Fuzzy AHP-improved entropy weight method-based household appliance green degree evaluation method and system

Abstract

The invention relates to the technical field of household appliance evaluation, and particularly provides a method and system for evaluating the green degree of a household appliance product based on a fuzzy AHP-improved entropy weight method, and the method comprises the following steps: obtaining a green household appliance evaluation index; subjective assignment is carried out on the green household appliance evaluation indexes according to the importance degree, and each green household appliance evaluation index of the household appliances is scored according to related evaluation standards; performing subjective assignment processing based on a fuzzy AHP method to obtain a subjective weight, processing the score of each green household electrical appliance evaluation index by using an improved entropy weight method to obtain an objective weight, and fitting the subjective weight and the objective weight to obtain a comprehensive weight of each green household electrical appliance evaluation index; performing weighted summation based on the comprehensive weight of each green household appliance evaluation index and the score to obtain an evaluation score which is used for representing the green degree of the product; according to the invention, the green degree of the household appliance product is reflected to a certain extent.

Description

Technical Field

[0001] The present invention relates to the technical field of household appliance evaluation, and in particular to a method and system for evaluating the greenness of household appliance products based on a fuzzy AHP-improved entropy weight method. Background Art

[0002] At present, environmental issues have become an important topic of global concern, and green, low-carbon and sustainable development have become the common themes of human life. How to more effectively reduce carbon emissions and achieve green, low-carbon and sustainable development has become the focus of research and application. Green design can solve this problem well. It focuses on the environmental attributes of products throughout their life cycle and takes them as design goals. While meeting environmental goals, green design also ensures that products have the required functions, service life and quality requirements.

[0003] Green modular design has been adopted at home and abroad to solve the problem of evaluating green home appliances. In the context of the era when environmental protection has become an important issue, many scholars have conducted in-depth research on green modular design. Home appliances are developing in an increasingly green direction, but there is currently a lack of methods to evaluate the green degree of home appliances. It is impossible to intuitively understand the defects and deficiencies of home appliances, and it is not known how to improve the performance of home appliances, which is not conducive to improving the impact of home appliances on the environment. In order to overcome the related problems, the present invention discloses a method and system for evaluating the green degree of home appliances based on fuzzy AHP-improved entropy weight method. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the green degree of household appliances based on fuzzy AHP-improved entropy weight method, so as to solve the problem of the current lack of methods for evaluating the green degree of household appliances.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method and system for evaluating the greenness of household appliances based on fuzzy AHP-improved entropy weight method, characterized in that the evaluation method comprises the following steps:

[0007] S10, obtaining green home appliance evaluation indicators, where the green home appliance evaluation indicators are indicators common to different home appliances and indicators related to the disassembly process of green home appliance products, used to evaluate the green design degree of the products;

[0008] S20: subjectively assign values ​​to green home appliance evaluation indicators according to their importance, and score each green home appliance evaluation indicator of the home appliance according to relevant evaluation standards;

[0009] S30: Based on the fuzzy AHP method, the subjective value assignment is processed to obtain the subjective weight, and the score of each green home appliance evaluation index is processed using the improved entropy weight method to obtain the objective weight, and the subjective weight and the objective weight are fitted to obtain the comprehensive weight of each green home appliance evaluation index;

[0010] S40: A weighted sum is performed based on the comprehensive weight of each green home appliance evaluation index and the score assigned to obtain an evaluation score for indicating the greenness of the product.

[0011] Preferably, the green home appliance evaluation indicators include disassembly energy consumption, disassembly time, material classification complexity, disassembly action complexity, disassembly material availability, disassembly material recyclability, renewable utilization rate, life cycle, packaging and packaging materials, recyclable labeling, noise, product energy consumption, product toxic substance content, product electrical safety, and product electromagnetic compatibility.

[0012] Preferably, the two green home appliance evaluation indicators are assigned values ​​based on their importance in comparison with each other, and a subjective value assignment table of green home appliance evaluation indicators with n rows and n columns is obtained. The method for calculating the subjective weights includes the following steps:

[0013] The fuzzy judgment matrix R is constructed based on the values ​​in the subjective value table of green home appliance evaluation indicators. ij ) n×n , where n is the number of green home appliance evaluation indicators;

[0014] Calculate the weight a i :

[0015]

[0016] Where n represents the number of green home appliance evaluation indicators; r ij is the corresponding value in the fuzzy judgment matrix R, that is, the value of the i-th row and j-th column in Table 2; Represents the sum of all values ​​in the i-th row.

[0017] Preferably, after calculating the weights, a consistency check is required, and the consistency check method includes the following steps:

[0018] Compute the feature matrix:

[0019] The characteristic matrix of matrix R is A=(a ij ) n×n , where a ij Calculate according to formula (2):

[0020]

[0021] Among them, a i and a jIt is obtained from formula (1);

[0022] Consistency check:

[0023] The consistency test of the fuzzy judgment matrix R and the characteristic matrix A is performed to ensure that the compatibility index I is less than or equal to 0.1, which means that the judgment matrix meets the consistency. The consistency test is calculated by formula (3):

[0024]

[0025] Among them, r ij Represents the value of the i-th row and j-th column in the judgment matrix R, a ji Represents the value of the j-th row and i-th column in the feature matrix A;

[0026] If I<0.1, it means that the consistency of the fuzzy judgment matrix R is considered to be within the allowable range. At this time, the judgment matrix can be used to perform weight calculation, that is, to solve the subjective weight a i ; If I ≥ 0.1, the fuzzy judgment matrix R should be considered for correction.

[0027] Preferably, the specific steps for solving the objective weight are as follows:

[0028] Data Standardization:

[0029] The sample data after scoring is standardized to eliminate the dimension differences of each green appliance evaluation index and compress the value of each index within the interval of [0-1]. Assuming that the matrix X formed by the scoring table consists of m samples and n indicators, X = (X ij ) m×n , as shown in the following matrix:

[0030]

[0031] Among them, m samples are formed by scoring by m experts;

[0032] The standardized formula for green home appliance evaluation index is shown in formula (4):

[0033]

[0034] After normalization, we get the new matrix As shown below:

[0035]

[0036] Calculate specific gravity:

[0037] The numerical weight P of the i-th sample in the j-th indicator is calculated by formula (5): ij :

[0038]

[0039] Among them, m represents the number of samples; n represents the number of indicators;

[0040] Calculate the entropy value:

[0041] The entropy value e of the jth indicator is calculated by formula (6): j :

[0042]

[0043] in, m represents the number of samples, Ln(m) represents the natural logarithm of m; calculate the objective weight:

[0044] The objective weight b of the jth indicator is calculated by formula (7): j :

[0045]

[0046] Among them, max(e j ) represents the maximum value among n entropy values; e j is the entropy value calculated by formula (6).

[0047] Preferably, the comprehensive weight is calculated by formula (8), as shown below:

[0048]

[0049] Where n represents the number of green appliance evaluation indicators.

[0050] Preferably, the calculation method of the evaluation score is as shown in formula (9):

[0051]

[0052] Where N represents the number of green home appliance evaluation indicators, w i Indicates the weight corresponding to each indicator, x i represents the score of the indicator, and Y represents the final result of the weighted summation, that is, the evaluation score.

[0053] The present invention also discloses a green degree evaluation system for household appliances based on fuzzy AHP-improved entropy weight method, the evaluation system comprising:

[0054] An acquisition module is used to obtain green home appliance evaluation indicators;

[0055] Scoring module, used to obtain expert scores;

[0056] A weight calculation module, used to calculate subjective weight and objective weight;

[0057] Evaluation module, used to calculate evaluation scores.

[0058] In summary, the present invention has the following beneficial effects compared with the prior art:

[0059] The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method disclosed in the present invention aims at the problem that the green degree of green household appliances cannot be evaluated in the current household appliances field. By selecting a series of quantifiable indicators related to household appliances, specifying the scoring standards of the indicators to score the household appliance indicators, calculating the indicator weights by the entropy weight method, and finally performing weighted summation to obtain the final score, this score reflects the green degree of the household appliances to a certain extent. This method can improve the scores of household appliances and develop in a greener direction at the same time through evaluation scores. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention discloses a schematic diagram of the steps of a method for evaluating the green degree of household appliances based on the fuzzy AHP-improved entropy weight method.

[0061] Figure 2 The present invention discloses a flowchart of a method for evaluating the green degree of household appliances based on the fuzzy AHP-improved entropy weight method.

[0062] Figure 3 The present invention discloses a flowchart for calculating subjective weights in a green degree evaluation method for household appliances based on fuzzy AHP-improved entropy weight method.

[0063] Figure 4 The invention discloses a flowchart for calculating objective weights in a green degree evaluation method for household appliances based on fuzzy AHP-improved entropy weight method. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0065] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the greenness of household appliances based on fuzzy AHP-improved entropy weight method, and the evaluation method includes the following steps:

[0066] S10, obtaining green home appliance evaluation indicators, where the green home appliance evaluation indicators are indicators common to different home appliances and indicators related to the disassembly process of green home appliance products, used to evaluate the green design degree of the products;

[0067] S20: subjectively assign values ​​to green home appliance evaluation indicators according to their importance, and score each green home appliance evaluation indicator of the home appliance according to relevant evaluation standards;

[0068] S30: Based on the fuzzy AHP method, the subjective value assignment is processed to obtain the subjective weight, and the score of each green home appliance evaluation index is processed using the improved entropy weight method to obtain the objective weight, and the subjective weight and the objective weight are fitted to obtain the comprehensive weight of each green home appliance evaluation index;

[0069] S40: A weighted sum is performed based on the comprehensive weight of each green home appliance evaluation index and the score assigned to obtain an evaluation score for indicating the greenness of the product.

[0070] Specifically, in step S10, the green home appliance evaluation index is obtained from relevant standards for green design of home appliances, such as the indicators involved in "Technical Specifications for Green Design Product Evaluation - Brushless DC Motor for Drum Washing Machine", "Technical Specifications for Green Design Product Evaluation - Electric Washing Machine", "Technical Specifications for Green Design Product Evaluation - Household Refrigerators", "Technical Specifications for Green Design Product Evaluation - Televisions", etc., wherein different evaluation standards are used to evaluate different products, such as the green home appliance evaluation index obtained from "Technical Specifications for Green Design Product Evaluation - Brushless DC Motor for Drum Washing Machine" is used to evaluate washing machines, and the green home appliance evaluation index obtained from "Technical Specifications for Green Design Product Evaluation - Televisions" is used to evaluate televisions;

[0071] On the other hand, it comes from indicators related to the disassembly process of household appliances, such as disassembly energy consumption, disassembly time, material classification complexity, disassembly action complexity, disassembly material availability, disassembly material recyclability, and recyclable utilization rate.

[0072] In this embodiment, disassembly energy consumption, disassembly time, material classification complexity, disassembly action complexity, disassembly material availability, disassembly material recyclability, renewable utilization rate, life cycle, packaging and packaging materials, recyclable label, noise, product energy consumption, product toxic substance content, product electrical safety and product electromagnetic compatibility are used as green home appliance evaluation indicators.

[0073] In step S20, the green home appliance evaluation index is assigned a value using a 0.1-0.9 scale method and the importance of the comparison between the two indicators to evaluate the importance of the green home appliance evaluation index to the green degree of the product. The 0.1-0.9 scale method is shown in Table 1:

[0074] Table 1 0.1-0.9 scaling method

[0075]

[0076] According to the method shown in Table 1, the subjective evaluation results of the green home appliance evaluation index in this embodiment are shown in Table 2:

[0077] Table 2 Subjective evaluation results of green home appliance evaluation indicators

[0078]

[0079] After assigning values ​​to the green home appliance evaluation indicators, score each green home appliance evaluation indicator according to the actual situation of the home appliance. The scoring table is shown in Table 3:

[0080] Table 3 Scoring table for green home appliance evaluation indicators

[0081]

[0082] In step S30, Figure 2 and Figure 3 As shown, the method for finding the subjective weight is as follows:

[0083] S31. Weight calculation

[0084] The fuzzy judgment matrix R constructed according to the assignment results in Table 2 is ij ) n×n , calculate the weight a according to formula (1) i and a j :

[0085]

[0086] Where n represents the number of green home appliance evaluation indicators; r ij is the corresponding value in the fuzzy judgment matrix R, that is, the value of the i-th row and j-th column in Table 2; Represents the sum of all values ​​in the i-th row;

[0087] S32. Calculate the feature matrix

[0088] The characteristic matrix of matrix R is A=(a ij ) n×n , where a ij Calculate according to formula (2):

[0089]

[0090] Among them, a i and a j It is obtained from formula (1);

[0091] Specifically, because the feature matrix A is nxn, when finding the n data in the first row, i=1 is taken unchanged, and then j is taken from 1 to n to obtain a in the feature matrix 11 ,a 12,...,a 1n , specifically a 11 =a1 / (a1+a1), a 12 =a1 / (a1+a2), ..., a 1n =a1 / (a1+a n ), so that we get n values ​​in the first row, that is, each time i is fixed and j is 1 to n.

[0092] S33, consistency check

[0093] The fuzzy judgment matrix R and the characteristic matrix A in step S32 are subjected to consistency check. The compatibility index I must be less than or equal to 0.1 to indicate that the judgment matrix meets the consistency. The consistency check is calculated by formula (3):

[0094]

[0095] Among them, r ij Represents the value of the i-th row and j-th column in the judgment matrix R, a ji Represents the value of the j-th row and i-th column in the feature matrix A.

[0096] If I<0.1, it means that the consistency of the fuzzy judgment matrix R is considered to be within the allowable range. At this time, the judgment matrix can be used to perform weight calculation, that is, to solve the subjective weight a i ; If I ≥ 0.1, the fuzzy judgment matrix R should be corrected, and the correction method is that the experts adjust the scoring results and the assignment results.

[0097] In this embodiment, the fuzzy AHP method is used to calculate the subjective weights of the green home appliance evaluation indicators. The fuzzy AHP method combines the fuzzy method with the AHP method, and can solve the problem that the AHP method is difficult to ensure the consistency of thinking when dealing with many evaluation indicators at a certain level.

[0098] like Figure 2 and Figure 4 As shown, the specific steps for solving the objective weight are as follows:

[0099] S34. Data Standardization

[0100] The sample data after scoring is standardized to eliminate the dimension differences of each green appliance evaluation index and compress the value of each index within the interval of [0-1]. Assuming that the matrix X formed by the scoring table consists of m samples and n indicators, X = (X ij ) m×n , as shown in the following matrix:

[0101]

[0102] Among them, m samples are formed by scoring by m experts;

[0103] The standardized formula for green home appliance evaluation index is shown in formula (4):

[0104]

[0105] After normalization, we get the new matrix As shown below:

[0106]

[0107] S35. Calculate specific gravity

[0108] The numerical weight P of the i-th sample in the j-th indicator is calculated by formula (5): ij :

[0109]

[0110] Among them, m represents the number of samples; n represents the number of indicators.

[0111] S36. Calculate entropy value

[0112] The entropy value e of the jth indicator is calculated by formula (6): j :

[0113]

[0114] in, m represents the number of samples, and Ln(m) represents the natural logarithm of m.

[0115] S37. Calculate objective weight

[0116] The objective weight b of the jth indicator is calculated by formula (7): j :

[0117]

[0118] Among them, e j is the entropy value calculated in step 36.

[0119] The subjective weight a i and objective weight b i Fitting is performed to obtain the comprehensive weight w i , the comprehensive weight is calculated by formula (8), as shown below:

[0120]

[0121] Where n represents the number of green appliance evaluation indicators.

[0122] In step S40, for the weight w obtained in step 37 i The weighted multiplication of the sample score in step 34 is performed using formula (9). The obtained score can represent the green degree of the home appliance product. The weighted sum is calculated using the following formula:

[0123]

[0124] Where N represents the number of green home appliance evaluation indicators, w i Indicates the weight corresponding to each indicator, x i represents the score of the indicator, and Y represents the final result of the weighted summation, that is, the evaluation score.

[0125] The entropy weight method used in steps S34 to S37 solves the objective weight. The entropy weight method calculates the entropy value of the indicator based on the impact of the numerical changes of each indicator on the whole, and then determines the weight. In information theory, entropy refers to the degree of chaos or disorder of the system (entropy is a measure of uncertainty). The larger the entropy value of the indicator, the smaller the weight. Its function is to eliminate the result deviation caused by human subjective assignment when dealing with the problem of multi-indicator weighting, avoid the influence of subjective factors, and improve the objectivity and accuracy of the evaluation results. Its advantages are strong operability and relatively simple algorithm. However, when the traditional entropy weight method calculates the weight, the change of entropy value will have a greater impact, so we improve the process of solving the weight of the entropy weight method, introduce a smoothing coefficient, and reduce the impact of the change of entropy value.

[0126] In this embodiment, taking a certain household appliance as an example, the green household appliance evaluation indicators are disassembly energy consumption, disassembly time, material classification complexity, disassembly action complexity, disassembly material availability, disassembly material recyclability, recyclable utilization rate, life cycle, packaging and packaging materials, recyclable label, noise, product energy consumption, product toxic substance content, product electrical safety and product electromagnetic compatibility. These green household appliance evaluation indicators are assigned values ​​to obtain the assignment results shown in Table 2. Combined with Table 3, each green household appliance evaluation indicator is scored as shown in Table (4):

[0127] Table 4 Scoring table for green home appliance evaluation indicators

[0128]

[0129] The weights obtained by using the above data through the traditional entropy weight method are shown in Table 5:

[0130] Table 5

[0131]

[0132] The comprehensive weights obtained based on the method disclosed in the present invention are shown in Table 6:

[0133] Table 6

[0134]

[0135] Among them, the fuzzy judgment matrix is ​​constructed by experts according to the importance of each indicator, which reflects the actual needs to a certain extent.

[0136] The subjective weights obtained by the fuzzy AHP method are shown in Table 7:

[0137] Table 7

[0138]

[0139] The scores obtained by using subjective weights for weighted scoring can reflect the experts' intentions to a certain extent, and can be used as the experts' subjective scores, which are compared with the weighted scores of the method of this application and the traditional entropy weight method. The scoring results of multiple commodity samples are statistically analyzed based on the above method and the traditional method, as shown in Table 8:

[0140] Table 8

[0141]

[0142] It can be seen that the scoring using the method of the present invention is closer to the result of subjective scoring by experts, which proves that the method of the present invention is more suitable for the evaluation of the green level of household appliances and further verifies the scientific nature and advancement of the method.

[0143] The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method disclosed in the present invention aims at the problem that the green degree of green household appliances cannot be evaluated in the current household appliances field. By selecting a series of quantifiable indicators related to household appliances, specifying the scoring standards of the indicators to score the household appliance indicators, calculating the indicator weights by the entropy weight method, and finally performing weighted summation to obtain the final score, this score reflects the green degree of the household appliances to a certain extent. This method can improve the scores of household appliances and develop in a greener direction at the same time through evaluation scores.

[0144] Example 2

[0145] The present invention also discloses a green degree evaluation system for household appliances based on fuzzy APH-improved entropy weight method, which is used to implement the green degree evaluation method for household appliances based on fuzzy APH-improved entropy weight method described in Example 1. The evaluation system includes:

[0146] An acquisition module is used to obtain green home appliance evaluation indicators;

[0147] Scoring module, used to obtain expert scores;

[0148] A weight calculation module, used to calculate subjective weight and objective weight;

[0149] Evaluation module, used to calculate evaluation scores;

[0150] In this embodiment, the acquisition module can be a computer, the scoring module can be an input device of the computer, the results acquired by the acquisition module and the scoring module are input into the weight calculation module, and the calculation results of the weight calculation module and the results of the scoring module are input into the evaluation module.

[0151] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0152] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0154] In a typical configuration of an embodiment of the present invention, the electronic device includes one or more processors (CPU), an input / output interface, a network interface, and a memory.

[0155] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash-RAM. The memory is an example of a computer-readable medium.

[0156] The readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be realized by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data.

[0157] Examples of storage media for electronic devices include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include non-transitory-med ia such as modulated data signals and carrier waves.

[0158] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. A method and system for evaluating the greenness of household appliances based on fuzzy AHP-improved entropy weight method, characterized in that: The evaluation method comprises the following steps: S10, obtaining green home appliance evaluation indicators, where the green home appliance evaluation indicators are indicators common to different home appliances and indicators related to the disassembly process of green home appliance products, used to evaluate the green design degree of the products; S20: subjectively assign values ​​to green home appliance evaluation indicators according to their importance, and score each green home appliance evaluation indicator of the home appliance according to relevant evaluation standards; S30: Based on the fuzzy AHP method, the subjective value assignment is processed to obtain the subjective weight, and the score of each green home appliance evaluation index is processed using the improved entropy weight method to obtain the objective weight, and the subjective weight and the objective weight are fitted to obtain the comprehensive weight of each green home appliance evaluation index; S40: A weighted sum is performed based on the comprehensive weight of each green home appliance evaluation index and the score assigned to obtain an evaluation score for indicating the greenness of the product.

2. The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method according to claim 1 is characterized in that: The green home appliance evaluation indicators include disassembly energy consumption, disassembly time, material classification complexity, disassembly action complexity, disassembly material availability, disassembly material recyclability, renewable utilization rate, life cycle, packaging and packaging materials, recyclable labeling, noise, product energy consumption, product toxic substance content, product electrical safety and product electromagnetic compatibility.

3. The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method according to claim 1 is characterized in that: The two green home appliance evaluation indicators are assigned values ​​by comparing their importance to each other, and a subjective value assignment table of green home appliance evaluation indicators with n rows and n columns is obtained. The method for calculating the subjective weights includes the following steps: The fuzzy judgment matrix R is constructed based on the values ​​in the subjective value table of green home appliance evaluation indicators. ij ) n×n , where n is the number of green home appliance evaluation indicators; Calculate the weight a i : Where n represents the number of green home appliance evaluation indicators; r ij is the corresponding value in the fuzzy judgment matrix R, that is, the value of the i-th row and j-th column in Table 2; Represents the sum of all values ​​in the i-th row.

4. The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method according to claim 3 is characterized in that: After calculating the weights, a consistency check is required, and the consistency check method includes the following steps: Compute the feature matrix: The characteristic matrix of matrix R is A=(a ij ) n×n , where a ij Calculate according to formula (2): Among them, a i and a j It is obtained from formula (1); Consistency check: The consistency test of the fuzzy judgment matrix R and the feature matrix A is performed to ensure that the compatibility index I is less than or equal to 0.1, which means that the judgment matrix meets the consistency. The consistency test is calculated by formula (3): Among them, r ij Represents the value of the i-th row and j-th column in the judgment matrix R, a ji Represents the value of the jth row and ith column in the feature matrix A; If I<0.1, it means that the consistency of the fuzzy judgment matrix R is considered to be within the allowable range. At this time, the judgment matrix can be used to perform weight calculation, that is, to solve the subjective weight a i ; If I ≥ 0.1, the fuzzy judgment matrix R should be considered for correction.

5. The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method according to claim 4 is characterized in that: The specific steps to solve the objective weight are as follows: Data Standardization: The sample data after scoring is standardized to eliminate the dimension differences of each green appliance evaluation index and compress the value of each index within the interval of [0-1]. Assuming that the matrix X formed by the scoring table consists of m samples and n indicators, X = (X ij ) m×n , as shown in the following matrix: Among them, m samples are scored by m experts; The standardized formula for green home appliance evaluation index is shown in formula (4): After normalization, we get the new matrix As shown below: Calculate specific gravity: The numerical weight P of the i-th sample in the j-th indicator is calculated by formula (5): ij : Among them, m represents the number of samples; n represents the number of indicators; Calculate the entropy value: The entropy value e of the jth indicator is calculated by formula (6): j : in, m represents the number of samples, Ln(m) represents the natural logarithm of m; Calculate objective weights: The objective weight b of the jth indicator is calculated by formula (7): j : Among them, max(e j ) represents the maximum value among the entropy values ​​from j = 1 to n; e j is the entropy value calculated by formula (6).

6. The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method according to claim 5 is characterized in that: The comprehensive weight is calculated by formula (8) as follows: Where n represents the number of green appliance evaluation indicators.

7. The green degree evaluation method of household appliances based on fuzzy AHP-improved entropy weight method according to claim 6 is characterized in that: The calculation method of the evaluation score is shown in formula (9): Where N represents the number of green home appliance evaluation indicators, w i Indicates the weight corresponding to each indicator, x i represents the score of the indicator, and Y represents the final result of the weighted summation, that is, the evaluation score.

8. A green evaluation system for household appliances based on fuzzy AHP-improved entropy weight method, characterized in that: The evaluation system includes: An acquisition module is used to obtain green home appliance evaluation indicators; Scoring module, used to obtain expert scores; A weight calculation module, used to calculate subjective weight and objective weight; Evaluation module, used to calculate evaluation scores.

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