CN107679754A - Power consumer satisfaction evaluation method based on advanced AHP and fuzzy theory - Google Patents
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Abstract
The present invention relates to power industry service technology field, and in particular to the power consumer satisfaction evaluation method based on advanced AHP and fuzzy theory, comprises the following steps：Comparator matrix is established according to power consumer satisfaction evaluation index system；The importance ranking index of indices is calculated based on the relative importance between each index in comparator matrix；To each level Index Establishment judgment matrix；Seek the optimum transfer matrix of judgment matrix；The excellent Consistent Matrix of plan of judgment matrix is sought, the characteristic vector for intending excellent Consistent Matrix is the weight vectors of indices；The weight vectors of indices are analyzed using fuzzy evaluation theory, is subordinate to angle value by what fuzzy evaluating matrix determined corresponding index, comprehensive evaluation result is determined according to degree of membership maximum principle.Present invention introduces the advanced AHP based on optimum transfer matrix to obtain each level index weights, can conveniently and effectively evaluate the satisfaction of power consumer, certain reference value can be provided for Utilities Electric Co.'s management.
Description
Technical Field
The invention relates to the technical field of power industry service, in particular to a power user satisfaction evaluation method based on improved AHP and fuzzy theory.
Background
With the continuous and deep advancement of the innovation of the power system, the user selection right is further improved after the diversified market main body is introduced, the operation idea of the power enterprise is also changed, and the user satisfaction degree is more and more concerned and valued. After the issue of the evaluation method of the quality of service of power supply of the national grid company (hereinafter referred to as the "method"), the power company focuses on the power supply in the past and gradually focuses on the improvement of the user demand and the service quality. The user is a direct source of business income of the enterprise, the improvement of the user satisfaction degree has important significance for enhancing the user viscosity and improving the brand benefits and competitiveness of the company, and the improvement has positive influence on the healthy and stable development of the enterprise.
The concept of "user satisfaction" was first proposed by cardoz, an american scholaree, and used in the field of marketing. At present, common user satisfaction models comprise a quartering chart model, a carnot model, an SCSB model, an ACSI model, an ECSI model and the like, and are applied to power supply companies in Fujian, hunan, shanghai and other places successively, but the evaluation models and indexes adopted by the companies are different, the comparability of evaluation results is not high, and the guidance on company operation decision is not strong. The national grid company provides an evaluation model taking a user satisfaction index (CSI) as a center in 'method', each structural variable has a plurality of corresponding evaluation indexes, the user requirements can be comprehensively reflected, but the relationship among the structural variables is not mentioned, and the user satisfaction is comprehensively evaluated to a certain extent.
On the basis of 'method' of the national power grid company, the requirements and expectations of power users are fully considered, and a user satisfaction evaluation model and an evaluation index system suitable for the power industry in China are provided; for the defects of the traditional analytic hierarchy process, the improved analytic hierarchy process based on the optimal transfer matrix is introduced, consistency check is not needed, the weight of each factor can be directly solved, and the evaluation efficiency is greatly improved; after the index weight matrix is established, the investigation results of all indexes are analyzed by adopting a fuzzy evaluation theory, the membership value of the corresponding index is determined by the fuzzy evaluation matrix, and the comprehensive evaluation result is determined according to the maximum membership principle. The example results show that the method can conveniently and effectively evaluate the satisfaction degree of the power users, and has certain reference value for power companies to master user requirements and improve service quality.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provide a power user satisfaction evaluation method based on improved AHP and fuzzy theory.
The invention is realized by the following technical scheme:
the power user satisfaction evaluation method based on the improved AHP and the fuzzy theory comprises the following steps:
s1, establishing a comparison matrix A according to an index system for evaluating the satisfaction degree of a power user;
s2, calculating importance ranking indexes r of all indexes based on the relative importance degree between the indexes in the comparison matrix A;
s3, establishing a judgment matrix B for each level of indexes according to the importance ranking index r;
s4, solving an optimal transfer matrix D of the B on the basis of establishing the judgment matrix B;
s5, after the optimal transfer matrix D is obtained, solving a quasi-optimal consistent matrix B' of the judgment matrix B, wherein a characteristic vector W of the quasi-optimal consistent matrix is a weight vector of each index;
and S6, analyzing the weight vector W of each index by adopting a fuzzy evaluation theory, determining the membership value of the corresponding index by using a fuzzy evaluation matrix, and determining a comprehensive evaluation result according to the maximum membership rule.
Preferably, the comparison matrix a = (a) in step S1 ij ) n×n Comprises the following steps:
in the formula: n is the number of indexes to be evaluated, a ij The relative importance degree of the ith index to the jth index is satisfied with a ij >0，a ij ＝1(i＝j)，a ij ＝1/a ji (i ≠ j), n, i, j are all natural numbers.
Preferably, said a ij The judgment criterion of (1) adopts Saaty 1-9 scaling method.
Preferably, in the step S2, based on the relative importance degree of the index i and the index j, the importance ranking index r of each index is calculated i Comprises the following steps:
in the formula: n is the number of indexes to be evaluated, a ij The relative importance degree of the ith index to the jth index is n, i and j are natural numbers.
Preferably, the determination matrix B = (B) in step S3 ij ) n×n And b is a ij The following equations are required:
in the formula: r is i And r j The importance ranking indexes of the ith index and the jth index are respectively; r is a radical of hydrogen max And r min The indices of importance are maximum and minimum, respectively, and r max ＝max(r i )，r min ＝min(r i )；k m ＝r max /r min (ii) a i. j is a natural number.
Preferably, the optimal transfer matrix D = (D) in step S4 ij ) n×n Comprises the following steps:
in the formula: n is the number of indexes to be evaluated, k is the number of columns of the matrix D, c ij ＝lgb ij N, k, i, j are natural numbers.
Preferably, the pseudo-optimal consistent matrix B' = (B) in step S5 ij ′) n×n Whereinn, i and j are natural numbers.
Preferably, the fuzzy evaluation theory analysis of step S6 comprises the following steps:
s61, setting the satisfaction degree of the power user as N evaluation levels, and calculating the membership degree of each level of N indexes to be evaluated which are subordinate to the N evaluation levels according to the questionnaire result; wherein N and N are both natural numbers;
s62, calculating a membership vector psi of the decision target fuzzy evaluation as follows:
ψ＝WF＝{ψ 1 ,ψ 2 ,...,ψ N } (5)
in the formula: w is a characteristic vector of the quasi-optimal consistent matrix, and F is a membership degree fuzzy evaluation matrix;
and S63, according to the maximum membership degree principle, the evaluation grade corresponding to max (psi) is the final evaluation result of the satisfaction degree of the power user.
The invention has the beneficial effects that:
(1) The method provides a user satisfaction evaluation model and an evaluation index system suitable for the power industry in China, and the requirements and expectations of power users are comprehensively considered from seventeen dimensions of four levels by taking the power user satisfaction as a general target.
(2) And the improved AHP based on the optimal transfer matrix is introduced to obtain the index weight of each level, so that the consistency test step is omitted, and the evaluation efficiency is effectively improved.
(3) The final evaluation result is determined according to the maximum membership principle by taking the eigenvector of the quasi-optimal consistent matrix as the basis and combining the fuzzy evaluation theory, so that the satisfaction degree of the power users can be conveniently and effectively evaluated, and a certain reference value can be provided for the operation management of the power company.
Drawings
Fig. 1 is a power consumer satisfaction evaluation model.
Fig. 2 is a flowchart of the power consumer satisfaction evaluation method based on the improved AHP and the fuzzy theory according to the present invention.
Detailed Description
For a better understanding of the present invention, the present invention will be further described with reference to the following examples and the accompanying drawings, which are set forth to illustrate, but are not to be construed to limit the present invention.
In order to establish a normalized service supervision mechanism, find weak links existing in company operation management in time and promote the improvement of the service quality and the service capacity of the company better, the embodiment uses a user satisfaction evaluation model suitable for the power industry in China, as shown in fig. 1. The model contains seven structural variables, enterprise image, user expectations, perceived quality, perceived value, user satisfaction, user complaints, and user loyalty, respectively. Wherein, the enterprise image is exogenous variable, and the rest variables are endogenous variables. It can also be considered that enterprise image, user expectation, perception quality and perception value are reason variables of user satisfaction, user satisfaction is intermediate variable, user complaint and user loyalty are result variables of user satisfaction, and both the reason variables and the result variables directly or indirectly influence the user satisfaction.
The selection of the user satisfaction evaluation index is an important work and is directly related to the quality and the reliability of the evaluation result. According to the power consumer satisfaction model and the relevant requirements of 'method', a four-level power consumer satisfaction evaluation index system is constructed and is shown in table 1. The primary index is the satisfaction degree of the power user and is a target value of evaluation; the secondary indexes are six structural variables of enterprise image, user expectation, perception quality, perception value, user complaint and user loyalty which influence the satisfaction degree of the user respectively; the third-level index is the specific refinement of the second-level index, and can fully reflect the meaning and the characteristics of the second-level index; the fourth-level index is the direct evaluation of the third-level index and is obtained by means of a questionnaire.
TABLE 1 evaluation index system for satisfaction degree of power consumer
A certain power supply company in a city is selected as a research object to be analyzed, and the research is widely carried out on different types of power users including residential users, industrial users, commercial users, users of public institution, users of science and education institution and the like through the assistance of marketing departments and 95598 customer service. 1000 parts of questionnaire are issued by adopting an SRS sampling mode, 968 parts are actually recovered, and the recovery rate reaches 96.8%. The evaluation procedure is shown in FIG. 2.
Taking the secondary indexes in the power user satisfaction evaluation index system as an example, according to the results of investigation on various users, a comparison matrix is established by a 1-9 scale method (table 2) as shown in the following table 3:
TABLE 2 Saaty1-9 Scale method
TABLE 3 comparison matrix
The importance ranking index r = {2.7000,6.9167, 18,9.8333, 12.3333,4.8333}, is calculated from equation (2). Establishing a judgment matrix according to the importance ranking index by the formula (3):
on the basis of establishing a judgment matrix B, solving an optimal transfer matrix of B by an equation (4):
and (3) solving a quasi-optimal consistent matrix and a characteristic vector of the quasi-optimal consistent matrix based on the optimal transfer matrix D:
W＝[0.0430,0.0878,0.4474,0.1441,0.2167,0.0610] T
therefore, the weights of the secondary indicators of business image, user expectation, perceived quality, perceived value, user complaint and user loyalty are 0.0430, 0.0878, 0.4474, 0.1441, 0.2167 and 0.0610 respectively. Similarly, the indicators for other levels can be similarly found, and the results are shown in table 4 below:
TABLE 4 Power customer satisfaction evaluation index system weights
When the satisfaction degree of the power consumer is investigated, a Likert five-point scale method is adopted, and five evaluation grades of very satisfactory, common, unsatisfactory and very unsatisfactory are set. For the same index, the evaluation of different users may be different, for example, user a is satisfied with index i, but user B is not satisfied with index i. Therefore, compared with a deterministic evaluation method, the fuzzy evaluation theory can well solve the problems of ambiguous classification and unclear boundary, and is widely applied to the fields of financial management, engineering technology, educational scientific research and the like.
For a given discourse domain E, let M be a fuzzy subset on E if forAll have mu M (x)∈[0,1]Corresponding to it, it is called mu M (x) Is the degree of membership of x to M. Fuzzy subsets are typically represented in discrete form: m = { μ = M (x 1 )|x 1 ,μ M (x 2 )|x 2 ,...,μ M (x n )|x n If the index domain and the evaluation grade domain of the evaluation target are respectively U = { U = } 1 ,u 2 ,...,u r H and V = { V = 1 ,v 2 ,...,v s }，u i Evaluating the index of the to-be-evaluated level in the index system for the satisfaction degree of the power user, v j Rating was determined by the Likert five-point scale method. For each element pairAll constitute the Desscartes product under the evaluation factor ifHas an evaluation factor value of f ij Then f is ij I.e. u i For v j The corresponding fuzzy evaluation matrix is:
since U is an r-dimensional evaluation space, the influence of each index on the decision target needs to be taken into account. Based on the eigenvector W of the pseudo-optimal consistent matrix in the improved AHP, the membership vector ψ of the fuzzy evaluation of the decision target is shown in formula (5).
According to analysis and statistics of user investigation results, a fuzzy evaluation matrix of the satisfaction degree of the power user is established, membership degree values of all indexes for different evaluation levels are determined, as shown in table 5, and the membership degree vector of the final evaluation is determined to be psi = {0.4627,0.8133,0.2701,0.1601 and 0.0839} by formula (5) in combination with the eigenvector W of the quasi-optimal consistent matrix. According to the maximum membership principle, the satisfaction level of the power user of the power supply company can be obtained as 'satisfaction'.
TABLE 5 fuzzy evaluation matrix for satisfaction of power consumers
The embodiment of the invention provides a user satisfaction evaluation model and an evaluation index system suitable for the power industry in China, and the requirements and expectations of power users are comprehensively considered from seventeen dimensions of four levels by taking the power user satisfaction as a general target. And the optimal transfer matrix-based improved AHP is introduced to obtain the index weight of each level, so that the consistency inspection step is omitted, and the evaluation efficiency is effectively improved. The method is based on the eigenvector of the quasi-optimal consistent matrix, combines the fuzzy evaluation theory, and determines the final evaluation result according to the maximum membership principle, so that the satisfaction degree of the power consumer can be conveniently and effectively evaluated, and a certain reference value can be provided for the operation management of the power company.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (8)
1. The method for evaluating the satisfaction degree of the power consumer based on the improved AHP and the fuzzy theory is characterized by comprising the following steps of:
s1, establishing a comparison matrix A according to an index system for evaluating the satisfaction degree of a power user;
s2, calculating importance ranking indexes r of all indexes based on the relative importance degree between the indexes in the comparison matrix A;
s3, establishing a judgment matrix B for each level of indexes according to the importance ranking index r;
s4, solving an optimal transfer matrix D of the B on the basis of establishing the judgment matrix B;
s5, after the optimal transfer matrix D is obtained, solving a quasi-optimal consistent matrix B' of the judgment matrix B, wherein a feature vector W of the quasi-optimal consistent matrix is a weight vector of each index;
and S6, analyzing the weight vector W of each index by adopting a fuzzy evaluation theory, determining the membership value of the corresponding index by using a fuzzy evaluation matrix, and determining a comprehensive evaluation result according to the maximum membership rule.
2. The method for evaluating satisfaction of power consumer based on improved AHP and fuzzy theory as claimed in claim 1, wherein step S1 said comparison matrix a = (a) ij ) n×n Comprises the following steps:
in the formula: n is the number of indexes to be evaluated, a ij Is the ith fingerThe relative importance degree of the mark to the jth index satisfies a ij >0，a ij ＝1(i＝j)，a ij ＝1/a ji (i ≠ j), n, i, j are all natural numbers.
3. The power consumer satisfaction evaluation method based on improved AHP and fuzzy theory according to claim 2, wherein: a is a mentioned ij The judgment criterion of (1) adopts Saaty 1-9 scaling method.
4. The method for evaluating satisfaction of power consumer based on improved AHP and fuzzy theory as claimed in claim 1, wherein said step S2 is to calculate the ranking index r of importance of each index based on the relative importance degree of index i and index j i Comprises the following steps:
in the formula: n is the number of indexes to be evaluated, a ij The relative importance of the ith index to the jth index is n, i and j are natural numbers.
5. The method for evaluating satisfaction of power consumer based on improved AHP and fuzzy theory as claimed in claim 1, wherein said determination matrix B = (B) in step S3 ij ) n×n And b is a ij The following equations are required:
in the formula: r is a radical of hydrogen i And r j The importance ranking indexes of the ith index and the jth index are respectively; r is a radical of hydrogen max And r min The indices are sorted by maximum and minimum importance, respectively, and r max ＝max(r i )，r min ＝min(r i )；k m ＝r max /r min (ii) a i. j is a natural number.
6. The method for evaluating satisfaction of power consumer according to claim 1, wherein in step S4 the optimal transfer matrix D = (D) ij ) n×n Comprises the following steps:
in the formula: n is the number of indexes to be evaluated, k is the number of columns of the matrix D, c ij ＝lgb ij N, k, i, j are natural numbers.
7. The power consumer satisfaction evaluation method based on improved AHP and fuzzy theory of claim 1, wherein: step S5, the pseudo-optimal consistent matrix B '= (B' ij ) n×n Whereinn, i and j are natural numbers.
8. The method of claim 1, wherein the fuzzy evaluation theory analysis of step S6 comprises the steps of:
s61, setting the satisfaction degree of the power user as N evaluation levels, and calculating the membership degree of each level of N indexes to be evaluated which are subordinate to the N evaluation levels according to the questionnaire result; wherein N and N are both natural numbers;
s62, calculating a membership vector psi of the decision target fuzzy evaluation as follows:
ψ＝WF＝{ψ 1 ,ψ 2 ,...,ψ N } (5)
in the formula: w is a characteristic vector of the quasi-optimal consistent matrix, and F is a membership fuzzy evaluation matrix;
and S63, according to the maximum membership degree principle, the evaluation grade corresponding to max (psi) is the final evaluation result of the satisfaction degree of the power user.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108694671A (en) * | 2018-06-27 | 2018-10-23 | 北京中电普华信息技术有限公司 | Marketing service site investment analysis method |
| CN109685372A (en) * | 2018-12-25 | 2019-04-26 | 国网北京市电力公司 | A kind of big data power customer good service quality evaluating method based on RATER index |
| CN110110989A (en) * | 2019-04-29 | 2019-08-09 | 国网河北省电力有限公司经济技术研究院 | The evaluation method and terminal device of the anti-bird effect of overhead transmission line |
| CN111415176A (en) * | 2018-12-19 | 2020-07-14 | 杭州海康威视数字技术股份有限公司 | Satisfaction evaluation method and device and electronic equipment |
| CN112116238A (en) * | 2020-09-16 | 2020-12-22 | 深圳市维度统计咨询股份有限公司 | Satisfaction evaluation method based on index weight system design |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108694671A (en) * | 2018-06-27 | 2018-10-23 | 北京中电普华信息技术有限公司 | Marketing service site investment analysis method |
| CN111415176A (en) * | 2018-12-19 | 2020-07-14 | 杭州海康威视数字技术股份有限公司 | Satisfaction evaluation method and device and electronic equipment |
| CN109685372A (en) * | 2018-12-25 | 2019-04-26 | 国网北京市电力公司 | A kind of big data power customer good service quality evaluating method based on RATER index |
| CN110110989A (en) * | 2019-04-29 | 2019-08-09 | 国网河北省电力有限公司经济技术研究院 | The evaluation method and terminal device of the anti-bird effect of overhead transmission line |
| CN112116238A (en) * | 2020-09-16 | 2020-12-22 | 深圳市维度统计咨询股份有限公司 | Satisfaction evaluation method based on index weight system design |
| CN117035887A (en) * | 2023-10-08 | 2023-11-10 | 中质国优测评技术(北京)有限公司 | Automobile user satisfaction evaluation method and system |
| CN117035887B (en) * | 2023-10-08 | 2023-12-26 | 中质国优测评技术(北京)有限公司 | Automobile user satisfaction evaluation method and system |
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