Automobile seat material performance evaluation method based on AHP-entropy weight method

Abstract

The invention relates to the field of material evaluation, in particular to an automobile seat material performance evaluation method based on an AHP-entropy weight method, and aims to solve the problem of non-objective evaluation of comprehensive performance of an automobile seat material in the prior art. The automobile seat material is weighted from subjective and objective angles according to the AHP method and the entropy weight method; and then the subjective evaluation result and the objective evaluation result are combined to obtain a combined weight, so that the coupling relationship between different materials can be better processed, and the comprehensive performance of each material can be more accurately evaluated. And the preference degree of the decision maker for the subjective evaluation method and the objective evaluation method is taken as a reference, so that the participation degree and the influence of the decision maker in the evaluation process are improved, and the evaluation result is more persuasive and representative.

Description

Technical Field

[0001] The invention relates to the field of material evaluation, and in particular to a method for evaluating the performance of automobile seat materials based on an AHP-entropy weight method. Background Art

[0002] With the continuous development of the automobile industry and the continuous improvement of consumption levels, people are paying more and more attention to the cost-effectiveness of automobiles. As an important component inside the car, seats are the focus of development by automobile manufacturers. The choice of surface material not only affects the performance of the seat, but also largely determines the interior level of the entire vehicle and affects consumers' purchasing desire. If the selected material is not good, it will affect the subsequent user experience, increase troubles and replacement costs.

[0003] There are many car seat materials developed so far, and the performance parameters of each material vary greatly. However, when selecting materials, decision makers only compare them based on a single evaluation index, or make judgments based on subjective feelings and experience, which results in one-sided and inaccurate selection results, affecting the future user experience and increasing later troubles. Therefore, there is currently a lack of an evaluation method for the comprehensive evaluation of seats of various materials in terms of the performance of car seat materials.

[0004] The Chinese invention patent with application number 202111480161.1 proposes a comprehensive evaluation model for the fire hazard characteristics of building insulation materials. The hierarchical analysis method is used to obtain the relationship between the various influencing factors of the fire hazard of building insulation materials, which is conducive to the comprehensive evaluation of the fire hazard of building insulation materials. However, this evaluation method is subjective and there is no objective data as a basis and comparison. The Chinese invention patent with application number 201510289536.4 proposes a multi-material door selection method based on recycling and reuse, but this method focuses on the recycling and reuse of materials and does not take into account the various performance parameters of each material. In addition, this method only uses an objective data model to select materials for car doors, and lacks corresponding expert investigation and experience judgment. The Chinese invention patent with application number 201510698717.2 uses the control variable method to compare the performance of different body materials, but the field involved is material processing and optimization, lacks expert investigation and experience judgment, and the method does not conduct a comprehensive analysis of the performance of the materials. The Chinese invention patent with application number 202010437369.4 proposes a comprehensive performance grading evaluation method for electrical engineering grounding materials, but this method tends to be objective and lacks subjective evaluation and expert experience. The Chinese invention patent with application number 201410030900.0 proposes a method and system for screening crop breeding materials, but it involves the field of biological breeding. At the same time, this method lacks expert experience and subjective judgment, and does not consider the participation of decision makers in the evaluation process. The Chinese invention patent with application number 202111413861.9 proposes a substation site selection method based on AHP-entropy weight method empowerment, combining subjective evaluation method with objective evaluation method, and quantitatively analyzing the substation site to obtain the optimal substation site. However, this method does not take into account the decision makers' preference for subjective evaluation methods and objective evaluation methods.

[0005] The existing evaluation methods for automobile seat material performance have the problem that the evaluation indicators are polymorphic and fuzzy, resulting in the objective evaluation of the comprehensive performance of each material. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the comprehensive performance evaluation of automobile seat materials (such as leather, fabric, PVC artificial leather and PU artificial leather) in the prior art is not objective, and provide a method for evaluating the performance of automobile seat materials. This method is based on the AHP method and the entropy weight method, combining subjective evaluation with objective evaluation. The specific process is:

[0007] Step 1: Build a car seat material performance evaluation model.

[0008] The automobile seat material performance evaluation model includes a target layer, a criterion layer, an evaluation layer and a solution layer;

[0009] Step 2: Use the AHP method to calculate the weighted value of each indicator in the criterion layer, evaluation layer and solution layer in the automobile seat material performance evaluation model;

[0010] Step 3: Based on the weighted values ​​of each indicator in the criterion layer, evaluation layer and solution layer, the total subjective weighted value of each indicator in the evaluation layer and solution layer in the automobile seat material performance evaluation model is calculated;

[0011] Step 4: Use the entropy weight method to calculate the total objective weight value of each indicator in the evaluation layer and solution layer in the automobile seat material performance evaluation model;

[0012] Step five: Based on the total subjective weighted value of each indicator in the evaluation layer and the solution layer and the total objective weighted value of each indicator in the evaluation layer and the solution layer, calculate the combined weighted value of each indicator in the solution layer to the target layer, and then complete the evaluation of the performance of the car seat material according to the combined weighted value of the solution layer to the target layer.

[0013] The beneficial effects of the present invention are:

[0014] This overcomes the problem that when evaluating the performance of automobile seat materials in the past, due to the polymorphism and ambiguity of the evaluation indicators, if the analysis was conducted through a single or independent indicator, it would lead to the inability to obtain the comprehensive performance of each material.

[0015] 2. It overcomes the problem that the evaluation results of the previous performance of automobile seat materials were greatly influenced by the personal preferences and experience of the judges, thus lacking accuracy and scientificity.

[0016] 3. Based on the AHP method and entropy weight method, the car seat materials are weighted from both subjective and objective perspectives, and then the subjective evaluation results are combined with the objective evaluation results to obtain the combined weight, which can better handle the coupling relationship between different materials and more accurately evaluate the comprehensive performance of each material.

[0017] 4. Taking the decision makers' preference for subjective and objective evaluation methods as a reference increases the decision makers' participation and influence in the evaluation process, making the evaluation results more convincing and representative. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of a method for evaluating the performance of automobile seat materials of the present invention;

[0019] Figure 2 It is a schematic diagram of the automobile seat material performance evaluation model of the present invention. DETAILED DESCRIPTION

[0020] Specific implementation method 1: Combination Figure 1 The invention is described, comprising:

[0021] Step 1: Build a car seat material performance evaluation model.

[0022] The automobile seat material performance evaluation model includes a target layer, a criterion layer, an evaluation layer and a solution layer;

[0023] Step 2: Use the AHP method to calculate the weighted value of each indicator in the criterion layer, evaluation layer and solution layer in the automobile seat material performance evaluation model;

[0024] Step 3: Based on the weighted values ​​of each indicator in the criterion layer, evaluation layer and solution layer, the total subjective weighted value of each indicator in the evaluation layer and solution layer in the automobile seat material performance evaluation model is calculated;

[0025] Step 4: Use the entropy weight method to calculate the total objective weight value of each indicator in the evaluation layer and solution layer in the automobile seat material performance evaluation model;

[0026] Step five: Based on the total subjective weighted value of each indicator in the evaluation layer and the solution layer and the total objective weighted value of each indicator in the evaluation layer and the solution layer, calculate the combined weighted value of each indicator in the solution layer to the target layer, and then complete the evaluation of the performance of automobile seat materials according to the combined weighted value of the solution layer to the target layer, so as to scientifically select automobile seat solutions and form the best solution that best suits decision makers.

[0027] Specific implementation method 2: The difference between this implementation method and specific implementation method 1 is that:

[0028] In the step 1, the target layer is the material performance evaluation of the car seat;

[0029] The car seat material performance evaluation is the result obtained by sorting according to the size of the combined weighted values ​​in step 5;

[0030] The criterion layer includes P criterion indicators; the evaluation layer includes Q evaluation indicators; the solution layer includes R solution indicators

[0031] The other steps and parameters are the same as those in the first specific implementation.

[0032] Specific implementation method three: This implementation method is different from the specific implementation method one in that:

[0033] In the step 2, the weighted values ​​of each indicator in the criterion layer, the evaluation layer and the solution layer are calculated by using the AHP method. The specific process includes:

[0034] S1. Construct judgment matrices at the criterion level, evaluation level and solution level respectively;

[0035] The criterion layer judgment matrix A is expressed by the formula:

[0036]

[0037] The evaluation layer judgment matrix B is expressed by the formula:

[0038]

[0039] The scheme layer judgment matrix C is expressed by the formula:

[0040]

[0041] Among them, u is the order of the criterion layer judgment matrix A, v is the order of the evaluation layer judgment matrix B, and w is the order of the solution layer judgment matrix C; is the pth i The criterion index is relative to the pth j The comparison results of the criteria indicators; i ,p j ∈[1,P]; is the qth z The evaluation index is relative to the qth x Comparison results of evaluation indicators; q z ,q x ∈[1,Q]; is the rth k The scheme indicator is relative to the rth l Comparison results of the indicators of the schemes; k ,r l ∈[1,R];

[0042] S2, calculating the weighted value of each criterion indicator of the criterion layer according to the criterion layer judgment matrix A;

[0043] The weighted value of each evaluation index of the evaluation layer is calculated according to the evaluation layer judgment matrix B;

[0044] The weighted value of each scheme indicator at the scheme level is calculated according to the scheme level judgment matrix C;

[0045] S3, respectively conduct consistency test on the criterion layer judgment matrix, the evaluation layer judgment matrix and the scheme layer judgment matrix;

[0046] When the criteria layer, evaluation layer and solution layer all pass the consistency test, proceed to step 3;

[0047] If the criterion layer judgment matrix fails the consistency test, the criterion layer judgment matrix is ​​reconstructed;

[0048] If the evaluation layer judgment matrix fails to pass the consistency test, the evaluation layer judgment matrix is ​​reconstructed;

[0049] If the scheme-level judgment matrix fails the consistency check, the scheme-level judgment matrix is ​​reconstructed; then the process returns to S2, and the other steps and parameters are the same as those in the first to second specific implementation modes.

[0050] Specific implementation method 4: This implementation method is different from specific implementation methods 1 to 4 in that:

[0051] In said S2, the weighted value of each criterion index of the criterion layer is calculated according to the criterion layer judgment matrix A;

[0052] The weighted value of each evaluation index of the evaluation layer is calculated according to the evaluation layer judgment matrix B;

[0053] The weighted value of each solution indicator at the solution level is calculated according to the solution level judgment matrix C; the specific process is as follows:

[0054] S2.1: Calculate the maximum eigenvalue of the criterion layer judgment matrix A With the maximum eigenvalue The corresponding eigenvector γ a ;

[0055] Calculate the maximum eigenvalue of the criterion layer judgment matrix B With the maximum eigenvalue The corresponding eigenvector γ b ;

[0056] Calculate the maximum eigenvalue of the criterion layer judgment matrix C With the maximum eigenvalue The corresponding eigenvector γ c ;

[0057] S2.2: For the eigenvector γ a Perform normalization processing to obtain the weighted value of each criterion indicator in the criterion layer;

[0058] For the eigenvector γ b Perform normalization processing to obtain the weighted value of each evaluation indicator of the evaluation layer;

[0059] For the eigenvector γ c Perform normalization to obtain the weighted value of each solution indicator at the solution level

[0060] The other steps and parameters are the same as those in the first to third embodiments.

[0061] Specific implementation method 5: This implementation method is different from specific implementation methods 1 to 4 in that:

[0062] In S2.2, the eigenvector γ a Normalize it to get the weighted value of each criterion indicator in the criterion layer; bNormalize it to get the weighted value of each evaluation index of the evaluation layer; c Perform normalization to obtain the weighted value of each scheme indicator at the scheme level; the specific process is as follows:

[0063] For the eigenvector γ a After normalization, we get the pth criterion layer i The weighted value of each criterion indicator The specific process is:

[0064]

[0065] In the formula, Represents γ a The corresponding criterion layer p i The element corresponding to the criterion index, u * Denotes the eigenvector γ a The label of the element in;

[0066] For the eigenvector γ b After normalization, we get the qth evaluation layer z The weighted value of the evaluation index The specific process is:

[0067]

[0068] In the formula, Represents γ b The corresponding evaluation layer is z The elements corresponding to the evaluation indicators, v * Denotes the eigenvector γ b The label of the element in;

[0069] For the eigenvector γ c After normalization, we get the qth solution layer z The weighted value of each scheme indicator The specific process is:

[0070]

[0071] In the formula, Represents γ c The corresponding solution layer r k The elements corresponding to the scheme indicators, w * Denotes the eigenvector γ c The other steps and parameters are the same as those in the first to fourth embodiments.

[0072] Specific implementation method 6: This implementation method is different from specific implementation methods 1 to 5 in that:

[0073] The specific process of the consistency check method in S3 is as follows:

[0074] Calculate the consistency test coefficient C of the judgment matrix R , when C R When <0.1, the judgment matrix is ​​considered to have passed the consistency test, otherwise, the judgment matrix is ​​considered to have failed the consistency test;

[0075] The consistency test coefficient C R The calculation formula is:

[0076]

[0077] In the formula, C R is the consistency test coefficient of the judgment matrix; C I is the consistency index of the judgment matrix; R I is the average random consistency index; max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix,

[0078] For example, when the judgment matrix A of the judgment criterion layer is used for consistency check, the maximum eigenvalue of the judgment matrix A of the judgment criterion layer is used. The order of the criterion layer judgment matrix is ​​u; the other steps and parameters are the same as those of the specific implementation modes one to five.

[0079] Specific implementation method 7: This implementation method is different from specific implementation methods 1 to 6 in that:

[0080] The total subjective weighted value of each indicator in the evaluation layer and the solution layer in the automobile seat material performance evaluation model calculated in step 3 is:

[0081] A1. Calculate the total subjective weight value of each evaluation indicator in the evaluation layer, where the pth evaluation indicator in the evaluation layer i The total subjective weighted value of the evaluation index is expressed as Dp i , the calculation formula is:

[0082]

[0083] A2. Calculate the total subjective weight value of each solution indicator in the solution layer, where the rth solution layer k The total subjective weight value of each scheme indicator is expressed as The calculation formula is:

[0084] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0085] Specific implementation eight: This implementation differs from specific implementations one to seven in that:

[0086] In step 4, the total objective weighted value of each indicator in the evaluation layer and the solution layer is calculated using the entropy weight method. The specific process is as follows:

[0087] B1. Construct the entropy weight matrix X. The specific process is as follows:

[0088]

[0089] in, Represented as the rth level of the scheme k The evaluation level q under the scheme indicators z The value of the evaluation index, It is the value after the original data related to the scheme is standardized. The calculation method of the original data xij is based on expert scores, statistical data and related standards, and then it is dimensionless. The standardization process is:

[0090]

[0091] B2. Calculate the contribution of each evaluation indicator under each scheme indicator, where the rth k The qth z The contribution of each evaluation index is expressed as Ir k q z :Ir k q z The calculation formula is:

[0092]

[0093] B3. Calculate the information entropy of each evaluation index, where the qth z The information entropy of the evaluation index is expressed as Eq z :Eq z The calculation formula is:

[0094]

[0095] B4. Calculate the weighted value of each evaluation indicator based on the information entropy of each evaluation indicator, where the qth z The weighted value of the evaluation index is expressed as Wq z :Wq z The calculation formula is:

[0096]

[0097] B5. Calculate the total objective weighted value of each program indicator at the program level under the entropy weight method based on the weighted value of each evaluation indicator. k The total objective weighted value of the program indicators is expressed as The calculation formula is:

[0098]

[0099] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0100] Specific implementation method 9: This implementation method is different from specific implementation methods 1 to 8 in that:

[0101] In step 5, the AHP method and the entropy weight method are combined to calculate the combined weight value of each solution indicator of the solution layer to the target layer, where the rth k The combined weighted value of the scheme indicators on the target layer is expressed as Wr k , the calculation formula is:

[0102]

[0103] Where: β is the decision maker preference coefficient

[0104] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0105] Specific implementation method 10: This implementation method is different from specific implementation methods 1 to 9 in that:

[0106] The P criteria indicators of the criterion layer include: safety and reliability P1, comfort and beauty P2, and economic processing P3;

[0107] The Q evaluation indicators of the evaluation layer include: flame retardancy Q1, tensile strength Q2, weather resistance Q3, high and low temperature performance Q4, air permeability Q5, cushioning Q6, touch feeling Q7, daily maintenance difficulty Q8, raw material processing difficulty Q9 and recycling rate Q10;

[0108] The R scheme indicators of the scheme layer include: leather R1, fabric R2, PVC artificial leather R3 and PU artificial leather R4

[0109] The other steps and parameters are the same as those in Specific Implementations 1 to 9.

[0110] Combining the simulation analysis of specific implementation methods one to ten

[0111] 1. Methods, steps and results of calculating subjective weighted values ​​of indicators using the AHP method

[0112] C1. Construct a material performance evaluation model for automobile seats, which includes a target layer, a criterion layer, an evaluation layer, and a solution layer. The criterion layer is safety and reliability, comfort and beauty, and economic processing; the evaluation layer is flame retardancy, tensile strength, weather resistance, high and low temperature performance, air permeability, cushioning, touch, daily maintenance difficulty, raw material processing difficulty, and recycling rate; the solution layer is leather, fabric, PVC artificial leather, and PU artificial leather.

[0113] C2. Construct judgment matrices at the criterion level, evaluation level, and solution level, score them based on expert experience, and quantify the expert scoring results. Use the indicator importance comparison standard to compare each indicator at each level to form a comparison matrix. The indicator importance comparison standard is shown in Table 1.

[0114] Table 1 Comparison criteria of indicator importance

[0115]

[0116]

[0117] Calculate the maximum eigenvalue λ of the judgment matrix at the criterion level, evaluation level, and solution level respectively max,a , max,b , max,c and the maximum eigenvalue λ max,a , max,b , max,c The corresponding eigenvector γ a , γ b , γ c , and then normalize the characteristic vector to obtain the weighted values ​​D1, D2, and D3 of the indicators at the criterion layer, the evaluation layer, and the solution layer respectively;

[0118] The formula for consistency check is:

[0119]

[0120] In the formula, C R is the consistency test coefficient of the judgment matrix; C I is the consistency index of the judgment matrix; R I is the average random consistency index, as shown in Table 2; max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix.

[0121] When C R When it is less than 0.1, the judgment matrix is ​​considered to have passed the consistency test, otherwise the judgment matrix needs to be readjusted and tested again.

[0122] Table 2 Average random consistency index

[0123]

[0124] The calculation of the judgment matrix and weighted values ​​of indicators at the criterion layer is shown in Table 3, the calculation of the judgment matrix and weighted values ​​of each indicator at the evaluation layer is shown in Tables 4 to 6, and the calculation of the judgment matrix and weighted values ​​of each indicator at the solution layer is shown in Tables 7 to 16.

[0125] Table 3 Criteria layer comparison matrix and indicator weights

[0126]

[0127] Table 4 Comparison matrix and weighted values ​​of evaluation layer indicators against criterion layer indicators safety and reliability (P1)

[0128]

[0129] Table 5 Comparison matrix and weighted values ​​of evaluation layer indexes versus standard layer indexes comfort and beauty (P2)

[0130]

[0131] Table 6 Comparison matrix and weighting values ​​of evaluation level indicators and criterion level indicators of economic processing (P3)

[0132]

[0133] Table 7 Comparison matrix and weighting values ​​of each scheme for the flame retardancy of the evaluation layer index (Q1)

[0134]

[0135]

[0136] Table 8 Comparison matrix and weighting values ​​of each scheme for evaluation layer index tensile strength (Q2)

[0137]

[0138] Table 9 Comparison matrix and weighting values ​​of various schemes for evaluation layer index weather resistance (Q3)

[0139]

[0140] Table 10 Comparison matrix and weighting values ​​of each scheme for high and low temperature performance (Q4) of evaluation layer indicators

[0141]

[0142]

[0143] Table 11 Comparison matrix and weighting values ​​of each scheme for the evaluation layer index air permeability (Q5)

[0144]

[0145] Table 12 Comparison matrix and weighting values ​​of various schemes for the buffering index (Q6) of the evaluation layer

[0146]

[0147] Table 13 Comparison matrix and weighting values ​​of various schemes for the evaluation layer indicator touch (Q7)

[0148]

[0149]

[0150] Table 14 Comparison matrix and weighting values ​​of each scheme for the evaluation layer indicator raw material processing difficulty (Q8)

[0151]

[0152] Table 15 Comparison matrix and weighting values ​​of daily maintenance difficulty (Q9) of each scheme for the evaluation layer index

[0153]

[0154] Table 16 Comparison matrix and weighting values ​​of various schemes for the recycling rate of evaluation layer indicators (Q10)

[0155]

[0156] C3, combined with weights D1 and D2, the total subjective weighted value D of each indicator at the evaluation layer is calculated, as shown in Table 17. Combined with weights D3 and D, the total subjective weighted value E of each indicator at the solution layer is calculated, as shown in Table 18.

[0157] Table 17 Weighting values ​​of each indicator in the evaluation layer

[0158] Weight value D2 Total subjective weight value D Flame retardant 0.5364 0.3343 Tensile Strength 0.1216 0.0758 Weather resistance 0.1216 0.0758 High and low temperature performance 0.2205 0.0758 Breathability 0.1481 0.0355 Cushioning 0.7672 0.1837 Touch 0.0848 0.0203 Difficulty of processing raw materials 0.2395 0.0329 Daily maintenance difficulty 0.6232 0.0856 Recycling rate 0.1373 0.0188

[0159] Table 18 Total subjective weights of each scheme at the scheme level

[0160]

[0161]

[0162] 2. Methods, steps and results of calculating objective weight values ​​of indicators using entropy weight method

[0163] Calculate the contribution, as shown in Table 19:

[0164] Calculate the information entropy Ej, as shown in Table 20:

[0165] The weighted values ​​Wj of each evaluation index are calculated according to the information entropy of each factor, as shown in Table 20: The total objective weighted values ​​of each scheme under the entropy weight method are calculated, as shown in Table 21

[0166] Table 19 Contribution of each evaluation index

[0167]

[0168] Table 20 Information entropy and weighting values ​​of each evaluation index

[0169]

[0170]

[0171] Table 21 Total objective weights of each scheme

[0172] Genuine Leather Fabric PVC artificial leather PU artificial leather Weighting 0.3249 0.3089 0.0992 0.2670

[0173] 3. Methods, steps and results for calculating the combined weighted value of the solution layer to the target layer

[0174] Combining the AHP method and the entropy weight method, the combined weighting formula is used to calculate the combined weighting values ​​of the evaluation layer indicators and the combined weighting values ​​of the solution layer to the target layer, as shown in Table 22 and Table 23. k The combined weighted value of the scheme indicators on the target layer is expressed as Wr k , the calculation formula is:

[0175]

[0176] Where: β is the decision maker's preference coefficient.

[0177] Table 22 Combined weight values ​​of each indicator in the evaluation layer

[0178]

[0179]

[0180] Table 23 Combined weighting values ​​of the solution layer to the target layer

[0181] Genuine Leather Fabric PVC artificial leather PU artificial leather Combined weight value 0.3124 0.3478 0.0899 0.2210

[0182] 4. According to the combined weighting results, fabric material has the best comprehensive benefit and the highest comprehensive evaluation, with a comprehensive weighting value of 0.3478; followed by genuine leather and PU artificial leather, with comprehensive weighting values ​​of 0.3124 and 0.2210 respectively; PVC artificial leather performed the worst in terms of comprehensive benefits, with a comprehensive weighting value of only 0.0899, far lower than other materials.

[0183] The above only describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above specific implementation methods. Although the present invention has been disclosed as above in the preferred embodiments, it is not used to limit the present invention. Any technician familiar with the profession can make some changes or modify the technical contents disclosed above into equivalent embodiments with equivalent changes without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent replacement and improvement made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the performance of automobile seat materials based on AHP-entropy weight method, characterized in that: The following steps are involved: Step 1: Build a car seat material performance evaluation model. The automobile seat material performance evaluation model includes a target layer, a criterion layer, an evaluation layer and a solution layer; Step 2: Use the AHP method to calculate the weighted value of each indicator in the criterion layer, evaluation layer and solution layer in the automobile seat material performance evaluation model; Step 3: Based on the weighted values ​​of each indicator in the criterion layer, evaluation layer and solution layer, the total subjective weighted value of each indicator in the evaluation layer and solution layer in the automobile seat material performance evaluation model is calculated; Step 4: Use the entropy weight method to calculate the total objective weight value of each indicator in the evaluation layer and solution layer in the automobile seat material performance evaluation model; Step 5: Based on the total subjective weighted value of each indicator in the evaluation layer and the solution layer and the total objective weighted value of each indicator in the evaluation layer and the solution layer, the combined weighted value of each indicator in the solution layer to the target layer is calculated.

2. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 1, characterized in that: In the step 1, the target layer is the material performance evaluation of the car seat; The criterion layer includes P criterion indicators; the evaluation layer includes Q evaluation indicators; and the solution layer includes R solution indicators.

3. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 2 is characterized in that: In the step 2, the weighted values ​​of each indicator in the criterion layer, the evaluation layer and the solution layer are calculated by using the AHP method. The specific process includes: S1. Construct judgment matrices at the criterion level, evaluation level and solution level respectively; The criterion layer judgment matrix A is expressed by the formula: The evaluation layer judgment matrix B is expressed by the formula: The scheme layer judgment matrix C is expressed by the formula: Among them, u is the order of the criterion layer judgment matrix A, v is the order of the evaluation layer judgment matrix B, and w is the order of the solution layer judgment matrix C; is the pth i The criterion index is relative to the pth j The comparison results of the criteria indicators; i ,p j ∈[1,P]; is the qth z The evaluation index is relative to the qth x Comparison results of evaluation indicators; q z ,q x ∈[1,Q]; is the rth k The scheme indicator is relative to the rth l Comparison results of the indicators of the schemes; k ,r l ∈[1,R]; S2, calculating the weighted value of each criterion indicator of the criterion layer according to the criterion layer judgment matrix A; The weighted value of each evaluation index of the evaluation layer is calculated according to the evaluation layer judgment matrix B; The weighted value of each scheme indicator at the scheme level is calculated according to the scheme level judgment matrix C; S3, respectively conduct consistency test on the criterion layer judgment matrix, the evaluation layer judgment matrix and the scheme layer judgment matrix; When the criteria layer, evaluation layer and solution layer all pass the consistency test, proceed to step 3; If the criterion layer judgment matrix fails the consistency check, the criterion layer judgment matrix is ​​reconstructed; then return to S2 If the evaluation layer judgment matrix fails the consistency check, the evaluation layer judgment matrix is ​​reconstructed; then return to S2 If the scheme-level judgment matrix fails the consistency check, the scheme-level judgment matrix is ​​reconstructed; then the process returns to S2.

4. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 3 is characterized in that: In said S2, the weighted value of each criterion index of the criterion layer is calculated according to the criterion layer judgment matrix A; The weighted value of each evaluation index of the evaluation layer is calculated according to the evaluation layer judgment matrix B; The weighted value of each scheme indicator at the scheme level is calculated according to the scheme level judgment matrix C; The specific process is: S2.1: Calculate the maximum eigenvalue of the criterion layer judgment matrix A With the maximum eigenvalue The corresponding eigenvector γ a ; Calculate the maximum eigenvalue of the criterion layer judgment matrix B With the maximum eigenvalue The corresponding eigenvector γ b ; Calculate the maximum eigenvalue of the criterion layer judgment matrix C With the maximum eigenvalue The corresponding eigenvector γ c ; S2.2: For the eigenvector γ a Perform normalization processing to obtain the weighted value of each criterion indicator in the criterion layer; For the eigenvector γ b Perform normalization processing to obtain the weighted value of each evaluation indicator of the evaluation layer; For the eigenvector γ c Normalization is performed to obtain the weighted value of each scheme indicator at the scheme layer.

5. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 4 is characterized in that: In S2.2, the eigenvector γ a Normalize it to get the weighted value of each criterion indicator in the criterion layer; b Normalize it to get the weighted value of each evaluation index of the evaluation layer; c Perform normalization to obtain the weighted value of each scheme indicator at the scheme level; the specific process is as follows: For the eigenvector γ a After normalization, we get the pth criterion layer i The weighted value of each criterion indicator The specific process is: In the formula, Represents γ a The corresponding criterion layer p i The element corresponding to the criterion index, u * Denotes the eigenvector γ a The label of the element in; For the eigenvector γ b After normalization, we get the qth evaluation layer z The weighted value of the evaluation index The specific process is: In the formula, Represents γ b The corresponding evaluation layer is z The elements corresponding to the evaluation indicators, v * Denotes the eigenvector γ b The label of the element in; For the eigenvector γ c After normalization, we get the qth solution layer z The weighted value of each scheme indicator The specific process is: In the formula, Represents γ c The corresponding solution layer r k The elements corresponding to the scheme indicators, w * Denotes the eigenvector γ c The label of the element in .

6. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 5, characterized in that: The specific process of the consistency check method in S3 is as follows: Calculate the consistency test coefficient C of the judgment matrix R , when C R When <0.1, the judgment matrix is ​​considered to have passed the consistency test, otherwise, the judgment matrix is ​​considered to have failed the consistency test; The consistency test coefficient C R The calculation formula is: In the formula, C R is the consistency test coefficient of the judgment matrix; C I is the consistency index of the judgment matrix; R I is the average random consistency index; max is the maximum eigenvalue of the judgment matrix; n is the order of the judgment matrix.

7. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 6, characterized in that: The total subjective weighted value of each indicator in the evaluation layer and the solution layer in the automobile seat material performance evaluation model calculated in step 3 is: A1. Calculate the total subjective weight value of each evaluation indicator in the evaluation layer, where the pth evaluation indicator in the evaluation layer i The total subjective weighted value of the evaluation index is expressed as Dp i , the calculation formula is: A2. Calculate the total subjective weight value of each solution indicator in the solution layer, where the rth solution layer k The total subjective weight value of the program indicators is expressed as The calculation formula is:

8. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 7, characterized in that: In step 4, the total objective weighted value of each indicator in the evaluation layer and the solution layer is calculated using the entropy weight method. The specific process is as follows: B1. Construct the entropy weight matrix X. The specific process is as follows: in, Represented as the solution layer r k The evaluation level q under the scheme indicators z The value of the evaluation index, B2. Calculate the contribution of each evaluation indicator under each scheme indicator, where the rth k The qth z The contribution of each evaluation index is expressed as Ir k q z :Ir k q z The calculation formula is: B3. Calculate the information entropy of each evaluation index, where the qth z The information entropy of the evaluation index is expressed as Eq z :Eq z The calculation formula is: B4. Calculate the weighted value of each evaluation indicator based on the information entropy of each evaluation indicator, where the qth z The weighted value of the evaluation index is expressed as Wq z :Wq z The calculation formula is: B5. Calculate the total objective weighted value of each program indicator at the program level under the entropy weight method based on the weighted value of each evaluation indicator. k The total objective weighted value of the program indicators is expressed as The calculation formula is:

9. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 8, characterized in that: In step 5, the AHP method and the entropy weight method are combined to calculate the combined weight value of each solution indicator of the solution layer to the target layer, where the rth k The combined weighted value of the scheme indicators on the target layer is expressed as Wr k , the calculation formula is: Where: β is the decision maker's preference coefficient.

10. The method for evaluating the performance of automobile seat materials based on the AHP-entropy weight method according to claim 9, characterized in that: The P criteria indicators of the criterion layer include: safety and reliability P1, comfort and beauty P2, and economic processing P3; The Q evaluation indicators of the evaluation layer include: flame retardancy Q1, tensile strength Q2, weather resistance Q3, high and low temperature performance Q4, air permeability Q5, cushioning Q6, touch feeling Q7, daily maintenance difficulty Q8, raw material processing difficulty Q9 and recycling rate Q10; The R solution indicators of the solution layer include: genuine leather R1, fabric R2, PVC artificial leather R3 and PU artificial leather R4.

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