i-th dimension.

[0017] In combination with the first aspect above, as an optional implementation method, according to the formula: Calculate the grey correlation coefficient between each scheme and each indicator, where ξ ij The grey correlation coefficient between the i-th scheme and the j-th index of the reference sequence, ρ is the resolution coefficient, y j0 The maximum value in each metric.

[0018] In combination with the first aspect, as an optional implementation method, the grey correlation coefficient ξ ij Construct a weighted standard matrix V, V = (v ij ) m×n (i=1,2,3...m;j=1,2,3...n), where v ij =w ij *ξ ij , which is the data obtained by weighting the grey correlation coefficient of the jth indicator of the i-th scheme;

[0019] Determine the maximum value and the minimum value of the preset number of indicators selected from the weighted standard matrix to respectively constitute the positive ideal solution and the negative ideal solution of the weighted standard matrix;

[0020] Determine the distance of each solution to the positive ideal solution and the negative ideal solution, and based on the distance of each solution to the positive ideal solution and the negative ideal solution, determine the relative closeness of each solution to the positive ideal solution;

[0021] The relative progress of each plan to the positive ideal solution is ranked, and the plan with the smallest relative progress is selected as the best evaluation plan.

[0022] In combination with the first aspect above, as an optional implementation method, according to the formula: Calculate the distance from the ith solution to the positive ideal solution, where Select the maximum value among the preset number of indicators in the weighted standard matrix;

[0023] According to the formula: Calculate the distance from the i-th solution to the negative ideal solution, where Select the minimum value among the preset number of indicators in the weighted standard matrix;

[0024] According to the formula: Calculate the relative closeness of the i-th solution to the positive ideal solution.

[0025] In a second aspect, the present application provides a vehicle interior material performance evaluation device, the device comprising:

[0026] A construction module, which is used to construct a hierarchical structure model, wherein the model includes a criterion layer, a control layer and a solution layer;

[0027] A calculation module, which is used to calculate the weight of each criterion layer and the weight of each indicator of the control layer in the model to determine the comprehensive weight of each indicator;

[0028] A determination module, which is used to determine the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model based on the grey correlation method;

[0029] A processing module is used to construct a weighted standard matrix through the comprehensive weight and the grey correlation coefficient, and to determine the best evaluation scheme by using the weighted standard matrix to evaluate the performance of vehicle interior materials.

[0030] In a third aspect, the present application further provides an electronic device, comprising: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the first aspects is implemented.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium storing computer program instructions, which, when executed by a computer, enables the computer to execute any of the methods described in the first aspect.

[0032] The present application provides a method, device, equipment and storage medium for evaluating the performance of vehicle interior materials, wherein the method includes the steps of: constructing a hierarchical structure model, the model including a criterion layer, a control layer and a scheme layer; calculating the weights of each criterion layer and the weights of each indicator of the control layer in the model to determine the comprehensive weight of each indicator; based on the grey correlation method, determining the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model; constructing a weighted standard matrix through the comprehensive weight and the grey correlation coefficient, and using the weighted standard matrix to determine the best evaluation scheme to evaluate the performance of vehicle interior materials. The present application can accurately evaluate the performance of automotive interior materials, and can also effectively evaluate the performance characteristics of different materials, optimize the design and selection of interior materials, promote the development and application of new materials, and improve the quality of automotive interiors and automotive quality.

[0033] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0035] Figure 1A flow chart of a vehicle interior material performance evaluation method provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of a vehicle interior material performance evaluation device provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of a multi-level evaluation index structure provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present application;

[0039] Figure 5 A schematic diagram of a computer-readable program medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0041] Furthermore, the drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities.

[0042] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings.

[0043] Reference Figure 1 , Figure 1 FIG. 1 is a flow chart of a vehicle interior material performance evaluation method provided by the present invention, such as Figure 1 As shown, the method comprises the steps of:

[0044] Step S101: construct a hierarchical structure model, which includes a criterion layer, a control layer and a solution layer.

[0045] It can be understood that the indicator system is determined and a hierarchical model is constructed, including a target layer, a criterion layer, a control layer (sub-criterion layer), and a solution layer.

[0046] According to the requirements of standards and regulations, performance and technical requirements, and user experience needs, the flame retardant performance, environmental performance, mechanical properties, durability, sensory evaluation and other factors of automotive interior materials are determined as the first-level criteria layer. In the flame retardant performance criteria layer, the second-level control layer indicators mainly include horizontal combustion, vertical combustion, smoke density level, oxygen index, molten dripping, and combustion level. If necessary, other indicators can be expanded.

[0047] In the mechanical properties criterion layer, the secondary control layer indicators mainly include tensile properties, elastic modulus, and dimensional stability. If necessary, other indicators can be expanded.

[0048] In the durability performance criterion layer, the secondary control layer indicators mainly include wear resistance, liquid resistance, friction color fastness, aging resistance (light aging resistance), and other indicators can be expanded if necessary.

[0049] In the environmental performance criteria layer, the secondary control layer indicators mainly include VOC, atomization, and banned substances. In the sensory evaluation criteria layer, the secondary control layer indicators mainly include odor evaluation and touch evaluation. If necessary, other indicators can be expanded.

[0050] It should be explained that the hierarchical model is a tool for decision analysis, which decomposes complex decision problems into different components and organizes them according to the interrelationships and affiliations between these components. This model usually includes the following levels: Target layer: This is the highest level of the hierarchical structure, representing the overall goal or ultimate purpose of the decision problem. Criteria layer: Located below the target layer, it contains various criteria or standards that need to be considered to achieve the goal. Indicator layer: Further subdivide the specific indicators or standards of the criterion layer to measure and evaluate the degree of realization of each criterion. This layer usually contains more specific quantitative or qualitative indicators. Solution layer: Located at the lowest level of the hierarchical structure, it contains optional solutions or measures to achieve the goal. For example, in this application, the solution layer data of each evaluation indicator is collected by experiments and reference to literature. The indicator data is collected by experimental method. It should also be explained that there are multiple solutions to evaluate the interior. The purpose of this application is to select an optimal evaluation solution from many solutions for interior evaluation. The method in Table 1 can be referred to, and the indicator data collected in Table 2.

[0051] Table 1

[0052]

[0053]

[0054] Table 2

[0055]

[0056] Step S102: Calculate the weight of each criterion layer and the weight of each indicator in the control layer in the model to determine the comprehensive weight of each indicator.

[0057] Specifically, a flame retardant performance, environmental performance, mechanical properties, durability performance and sensory evaluation index system is established as the first-level criterion layer for vehicle interior materials;

[0058] The evaluation indicators in the criterion layer are compared and assigned in pairs, and the average value is taken to obtain the judgment matrix element a ij , where the judgment matrix expression is: A = (a ij ) k×k (i,j=1,2,3,...,k);

[0059] According to the formula: Calculate the weight of the i-th criterion layer indicator, where is the square root of the product of the judgment matrix, and its expression is:

[0060] Based on the subjective weighting method (analytic hierarchy process AHP), the weights of each factor in the criterion layer are determined, specifically:

[0061] 1) According to Santy's 1-9 scaling method, as shown in Table 3, the evaluation dimensions in the criterion layer are compared and assigned values ​​in pairs, and the judgment matrix elements aij are obtained after taking the average value, and the judgment matrix A is constructed. A=(a ij ) k×k (i, j = 1, 2, 3, ..., k), where a ij is the importance of index i relative to index j, i, j = 1, 2, 3, ..., k, k is the number of index dimensions of the criterion layer, that is, the order of the judgment matrix, satisfying: a ij >0, a ii =1, a ij =1 / a ji .

[0062] Table 3

[0063]

[0064]

[0065] 2) Calculate the weight of indicators at each criterion level:

[0066] According to the formula: Calculate the weight of the i-th criterion layer indicator, where is the square root of the product of the judgment matrix, and its expression is: Among them, w AiThe weight of the indicator of the i-th criterion layer, i, j = 1, 2, 3, ..., k, k is the number of indicator dimensions of the criterion layer, a ij are the elements of the judgment matrix A, i, j = 1, 2, 3, ..., k, k is the number of indicator dimensions of the criterion layer, that is, the order of the judgment matrix.

[0067] The calculation results of the criterion layer weights are shown in Table 4:

[0068] Table 4

[0069]

[0070] 3)Consistency check:

[0071] In order to ensure the credibility of the weight value, it is necessary to perform a consistency check on the judgment matrix and calculate the consistency ratio CR:

[0072]

[0073] Where CR is the consistency ratio; max To determine the maximum characteristic root of matrix A, the formula is: Where W is the eigenvector (i.e., weight vector) of the judgment matrix A, satisfying AW = λ max W, RI is the random consistency index, which is obtained by querying the random consistency index table (as shown in Table 5).

[0074] Table 5

[0075] Matrix order k 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

[0076] When CR < 0.1, it indicates that the consistency of the judgment matrix A is considered to be within the permissible range;

[0077] If CR≥0.1, it is judged that the matrix A deviates too much from the consistency and the element values ​​in A need to be corrected.

[0078] The weights of each indicator in the control layer are determined based on the objective weighting method (entropy weight method). Specifically, the evaluation matrix X is constructed and the evaluation matrix is ​​standardized to obtain the standardized matrix Y, where X = (x ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), Y=(y ij ) m×n (i=1, 2, 3…m; j=1, 2, 3…n), where x ij is the collected data corresponding to the jth evaluation index of the i-th scheme, y ij The data obtained after dimensionless normalization of the jth evaluation index of the i-th scheme is performed according to the following process:

[0079] a): The dimensionless standardization of benefit indicators (the larger the indicator value, the better) is:

[0080]

[0081] b) The dimensionless standardization of cost indicators (the smaller the indicator value, the better) is:

[0082]

[0083] c) The dimensionless normalization of a specific interval indicator (with the same effect within the interval) is:

[0084]

[0085] d) For indicators related to standards and regulations, when x ij When within the limit, refer to a), b), and c) for standardization. ij If the regulatory limit is exceeded, y ij =0.

[0086] According to the formula: Calculate the information entropy of each indicator, where It is the proportion of the jth indicator in the ith solution. If p ij =0, then define M is the number of evaluation objects at the solution level, and n is the number of evaluation indicators at the control level;

[0087] According to the information entropy of each indicator, the weight w of each indicator in the control layer is determined Bj ,in, n is the number of evaluation indicators in the control layer.

[0088] Among them, the results of calculating the control layer weights using the entropy weight method are shown in Table 6.

[0089] Table 6

[0090] Control layer indicators Information entropy Weight (%) Color fastness to rubbing 0.667 11.836 Touch evaluation 0.865 4.793 Photoaging resistance 0.641 12.764 Oxygen Index 0.856 5.128 Melt dripping 0.774 8.027 Liquid resistance 0.833 5.922 Prohibited substances 0.862 4.914 Elastic modulus 0.797 7.231 Tensile properties 0.834 5.902 Atomization 0.897 3.669 Wear resistance 0.858 5.043 Odor Rating 0.894 3.772 Horizontal burning 0.907 3.313 Formaldehyde emission 0.877 4.367 Dimensional stability 0.881 4.246 Vertical combustion 0.881 4.244 Smoke density level 0.864 4.83

[0091] The obtained criterion layer weight is combined with the obtained control layer weight to obtain the comprehensive weight, that is, according to the formula: Calculate the comprehensive weight of each indicator, where w ij is the comprehensive weight of the jth indicator under the i-th criterion layer, w Ai is the weight of the index at the i-th criterion layer, w Bj is the information weight of the jth indicator in the control layer, C(i) is the number of indicators contained in the i-th dimension, and C(0)=0, C(i)≥1 is defined.

[0092] The comprehensive weight calculation results are shown in Table 7.

[0093] Table 7

[0094] Control layer indicators Comprehensive weight Horizontal burning 6.77％ Vertical combustion 8.67％ Smoke density level 9.87％ Oxygen Index 10.47％ Melt dripping 16.40％ Tensile properties 5.99％ Elastic modulus 7.34％ Dimensional stability 4.31％ Formaldehyde emission 1.17％ Atomization 0.98％ Prohibited substances 1.31％ Wear resistance 3.60％ Liquid resistance 4.23％ Photoaging resistance 9.11％ Color fastness to rubbing 8.45％ Odor Rating 0.59％ Touch evaluation 0.75％

[0095] Step S103: Based on the grey correlation method, determine the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model.

[0096] Specifically, according to the formula: Calculate the grey correlation coefficient between each scheme and each indicator, where ξ ij The grey correlation coefficient between the i-th scheme and the j-th index of the reference sequence, ρ is the resolution coefficient, y j0 The maximum value in each metric.

[0097] For easy understanding, an example is given. In the matrix Y (normalized matrix), the most ideal values ​​of n indicators are selected to form a reference sequence Y0={yj 0|j=1, 2..., n}. This reference sequence is the ideal solution. The grey correlation coefficient ξ between the i-th solution and the j-th indicator of the reference sequence is calculated. ij , where: ij is the grey correlation coefficient between the i-th scheme and the j-th index of the reference sequence, ξi j The value reflects the closeness between the i-th solution and the optimal solution. If ξ ij =1, the correlation coefficient is the largest, indicating that the i scheme is the optimal scheme. ij The smaller it is, the greater the difference between solution i and the optimal solution. ρ is the resolution coefficient, which usually takes a value within [0, 1] and is generally 0.5.

[0098] Among them, it should be noted that eight kinds of plastics commonly used in automobile interior materials, including PP (polypropylene), ABS (terpolymer of three monomers: acrylonitrile (A), butadiene (B), and styrene (S)), PE (polyethylene), PVC (polyvinyl chloride), PU (polyurethane), PA (polyamide), POM (polyoxymethylene resin), and PC (polycarbonate), were selected for comprehensive performance evaluation.

[0099] The calculation results of the grey correlation coefficient of each scheme are shown in Table 8.

[0100] Table 8

[0101]

[0102]

[0103] Step S104: constructing a weighted standard matrix through the comprehensive weight and the grey correlation coefficient, and using the weighted standard matrix to determine the best evaluation scheme to evaluate the performance of vehicle interior materials.

[0104] Specifically, the grey correlation coefficient ξi j Construct a weighted standard matrix V, V = (v ij ) m×n (i=1,2,3…m;j=1,2,3…n), where v ij =w ij *ξ ij , which is the data obtained by weighting the grey correlation coefficient of the jth indicator of the i-th scheme;

[0105] Determine the maximum value and the minimum value of the preset number of indicators selected from the weighted standard matrix to respectively constitute the positive ideal solution and the negative ideal solution of the weighted standard matrix;

[0106] Determine the distance of each solution to the positive ideal solution and the negative ideal solution, and based on the distance of each solution to the positive ideal solution and the negative ideal solution, determine the relative closeness of each solution to the positive ideal solution;

[0107] The relative progress of each plan to the positive ideal solution is ranked, and the plan with the smallest relative progress is selected as the best evaluation plan.

[0108] Among them, according to the formula: Calculate the distance from the ith solution to the positive ideal solution, where Select the maximum value among the preset number of indicators in the weighted standard matrix;

[0109] According to the formula: Calculate the distance from the i-th solution to the negative ideal solution, where Select the minimum value among the preset number of indicators in the weighted standard matrix;

[0110] According to the formula: Calculate the relative closeness of the i-th solution to the positive ideal solution.

[0111] Specifically, an evaluation model is constructed based on the TOPSIS method to determine the relative closeness of each solution:

[0112] 1) Construct a weighted standard matrix:

[0113] Grey correlation coefficient ξ ij Construct a weighted standard matrix V: V = (v ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), v ij =w ij *ξij , where V is the grey correlation coefficient ξi j Construct a weighted standard matrix;

[0114] v ij The data obtained by weighting the grey correlation coefficient of the jth indicator of the i-th scheme;

[0115] ξ ij is the grey correlation coefficient between the i-th scheme and the j-th index of the reference sequence;

[0116] w ij is the comprehensive weight of the jth indicator under the i-th criterion layer, satisfying

[0117]

[0118] 2) Determine the positive ideal solution and the negative ideal solution:

[0119] In the matrix V, select the optimal value (maximum value) among n indicators to form the positive ideal solution of V Select the worst value (minimum value) among n indicators to form the negative ideal solution of V

[0120] 3) Determine the distance of each solution to the positive ideal solution and the negative ideal solution:

[0121] The distance from the i-th solution to the positive ideal solution is

[0122]

[0123] The distance from the i-th solution to the negative ideal solution is

[0124]

[0125] in, is the distance from the ith solution to the positive ideal solution; v ij The data obtained by weighting the grey correlation coefficient of the jth indicator of the i-th scheme; is the optimal value (maximum value) among the n indicators in the matrix V; is the distance from the i-th solution to the negative ideal solution; It is the worst value (minimum value) among the n indicators in the matrix V.

[0126] 4) Determine the relative closeness of each solution to the positive ideal solution:

[0127] According to the formula: Calculate the relative closeness of the ith solution to the positive ideal solution, where C i is the relative closeness of the ith solution to the positive ideal solution; is the distance from the ith solution to the positive ideal solution; is the distance from the ith solution to the negative ideal solution. The solutions are sorted according to their relative closeness Ci values. The larger the relative closeness, the better the solution. The best solution is screened. The closeness of each solution can also be converted into a percentage system for scoring or grading.

[0128] It can be understood that the evaluation model based on the TOPSIS method is used to determine the relative closeness of each solution. The weighted standard matrix is ​​constructed with the grey correlation coefficient, and the optimal value (maximum value) of the weighted standard matrix is ​​determined to constitute the positive ideal solution, and the worst value (minimum value) constitutes the negative ideal solution. The distance and relative closeness of each solution to the positive ideal solution and the negative ideal solution are calculated. Table 9 shows the calculation results of the closeness of each solution.

[0129] Table 9

[0130]

[0131]

[0132] Among them, the schemes are ranked according to their relative closeness. The smaller the relative closeness, the better the scheme, and the best scheme is screened. Table 10 shows the closeness ranking and evaluation results of each scheme.

[0133] Table 10

[0134] plan Evaluation sorting Ranking by share of actual engineering applications PP 1 1 ABS 2 3 PE 3 4 PVC 4 8 PU 5 2 PA 6 5 POM 7 7 PC 8 6

[0135] Through evaluation and analysis, PP plastic has excellent performance and is widely used in engineering such as instrument panels. Although ABS has excellent performance, its engineering application share is relatively low due to cost issues. Although PU has poor overall performance, it has a low production cost and is widely used in seat cushions.

[0136] Reference Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a vehicle interior material performance evaluation device provided by the present invention, such as Figure 2 As shown, the device comprises:

[0137] Construction module 201: It is used to construct a hierarchical structure model, which includes a criterion layer, a control layer and a solution layer.

[0138] Calculation module 202: It is used to calculate the weight of each criterion layer and the weight of each indicator of the control layer in the model to determine the comprehensive weight of each indicator.

[0139] Determination module 203: It is used to determine the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model based on the grey correlation method.

[0140] Processing module 204 is used to construct a weighted standard matrix through the comprehensive weight and the grey correlation coefficient, and use the weighted standard matrix to determine the best evaluation scheme to evaluate the performance of vehicle interior materials.

[0141] Furthermore, in a possible implementation manner, the processing module is further used to compare and assign values ​​to the evaluation indicators in the criterion layer in pairs, and obtain the judgment matrix element a after taking the average value. ij , where the judgment matrix expression is: A = (a ij ) k×k (i,j=1,2,3,...,k);

[0142] The calculation module is also used according to the formula: Calculate the weight of the i-th criterion layer indicator, where is the square root of the product of the judgment matrix, and its expression is:

[0143] Furthermore, in a possible implementation manner, the processing module is further used to construct an evaluation matrix X, and perform standardization processing on the evaluation matrix to obtain a standardized matrix Y, where X=(x ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), Y=(y ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), where x ij is the collected data corresponding to the jth evaluation index of the i-th scheme, y ij The data obtained after dimensionless normalization of the jth evaluation index of the i-th scheme;

[0144] The calculation module is also used according to the formula: Calculate the information entropy of each indicator, where It is the proportion of the jth indicator in the ith scheme, M is the number of evaluation objects at the scheme level, and n is the number of evaluation indicators at the control level;

[0145] According to the information entropy of each indicator, the weight w of each indicator in the control layer is determined Bj ,in, n is the number of evaluation indicators in the control layer.

[0146] Furthermore, in a possible implementation manner, the determination module is further configured to determine the following according to the formula: Calculate the comprehensive weight of each indicator, where w ij is the comprehensive weight of the jth indicator under the i-th criterion layer, w Ai is the weight of the index at the i-th criterion layer, wBj is the information weight of the j-th indicator in the control layer, and C(i) is the number of indicators contained in the i-th dimension.

[0147] Furthermore, in a possible implementation manner, the determination module is further configured to determine the following according to the formula: Calculate the grey correlation coefficient between each scheme and each indicator, where ξ ij The grey correlation coefficient between the i-th scheme and the j-th index of the reference sequence, ρ is the resolution coefficient, y j0 The maximum value in each metric.

[0148] Further, in a possible implementation manner, the processing module is also used to use the grey correlation coefficient ξ ij Construct a weighted standard matrix V, V = (v ij ) m×n (i=1,2,3...m;j=1,2,3...n), where v ij =w ij *ξ ij , which is the data obtained by weighting the grey correlation coefficient of the jth indicator of the i-th scheme;

[0149] Determine the maximum value and the minimum value of the preset number of indicators selected from the weighted standard matrix to respectively constitute the positive ideal solution and the negative ideal solution of the weighted standard matrix;

[0150] Determine the distance of each solution to the positive ideal solution and the negative ideal solution, and based on the distance of each solution to the positive ideal solution and the negative ideal solution, determine the relative closeness of each solution to the positive ideal solution;

[0151] The relative progress of each plan to the positive ideal solution is ranked, and the plan with the smallest relative progress is selected as the best evaluation plan.

[0152] Furthermore, in a possible implementation manner, the calculation module is further configured to calculate the following formula: Calculate the distance from the ith solution to the positive ideal solution, where Select the maximum value among the preset number of indicators in the weighted standard matrix;

[0153] According to the formula: Calculate the distance from the i-th solution to the negative ideal solution, where Select the minimum value among the preset number of indicators in the weighted standard matrix;

[0154] According to the formula: Calculate the relative closeness of the i-th solution to the positive ideal solution.

[0155] Reference Figure 3 , Figure 3 The figure shows a schematic diagram of the multi-level evaluation index structure provided by the present invention. Figure 3 As shown:

[0156] According to the requirements of standards and regulations, performance and technical requirements, and user experience needs, the flame retardant performance, environmental performance, mechanical properties, durability, and other factors of automotive interior materials such as sensory evaluation are determined as the first-level criterion layer. Other factors such as cost and processing technology performance can be expanded when necessary. In the flame retardant performance criterion layer, the secondary control layer indicators mainly include horizontal combustion, vertical combustion, smoke density level, oxygen index, molten dripping, combustion level, etc. If necessary, other indicators can be expanded. In the mechanical performance criterion layer, the secondary control layer indicators mainly include tensile performance, elastic modulus, dimensional stability, etc. If necessary, other indicators can be expanded. In the durability performance criterion layer, the secondary control layer indicators mainly include wear resistance, liquid resistance, friction color fastness, aging resistance (light aging resistance, heat aging resistance), etc. If necessary, other indicators can be expanded. In the environmental performance criterion layer, the secondary control layer indicators mainly include VOC, atomization, banned substances, etc. If necessary, other indicators can be expanded. In the sensory evaluation criterion layer, the secondary control layer indicators mainly include odor evaluation, touch evaluation, etc. If necessary, other indicators can be expanded. In this application, the scheme layer data of each evaluation indicator is collected through experiments and reference to literature. The indicator data is collected by experimental method. It can be understood that if there are 8 different schemes in the scheme layer, the criterion layer data and the control layer data can be collected, and the purpose of this application is to select an optimal scheme from these 8 schemes to evaluate the interior performance.

[0157] Refer to the following Figure 4 An electronic device 400 according to this embodiment of the present invention will be described. Figure 4 The electronic device 400 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0158] like Figure 4 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410).

[0159] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0160] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0161] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0162] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0163] The electronic device 400 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 450. Furthermore, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 via a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0164] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0165] According to the solution of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present invention described in the above "Exemplary Method" section of the present specification.

[0166] refer to Figure 5 As shown, a program product 500 for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0167] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The 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 of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 of the above.

[0168] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. 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. Readable signal media may also be any readable medium other than a 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.

[0169] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0170] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0171] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0172] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

[0173] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 processor 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 process in the flowchart. 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.

Claims

1. A method for evaluating the performance of vehicle interior materials, characterized in that: include: Constructing a hierarchical structure model, the model comprising a criterion layer, a control layer and a solution layer; Calculate the weight of each criterion layer and the weight of each indicator of the control layer in the model to determine the comprehensive weight of each indicator; Based on the grey correlation method, the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model is determined; A weighted standard matrix is ​​constructed by using the comprehensive weight and the grey correlation coefficient, and the weighted standard matrix is ​​used to determine the best evaluation scheme to evaluate the performance of vehicle interior materials.

2. The method according to claim 1, characterized in that The calculating of the weights of each criterion layer in the model includes: The evaluation indicators in the criterion layer are compared and assigned in pairs, and the average value is taken to obtain the judgment matrix element a ij , where the judgment matrix expression is: A = (a ij ) k×k (i,j=1,2,3,...,k); According to the formula: Calculate the weight of the i-th criterion layer indicator, where is the square root of the product of the judgment matrix, and its expression is:

3. The method according to claim 1, characterized in that The calculating of the weights of the indicators of the control layer in the model includes: Construct an evaluation matrix X, and standardize the evaluation matrix to obtain a standardized matrix Y, where X = (x ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), Y= (y ij ) m×n (i=1, 2, 3...m; j=1, 2, 3...n), where x ij is the collected data corresponding to the jth evaluation index of the i-th scheme, y ij The data obtained after dimensionless normalization of the jth evaluation index of the i-th scheme; According to the formula: Calculate the information entropy of each indicator, where It is the proportion of the jth indicator in the ith scheme, M is the number of evaluation objects at the scheme level, and n is the number of evaluation indicators at the control level; According to the information entropy of each indicator, the weight w of each indicator in the control layer is determined Bj ,in, n is the number of evaluation indicators in the control layer.

4. The method according to claim 1, characterized in that: Determining the comprehensive weight of each indicator includes: According to the formula: Calculate the comprehensive weight of each indicator, where w ij is the comprehensive weight of the jth indicator under the i-th criterion layer, w Ai is the weight of the index at the ith criterion level, w Bj is the information weight of the j-th indicator in the control layer, and C(i) is the number of indicators contained in the i-th dimension.

5. The method according to claim 1, characterized in that The grey correlation coefficient between each scheme and each indicator in the scheme layer in the model is determined based on the grey correlation method, including: According to the formula: Calculate the grey correlation coefficient between each scheme and each indicator, where ξ ij The grey correlation coefficient between the i-th scheme and the j-th index of the reference sequence, ρ is the resolution coefficient, y j0 The maximum value in each metric.

6. The method according to claim 1, characterized in that The step of constructing a weighted standard matrix through the comprehensive weight and the grey correlation coefficient, and determining the best evaluation scheme by using the weighted standard matrix, comprises: Grey correlation coefficient ξ ij Construct a weighted standard matrix V, V = (v ij ) m×n (i=1,2,3...m;j=1,2,3...n), where v ij =w ij *ξ ij , which is the data obtained by weighting the grey correlation coefficient of the jth indicator of the i-th scheme; Determine the maximum value and the minimum value of the preset number of indicators selected from the weighted standard matrix to respectively constitute the positive ideal solution and the negative ideal solution of the weighted standard matrix; Determine the distance of each solution to the positive ideal solution and the negative ideal solution, and based on the distance of each solution to the positive ideal solution and the negative ideal solution, determine the relative closeness of each solution to the positive ideal solution; The relative progress of each plan to the positive ideal solution is ranked, and the plan with the smallest relative progress is selected as the best evaluation plan.

7. The method according to claim 6, characterized in that include: According to the formula: Calculate the distance from the ith solution to the positive ideal solution, where Select the maximum value among the preset number of indicators in the weighted standard matrix; According to the formula: Calculate the distance from the i-th solution to the negative ideal solution, where Select the minimum value among the preset number of indicators in the weighted standard matrix; According to the formula: Calculate the relative closeness of the i-th solution to the positive ideal solution.

8. A vehicle interior material performance evaluation device, characterized in that: include: A construction module, which is used to construct a hierarchical structure model, wherein the model includes a criterion layer, a control layer and a solution layer; A calculation module, which is used to calculate the weight of each criterion layer and the weight of each indicator of the control layer in the model to determine the comprehensive weight of each indicator; A determination module, which is used to determine the grey correlation coefficient between each scheme and each indicator in the scheme layer in the model based on the grey correlation method; A processing module is used to construct a weighted standard matrix through the comprehensive weight and the grey correlation coefficient, and to determine the best evaluation scheme by using the weighted standard matrix to evaluate the performance of vehicle interior materials.

9. An electronic device, characterized in that: The electronic device comprises: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer program instructions are stored therein, and when the computer program instructions are executed by a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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