Power grid supply chain greening level analysis method and device

Abstract

The invention provides a power grid supply chain greening level analysis method and apparatus. The method comprises the steps of obtaining interval relative importance data and interval sample data of an evaluation index of a greening level; generating a first weight of the evaluation index according to the interval relative importance data; generating a second weight of the evaluation index according to the interval sample data; performing combined weighting on the first weight and the second weight to generate a comprehensive weight of the evaluation index; and carrying out green level analysis on the power grid supply chain by using the comprehensive weight. According to the power grid supply chain green level analysis method and device provided by the invention, more scientific and accurate power grid supply chain green performance evaluation is realized, and sustainable and healthy development of the power grid green supply chain is promoted.

Description

Technical Field

[0001] The present application relates to the technical field of green supply chain management, and in particular to a method and device for analyzing the greening level of a power grid supply chain. Background Art

[0002] Green supply chain management has now become an important means for the sustainable development of enterprises and even industries. Analysis of the green level of the supply chain can improve resource utilization efficiency, reduce environmental load, and enhance corporate competitiveness. At the same time, it can promote the green transformation of the entire power industry supply chain. By evaluating all aspects of the enterprise, it can encourage enterprises to improve their green level, promote enterprises to improve resource utilization efficiency and reduce environmental load, thereby enhancing corporate competitiveness and promoting the continued vigorous development of the industry.

[0003] There is no unified standard for the research on green level analysis indicators and weight setting. In terms of the evaluation weight setting method, the analytic hierarchy process (AHP) is generally used, that is, when conducting multi-objective optimization and multi-scheme decision-making, a hierarchical structure of the goals, criteria, and schemes of the decision-making problem is established to determine the final weight of each scheme.

[0004] At present, the weight setting of green level analysis mainly adopts the analytic hierarchy process (AHP). When constructing indicators, the indicator system is set to three levels: target layer, criterion layer and indicator layer to determine the indicator weights based on the basic construction principles such as scientificity and comprehensiveness. However, the analytic hierarchy process is too dependent on expert experience and subjective feelings when constructing the judgment matrix. Especially when there are different opinions among multiple decision makers, the judgment matrix has a large error, and direct use will cause a large error. For green level analysis, not only subjective expert experience is needed, but more objective data analysis is combined to make the final result scientific and reliable, so more detailed and accurate research can be carried out.

[0005] The current main basis for analyzing the greening level of the supply chain is "Green Manufacturing - Green Supply Chain Management of Manufacturing Enterprises - Evaluation Specifications" (GB / T39257-2020), in which the evaluation index system of the green supply chain includes 7 first-level indicators: strategy and goals, green design, green procurement, green production, green logistics, recycling and terminal disposal, green information management and disclosure, and 35 second-level indicators. Specific indicators include but are not limited to: energy consumption, waste emissions, social responsibility, product reuse rate, etc. The evaluation standard has the following problems:

[0006] First, it is highly subjective. In the construction of various evaluation index systems for green supply chains, the evaluation factors and importance ranking issues are inevitably subjective. Although the specification attempts to reduce this subjectivity through standardized methods, in actual application, it may still be affected by the subjective opinions of the evaluators.

[0007] Second, the updating of standards lags behind. With the continuous development of green manufacturing and green supply chain management technologies, new concepts, methods and technologies continue to emerge. However, the formulation and updating of standards requires a certain amount of time and procedures, which may cause the standards to lag behind the needs of practical application in some aspects. Summary of the invention

[0008] In response to the problems in the prior art, the embodiments of the present application provide a method and device for analyzing the greening level of a power grid supply chain, which can at least partially solve the problems in the prior art.

[0009] In a first aspect, a method for analyzing the greening level of a power grid supply chain includes:

[0010] Obtain interval relative importance data and interval sample data of green level evaluation indicators;

[0011] Generating a first weight of the evaluation index according to the interval relative importance data;

[0012] Generating a second weight of the evaluation index according to the interval sample data;

[0013] Combining and weighting the first weight and the second weight to generate a comprehensive weight of the evaluation index;

[0014] The comprehensive weight is used to analyze the greening level of the power grid supply chain.

[0015] Furthermore, generating the first weight of the evaluation index according to the interval relative importance data includes:

[0016] Layering the evaluation indicators to obtain indicator levels;

[0017] Construct the corresponding interval judgment matrix according to the interval relative importance data of the evaluation indicators at each indicator level;

[0018] Calculating the interval eigenvector of the interval judgment matrix;

[0019] The interval feature vector is normalized to obtain the first weight.

[0020] Furthermore, after normalizing the interval feature vector, the method further includes:

[0021] Calculate the maximum eigenvalue according to the interval judgment matrix and the first weight;

[0022] The first weight is checked for consistency using the maximum eigenvalue.

[0023] Furthermore, generating the second weight of the evaluation index according to the interval sample data includes:

[0024] Calculate the upper bound information entropy and the lower bound information entropy of the evaluation index according to the interval sample data;

[0025] Calculating the interval information entropy of the evaluation index using the upper bound information entropy and the lower bound information entropy;

[0026] The second weight is generated according to the interval information entropy.

[0027] Furthermore, the combining and weighting the first weight and the second weight includes:

[0028] Constructing a linear combination model according to the first weight and the second weight;

[0029] Using the linear combination model to solve the linear combination coefficients for the purpose of minimizing the deviation;

[0030] The comprehensive weight is calculated according to the linear combination coefficient.

[0031] In a second aspect, a device for analyzing the greening level of a power grid supply chain includes:

[0032] A data acquisition unit, used to acquire interval relative importance data and interval sample data of the evaluation index of the greening level;

[0033] A first weight generating unit, configured to generate a first weight of the evaluation index according to the interval relative importance data;

[0034] A second weight generating unit, used for generating a second weight of the evaluation index according to the interval sample data;

[0035] A comprehensive weight generating unit, used for combining and weighting the first weight and the second weight to generate a comprehensive weight of the evaluation index;

[0036] An evaluation unit is used to analyze the greening level of the power grid supply chain using the comprehensive weight.

[0037] Furthermore, the first weight generating unit includes:

[0038] An indicator stratification module, used to stratify the evaluation indicators to obtain indicator levels;

[0039] An interval judgment matrix construction module is used to construct a corresponding interval judgment matrix according to the interval relative importance data of the evaluation indicators at each indicator level;

[0040] An interval characteristic vector calculation module, used to calculate the interval characteristic vector of the interval judgment matrix;

[0041] The first weight generating module is used to normalize the interval feature vector to obtain the first weight.

[0042] Furthermore, the first weight generating unit further includes:

[0043] A maximum eigenvalue calculation module, used for calculating the maximum eigenvalue according to the interval judgment matrix and the first weight;

[0044] A consistency check module is used to perform a consistency check on the first weight using the maximum eigenvalue.

[0045] Furthermore, the second weight generating unit includes:

[0046] A boundary information entropy calculation module, used to calculate the upper bound information entropy and the lower bound information entropy of the evaluation index according to the interval sample data;

[0047] An interval information entropy calculation module, used to calculate the interval information entropy of the evaluation index using the upper bound information entropy and the lower bound information entropy;

[0048] The second weight generating module is used to generate the second weight according to the interval information entropy.

[0049] Furthermore, the comprehensive weight generating unit includes:

[0050] A model building module, used for building a linear combination model according to the first weight and the second weight;

[0051] A coefficient solving module, used for solving the linear combination coefficients by using the linear combination model to minimize the deviation;

[0052] A comprehensive weight generation module is used to calculate the comprehensive weight according to the linear combination coefficient.

[0053] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the above embodiments when executing the computer program.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0055] In a fifth aspect, the present application provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0056] The method and device for analyzing the greening level of the power grid supply chain provided in the present application obtain the interval relative importance data and interval sample data of the evaluation index of the greening level; generate the first weight of the evaluation index according to the interval relative importance data; generate the second weight of the evaluation index according to the interval sample data; combine and weight the first weight and the second weight to generate the comprehensive weight of the evaluation index; use the comprehensive weight to analyze the greening level of the power grid supply chain, so as to achieve a more scientific and accurate green performance evaluation of the power grid supply chain and promote the sustainable and healthy development of the green supply chain of the power grid. Among them, by combining and weighting the first weight and the second weight, it is possible to take into account both subjective and objective weights at the same time, scientifically combine subjective judgment and objective data, and then form a more complete and standard power grid supply chain evaluation index weight establishment method, which meets scientificity and rationality; by obtaining the interval relative importance data and interval sample data of the evaluation index of the greening level, it is possible to consider the various factors of the supply chain and the uncertainty of the data, and provide a reference basis for managers in the actual supply chain operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in one embodiment of the present application;

[0059] Figure 2 It is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in one embodiment of the present application;

[0060] Figure 3 It is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in one embodiment of the present application;

[0061] Figure 4 It is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in one embodiment of the present application;

[0062] Figure 5 It is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in one embodiment of the present application;

[0063] Figure 6 It is a structural schematic diagram of a power grid supply chain greening level analysis device provided in one embodiment of the present application;

[0064] Figure 7 It is a structural schematic diagram of a power grid supply chain greening level analysis device provided in one embodiment of the present application;

[0065] Figure 8 It is a structural schematic diagram of a power grid supply chain greening level analysis device provided in one embodiment of the present application;

[0066] Fig. 9 It is a structural schematic diagram of a power grid supply chain greening level analysis device provided in one embodiment of the present application;

[0067] Fig.10 It is a structural schematic diagram of a power grid supply chain greening level analysis device provided in one embodiment of the present application;

[0068] Fig.11 It is a schematic diagram of the physical structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined arbitrarily with each other.

[0070] The following takes the server as an example of the execution entity to illustrate the specific implementation process of the power grid supply chain greening level analysis method provided in the embodiment of the present application.

[0071] Figure 1 : is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in an embodiment of the present application, such as Figure 1 As shown, the green level analysis method of the power grid supply chain provided by this application includes:

[0072] S101: Obtain interval relative importance data and interval sample data of the evaluation index of the greening level;

[0073] S102: generating a first weight of the evaluation index according to the interval relative importance data;

[0074] S103: generating a second weight of the evaluation index according to the interval sample data;

[0075] S104: Combining and weighting the first weight and the second weight to generate a comprehensive weight of the evaluation index;

[0076] S105: Analyze the greening level of the power grid supply chain using the comprehensive weight.

[0077] from Figure 1 It can be seen from the shown process that the power grid supply chain greening level analysis method provided in the present application obtains interval relative importance data and interval sample data of the greening level evaluation index; generates a first weight of the evaluation index based on the interval relative importance data; generates a second weight of the evaluation index based on the interval sample data; combines and weights the first weight and the second weight to generate a comprehensive weight of the evaluation index; and uses the comprehensive weight to analyze the greening level of the power grid supply chain, thereby achieving a more scientific and accurate green performance evaluation of the power grid supply chain and promoting the sustainable and healthy development of the power grid green supply chain.

[0078] Each step is explained in detail below.

[0079] S101: Obtain interval relative importance data and interval sample data of the evaluation index of the greening level;

[0080] Specifically, the server obtains the interval relative importance data of the evaluation indicators to generate the first weight of the evaluation indicators. The interval relative importance data is provided by the expert evaluation team, which reflects the importance of each evaluation indicator to the overall goal, that is, the importance relationship between the evaluation indicators. Among them, the overall goal is the green efficiency of the power grid supply chain. The first weight obtained according to the interval relative importance data provided by the expert evaluation team is the subjective weight.

[0081] The server obtains interval sample data of the evaluation index to generate a second weight of the evaluation index. For quantitative indicators, the server collects and standardizes the data generated in the actual operation of the power grid supply chain to obtain interval sample data; for qualitative indicators, the server obtains the scores of expert evaluation to obtain interval sample data, and the second weight obtained according to the interval sample data is the objective weight.

[0082] S102: generating a first weight of the evaluation index according to the interval relative importance data;

[0083] Specifically, the server calculates the first weight of the evaluation indicator based on the acquired interval relative importance data of the evaluation indicator, and obtains the comprehensive weight of the evaluation indicator based on the first weight combined with the second weight, and scientifically combines subjective judgment and objective data to analyze the green level of the power grid supply chain.

[0084] Figure 2 : is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in an embodiment of the present application, such as Figure 2 As shown, S102 includes:

[0085] S201: stratifying the evaluation indicators according to a preset indicator-level mapping table to obtain an indicator level;

[0086] Specifically, the overall goal (green efficiency of the power grid supply chain) is decomposed into n "elements" (n≥1), namely evaluation indicators. The server hierarchizes the evaluation indicators according to the preset mapping relationship table between indicators and levels to construct a green level analysis system for the power grid supply chain. The hierarchical indicator levels include the criterion layer and the indicator layer. The criterion layer includes six aspects (first-level evaluation indicators) of strategy and goals, green procurement, green logistics, green construction, green recycling and green chain carbon management. The indicator layer includes 20 indicators (second-level evaluation indicators) of green development planning and goals, green supply chain management system, management system and standards, EPC business process efficiency, emergency management and response, supplier performance evaluation, green procurement requirements, management system, logistics plan, product transportation and storage requirements, transportation tools, pollutant emissions, energy and water metering systems, recycling labels, recycling performance, recycling system, harmless treatment, downstream enterprise collaboration, green information management level and green information disclosure for weight setting.

[0087] Among them, the correspondence between the first-level evaluation indicators and the second-level evaluation indicators is as follows: strategies and goals include green development plans and goals and green supply chain management system; green procurement includes management systems and standards, EPC business process efficiency, emergency management and response, supplier performance evaluation and green procurement requirements; green logistics includes management systems, logistics plans, product transportation and storage requirements and transportation tools; green construction includes pollutant emissions and energy and water metering systems; green recycling includes recycling labels, recycling performance, recycling system, harmless treatment and downstream enterprise collaboration; green chain carbon management includes green information management level and green information disclosure.

[0088] S202: constructing a corresponding interval judgment matrix according to the interval relative importance data of the evaluation indicators of each indicator level;

[0089] Specifically, the server further determines the judgment matrix of each expert on each evaluation indicator (primary evaluation indicator and secondary evaluation indicator) through the interval relative importance data of the evaluation indicators at each indicator level.

[0090] Constructing interval judgment matrix based on relative importance in is the interval number, i=1,2,…,n, j=1,2,…,n, Indicates the importance of index i and index j relative to the target. The expert evaluation team compares the importance of evaluation indicators. Assuming there are m experts, the judgment matrix of the kth expert is expressed as in Then the final interval judgment matrix is:

[0091]

[0092] In the formula, k = 1, 2,…, m, i = 1, 2,…, n, j = 1, 2,…, n.

[0093] In one embodiment, the interval relative importance data is a comparative hierarchical structure diagram of the expert evaluation group, showing the relationship between the evaluation indicators at each level.

[0094] In one embodiment, the judgment matrix of the criterion layer indicators is shown in Table 1.

[0095] Table 1

[0096]

[0097]

[0098] In one embodiment, the judgment matrix of each secondary indicator under the green procurement indicator of the criterion layer is shown in Table 2.

[0099] Table 2

[0100]

[0101] S203: Calculate the interval eigenvector of the interval judgment matrix;

[0102] Specifically, the server uses the square root method to calculate the judgment matrix The eigenvector is obtained by multiplying the number of elements in each row and raising it to the power of n.

[0103]

[0104] S204: Normalize the interval feature vector to obtain the first weight.

[0105] Specifically, the server After normalization, we get: That is the interval matrix The weight vector is:

[0106]

[0107] In one embodiment, the calculated criterion layer indicator weights are shown in Table 3.

[0108] Table 3

[0109]

[0110] In one embodiment, the weights of the secondary indicators under the green procurement indicators of the criterion layer are calculated as shown in Table 4.

[0111] Table 4

[0112]

[0113] In one embodiment, the server converts the first weight into a single point value according to the following formula:

[0114]

[0115] The first weights of each indicator in the indicator layer are shown in Table 5.

[0116] Table 5

[0117]

[0118] In one embodiment, the server uses Interval AHP (IAHP for short) to determine the first weight.

[0119] Figure 3 : is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in an embodiment of the present application, such as Figure 3 As shown, after S204, it includes:

[0120] S301: Calculating the maximum eigenvalue according to the interval judgment matrix and the first weight;

[0121] Specifically, the server judges the matrix Add the values ​​of each column in Calculate the largest eigenvalue as follows:

[0122]

[0123] S302: Using the maximum eigenvalue root to perform a consistency check on the first weight.

[0124] Specifically, the server performs a consistency check on the first weight. Calculate the consistency ratio CR, because the maximum characteristic root is the interval number, so when calculating CI, it is necessary to use formula (4) to convert Converted to a single point value, as follows:

[0125]

[0126] Among them, CI is the consistency index, RI is the average random consistency index, and CR<0.1 is used to check whether it meets the consistency. If CR<0.1, it passes the consistency test.

[0127] In one embodiment, the consistency check is performed on the criterion layer indicator weights shown in Table 3. CI=0.0926, CR=0.0735<0.1, the judgment matrix has satisfactory consistency, that is, it passes the consistency test.

[0128] S103: generating a second weight of the evaluation index according to the interval sample data;

[0129] Specifically, the server calculates the second weight of the evaluation indicator based on the obtained interval sample data of the evaluation indicator, combines it with the first weight to obtain the comprehensive weight of the evaluation indicator, and scientifically combines subjective judgment and objective data to analyze the green level of the power grid supply chain.

[0130] Figure 4 : is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in an embodiment of the present application, such as Figure 4 As shown, S103 includes:

[0131] S401: Calculating the upper bound information entropy and the lower bound information entropy of the evaluation index according to the interval sample data;

[0132] Specifically, the server calculates and processes the interval sample data to obtain the weight of each indicator. If there are n indicators and m samples to be evaluated, the measured value of sample i under indicator j represents the interval number. Right now And the information entropy of index j is recorded as Let E j - represents the lower bound information entropy, E j + Denotes the upper bound information entropy, then:

[0133]

[0134] in,

[0135]

[0136] S402: Calculate the interval information entropy of the evaluation index using the upper bound information entropy and the lower bound information entropy; specifically, the server calculates the interval information entropy E according to the expected number of intervals. j :

[0137]

[0138] S403: Generate the second weight according to the interval information entropy.

[0139] Specifically, the server calculates the weight w of indicator j j :

[0140]

[0141] Then W=(w1,w2,...,w n ) is the second weight vector of the obtained evaluation index.

[0142] In one embodiment, the calculated second weights of the indicators in the indicator layer are shown in Table 6.

[0143] Table 6

[0144]

[0145] In one embodiment, the second weight is determined using an entropy weighting method of interval numbers.

[0146] S104: Combining and weighting the first weight and the second weight to generate a comprehensive weight of the evaluation index;

[0147] Specifically, based on the principle of combining subjective and objective factors, the server will calculate the first weight and the second weight and determine the final comprehensive weight through an algorithm.

[0148] Figure 5 : is a flow chart of a method for analyzing the greening level of a power grid supply chain provided in an embodiment of the present application, such as Figure 5 As shown, S104 includes:

[0149] S501: constructing a linear combination model according to the first weight and the second weight;

[0150] Specifically, suppose that the i-th criterion layer has a total of p i indicators, i = 1, ..., n, then The linear combination model constructed by the server is expressed as:

[0151]

[0152] Where α1 and α2 are linear combination coefficients.

[0153] S502: using the linear combination model to solve the linear combination coefficients for the purpose of minimizing the deviation;

[0154] Specifically, the server optimizes the two linear combination coefficients α1 and α2 to minimize the deviation, and obtains the most reasonable weight. The determined objective function is expressed as:

[0155]

[0156] The above equation is equivalently transformed into a system of linear equations with the optimal first-order derivative conditions by using matrix differential properties:

[0157]

[0158] The optimal linear combination coefficients α1 and α2 are obtained from the above formula and normalized, which are recorded as

[0159] S503: Calculate the comprehensive weight according to the linear combination coefficient.

[0160] Specifically, the server obtains Calculate the comprehensive weight W as:

[0161]

[0162] In one embodiment, the calculated comprehensive weight is shown in Table 7.

[0163] Table 7

[0164]

[0165] In one embodiment, the comprehensive weight is based on game theory combination weighting.

[0166] S105: Analyze the greening level of the power grid supply chain using the comprehensive weight.

[0167] Specifically, the server obtains the data of each evaluation indicator in the actual operation of the power grid supply chain, and applies the generated comprehensive weight to the data of each indicator. By calculating the weighted value of each indicator and combining the performance of each indicator, the overall green level score is obtained. According to the weighted scores of each indicator of the supply chain, the overall green level of the power grid supply chain is quantitatively analyzed.

[0168] The present application provides a method for analyzing the greening level of a power grid supply chain, by obtaining interval relative importance data and interval sample data of the evaluation index of the greening level; generating the first weight of the evaluation index according to the interval relative importance data; generating the second weight of the evaluation index according to the interval sample data; combining and weighting the first weight and the second weight to generate the comprehensive weight of the evaluation index; using the comprehensive weight to analyze the greening level of the power grid supply chain, achieving a more scientific and accurate green performance evaluation of the power grid supply chain, and promoting the sustainable and healthy development of the green supply chain of the power grid. Among them, by combining and weighting the first weight and the second weight, it is possible to take into account both subjective and objective weights at the same time, scientifically combine subjective judgments and objective data, and then form a more complete and standard method for establishing the weights of power grid supply chain evaluation indicators, which meets scientificity and rationality; by obtaining interval relative importance data and interval sample data of the evaluation index of the greening level, it is possible to consider various factors of the supply chain and the uncertainty of the data, and provide a reference basis for managers in the actual supply chain operation.

[0169] Based on the same inventive concept, the embodiments of the present application also provide a power grid supply chain green level analysis device, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of solving the problem by the power grid supply chain green level analysis device is similar to the power grid supply chain green level analysis method, the implementation of the power grid supply chain green level analysis device can refer to the implementation of the method based on software performance benchmark determination, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0170] Figure 6 is a schematic diagram of the structure of a power grid supply chain greening level analysis device provided in an embodiment of the present application, such as Figure 6 As shown, the device comprises:

[0171] The data acquisition unit 601 is used to acquire interval relative importance data and interval sample data of the evaluation index of the greening level;

[0172] Specifically, the data acquisition unit 601 acquires the interval relative importance data of the evaluation index to generate the first weight of the evaluation index. The interval relative importance data is provided by the expert evaluation team, which reflects the importance of each evaluation index to the overall goal, that is, the importance relationship between the evaluation indicators, where the overall goal is the green efficiency of the power grid supply chain.

[0173] The data acquisition unit 601 acquires the interval sample data of the evaluation index to generate the second weight of the evaluation index, wherein, for the quantitative index, the server collects and standardizes the data generated in the actual operation of the power grid supply chain to obtain the interval sample data; for the qualitative index, the server acquires the scores of the expert evaluation to obtain the interval sample data.

[0174] A first weight generating unit 602, configured to generate a first weight of the evaluation index according to the interval relative importance data;

[0175] Specifically, the first weight generating unit 602 calculates the first weight of the evaluation indicator according to the acquired interval relative importance data of the evaluation indicator, so as to obtain the comprehensive weight of the evaluation indicator according to the first weight combined with the second weight, and scientifically combines the subjective judgment and objective data to analyze the green level of the power grid supply chain.

[0176] A second weight generating unit 603, configured to generate a second weight of the evaluation index according to the interval sample data;

[0177] Specifically, the second weight generating unit 603 calculates the second weight of the evaluation index based on the obtained interval sample data of the evaluation index, and combines it with the first weight to obtain the comprehensive weight of the evaluation index, scientifically combining subjective judgment and objective data to analyze the green level of the power grid supply chain.

[0178] A comprehensive weight generating unit 604, configured to combine and weight the first weight and the second weight to generate a comprehensive weight of the evaluation index;

[0179] Specifically, based on the principle of combining subjective and objective factors, the comprehensive weight generation unit 604 uses an algorithm to determine the final comprehensive weight using the calculated first weight and second weight.

[0180] The evaluation unit 605 is used to analyze the greening level of the power grid supply chain using the comprehensive weight.

[0181] Specifically, the evaluation unit 605 obtains the data of each evaluation index in the actual operation of the power supply chain, and applies the generated comprehensive weight to each index, and forms an overall green level score by calculating the weighted value of each index and combining the performance of each index. According to the weighted scores of each link in the supply chain, the overall green level of the power supply chain is quantitatively evaluated.

[0182] Figure 7 is a schematic diagram of the structure of a power grid supply chain greening level analysis device provided in an embodiment of the present application. Figure 6 Based on the embodiment, further, Figure 7 As shown, the power grid supply chain greening level analysis device provided by the present application also includes:

[0183] An indicator stratification module 701 is used to stratify the evaluation indicators to obtain indicator levels;

[0184] An interval judgment matrix construction module 702 is used to construct a corresponding interval judgment matrix according to the interval relative importance data of the evaluation indicators of each indicator level;

[0185] An interval feature vector calculation module 703 is used to calculate the interval feature vector of the interval judgment matrix;

[0186] The first weight generating module 704 is used to normalize the interval feature vector to obtain the first weight.

[0187] Figure 8 is a schematic diagram of the structure of a power grid supply chain greening level analysis device provided in an embodiment of the present application. Figure 7 Based on the embodiment, further, Figure 8 As shown, the power grid supply chain greening level analysis device provided by the present application also includes:

[0188] A maximum eigenvalue calculation module 801 is used to calculate the maximum eigenvalue according to the interval judgment matrix and the first weight;

[0189] The consistency check module 802 is used to perform a consistency check on the first weight using the maximum eigenvalue.

[0190] Fig. 9 is a schematic diagram of the structure of a power grid supply chain greening level analysis device provided in an embodiment of the present application. Figure 6 Based on the embodiment, further, Fig. 9 As shown, the power grid supply chain greening level analysis device provided by the present application also includes:

[0191] A boundary information entropy calculation module 901 is used to calculate the upper bound information entropy and the lower bound information entropy of the evaluation index according to the interval sample data;

[0192] An interval information entropy calculation module 902 is used to calculate the interval information entropy of the evaluation index using the upper bound information entropy and the lower bound information entropy;

[0193] The second weight generating module 903 is used to generate the second weight according to the interval information entropy.

[0194] Fig.10 is a schematic diagram of the structure of a power grid supply chain greening level analysis device provided in an embodiment of the present application. Figure 6 Based on the embodiment, further, Fig.10 As shown, the power grid supply chain greening level analysis device provided by the present application also includes:

[0195] A model building module 1001, configured to build a linear combination model according to the first weight and the second weight;

[0196] A coefficient solving module 1002, used for solving the linear combination coefficients by using the linear combination model to minimize the deviation;

[0197] The comprehensive weight generating module 1003 is used to calculate the comprehensive weight according to the linear combination coefficient.

[0198] The present application provides a method and device for analyzing the greening level of a power grid supply chain, by obtaining interval relative importance data and interval sample data of the evaluation index of the greening level; generating the first weight of the evaluation index according to the interval relative importance data; generating the second weight of the evaluation index according to the interval sample data; combining and weighting the first weight and the second weight to generate the comprehensive weight of the evaluation index; using the comprehensive weight to analyze the greening level of the power grid supply chain, achieving a more scientific and accurate green performance evaluation of the power grid supply chain, and promoting the sustainable and healthy development of the green supply chain of the power grid. Among them, by combining and weighting the first weight and the second weight, it is possible to take into account both subjective and objective weights at the same time, scientifically combine subjective judgments and objective data, and then form a more complete and standard power grid supply chain evaluation index weight establishment method that meets scientificity and rationality; by obtaining interval relative importance data and interval sample data of the evaluation index of the greening level, it is possible to consider multiple factors of the supply chain and the uncertainty of the data, and provide a reference basis for managers in the actual supply chain operation.

[0199] Fig.11 is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application, such as Fig.11 As shown, the electronic device may include: a processor 1101, a communication interface 1102, a memory 1103 and a communication bus 1104, wherein the processor 1101, the communication interface 1102 and the memory 1103 communicate with each other through the communication bus 1104. The processor 1101 may call the logic instructions in the memory 1103 to execute the following method: determine a symmetrical current signal and an asymmetrical current signal from the acquired ground fault current; generate a current value of the asymmetrical current signal according to the current value of the symmetrical current signal and the acquired reactance-resistance ratio; and analyze the arc flash level using the current value of the asymmetrical current signal and the current value of the symmetrical current signal.

[0200] In addition, the logic instructions in the above-mentioned memory 1103 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0201] The present embodiment discloses a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments, for example, including: determining a symmetrical current signal and an asymmetrical current signal from the acquired ground fault current; generating a current value of the asymmetrical current signal according to the current value of the symmetrical current signal and the acquired reactance-resistance ratio; and analyzing an arc flash level using the current value of the asymmetrical current signal and the current value of the symmetrical current signal.

[0202] The present embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program enables the computer to execute the methods provided by the above-mentioned method embodiments, for example, including: determining a symmetrical current signal and an asymmetrical current signal from the acquired ground fault current; generating a current value of the asymmetrical current signal according to the current value of the symmetrical current signal and the acquired reactance-resistance ratio; and analyzing an arc flash level using the current value of the asymmetrical current signal and the current value of the symmetrical current signal.

[0203] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0204] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0205] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0206] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0207] In the description of this specification, the description with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0208] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for analyzing the greening level of a power grid supply chain, characterized in that: include: Obtain interval relative importance data and interval sample data of green level evaluation indicators; Generating a first weight of the evaluation index according to the interval relative importance data; Generating a second weight of the evaluation index according to the interval sample data; Combining and weighting the first weight and the second weight to generate a comprehensive weight of the evaluation index; The comprehensive weight is used to analyze the greening level of the power grid supply chain.

2. The method for analyzing the greening level of the power grid supply chain according to claim 1 is characterized in that: Generating the first weight of the evaluation index according to the interval relative importance data includes: The evaluation indicators are layered according to a preset mapping relationship table between indicators and levels to obtain an indicator level; Construct the corresponding interval judgment matrix according to the interval relative importance data of the evaluation indicators at each indicator level; Calculating the interval eigenvector of the interval judgment matrix; The interval feature vector is normalized to obtain the first weight.

3. The method for analyzing the greening level of the power grid supply chain according to claim 2 is characterized in that: After normalizing the interval feature vector, the method further includes: Calculate the maximum eigenvalue according to the interval judgment matrix and the first weight; The first weight is checked for consistency using the maximum eigenvalue.

4. The method for analyzing the greening level of a power grid supply chain according to claim 1, characterized in that: Generating the second weight of the evaluation index according to the interval sample data includes: Calculate the upper bound information entropy and the lower bound information entropy of the evaluation index according to the interval sample data; Calculating the interval information entropy of the evaluation index using the upper bound information entropy and the lower bound information entropy; The second weight is generated according to the interval information entropy.

5. The method for analyzing the greening level of a power grid supply chain according to claim 1, characterized in that: The combining and weighting the first weight and the second weight includes: Constructing a linear combination model according to the first weight and the second weight; Using the linear combination model to solve the linear combination coefficients for the purpose of minimizing the deviation; The comprehensive weight is calculated according to the linear combination coefficient.

6. A device for analyzing the greening level of a power grid supply chain, characterized in that: include: A data acquisition unit, used to acquire interval relative importance data and interval sample data of the evaluation index of the greening level; A first weight generating unit, configured to generate a first weight of the evaluation index according to the interval relative importance data; A second weight generating unit, used for generating a second weight of the evaluation index according to the interval sample data; A comprehensive weight generating unit, used for combining and weighting the first weight and the second weight to generate a comprehensive weight of the evaluation index; An evaluation unit is used to analyze the greening level of the power grid supply chain using the comprehensive weight.

7. The power grid supply chain greening level analysis device according to claim 6, characterized in that: The first weight generating unit comprises: An indicator stratification module, used to stratify the evaluation indicators according to a preset indicator-level mapping relationship table to obtain an indicator level; An interval judgment matrix construction module is used to construct a corresponding interval judgment matrix according to the interval relative importance data of the evaluation indicators at each indicator level; An interval characteristic vector calculation module, used to calculate the interval characteristic vector of the interval judgment matrix; The first weight generating module is used to normalize the interval feature vector to obtain the first weight.

8. The power grid supply chain greening level analysis device according to claim 7, characterized in that: The first weight generating unit further includes: A maximum eigenvalue calculation module, used for calculating the maximum eigenvalue according to the interval judgment matrix and the first weight; A consistency check module is used to perform a consistency check on the first weight using the maximum eigenvalue.

9. The power grid supply chain greening level analysis device according to claim 6, characterized in that: The second weight generating unit comprises: A boundary information entropy calculation module, used to calculate the upper bound information entropy and the lower bound information entropy of the evaluation index according to the interval sample data; An interval information entropy calculation module, used to calculate the interval information entropy of the evaluation index using the upper bound information entropy and the lower bound information entropy; The second weight generating module is used to generate the second weight according to the interval information entropy.

10. The power grid supply chain greening level analysis device according to claim 6, characterized in that: The comprehensive weight generating unit comprises: A model building module, used for building a linear combination model according to the first weight and the second weight; A coefficient solving module, used for solving the linear combination coefficients by using the linear combination model to minimize the deviation; A comprehensive weight generation module is used to calculate the comprehensive weight according to the linear combination coefficient.

11. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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