Evaluation method and system for green factory in engineering machinery industry

Abstract

The invention discloses an engineering machinery industry green factory evaluation method and system. The method comprises the steps of obtaining a pre-formulated green factory evaluation index system; calculating the subjective weight of each secondary index relative to the target layer; introducing a Monte Carlo simulation algorithm, and calculating an objective weight and an evaluation matrix of each secondary index corresponding to each Monte Carlo simulation relative to the target layer; based on a minimum relative information entropy principle and a Lagrange multiplier method, combined with the subjective weight and the objective weight of each secondary index relative to the target layer, calculating the comprehensive weight of each secondary index relative to the target layer corresponding to each Monte Carlo simulation; on the basis of the comprehensive weight and evaluation matrix of each secondary index relative to the target layer, constructing a fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation; and obtaining a green factory grade evaluation result based on the fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation and a preset score interval corresponding to each comment grade. According to the method, the accuracy and objectivity of green factory evaluation can be remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of green level evaluation of green factories in the engineering machinery industry, and specifically relates to an engineering machinery industry green factory evaluation method and system. BACKGROUND

[0002] A scientific and reasonable green factory evaluation method is not only a core tool for measuring the environmental protection performance of an enterprise, but also an important basis for guiding the transformation and upgrading of the industry. However, the traditional evaluation methods either focus on subjective experience judgment or rely on single data calculation, and it is difficult to comprehensively cover multi-dimensional indicators such as environment, energy, and management in factory operation. In the complex engineering machinery production and remanufacturing scene, the evaluation results often deviate from the actual situation.

[0003] Under this background, the industry has an increasingly urgent demand for more accurate and comprehensive evaluation methods. At present, the research methods for green factory evaluation show a trend of deep integration of qualitative and quantitative methods. Qualitative evaluation is dominated by expert experience method and life cycle assessment (LCA). The former constructs an index system through Delphi method or analytic hierarchy process (AHP), and focuses on soft indicators such as management mechanism and policy response, but the subjective weight assignment is easily affected by cognitive bias. The latter quantifies the environmental impact of the whole life cycle based on ISO 14040 standard, but in the scene of engineering machinery remanufacturing process, it lacks industry-specific emission factor database, resulting in 42% of the emission reduction contribution not being effectively identified. Quantitative evaluation focuses on data-driven models such as data envelopment analysis (DEA) and random forest algorithm, which can accurately measure hard indicators such as energy intensity and carbon emission intensity, but have insufficient adaptability to dynamic working conditions and rely on high-quality historical data. Qualitative and quantitative combination methods try to make up for the limitations of single methods, such as combination weight method (entropy method + AHP), which balances the subjective and objective weights to improve the comprehensiveness of evaluation. However, existing research still has the problem of imbalance between subjective and objective weights, and does not consider the non-linear correlation between indicators, resulting in deviation of evaluation results from actual production scenes and restricting their precise application in the engineering machinery field.

[0004] For the zero-carbon development level of industrial parks, the existing research discloses "a construction and evaluation method of zero-carbon index system for industrial parks". This method first constructs the framework of the zero-carbon index system for industrial parks, and then based on the combination weight method, combines the weights of analytic hierarchy process and entropy method, and introduces a linear weighted evaluation model to comprehensively evaluate the target park. This method does not consider the non-linear relationship between indicators in the allocation of subjective and objective weights, resulting in unstable evaluation results and large differences from the actual situation, which further affects the subsequent decision-making and implementation of the scheme.

[0005] In addition, there are other studies such as "Regional Industrial Green Manufacturing Level Evaluation Research Based on Entropy Weight TOPSIS", which objectively determines the weight of each evaluation index through entropy method, and combines TOPSIS model to calculate the relative distance of each evaluation object and the optimal (ideal) and worst (negative ideal) scheme, so as to comprehensively sort and evaluate the regional industrial green manufacturing level. This method is mainly data-driven, has the characteristics of strong objectivity and simple operation, and is suitable for multi-index decision-making problems, but has certain limitations in subjective experience integration and uncertainty analysis. At the same time, this method lacks simulation analysis of uncertainty, and the evaluation stability and decision support ability are relatively weak. SUMMARY

[0006] In view of the above problems, the present application provides an engineering machinery industry green factory evaluation method and system, which can significantly improve the accuracy and objectivity of green factory evaluation.

[0007] In order to achieve the above technical purpose and achieve the above technical effect, the present application realizes the following technical scheme:

[0008] In a first aspect, the present application provides an engineering machinery industry green factory evaluation method, comprising:

[0009] obtaining a pre-prepared green factory evaluation index system, the green factory evaluation index system comprising a target layer, a first index layer and a second index layer arranged in turn;

[0010] calculating the subjective weight of each second index relative to the target layer;

[0011] introducing Monte Carlo simulation algorithm, respectively calculating the objective weight of each second index relative to the target layer and the evaluation matrix corresponding to each Monte Carlo simulation;

[0012] Based on the principle of minimum relative information entropy and Lagrange multiplier method, combining the subjective weight of each second index relative to the target layer and the objective weight of each second index relative to the target layer corresponding to each Monte Carlo simulation, the comprehensive weight of each second index relative to the target layer corresponding to each Monte Carlo simulation is calculated;

[0013] Based on the comprehensive weight of each second index relative to the target layer corresponding to each Monte Carlo simulation and the evaluation matrix, a fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation is constructed;

[0014] Based on the fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation and the preset score interval corresponding to each evaluation grade, the green factory grade evaluation result is obtained.

[0015] In combination with the first aspect, optionally, the calculation method of the subjective weight of each second index relative to the target layer comprises:

[0016] Construct the interval number judgment matrix corresponding to the secondary indicator layer. ;

[0017] Based on the interval number judgment matrix corresponding to the secondary indicator layer, the interval number eigenvalue method is used to calculate the subjective weight of each secondary indicator relative to the target layer.

[0018] In conjunction with the first aspect, optionally, the interval number judgment matrix corresponding to the secondary indicator layer. The construction methods include:

[0019] Based on interval number The lower bound matrix is ​​obtained. and upper bound matrix ,in, = , , The interval width, Indicates the row number of the element. Indicates the column number of the element. , , This indicates the number of secondary indicators. ;

[0020] Based on the lower bound matrix and upper bound matrix The interval number judgment matrix is ​​obtained. ;

[0021] The calculation methods for the subjective weights of each secondary indicator relative to the target layer include:

[0022] The lower bound matrix is ​​respectively and upper bound matrix Substitute into formula (2) to solve for the lower bound matrix. and upper bound matrix Maximum eigenvalue , and the corresponding feature vectors and eigenvectors , , ;

[0023] (2),

[0024] In the formula, Represents the lower bound matrix or upper bound matrix , Representing the eigenvector or feature vector , express or ; and Represent the lower bound matrix respectively and upper bound matrix The Middle The subjective weight of each secondary indicator relative to the primary indicator layer;

[0025] The consistency coefficient is calculated according to formula (3). and ,0< <1< ,and , :

[0026] , (3);

[0027] Calculate the number of weight intervals for each secondary indicator relative to the primary indicator layer. , ;

[0028] Following the same steps as calculating the weight intervals of each secondary indicator relative to the primary indicator layer, calculate the weight intervals of each primary indicator relative to the target layer. , , ;

[0029] Use formula (4) to calculate the number of weight intervals. and weighted interval number Defuzzing is performed, and the subjective weight vector of the first-level index relative to the target layer is calculated using a linear weighting method. And the subjective weight vector of secondary indicators relative to the primary indicator layer. :

[0030] (4);

[0031] in, It is the optimism coefficient, and ; express or ; express or ; express or ;

[0032] The subjective weights of the secondary indicators relative to the target layer are calculated using formula (5):

[0033] = (5);

[0034] in, The subjective weight of secondary indicator i relative to the target layer. The conditions for multiplication must meet the secondary indicators. Belongs to the corresponding first-level indicator .

[0035] In conjunction with the first aspect, optionally, the calculation method for the subjective weights of each secondary indicator relative to the target layer may also include:

[0036] A consistency judgment is performed on the interval number judgment matrix corresponding to the secondary indicator layer. The consistency judgment steps include:

[0037] The consistency index is calculated based on formula (6). :

[0038] (6);

[0039] In the formula, t represents the number of indicators contained in the interval number judgment matrix constructed by the current indicator layer. The largest eigenvalue of the interval number judgment matrix constructed for the current indicator layer;

[0040] A random consistency index RI is introduced, and the relationship between the random consistency index RI and the number of indices t is a fixed numerical mapping relationship.

[0041] Calculate the consistency ratio according to formula (7). :

[0042] (7);

[0043] like If the interval values ​​exceed a set threshold, it indicates low consistency of the interval value judgment matrix. In this case, the interval values ​​in the interval value judgment matrix corresponding to the secondary indicator layer will be adjusted. until Less than the set threshold.

[0044] In conjunction with the first aspect, optionally, the calculation methods for the objective weights of each secondary indicator relative to the target layer include:

[0045] right Each secondary indicator is set separately. Each rating level, combined with Monte Carlo simulation, constructs... Evaluation matrix As shown in (11):

[0046] (11);

[0047] in, Indicates the first The second-level indicator for the first The degree of membership of each rating level, ;

[0048] Calculate the secondary indicators according to formula (12) Information entropy :

[0049] (12);

[0050] In the formula, It is a constant. = ,when When =0, it is the default. ;

[0051] Calculate the secondary indicators according to formula (13) coefficient of difference :

[0052] (13);

[0053] Calculate the secondary indicators according to formula (14) Entropy weight :

[0054] (14);

[0055] Calculate sequentially according to formulas (11) to (14). Entropy weight of each secondary indicator That is, the objective weight of each secondary indicator relative to the target layer.

[0056] In conjunction with the first aspect, optionally, the calculation method for the comprehensive weight of each secondary indicator relative to the target layer includes:

[0057] Based on the subjective weights of each secondary indicator relative to the target layer and objective weight ;

[0058] The initial comprehensive weight vector is calculated according to formula (15). :

[0059] (15);

[0060] Introducing the principle of minimum relative information entropy, we define the objective function of formula (16):

[0061] (16);

[0062] in, The degree of dispersion between subjective weights and comprehensive weights; The degree of dispersion between objective weights and comprehensive weights; Equivalent to making Approaching at the same time and ;

[0063] The objective function corresponding >0;

[0064] Introducing the Lagrange multiplier method, constructing the Lagrange function. As shown in formula (17):

[0065] = (17);

[0066] In the formula, For Lagrange multipliers;

[0067] The optimized secondary indicators can be derived. relative to the overall weight of the target layer The calculation formula is shown in (15):

[0068] (18).

[0069] In conjunction with the first aspect, optionally, the method for calculating the evaluation matrix includes:

[0070] Give each secondary indicator Scoring is performed, and the score result for each secondary indicator is a score range;

[0071] For the stated score range, a set of score values ​​corresponding to each secondary evaluation indicator is randomly generated based on Monte Carlo simulation, and the result is denoted as follows. , ;

[0072] The secondary indicators are calculated using ascending and descending trapezoidal functions. Membership degree of the comment set V, each secondary indicator The formulas for calculating the membership degree of the comment set V are shown in (8)-(10), where , This represents the threshold of each piecewise function. < :

[0073] (8);

[0074] (9);

[0075] (10);

[0076] In the formula, Indicates secondary indicators right The membership degree of the rating level, h = 1, 2, ..., m;

[0077] Constructing secondary indicators The evaluation vector of the evaluation set V =( );

[0078] By summing the evaluation vectors of all secondary indicators for the evaluation set V, an evaluation matrix is ​​generated. :

[0079] (11).

[0080] In conjunction with the first aspect, optionally, the method for constructing the fuzzy comprehensive evaluation matrix includes:

[0081] Based on secondary indicators relative to the overall weight of the target layer Construct a comprehensive weight vector for secondary indicators. , ;

[0082] The comprehensive weight vector of secondary indicators With evaluation matrix Combined to form a fuzzy comprehensive evaluation matrix As shown in formula (19):

[0083] = ( (19)。

[0084] In conjunction with the first aspect, optionally, the calculation method for the green factory rating result includes:

[0085] The calculation results of the fuzzy comprehensive evaluation matrix E are standardized.

[0086] The evaluation vector V is constructed by taking the median of the corresponding score interval for each preset evaluation level;

[0087] The green factory rating result is obtained according to formula (21). :

[0088] (twenty one).

[0089] Secondly, the present invention provides a green factory evaluation system for the construction machinery industry, including a storage medium and a processor;

[0090] The storage medium is used to store instructions;

[0091] The processor is configured to operate according to the instructions to perform the method according to any one of the first aspects.

[0092] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0093] This invention introduces the concept of intervals into the interval-based analytic hierarchy process (I-AHP) to address uncertainty, enhancing the applicability of traditional I-AHP in complex systems and multi-source uncertainty scenarios. For the weight analysis of quantitative indicators, the entropy method is introduced to calculate the objective weights of each indicator, reflecting the differences in the information content of objective data. Then, an objective function is constructed based on the Lagrangian function to coordinate the weight coefficients of I-AHP and the entropy method, minimizing their differences while satisfying normalization constraints and enhancing the robustness of the evaluation. Finally, by combining Monte Carlo simulation algorithms and introducing probability distributions and random sampling, uncertainty is effectively handled, further improving the adaptability and reliability of green factory evaluation. Attached Figure Description

[0094] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0095] Figure 1 This is a flowchart illustrating a green factory evaluation method for the construction machinery industry according to an embodiment of the present invention.

[0096] Figure 2 This is a schematic diagram of index weight analysis according to an embodiment of the present invention;

[0097] Figure 3 This is a schematic diagram of the green factory evaluation index results according to an embodiment of the present invention. Detailed Implementation

[0098] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0099] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0100] Example 1

[0101] To achieve the above objectives, this invention discloses a method for evaluating green factories in the construction machinery industry, such as... Figure 1 As shown, it mainly includes the following steps:

[0102] Step 1: Determine the framework of the green factory evaluation index system for the construction machinery industry based on the green factory evaluation indicators formulated in the national standard GB / T 36132-2018 "General Rules for Green Factory Evaluation".

[0103] Step 2: In this embodiment of the invention, the green factory evaluation of the construction machinery industry is taken as the target layer D. Based on the industry's requirements for green factory evaluation, the target layer D includes... The first-level indicator layer of each first-level indicator, and the subordinate levels of each first-level indicator. The secondary indicator layer of each secondary indicator.

[0104] Step 3: Based on the evaluation of the relative importance of each primary and secondary indicator by experts in the green factory industry, qualitatively obtain the score range of the relative importance values ​​of each indicator under different dimensions from a subjective perspective, and calculate the subjective weight of each level of indicator using the interval analytic hierarchy process. The specific operation steps are as follows:

[0105] (1) Construct the interval number judgment matrix of the second-level indicator layer and the interval number judgment matrix of the first-level indicator layer.

[0106] First, define the interval number judgment matrix of the secondary indicator layer as B. Its elements Let be the interval number, where i represents the row number of the element and j represents the column number of the element. {1, 2, 3…n}、j {1, 2, 3…n}, , and Let these represent the lower and upper limits of the interval number, respectively. and The calculation formula is shown in (1).

[0107] = , (1);

[0108] in, It represents the interval number and also the midpoint of the interval (used to reflect the judgment criterion). This represents the interval width (used to reflect the uncertainty in the judgment). and These were all provided by experts; for any i and j, .

[0109] For interval numbers Experts scored the relative importance of each pair of indicators using a 1-9 scale, and assessed the uncertainty of their judgments to determine the final score. Generate the interval number judgment matrix B corresponding to the secondary indicator layer. The specific scaling is shown in Table 1-1.

[0110] Table 1-1 Scale Division

[0111]

[0112] Based on the above scoring criteria, construct an interval number judgment matrix B, and let the secondary indicators be... , , ..., Indicators belonging to the first-level indicator layer By comparing the relative importance values ​​of each factor pairwise, the interval number judgment matrix of the secondary indicator layer is obtained. As shown in Table 2-1.

[0113] Table 2-1 Interval Index Judgment Matrix for Secondary Indicator Layer of Green Factory Evaluation

[0114]

[0115] Interval number judgment matrix for the first-level indicator layer The construction also follows the same steps, among which, The target layer is shown in Table 2-2.

[0116] Table 2-2 shows the interval numerical judgment matrix of the secondary indicator layer for green factory evaluation.

[0117]

[0118] (2) Calculation of subjective weights

[0119] The Interval-number Eigenvector Method is introduced to calculate the subjective weights of each secondary indicator relative to the target layer. The specific steps are as follows:

[0120] 1) Based on the interval number Let the lower bound matrix be... and upper bound matrix Then the interval number judgment matrix .

[0121] 2) Solve for the number of judgment intervals and the judgment matrix. Maximum eigenvalues ​​and eigenvectors: The lower bound matrix is ​​defined as follows. and upper bound matrix Substitute into formula (2) to solve for the lower bound matrix. and upper bound matrix Maximum eigenvalue and the corresponding feature vectors and eigenvectors , , ;

[0122] (2);

[0123] In the formula, Represents the lower bound matrix or upper bound matrix , Representing the eigenvector or feature vector , express or ; and Represent the lower bound matrix respectively and upper bound matrix The Middle The subjective weight of each secondary indicator relative to the target layer;

[0124] 3) Calculate the consistency coefficient of the secondary indicator layer. and :

[0125] Feature vector eigenvectors These are the lower bound matrices. Upper bound matrix Corresponding to the largest eigenvalue , , where the parameters are . and The value range is 0 < <1< ,and , Calculate the consistency coefficient. and As shown in formula (3).

[0126] , (3);

[0127] 4) Calculate the number of weight intervals for each secondary indicator relative to the primary indicator layer. i .

[0128] 5) Following steps 1)-4), calculate the number of weight intervals for each primary indicator relative to the target layer. , .

[0129] 6) Use formula (4) to calculate the number of weight intervals. and weighted interval number Defuzzing is performed, and the subjective weight vector of the first-level index relative to the target layer is calculated using a linear weighting method. And the subjective weight vector of the secondary indicators relative to the primary indicator layer. .

[0130] (4);

[0131] in, It is the optimism coefficient, and In the embodiments of the present invention The value is 0.5. The value can be set according to actual needs. express or ; express or ; express or .

[0132] 7) Use formula (5) to obtain the subjective weights of each secondary indicator relative to the target layer:

[0133] = (5);

[0134] in, The subjective weight of the secondary indicator i relative to the target layer is the target subjective weight in this embodiment of the invention. The conditions for multiplication must meet the secondary indicators. Belongs to the corresponding first-level indicator .

[0135] (3) Matrix consistency test

[0136] To verify the consistency of the interval number judgment matrix, a consistency index must first be calculated. The formula for calculating the consistency index is shown in (6):

[0137] (6);

[0138] Where t represents the number of evaluation indicators contained in the interval number judgment matrix constructed in the current indicator layer. The largest eigenvalue of the interval number matrix is ​​determined.

[0139] To eliminate the influence of the number of indicators on the consistency judgment, a random consistency index (RI) is introduced, and its value is related to the number of indicators t as shown in Table 3-1.

[0140] Table 3-1 Random Consistency Index (RI)

[0141]

[0142] Based on the consistency ratio ( The degree of inconsistency of the matrix is ​​determined by the formula shown in (7).

[0143] (7);

[0144] when The higher the consistency of the interval number judgment matrix, the better; otherwise, adjust the relative importance values ​​of the indicators in the interval number judgment matrix to achieve a higher consistency ratio. .

[0145] Step 4: Using expert interviews, organize industry experts with backgrounds in green manufacturing and factory management to develop a set of evaluation criteria based on official evaluation requirements and technical specifications for different levels of green factories. Divide this set into m levels (typically 3-5 levels). Experts then assign interval scores to assess the compliance and importance of secondary indicators based on these evaluation levels. The evaluation level set in this invention is V = ( Finally, the entropy method is introduced to quantify the information entropy and difference of the secondary indicators to obtain the objective weights of the evaluation indicators. The specific operation steps are as follows:

[0146] (1) Define the evaluation index system

[0147] Based on the c primary evaluation indicators set in step 3 for green factory evaluation, each primary indicator contains corresponding secondary indicators. In order to quantify these indicators, experts score each secondary indicator according to the set rating levels, and the score result of each secondary indicator is the score range.

[0148] For the stated score range, a set of score values ​​corresponding to each secondary evaluation indicator is randomly generated based on Monte Carlo simulation, and the result is denoted as follows. , Specifically, it includes:

[0149] Let the random variable representing the uncertainty of the impact assessment results be... Monte Carlo simulation was introduced to establish the functional relationship between random variables as shown in (21). A sample was randomly drawn from the expert's scoring results for the secondary indicators (i.e., the above score range). By substituting this function into the equation, a single Monte Carlo simulation is performed to determine the value of variable Y. Then, iterative calculations are conducted to obtain the function distribution characteristics that are more consistent with the actual situation of the research objective.

[0150] (20);

[0151] (2) Constructing the evaluation matrix

[0152] This invention uses ascending and descending trapezoidal functions to calculate the membership degree of each secondary index to the comment set V. The function expressions are shown in formulas (8), (9), and (10), where... , The threshold of each piecewise function ( < ).

[0153] (8);

[0154] (9);

[0155] (10);

[0156] The membership degree of each secondary indicator's individual factor to each level of evaluation is obtained using the above formula. , constitute secondary indicators The evaluation vector of the evaluation set V =( Summarize the membership degree evaluation vectors of all secondary indicators to the comment set V, and construct the evaluation matrix. .

[0157] (11);

[0158] In the formula, The degree of membership of secondary indicator i to comment h is represented (i=1, 2, ..., n; h=1, 2, ..., m).

[0159] Repeat the above steps until the objective weights and evaluation matrices of each secondary index relative to the target layer are calculated for each Monte Carlo simulation.

[0160] (3) Calculate each secondary indicator Information entropy

[0161] Secondary indicators Information entropy The formula used to quantify the dispersion of indicator data is shown in formula (12), where, It is a constant. = (m is the set number of membership levels to ensure non-negative information entropy). To avoid the logarithm being meaningless, when... When =0, it is the default. .

[0162] (12);

[0163] (3) Calculate each secondary indicator coefficient of difference

[0164] The differentiation coefficient is used to reflect the complementarity of the information entropy of the indicators. The smaller the information entropy, the stronger the indicator differentiation and the larger the differentiation coefficient. The first coefficient is calculated according to formula (13). Secondary indicators (i.e., secondary indicators) The coefficient of difference .

[0165] (13);

[0166] (4) Calculate each secondary indicator Entropy weight

[0167] Based on the above differentiation coefficients, the entropy weights of each secondary indicator are calculated using formula (14). .

[0168] (14);

[0169] In the formula, n represents the number of secondary indicators.

[0170] The entropy weights of the n secondary evaluation indicators are calculated sequentially according to formulas (11) to (14). , , ..., That is, the objective weights of each secondary indicator relative to the target layer, which ultimately constitute the objective weight vector W of the secondary indicators. , , ..., ).

[0171] Step 5: Construct an optimization model using the Lagrange multiplier method, solve for the optimal coordination coefficients of subjective and objective weights, and finally obtain the comprehensive weight results of each secondary indicator using a vector. = , The specific steps for implementing the comprehensive weight of the i-th secondary indicator are as follows:

[0172] (1) Combine steps 3 and 4 to obtain the subjective and objective weights of the secondary indicators. and ;

[0173] (2) Calculate the overall weight Overall weight The initial calculation formula is shown in (15). This formula is based on the idea of ​​a simple combination of subjective and objective weights. It will be adjusted by optimizing the model to make the comprehensive weight more reasonable.

[0174] (15);

[0175] (3) To ensure the overall weight As close as possible to subjective weight and objective weight Since they are all close, the principle of minimum relative information entropy is introduced, and the objective function is defined as shown in formula (16). The degree of dispersion between subjective weights and comprehensive weights; The degree of dispersion between objective weights and comprehensive weights; Equivalent to making Approaching at the same time and .

[0176] (16);

[0177] >0 ( i) This ensures that the overall weight of each evaluation indicator is positive.

[0178] (4) The objective function has constraints. In order to solve the optimization problem, the Lagrange multiplier method is introduced and the Lagrange function is constructed as shown in formula (17).

[0179] = (17);

[0180] Finally, the optimized secondary indicators can be derived. relative to the overall weight of the target layer The calculation formula is shown in (18).

[0181] (18);

[0182] Step 6: Quantify the green factory evaluation indicators based on the membership concept of the fuzzy comprehensive evaluation method. Use the degree of belonging to replace "belongs to" or "does not belong to" to quantify the scores of the evaluation indicators. The specific steps are as follows, considering the green factory evaluation scenario:

[0183] (1) Calculate the comprehensive weight vector of the secondary indicators.

[0184] Combining the calculation method for the comprehensive weight in step 5, the final comprehensive weight vector of the indicators is obtained. = ( ),in, This represents the overall weight of the i-th secondary indicator.

[0185] (4) Construct a fuzzy comprehensive evaluation model and output the results.

[0186] Combine the comprehensive weight vector of the secondary indicators with the evaluation matrix Combined to form a fuzzy comprehensive evaluation matrix As shown in formula (19).

[0187] = ( (19);

[0188] Step 7: Standardize the calculation results of the fuzzy comprehensive evaluation matrix E, assign a corresponding score range to each level of the green factory evaluation index, such as {90-100, 80-90, 70-80, 60-70, 50-60}, and construct the evaluation vector V by taking the median of the corresponding score ranges of each level of evaluation. Obtain the evaluation result according to formula (21). .

[0189] (twenty one).

[0190] Repeat steps (4)-(7) to obtain the fuzzy comprehensive evaluation matrix based on each Monte Carlo simulation and the score range corresponding to each preset evaluation level, and obtain the final green factory level evaluation result.

[0191] The following describes in detail the green factory evaluation method for the construction machinery industry in this invention, using a specific implementation method as an example.

[0192] As described in steps 1 and 2, this embodiment takes the green factory rating of a construction machinery company as a case study. Based on the national standard GB / T36132-2018 "Green Factory Evaluation Criteria" and combined with the characteristics of the construction machinery factory itself, an evaluation index system that conforms to the green factory of construction machinery is formulated. The evaluation system mainly consists of nine evaluation indicators, including buildings, lighting, equipment, energy, materials, waste, noise, greenhouse gases, and performance. The nine secondary indicators are further divided into dimensional attributes from four dimensions: infrastructure, energy and resource input, environmental emissions, and performance. The final construction of the green factory evaluation index system for construction machinery is shown in Table 4-1.

[0193] Table 4-1 Evaluation Index System for Green Factories of Construction Machinery

[0194]

[0195] According to step 3, by surveying 8 experts related to the green factory industry, the relative importance of the indicators in the constructed green factory evaluation index system for construction machinery was scored in intervals based on the national standard GB / T36132-2018 "Evaluation Criteria for Green Factories". The weight scores of each level of indicators by the experts are shown in Tables 5-1 to 9-1. In this invention, the data is processed virtually.

[0196] Table 5-1 Primary Evaluation Indicators

[0197]

[0198] Table 6-1 Secondary Evaluation Indicators

[0199]

[0200] Table 7-1 Secondary Evaluation Indicators

[0201]

[0202] Table 8-1 Secondary Evaluation Indicators

[0203]

[0204] Table 9-1 Secondary Evaluation Indicators

[0205]

[0206] Combining expert scoring and the calculation formula (2) for the subjective weight of indicators in step 3, the feature vector of the first-level indicators is obtained. With feature vectors .

[0207] =[0.1086, 0.2365, 0.5840, 0.0710]、 =[0.1107, 0.2407, 0.5769, 0.0717]

[0208] By combining formulas (3) and (4), the indicator weights are defuzzified to obtain the corrected subjective weights of the first-level indicators relative to the target layer. .

[0209] [0.1096, 0.2386, 0.5804, 0.0713]

[0210] The same method was used to calculate the subjective weights of the secondary indicators relative to the primary indicator layer. And combined with formula (5), the subjective weight calculation results of the secondary indicators relative to the target layer are finally obtained.

[0211] ( )

[0212] Based on the expert interview method in step 4, the scoring results of each secondary indicator are shown in Table 10-1, combined with the green factory evaluation criteria.

[0213] Table 10-1 Scoring of Secondary Indicators Based on Comment Set

[0214] A set of scores for each secondary evaluation indicator (74, 83, 82, 71, 66, 85, 87, 89, 84) was randomly generated using the Monte Carlo simulation algorithm. Then, the membership degree of the randomly generated set of secondary indicator scores to the five comment levels was calculated according to the membership function formulas (8)-(10). Finally, the evaluation matrix was obtained by integrating the results. The results are shown below.

[0215]

[0216] Then, combining formulas (12), (13), and (14) from step 4, calculate the objective weights W=( ).

[0217] The subjective and objective weights of the indicators are obtained through the above steps. Step 5 introduces the Lagrange multiplier method to calculate the comprehensive weight of the index. =( The analysis of the three weighting calculation results is attached. Figure 2 As shown.

[0218] Based on the fuzzy comprehensive evaluation model The calculation formula (19) is used to obtain the fuzzy evaluation model results of the secondary indicators. .

[0219] The evaluation criteria for green factories are divided into 5 levels, V=( Each level is assigned a corresponding score range {90-100, 80-90, 70-80, 60-70, 50-60}, and the median of each score range is used as the evaluation vector V=( Combined with formula (21), the comprehensive evaluation results of a single Monte Carlo simulation are obtained.

[0220] 80.379;

[0221] According to the evaluation results, the factory's green rating is between 80 and 90 points.

[0222] Finally, combining Monte Carlo simulation with 10,000 iterative calculations, the final evaluation results are shown in the appendix. Figure 3 As shown in the chart, the green factory rating is between 78.5 and 83 points, with the majority concentrated around 80.5 points.

[0223] Example 3

[0224] Based on the same inventive concept as in Embodiment 1, this embodiment of the invention provides a green factory evaluation system for the construction machinery industry, including a storage medium and a processor;

[0225] The storage medium is used to store instructions;

[0226] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1.

[0227] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0228] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0229] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0230] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0231] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

[0232] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating green factories in the construction machinery industry, characterized in that, include: Obtain a pre-defined green factory evaluation index system, which includes a target layer, a primary index layer, and a secondary index layer set sequentially. Calculate the subjective weight of each secondary indicator relative to the target layer; The Monte Carlo simulation algorithm is introduced to calculate the objective weights and evaluation matrices of each secondary index relative to the target layer for each Monte Carlo simulation. Based on the principle of minimum relative information entropy and the Lagrange multiplier method, and combining the subjective weights of each secondary indicator relative to the target layer, as well as the objective weights of each secondary indicator relative to the target layer in each Monte Carlo simulation, the comprehensive weights of each secondary indicator relative to the target layer in each Monte Carlo simulation are calculated. Based on the comprehensive weights and evaluation matrices of each secondary indicator relative to the target layer in each Monte Carlo simulation, a fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation is constructed. Based on the fuzzy comprehensive evaluation matrix corresponding to each Monte Carlo simulation and the pre-set score range for each evaluation level, the green factory level evaluation results are obtained.

2. The method for evaluating green factories in the construction machinery industry according to claim 1, characterized in that: The calculation methods for the subjective weights of each secondary indicator relative to the target layer include: Construct the interval number judgment matrix corresponding to the secondary indicator layer. ; Based on the interval number judgment matrix corresponding to the secondary indicator layer, the interval number eigenvalue method is used to calculate the subjective weight of each secondary indicator relative to the target layer.

3. The method for evaluating green factories in the construction machinery industry according to claim 2, characterized in that: Interval number judgment matrix corresponding to the secondary indicator layer The construction methods include: Based on interval number The lower bound matrix is ​​obtained. and upper bound matrix ,in, = , , The interval width, Indicates the row number of the element. Indicates the column number of the element. , , This indicates the number of secondary indicators. ; Based on the lower bound matrix and upper bound matrix The interval number judgment matrix is ​​obtained. ; The calculation methods for the subjective weights of each secondary indicator relative to the target layer include: The lower bound matrix is ​​respectively and upper bound matrix Substitute into formula (2) to solve for the lower bound matrix. and upper bound matrix Maximum eigenvalue , and the corresponding feature vectors and eigenvectors , , ; （2）， In the formula, Represents the lower bound matrix or upper bound matrix , Representing the eigenvector or feature vector , express or ; and Represent the lower bound matrix respectively and upper bound matrix The Middle The subjective weight of each secondary indicator relative to the primary indicator layer; The consistency coefficient is calculated according to formula (3). and ,0< <1< ,and , : ， （3）； Calculate the number of weight intervals for each secondary indicator relative to the primary indicator layer. , ; Following the same steps as calculating the weight intervals of each secondary indicator relative to the primary indicator layer, calculate the weight intervals of each primary indicator relative to the target layer. , , ; Use formula (4) to calculate the number of weight intervals. and weighted interval number Defuzzing is performed, and the subjective weight vector of the first-level index relative to the target layer is calculated using a linear weighting method. And the subjective weight vector of secondary indicators relative to the primary indicator layer. : （4）； in, It is the optimism coefficient, and ; express or ; express or ; express or ; The subjective weights of the secondary indicators relative to the target layer are calculated using formula (5): = （5）； in, The subjective weight of secondary indicator i relative to the target layer. The conditions for multiplication must meet the secondary indicators. Belongs to the corresponding first-level indicator .

4. The method for evaluating green factories in the construction machinery industry according to claim 2, characterized in that: The calculation methods for the subjective weights of each secondary indicator relative to the target layer also include: A consistency judgment is performed on the interval number judgment matrix corresponding to the secondary indicator layer. The consistency judgment steps include: The consistency index is calculated based on formula (6). : （6）； In the formula, t represents the number of indicators contained in the interval number judgment matrix constructed by the current indicator layer. The largest eigenvalue of the interval number judgment matrix constructed for the current indicator layer; A random consistency index RI is introduced, and the relationship between the random consistency index RI and the number of indices t is a fixed numerical mapping relationship. Calculate the consistency ratio according to formula (7). : （7）； like If the interval values ​​exceed a set threshold, it indicates low consistency of the interval value judgment matrix. In this case, the interval values ​​in the interval value judgment matrix corresponding to the secondary indicator layer will be adjusted. until Less than the set threshold.

5. The method for evaluating green factories in the construction machinery industry according to claim 1, characterized in that: The calculation methods for the objective weights of each secondary indicator relative to the target layer include: right Each secondary indicator is set separately. Each rating level, combined with Monte Carlo simulation, constructs... Evaluation matrix As shown in (11): （11）； in, Indicates the first The second-level indicator for the first The degree of membership of each rating level, ; Calculate the secondary indicators according to formula (12) Information entropy : （12）； In the formula, It is a constant. = ,when When =0, it is the default. ; Calculate the secondary indicators according to formula (13) coefficient of difference : （13）； Calculate the secondary indicators according to formula (14) Entropy weight : （14）； Calculate sequentially according to formulas (11) to (14). Entropy weight of each secondary indicator That is, the objective weight of each secondary indicator relative to the target layer.

6. The method for evaluating green factories in the construction machinery industry according to claim 1, characterized in that: The calculation methods for the overall weight of each secondary indicator relative to the target layer include: Based on the subjective weights of each secondary indicator relative to the target layer and objective weight ; The initial comprehensive weight vector is calculated according to formula (15). : （15）； Introducing the principle of minimum relative information entropy, we define the objective function of formula (16): （16）； in, The degree of dispersion between subjective weights and comprehensive weights; The degree of dispersion between objective weights and comprehensive weights; Equivalent to making Approaching at the same time and ; The objective function corresponding to >0; Introducing the Lagrange multiplier method, constructing the Lagrange function. As shown in formula (17): = （17）； In the formula, For Lagrange multipliers; The optimized secondary indicators can be derived. relative to the overall weight of the target layer The calculation formula is shown in (15): （18）。 7. The method for evaluating green factories in the construction machinery industry according to claim 5, characterized in that: The method for calculating the evaluation matrix includes: Give each secondary indicator Scoring is performed, and the score result for each secondary indicator is a score range; For the stated score range, a set of score values ​​corresponding to each secondary evaluation indicator is randomly generated based on Monte Carlo simulation, and the result is denoted as follows. , ; Each secondary index is calculated using ascending and descending trapezoidal functions. Membership degree of the comment set V, each secondary indicator The formulas for calculating the membership degree of the comment set V are shown in (8)-(10), where , This represents the threshold of each piecewise function. < : （8）； （9）； （10）； In the formula, Indicates secondary indicators right The membership degree of the rating level, h = 1, 2, ..., m; Constructing secondary indicators The evaluation vector of the evaluation set V =( ); By summing the evaluation vectors of all secondary indicators for the evaluation set V, an evaluation matrix is ​​generated. : （11）。 8. The method for evaluating green factories in the construction machinery industry according to claim 7, characterized in that: The method for constructing the fuzzy comprehensive evaluation matrix includes: Based on secondary indicators relative to the overall weight of the target layer Construct a comprehensive weight vector for secondary indicators. , ; The comprehensive weight vector of secondary indicators With evaluation matrix Combined to form a fuzzy comprehensive evaluation matrix As shown in formula (19): =（ ） （19）。 9. The method for evaluating green factories in the construction machinery industry according to claim 8, characterized in that: The calculation method for the green factory rating results includes: The calculation results of the fuzzy comprehensive evaluation matrix E are standardized. The evaluation vector V is constructed by taking the median of the corresponding score interval for each preset evaluation level; The green factory rating result is obtained according to formula (21). : （21）。 10. A green factory evaluation system for the construction machinery industry, characterized in that, Including storage media and processor; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the method according to any one of claims 1-9.

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