Product Solution Evaluation Method, System, Storage Medium and Device

Abstract

The present invention relates to a product solution evaluation method, system, storage medium and device. The product solution evaluation method includes the following steps: constructing a disassemblable design quality house and disassembling technical feature indicators based on interval dual semantic orthogonal fuzzy sets, and constructing a non-linear programming model to determine the weights of each disassemblable indicator; conducting expert scoring on each disassemblable design solution, and constructing a disassemblable technical feature evaluation matrix, which is standardized and weighted to obtain a standardized comprehensive disassemblable technical feature evaluation matrix; according to the standardized comprehensive disassemblable technical feature evaluation matrix, establishing a comprehensive utility matrix by using the utility value and the regret-joy value of each disassemblable design solution; determining the average comprehensive utility value, the best comprehensive utility value and the worst comprehensive utility value of each disassemblable technical feature, and based on the comprehensive utility matrix and the average distance solution method of regret theory, conducting a priority selection and ranking of each disassemblable design solution to obtain an evaluation result.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and specifically to a product solution evaluation method, system, storage medium and device. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the upgrading of mechanical products, there are a large number of waste products, which cause environmental pollution and waste of resources. Therefore, recyclable design needs to be considered in the product design stage. At the same time, mechanical products are designed for specific customer needs. Customer needs are subjective and need to be disassembled to respond to them.

[0004] Product disassembly design is a product design technology that serves the product recycling stage of the entire life cycle. When designing a mechanical product for disassembly, it is necessary not only to consider the economic benefits brought by disassembly, but also to consider the impact of the product design on the environment.

[0005] The selection of a disassembled design for mechanical products is a multi-criteria, multi-objective, and multi-scheme decision-making problem, which requires consideration of the ambiguity and uncertainty of the decision-makers. The commonly used multi-attribute decision-making method has certain limitations and some shortcomings: 1) The multi-attribute decision-making process is fuzzy and uncertain; 2) There is a lack of design solutions that incorporate customer needs, resulting in excessive subjectivity in the design solutions; 3) The limitations of a single decision-making method. Summary of the invention

[0006] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a product solution evaluation method, system, storage medium and device to dilute the subjectivity of traditional disassembly design solutions, solve the limitations of the single decision-making method of disassembly design solutions for mechanical products, and provide relevant practitioners with a more practical guidance for design goals.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides a product solution evaluation method, comprising the following steps:

[0009] Based on interval binary semantic orthogonal fuzzy sets, the disassembly design quality house and disassembly technology characteristic indicators are constructed, and a nonlinear programming model is constructed to determine the weight of each disassembly indicator.

[0010] Experts scored each decomposable design scheme based on interval binary semantic orthogonal fuzzy sets, and constructed a decomposable technical feature evaluation matrix. After standardization and weighting, a standardized comprehensive decomposable technical feature evaluation matrix was obtained.

[0011] According to the standardized comprehensive decomposable technical feature evaluation matrix, a comprehensive utility matrix is ​​established using the utility value and the regret-joy value of each decomposable design scheme;

[0012] The average comprehensive utility value, the best comprehensive utility value and the worst comprehensive utility value of each disassembly technical feature are determined. Based on the comprehensive utility matrix of regret theory and the average distance solution method, the disassembly design schemes are prioritized and ranked to obtain the evaluation results.

[0013] A second aspect of the present invention provides a system for implementing the above method, comprising:

[0014] The first module is configured to: construct decomposable design quality house and decomposable technical characteristic indicators based on interval binary semantic orthogonal fuzzy sets, and construct a nonlinear programming model to determine the weight of each decomposable indicator;

[0015] The second module is configured to: perform expert scoring on each decomposable design scheme based on interval binary semantic orthogonal fuzzy sets, and construct a decomposable technical feature evaluation matrix;

[0016] The third module is configured to: obtain a standardized comprehensive disassembly technical feature evaluation matrix by standardizing and weighting the disassembly technical feature evaluation matrix;

[0017] The fourth module is configured to: establish a comprehensive utility matrix using the utility value and the regret-joy value of each decomposable design scheme according to the standardized comprehensive decomposable technical feature evaluation matrix;

[0018] The fifth module is configured to: determine an average comprehensive utility value, an optimal comprehensive utility value, and a worst comprehensive utility value of each decomposable technical feature;

[0019] The sixth module is configured as follows: based on the comprehensive utility matrix of regret theory and the average distance solution method, each decomposable design scheme is prioritized and ranked to obtain the evaluation results.

[0020] A third aspect of the present invention provides a computer-readable storage medium.

[0021] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the product solution evaluation method as described above.

[0022] A fourth aspect of the present invention provides a computer device.

[0023] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the product solution evaluation method as described above when executing the program.

[0024] Compared with the prior art, one or more of the above technical solutions have the following beneficial effects:

[0025] The binary semantic orthogonal fuzzy set theory is adopted to reduce the influence of subjective decisions on the results in the scheme decision-making process. The avoidance psychology of regret theory and the rational decision-making of the average distance solution method are integrated to make the evaluation process more reasonable and effective. The BWM (Best Worst Method)-FQFD (Fuzzy Quality Function Deployment) nonlinear model is used to calculate the weights of decomposable technical features, which solves the subjective problem caused by direct scoring by experts. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 It is a schematic diagram of a quality house architecture of a disassembled design in a product solution evaluation method provided by one or more embodiments of the present invention;

[0028] Figure 2 It is a decision flow diagram of a product solution evaluation method provided by one or more embodiments of the present invention;

[0029] Figure 3 The content of Table 7 in the product solution evaluation method provided by one or more embodiments of the present invention;

[0030] Figure 4 The content of Table 8 in the product solution evaluation method provided by one or more embodiments of the present invention;

[0031] Figure 5 The content of Table 9 in the product solution evaluation method provided by one or more embodiments of the present invention;

[0032] Figure 6 This is the content of Table 15 in the product solution evaluation method provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0034] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0035] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0036] Terminology explanation:

[0037] I2q: interval binary semantic orthogonal fuzzy sets;

[0038] BWM: best-worst method;

[0039] I2q-RT-EDAS: Interval binary semantic orthogonal fuzzy sets-regret theory-average distance solution;

[0040] VIKOR: A multi-criteria compromise solution ranking method;

[0041] PROMETHEE: Preference Ordinal Structure Evaluation Method;

[0042] AHP: Analytical Hierarchy Process;

[0043] GRA: Grey relational analysis;

[0044] TOPSIS: A ranking method that approximates the ideal solution;

[0045] MEW: Multiplicative Exponential Weighting;

[0046] SAW: Simple Additive Weighting;

[0047] FQFD: Fuzzy Quality Function Deployment.

[0048] As described in the background technology, the selection of a disassembly design for a mechanical product is a multi-criteria, multi-objective, and multi-scheme decision-making problem that requires consideration of the ambiguity and uncertainty of the decision-maker's decision. The commonly used multi-attribute decision-making method has certain limitations and some shortcomings: 1) The multi-attribute decision-making process is fuzzy and uncertain; 2) There is a lack of design solutions that incorporate customer needs, resulting in excessive subjectivity in the design solution; 3) The limitations of a single decision-making method.

[0049] Therefore, the following embodiments provide a product solution evaluation method, system, storage medium and device to reduce the subjectivity of traditional disassembly design solutions, solve the existing technical problems of the limitations of the single decision-making method for disassembly design solutions of mechanical products, and provide relevant practitioners with a more practical design goal guidance.

[0050] Embodiment 1:

[0051] The product contains multiple design solutions, and the design solutions contain the technical features of the product, but cannot show customer needs. In order to select the best solution to put on the market, it is necessary to investigate market demand (customer needs) and establish a quality house to analyze the correlation between customer needs and the technical features of the product design solution, and then explore the importance of technical feature weights to meet market needs. The following steps are included:

[0052] Analyze the correlation between customer needs and technical features;

[0053] Obtaining technical feature weights;

[0054] Make decisions on design options and select the best one;

[0055] Specific:

[0056] like Figure 1-2 As shown, the product solution evaluation method includes the following steps:

[0057] Before evaluating the disassembly design scheme of mechanical products, this embodiment proposes interval binary semantic orthogonal fuzzy sets (I2q-ROFSs) to describe the fuzzy environment in the decision-making process:

[0058] Definition 1: is an I2q-ROFS.

[0059] in, is the membership degree of the set, represents the lower limit of membership, represents the upper limit of membership degree;

[0060] is the set non-membership degree, represents the lower limit of non-membership degree, Represents the upper limit of non-membership and satisfies the following conditions:

[0061] (1)

[0062] (2)

[0063] (3)

[0064] (4)

[0065] in, It's hesitation. represents the lower limit of hesitation, represents the upper limit of hesitation; and

[0066] Definition 2:

[0067]

[0068] and

[0069]

[0070] They are all I2q-ROFS, and the calculation rules are as follows:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] Definition 3: Score function of I2q-ROFSs and accuracy function As shown below:

[0077]

[0078]

[0079] Based on the basic knowledge of interval binary semantic orthogonal fuzzy sets (I2q-ROFSs) mentioned above, the evaluation method of the disassembly design scheme of the mechanical product in this embodiment is as follows:

[0080] Module 1: Use interval binary orthogonal fuzzy sets to construct the quality house (HOQ) of disassembled design, solve the optimal customer demand and the optimal correlation matrix through the nonlinear model of I2q-BWM-FQFD, and obtain the optimal weights of disassembled technical features through HOQ;

[0081] Module 2: Use interval binary semantic orthogonal fuzzy sets to conduct expert scoring on each decomposable design scheme and construct a fuzzy decomposable technical characteristic index evaluation matrix;

[0082] The third module: standardize and weight it to obtain a standardized comprehensive decomposable design technical feature evaluation matrix;

[0083] Module 4: Calculate the utility value and regret-joy value of the decomposable design scheme based on the standardized comprehensive decomposable design scheme evaluation matrix to establish a comprehensive utility matrix;

[0084] Module 5: Determine the average comprehensive utility value, the best comprehensive utility value, and the worst comprehensive utility value of each decomposable technical feature;

[0085] Module 6: Use the comprehensive utility matrix of regret theory and EDAS method to prioritize and rank the disassembled design schemes.

[0086] Furthermore, the disassembled design HOQ in the first module (such as Figure 1 The process of constructing the I2q-BWM-FQFD nonlinear model and solving the weights of the detachable technical characteristics is as follows:

[0087] The I2q-BWM-FQFD nonlinear model is as follows:

[0088]

[0089] In the above model, the first four formulas are the BWM optimization formulas for the best indicator and other indicators, the 5th to 8th formulas represent the BWM optimization formulas for other indicators and the worst indicator, and the last two formulas standardize the weights and satisfy non-negativity.

[0090] use The disassembled technical features of l decisions are integrated, and then the standard weight of each disassembled technical feature is solved by the following formula.

[0091]

[0092] Furthermore, the specific process of establishing the fuzzy decomposable technical feature evaluation matrix in the second module is as follows:

[0093] DM k Using interval binary semantic orthogonal fuzzy sets for the decomposable technical feature DT={DT 1 ,DT 2 ,...,DT n}, for each decomposable design A={A 1 ,A 2 ,...,A m}Evaluate to obtain the evaluation index matrix of disassembly technical characteristics

[0094] Fuzzy evaluation matrix

[0095]

[0096] Among them, D krepresents the kth decision maker, k represents the number of the decision maker, l represents the number of decision makers, i represents the i-th row of the fuzzy evaluation matrix, i.e., the i-th mechanical product disassembly design scheme, j represents the j-th column of the fuzzy evaluation matrix, i.e., the j-th disassembly technical feature, m represents the total number of mechanical product disassembly design schemes, n represents the total number of disassembly technical features, It represents the evaluation of the j-th decomposable technical feature of the ith solution by the k-th decision maker using interval binary semantic orthogonal fuzzy sets.

[0097] Furthermore, the specific process of standardization and weighting in the third module is as follows:

[0098] The normalized matrix is ​​DT = [dt ij ] m×n ,i=1,2,…,m,j=1,2,…,n;

[0099]

[0100] Among them, J + Indicates that the corresponding attribute is a benefit attribute; J - Indicates that the corresponding attribute is a cost attribute.

[0101] Weighted processing: Establish a comprehensive evaluation matrix of disassembly technology characteristics for each decision maker [DT m×n ]=[dt ij ].

[0102]

[0103] Furthermore, the calculation method of the comprehensive utility matrix in the fourth module is as follows:

[0104]

[0105]

[0106]

[0107] Among them, t μ is the membership utility value, the larger the better, and t v is the non-membership utility value, the smaller the better. V(x) is the utility function, β is the risk aversion coefficient, 0<β<1, the larger β is, the greater the risk aversion the decision maker will face. μ (x) and f v (x) is Related distribution function distribution function f n It follows the normal distribution as follows:

[0108] in,

[0109]

[0110]

[0111] u ij =g ij +h ij +2

[0112] in, g ij and h ij Respectively represent the attributes C j Alternative A i Regret and joy and Respectively represent the attributes C j Alternative A i The regret and joy values ​​of the membership degree; and Respectively represent the attributes C j Alternative A i The regret and joy value of the non-membership degree of u; ij It is attribute C j Alternative A i The comprehensive utility value.

[0113] Furthermore, the calculation process of the average comprehensive utility value, the best comprehensive utility value and the worst comprehensive utility value in the fifth module is as follows.

[0114] Average comprehensive utility value:

[0115] Best comprehensive utility value:

[0116] Worst comprehensive utility value:

[0117] Furthermore, the scoring method for the disassembly design solution in the sixth module is as follows:

[0118]

[0119] The scores obtained by the above formula are sorted, and the sorting result is the evaluation result.

[0120] The above process uses interval binary semantic orthogonal fuzzy sets and FQFD to reduce the subjectivity of experts in the decision-making process of the demountable design scheme, making the evaluation results more objective; the optimization nonlinear model of BWM is used to improve the accuracy and consistency of the normalized weights in the relationship matrix. The proposed hybrid multi-attribute decision-making method can be used to guide decision makers / manufacturers to make better decisions when selecting the most sustainable demountable design scheme.

[0121] The embodiment of the present invention combines four refrigerator disassembly design schemes (as shown in Table 1) to select the best disassembly design scheme to verify the evaluation method of the disassembly design scheme of mechanical products. The specific process is as follows: Figure 1 shown.

[0122] Table 1 Disassembly design of refrigerator

[0123]

[0124] In Table 1, customer demand indicators are established based on relevant research, CR 1 : Disassembly cost; CR 2 : Environmental performance; CR 3 : Safety; CR 4 : Convenience of operation; CR 5 : Disassembly income; CR 6 : Disassembly time and disassembly technical features; DT 1 : Disassembling accessibility; DT 2 : Toxic material ratio; DT 3 : Material recovery rate; DT 4 : Disassembly of energy consumption; DT 5 : Waste discharge; DT 6 : Production and use noise; DT 7 : Fastener ratio.

[0125] The following steps are involved:

[0126] Module 1: Construct the BWM-FQFD fuzzy evaluation language level variables based on interval binary semantic orthogonal fuzzy sets as shown in Table 2 and use interval binary orthogonal fuzzy sets to construct the quality house (HOQ) of the disassembled design, and solve the optimal customer demand and the optimal correlation matrix through the nonlinear model of BWM-FQFD. And obtain the optimal weight of the disassembled technical features through HOQ;

[0127] Table 2. I2q-BWM-FQFD fuzzy evaluation language level.

[0128] Language variables Interval binary semantic orthogonal fuzzy numbers Weakly important [(1,2),(0,1)] Strongly important [(3,4),(2,3)] Very important [(4,5),(3,4)] Absolutely important [(4,5),(0,1)] Equally important [(6,6),(0,0)]

[0129] Step 1. According to the decision maker, determine the best (most important) customer demand and the worst (least important) customer demand as well as the Best-to-Others vector and Others-to-Worst vector as shown in Table 3-6.

[0130] Table 3. Best and Worst Customer Demands (Expert 1)

[0131]

[0132] Table 4. Best and Worst Customer Demands (Expert 2)

[0133]

[0134] Table 5. Best and Worst Customer Demands (Expert 3)

[0135]

[0136] Table 6. Best and Worst Customer Demands (Expert 4)

[0137]

[0138] Step 2. According to the decision maker, determine the disassembled technical features that have the greatest impact on customer needs and the disassembled technical features that have the least impact on customer needs, as well as the Best-to-Others vector and Others-to-Worst vector for each customer demand. Figure 3 (Table 7) shows.

[0139] Table 7. Correlation matrix (Expert 1)

[0140]

[0141] Step 3. Solve the optimal weight and optimal correlation matrix of the customer demand of the kth decision maker by I2q-BWM-FQFD nonlinearly, as Figure 4 (Table 8)

[0142] Table 8. Best HOQ (Expert 1)

[0143]

[0144] Step 4. Obtain the optimal weight of the disassembly technology features of the kth decision maker through HOV calculation, such as Figure 4 (Table 8)

[0145] Step 5. Calculate the comprehensive optimal weights of the disassembled technical features of all decision makers, as shown in 5 (Table 9).

[0146] Table 9. Weights of foldable technical features

[0147]

[0148] Module 2: Step 6. Construct comprehensive fuzzy evaluation language level variables based on interval binary semantic orthogonal fuzzy sets as shown in Table 10 and use interval binary semantic orthogonal fuzzy sets to conduct expert scoring on each decomposable design scheme to construct a fuzzy decomposable technical feature index evaluation matrix as shown in Tables 11-14;

[0149] Table 10. Variables of comprehensive fuzzy evaluation language level.

[0150] Language variables Interval binary semantic orthogonal fuzzy numbers Extremely fine <![CDATA[([(s 4 ,0),(s 5 ,0)],[(s 0 ,0),(s 1 ,0)])]]> Very fine <![CDATA[([(s 4 ,0),(s 5 ,0)],[(s 3 ,0),(s 4 ,0)])]]> Fine <![CDATA[([(s3,0),(s 4 ,0)],[(s 2 ,0),(s 3 ,0)])]]> Weakly fine <![CDATA[([(s 1 ,0),(s 2 ,0)],[(s 0 ,0),(s1,0)])]]> Medium <![CDATA[([(s 3 ,0),(s 4 ,0)],[(s 3 ,0),(s 4 ,0)])]]> Weakly terrible <![CDATA[([(s 0 ,0),(s 1 ,0)],[(s 1 ,0),(s 2 ,0)])]]> Terrible <![CDATA[([(s 2 ,0),(s 3 ,0)],[(s 3 ,0),(s 4 ,0)])]]> Very terrible <![CDATA[([(s 3 ,0),(s 4 ,0)],[(s 4 ,0),(s 5 ,0)])]]> Extremely terrible <![CDATA[([(s 0 ,0),(s 1 ,0)],[(s 4 ,0),(s 5 ,0)])]]>

[0151] Table 11. Fuzzy evaluation matrix (expert one)

[0152]

[0153] Table 12. Fuzzy evaluation matrix (expert 2)

[0154]

[0155] Table 13. Fuzzy evaluation matrix (expert three)

[0156]

[0157] Table 14. Fuzzy evaluation matrix (expert 4)

[0158]

[0159]

[0160] Module 3: Step 7. Standardize and weight the data to obtain a standardized comprehensive decomposable design technical feature evaluation matrix, such as Figure 6 (Table 15)

[0161] Table 15. Comprehensive fuzzy evaluation matrix

[0162]

[0163] Module 4: Step 8. Calculate the utility value and regret-joy value of the decomposable design scheme based on the standardized comprehensive decomposable design scheme evaluation matrix to establish a comprehensive utility matrix, as shown in Table 16;

[0164] Table 16. Utility matrix

[0165]

[0166] Module 5: Step 9. Determine the average comprehensive utility value of each decomposable technical feature:

[0167]

[0168] The best comprehensive utility value and the worst comprehensive utility value are shown in Table 17 and Table 18 respectively.

[0169] Table 17. Best utility distance matrix

[0170]

[0171] Table 18. Worst utility distance matrix

[0172]

[0173] Module 6: Step 10. Use the comprehensive utility matrix of regret theory and EDAS method to prioritize the disassembled design solutions. The AS values ​​are: AS 1 =0.2345AS 2 =0.0117AS 3 =0.2270AS 4 =0.7276. Therefore, the ranking result of the disassembled design schemes is: A 4 >A 1 >A 3 >A 2， The best decomposable design solution is determined to be A 4 .

[0174] In summary, this embodiment adopts the I2q-ROFSs fuzzy theory to reduce the influence of the subjective judgment of experts on the results in the solution decision-making process, and integrates the avoidance psychology of regret theory and the rational decision-making of the EDAS method to make the evaluation process more reasonable and effective. The BWM-FQFD nonlinear model is used to calculate the weight of the decomposable technical features, which solves the subjective problem caused by the direct scoring of experts.

[0175] Embodiment 2:

[0176] A system for implementing the above method comprises:

[0177] The first module is configured to: construct decomposable design quality house and decomposable technical characteristic indicators based on interval binary semantic orthogonal fuzzy sets, and construct a nonlinear programming model to determine the weight of each decomposable indicator;

[0178] The second module is configured to: perform expert scoring on each decomposable design scheme based on interval binary semantic orthogonal fuzzy sets, and construct a decomposable technical feature evaluation matrix;

[0179] The third module is configured to: obtain a standardized comprehensive disassembly technical feature evaluation matrix by standardizing and weighting the disassembly technical feature evaluation matrix;

[0180] The fourth module is configured to: establish a comprehensive utility matrix using the utility value and the regret-joy value of each decomposable design scheme according to the standardized comprehensive decomposable technical feature evaluation matrix;

[0181] The fifth module is configured to: determine an average comprehensive utility value, an optimal comprehensive utility value, and a worst comprehensive utility value of each decomposable technical feature;

[0182] The sixth module is configured as follows: based on the comprehensive utility matrix of regret theory and the average distance solution method, each decomposable design scheme is prioritized and ranked to obtain the evaluation results.

[0183] The binary semantic orthogonal fuzzy set theory is adopted to reduce the influence of subjective decisions on the results in the scheme decision-making process. The avoidance psychology of regret theory and the rational decision-making of the average distance solution method are integrated to make the evaluation process more reasonable and effective. The BWM (Best Worst Method)-FQFD nonlinear model is used to calculate the weights of decomposable technical features, which solves the subjective problem caused by direct scoring by experts.

[0184] Embodiment three:

[0185] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the product solution evaluation method described in the above embodiment 1 are implemented.

[0186] The product scheme evaluation method adopts binary semantic orthogonal fuzzy set theory, which reduces the influence of subjective decisions on the results in the scheme decision-making process. It integrates the avoidance psychology of regret theory and the rational decision-making of the average distance solution method, making the evaluation process more reasonable and effective. The BWM (Best Worst Method)-FQFD nonlinear model is used to calculate the weights of decomposable technical features, which solves the subjective problem caused by direct scoring by experts.

[0187] Embodiment 4:

[0188] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the product solution evaluation method described in the first embodiment are implemented.

[0189] The product scheme evaluation method adopts binary semantic orthogonal fuzzy set theory, which reduces the influence of subjective decisions on the results in the scheme decision-making process. It integrates the avoidance psychology of regret theory and the rational decision-making of the average distance solution method, making the evaluation process more reasonable and effective. The BWM (Best Worst Method)-FQFD nonlinear model is used to calculate the weights of decomposable technical features, which solves the subjective problem caused by direct scoring by experts.

[0190] The steps or modules involved in the above embodiments 2 to 4 correspond to those in embodiment 1. For the specific implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0191] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Product solution evaluation method, Features: The following steps are involved: Based on interval binary semantic orthogonal fuzzy sets, the disassembly design quality house and disassembly technology characteristic indicators are constructed, and a nonlinear programming model is constructed to determine the weights of each disassembly indicator; among them, the disassembly technology characteristic indicators include disassembly accessibility, toxic material ratio, material recovery rate, disassembly energy consumption, waste emissions, production and use noise, and fastener ratio; Experts scored each decomposable design scheme based on interval binary semantic orthogonal fuzzy sets, and constructed a decomposable technical feature evaluation matrix. After standardization and weighting, a standardized comprehensive decomposable technical feature evaluation matrix was obtained. According to the standardized comprehensive decomposable technical feature evaluation matrix, a comprehensive utility matrix is ​​established using the utility value and the regret-joy value of each decomposable design scheme; Determine the average comprehensive utility value, the best comprehensive utility value and the worst comprehensive utility value of each disassembly technical feature, and prioritize and rank the disassembly design schemes based on the comprehensive utility matrix of regret theory and the average distance solution method to obtain the evaluation results; specifically: The calculation process of the average comprehensive utility value, the best comprehensive utility value and the worst comprehensive utility value is as follows: Average comprehensive utility value: Best comprehensive utility value: Worst comprehensive utility value: The scoring method for the disassembly design solution is as follows: Sort the score results obtained by the above formula to obtain the evaluation results; Among them, u ij It is attribute C j Alternative A i The comprehensive utility value of n is the number of disassembled technical features.

2. The product solution evaluation method according to claim 1, Features: Construct a disassembly technology feature evaluation matrix, including: DM k Using interval binary semantic orthogonal fuzzy sets, the decomposable technical feature DT={DT 1 ,DT 2 ,…,DT n }、Disassembly design A={A 1 ,A 2 ,…,A m } to evaluate and obtain the evaluation index matrix of disassembly technical characteristics 3. The product solution evaluation method according to claim 2, Features: Construct a disassembly technology feature evaluation matrix, including: Fuzzy evaluation matrix Among them, DT k represents the kth decision maker, l represents the number of decision makers, i represents the i-th row of the fuzzy evaluation matrix, i.e., the disassembly design scheme of the i-th mechanical product, j represents the j-th column of the fuzzy evaluation matrix, i.e., the j-th disassembly technical feature, m represents the total number of disassembly design schemes of mechanical products, n represents the total number of disassembly technical features, It represents the evaluation of the j-th decomposable technical feature of the ith solution by the k-th decision maker using interval binary semantic orthogonal fuzzy sets.

4. The product solution evaluation method according to claim 1, Features: A comprehensive utility matrix is ​​established using the utility values ​​and the regret-joy values ​​of each decomposable design solution, including: Among them, t μ is the membership utility value, t v is the non-membership utility value, V(x) is the utility function, β is the risk aversion coefficient, 0<β<1, the larger β is, the greater the risk aversion the decision maker will face.

5. The product solution evaluation method according to claim 4, Features: A comprehensive utility matrix is ​​established using the utility values ​​and the regret-joy values ​​of each decomposable design solution, including: f μ (x) and f v (x) is Related distribution functions.

6. The product solution evaluation method according to claim 5, Features: The distribution function f n It obeys the normal distribution, as follows: in, u ij =g ij +h ij +2; In the above formula, g ij and h ij Respectively represent the attributes C j Alternative A i The regret value and joy value, and Respectively represent the attributes C j Alternative A i The regret and joy values ​​of the membership degree; and Respectively represent the attributes C j Alternative A i The regret and joy value of the non-membership degree of u; ij It is attribute C j Alternative A i The comprehensive utility value.

7. A product solution evaluation system implementing the method according to any one of claims 1 to 6, Features: include: The first module is configured to: construct decomposable design quality house and decomposable technical characteristic indicators based on interval binary semantic orthogonal fuzzy sets, and construct a nonlinear programming model to determine the weight of each decomposable indicator; The second module is configured to: perform expert scoring on each decomposable design scheme based on interval binary semantic orthogonal fuzzy sets, and construct a decomposable technical feature evaluation matrix; The third module is configured to: obtain a standardized comprehensive disassembly technical feature evaluation matrix by standardizing and weighting the disassembly technical feature evaluation matrix; The fourth module is configured to: establish a comprehensive utility matrix using the utility value and the regret-joy value of each decomposable design scheme according to the standardized comprehensive decomposable technical feature evaluation matrix; The fifth module is configured to: determine an average comprehensive utility value, an optimal comprehensive utility value, and a worst comprehensive utility value of each decomposable technical feature; The sixth module is configured as follows: based on the comprehensive utility matrix of regret theory and the average distance solution method, each decomposable design scheme is prioritized and ranked to obtain the evaluation results.

8. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps in the product solution evaluation method as described in any one of claims 1 to 6 are implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the product solution evaluation method according to any one of claims 1 to 6 are implemented.

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