CN108921376A - A kind of intelligent distribution network electricity consumption reliability promotes the preferred method and system of object - Google Patents
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Abstract
The present invention discloses the preferred method and system of a kind of intelligent distribution network electricity consumption reliability promotion object.System includes data acquisition module, evaluation module and storage module.Preferred method includes：It establishes intelligent distribution network electricity consumption reliability and promotes demand degree assessment indicator system；Data acquisition module obtains index of correlation data and is stored into storage module；Evaluation module is again normalized each index value of all objects to be selected, nondimensionalization, forms standardization index matrix；Master, the objective weight value of each index are calculated, and seeks the synthetic weights weight values of each index；Weighted normal index matrix is calculated, determines positive and negative absolute ideal solution, calculates the grey relational grade of each object to be selected；Relative similarity degree of each object to be selected relative to positive ideal solution is calculated, and object, output preferred result to storage module are promoted according to the preferred reliability of its size.The present invention can fully assess the true electricity consumption situation of user in intelligent distribution network, and intelligent distribution network electricity consumption reliability is instructed to promote the development of engineering.
Description
Technical Field
The invention relates to the field of power supply reliability assessment, in particular to a preferred method and a system for improving the power utilization reliability of an intelligent power distribution network.
Background
As an important way for improving the operation management level and the service capability of the power supply enterprises, reliability transformation and improvement work are always paid much attention by the power supply enterprises. Along with the development of intelligent power distribution network, the mode that obtains the electric energy such as distributed power generation, energy storage is constantly abundant, and distribution network electric energy quality problem is salient day by day, and the limitation of the power supply reliability evaluation system that domestic long-term medium voltage user obtained for statistics bore is more obvious: 1) the power supply reliability index, especially the power supply reliability of the medium-voltage caliber, only roughly considers the problem of power supply continuity, and cannot comprehensively reflect the real power consumption experience of a user; 2) the traditional evaluation index system which is system-oriented and does not consider the availability of electric energy cannot meet the new requirements of fine management of a power distribution network of a power selling enterprise and deep development of a power selling market; 3) traditional power supply reliability assessment cannot be applied to the intelligent power distribution network. Influences of new factors such as distributed power sources and energy storage in the intelligent power distribution network on the power utilization process of the user are difficult to reflect in power supply reliability assessment.
In addition, the reliability of the power distribution network is improved only by taking the power supply reliability as the primary basis in the current stage, and the power consumption experience is difficult to be practically improved. On the other hand, although research on reliability prediction and improvement methods has achieved a lot of achievements, currently, improvement of reliability of smart distribution networks is preferred mainly in a scheme, and preference of reliability improvement implementation objects is not considered. Therefore, under the condition that the investment of the reliability transformation project is limited, the power utilization reliability improvement requirements of each power distribution network are reasonably evaluated, the power distribution network which urgently needs to improve the power utilization reliability is preferably selected, the reliability improvement effect is maximized, and the reliability transformation method has more important significance for improving the user satisfaction and the service level undoubtedly.
The invention constructs an intelligent power distribution network power utilization reliability improvement demand evaluation index system from three aspects of power distribution network power supply reliability, user power utilization experience and engineering economy improvement, and provides a preferred method and system for an intelligent power distribution network power utilization reliability improvement object.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for optimizing an object for improving the power utilization reliability of an intelligent power distribution network.
The object of the present invention is achieved by the following means.
A preferred system for improving the object of the power utilization reliability of a smart distribution network comprises the following components:
and the data acquisition module is communicated with a background production management system and a marketing information management system of the power grid enterprise, acquires data of corresponding indexes of each intelligent power distribution network to be selected, and can also manually input part of index data by power grid workers.
The evaluation module evaluates the power utilization reliability improvement requirements of each intelligent power distribution network to be selected by using a comprehensive evaluation method based on improvement approximation ideal to ideal solution (TOPSIS) and gray correlation analysis, and preferably selects the intelligent power distribution network which needs the most power utilization reliability improvement.
And the storage module stores the data and the evaluation result.
The method for utilizing the optimal system for improving the object of the power utilization reliability of the intelligent power distribution network comprises the following steps:
s1, establishing an intelligent power distribution network power utilization reliability improvement demand evaluation index system from three aspects of power distribution network power supply reliability, user power utilization experience and engineering economy improvement.
S2, the data acquisition module acquires data of related indexes through a power grid background production management system, a marketing information management system and a manual input mode, and stores the data in the storage module
S3, the evaluation module calls the relevant data, and normalizes and dimensionless the index values of all the objects to be selected to form a normalized index matrix.
S4, the evaluation module obtains the subjective weight value of each index by adopting an Analytic Hierarchy Process (AHP), obtains the objective weight value of each index by adopting an improved entropy weight method, and obtains the comprehensive weight value of each index.
And S5, calculating a weighted normalized index matrix by the evaluation module, determining positive and negative absolute ideal solutions, and calculating the gray correlation degree of each object to be selected.
S6, the evaluation module calculates the relative closeness R (i) of each object to be selected relative to the positive ideal solution, optimizes the reliability promotion object according to the size of R (i), and outputs the optimization result to the storage module.
The intelligent power distribution network power utilization reliability improvement demand evaluation index system mentioned in the step S1 specifically includes the following indexes:
1) island adequacy probability
At different times during the island operation process, the output power of the distributed power supply in the island does not necessarily meet the load requirement, and the load and the power of the distributed power supply may be one of their respective probability models. Under the condition that all possible combination modes of the two are considered, the probability that the distributed power supply in the island i meets the power demand of the load point is the available island adequacy probability rhoiExpressed, its calculation formula is as follows:
in the formula,andrespectively representing the load size of the islanding i in the jth running state and the total output of the distributed power supply; n is a radical ofdiThe number of all possible operating states for island i (all possible combinations of load and distributed power source power within island); rhoi,jIndicating the probability that island i is in the j-th operating state.
In order to reflect the overall influence, the operating adequacy probability of the system island is defined, and the calculation formula is as follows:
in the formula,the specific numerical value can be obtained by simulation of a Monte Carlo method for the probability of forming the operation of an island i after the system fails; n is a radical ofdFor all possible islanding scenarios.
2) Load transfer efficiency
The N-1 fault is the most common fault of the power distribution network, and after the fault occurs, if the power distribution network can provide more load transfer line channels, the network structure of the power distribution network is stronger. In fact, the line capacity for load transfer provided by the distribution network after the N-1 fault occurs can only meet the safe transfer of partial load, and in order to evaluate the load transfer efficiency of the intelligent distribution network and consider the situation of load loss caused by the fault, a load transfer efficiency index is defined, and the calculation formula is as follows:
in the formula,indicating the limit transmission power of the line j under the ith N-1 fault condition;respectively representing the power which needs to be transmitted by the line j to ensure that no load is cut off; pLTotal load before failure occurs;number of N-1 faults when ηiWhen the value is negative, it indicates that the distribution network has a load loss under the fault, and ηiThe smaller the loss amount, the larger the demand for improvement in power consumption reliability.
To reflect the overall influence, the system load transfer efficiency is defined, and the calculation formula is as follows:
in the formula,the probability of the ith N-1 fault occurring in the system; n is a radical ofN-1Is the number of all possible N-1 faults.
3) Power supply reliability index completion rate
According to the southern power grid 110kV and the following power distribution network planning guiding principles, power distribution networks are required to be classified into power supply areas according to load density and planning development positioning during planning, and different power supply reliability assessment targets are made for the power supply areas of different grades, which is shown in the following table:
TABLE 1 evaluation of Power supply reliability (%)
According to the power supply reliability assessment target of the power supply area and the power supply reliability of actual statistics, the invention defines the completion rate of the power supply reliability index, and the calculation formula is as follows:
in the formula, RSaRepresenting a power supply reliability assessment target value, RS-1Representing the actual statistical power reliability.
4) Power utilization reliability: the ratio of the number of hours that all users have acquired available power supply to the statistical time, denoted as RRSL。
In the formula: t is tmRepresenting the total power failure time of the mth user in the power distribution network within the statistical time; m represents the total number of the charging users in the power distribution network; t represents the statistical duration.
5) User power consumption satisfaction degree: the ratio of the number of complaints of users to the total number of users under the distribution network in one year of the distribution network to be evaluated is recorded
In the formula: n is a radical ofucRepresenting the total number of complaints of the user received in one year of the distribution network; and M represents the total number of the charged users in the power distribution network.
6) User load level: according to the design specification GB 50052-2009 of power supply and distribution systems in China, user loads are divided into special loads, primary loads, secondary loads and tertiary loads according to the load properties and the importance degree of the user loads. When a plurality of loads with different grades exist in an object to be evaluated, the highest load grade is taken as the load grade of the object.
Since the user load level is a non-quantization index, the present example quantizes the loads of all levels from the low level to the high level by 1, 0.75, 0.5, and 0.25, that is, the index value of the special level load is assigned to be 1, and the amplitude of the three levels of loads is assigned to be 0.25.
7) Voltage qualification rate: and in the counting time, the ratio of the voltage qualified time of the subscriber incoming line unit to the counting time is recorded as VER (%).
In the formula: t is tvmAnd representing the voltage qualified hours of the mth billing user in the power distribution network in the statistical time.
8) Investment, operation and maintenance cost
Wherein
In the formula, CiEqual annual value for initial investment cost; ceThe annual value of the residual value of the equipment; cmCosts for operating maintenance, and network losses; pLThe load prediction value of the distribution network planning period is obtained; coIs the initial investment cost; k is the discount rate; n is the equipment operating life; crIs the residual value at the end of the life of the device.
9) Electricity generation ratio: during the statistical period, the ratio of the national production total value and the total electricity consumption of all users in the evaluated object is recorded as: VOC (yuan/kW. h)
In the formula: GDP represents the total domestic (regional) production value of a certain distribution network or region within a statistical period, in units: billion yuan; q represents the total electricity consumption of the distribution network or area during the statistical period, unit: billion kWh. (times/year).
The step S3 includes the steps of:
there are m objects to be selected, n evaluation indexes, the j evaluation index value of the ith evaluation object is marked as xij. Preprocessing the original data of all objects to be selected, wherein the processing formula is as follows:
the larger the index value is, the larger the demand for improving the reliability of power utilization is, and the following indexes are provided:
rij＝xij/miax xij(12)
the smaller the index value is, the greater the demand for improving the reliability of power utilization is, and the following indexes are provided:
after dimensionless index data is converted, the normalized index matrix of each object to be selected is obtained as follows:
wherein r isijThe value after the dimensionless processing of the j index of the ith candidate object is closer to 1, which indicates that the index of the object is worse in performance and has a requirement for improving reliability.
Step S4 includes the following steps:
s401: the subjective weight value of each index is obtained by adopting an AHP method,
and comparing the importance of each index pairwise by adopting three scales to establish a comparison matrix A:
wherein,
constructing judgment matrix C ═ C by range methodij)n×n：
In the formula, riIs the sum of the elements of each row of matrix A; c. CbIs a constant and is determined according to the relative importance of the range element pair under a certain standard, and c is taken as a general caseb＝9；R＝rmax-rmin，rmax＝max(r1， r2，…，rn)，rmin＝min(r1，r2，…，rn)。
Obtaining the maximum characteristic value of the judgment matrix C, carrying out consistency check, introducing a compatibility index CI to check the consistency of the judgment matrix, wherein:
CI＝(λmax-n)/(n-1) (18)
generally, when CI is less than 0.1, the consistency of the judgment matrix is considered to be acceptable; when CI is greater than 0.1, the judgment matrix should be modified again, and then the weight of the modified matrix is recalculated and consistency check is carried out.
When the maximum eigenvalue of the judgment matrix passes consistency check, calculating eigenvector omega 'corresponding to the maximum eigenvalue'A＝(ω’A1，ω’A2，…ω’An) The normalization processing is carried out to obtain the relative weight omega of each index of a certain level relative to the index of the previous levelA＝(ωA1，ωA2，…ωAn)。
S402: an improved entropy weight method is adopted to obtain objective weight values of all indexes,
for the index j, the characteristic proportion of the object i to be selected is as follows:
the entropy of index j is:
the objective weight of index j is then:
in the formula,is the average of all entropy values other than 1;
s403: the comprehensive weight value of each index is as follows:
ωj＝φωAj+(1-φ)ωEj(23)
the invention considers the characteristics of two weighting methods, and takes phi as 0.5.
Normalizing the weight vector omega, wherein
Step S5 includes the following steps:
s501: through the normalized matrix and the standard comprehensive weight vector, the weighted normalized matrix B ═ B can be obtainedij]m×nWherein:
s502: the positive and negative ideal solution data sequences of the ideal object are respectively defined as:
s503: calculating the grey correlation degree, wherein the calculation method of the grey correlation degree comprises the following steps:
in the formula,ρ is a resolution coefficient, and is generally 0.5.
Step S6 includes the following steps:
s601: calculating relative closeness, and based on gray correlation analysis, calculating the relative closeness R (i) of each object to be selected relative to a positive ideal solution:
the relative closeness degree reflects the closeness degree of the object to be selected to the positive ideal object or the negative ideal object in the situation change. And (e) sorting the objects to be selected according to the size of the R (i), preferably selecting the objects, and outputting the preferred result to the storage module.
Compared with the prior art, the invention has the following advantages and technical effects:
1. according to the optimal selection method and system for the power utilization reliability improvement object of the intelligent power distribution network, a power utilization reliability improvement demand degree evaluation index system which can comprehensively reflect the real power utilization condition of a user and guide the improvement of the intelligent power distribution network is established for the first time from three aspects of power supply reliability of a power grid, user power utilization experience and improvement engineering economy.
2. The comprehensive evaluation method based on the improved approximation method and the grey correlation analysis utilizes the improved entropy weight method to calculate the objective weight of each index, utilizes the AHP method to calculate the subjective weight of each index, and further obtains the comprehensive weight of each index, not only reflects the characteristics of original data, but also considers the actual experience of experts.
3. The comprehensive evaluation method based on the improved approximation method and the grey correlation analysis, which is designed by the invention, utilizes the absolute ideal solution to better solve the problem of 'inverse sorting' caused by the quantity change of the objects to be selected, can accurately and optimally select the intelligent power distribution network which is urgently needed to be subjected to power utilization reliability improvement, and helps a power grid enterprise to realize the maximum benefit by utilizing limited resources.
Drawings
FIG. 1 is a preferred flow chart of a preferred method for improving an object of power utilization reliability of a smart distribution network in an example;
fig. 2 is an evaluation index system diagram of the reliability improvement demand of the power utilization of the intelligent power distribution network mentioned in step S1 in the example.
Fig. 3 is a diagram of a preferred system structure of an object for improving power utilization reliability of a smart distribution network in an example.
FIG. 4 is a graph of the closeness contrast calculated by Euclidean distance method for different ideal solutions in the examples.
FIG. 5 is a comparison graph of the closeness calculated by the Euclidean distance method and the projection method in the example of the gray correlation analysis method.
Detailed Description
The present invention is described in further detail below with reference to examples and drawings, but the mode of implementation of the present invention is not limited thereto, and it should be noted that the following can be understood or implemented by those skilled in the art without specific details.
FIG. 1 is a flow chart of an optimal method for improving an object of power utilization reliability of an intelligent power distribution network, and the method comprises the following basic steps: firstly, establishing an evaluation index system for improving the power utilization reliability of the intelligent power distribution network on three aspects of power supply reliability of the power grid, user power utilization experience and improvement engineering economy; the data acquisition module acquires related index data in an automatic acquisition or manual input mode of a system and stores the related index data in the storage module; the evaluation module normalizes and dimensionless the index values of all the objects to be selected to form a normalized index matrix; then, obtaining subjective weight values of the indexes by adopting an AHP method, obtaining objective weight values of the indexes by adopting an improved entropy weight method, and solving a comprehensive weight value of each index; secondly, calculating a weighted normalized index matrix, determining positive and negative absolute ideal solutions, and calculating the grey correlation degree of each object to be selected; and thirdly, calculating the relative closeness R (i) of each object to be selected relative to the positive ideal solution, optimizing the reliability promotion object according to the size of R (i), and outputting an optimized result to the storage module.
Fig. 2 is an index system diagram of the reliability improvement demand evaluation of the power utilization of the intelligent power distribution network mentioned in step S1. The concrete contents are as follows:
the primary indexes of the demand evaluation index system for improving the power utilization reliability of the intelligent power distribution network comprise a power grid power supply reliability index, a user power utilization experience index and a modification engineering economy index. The power supply reliability indexes of the power grid comprise 3 secondary indexes: island adequacy probability, load transfer efficiency and power supply reliability index completion rate; the user electricity utilization experience indexes comprise 4 secondary indexes: the electricity utilization reliability, the electricity utilization satisfaction, the user load grade and the voltage qualification rate; the economic indexes of the reconstruction project comprise 2 secondary indexes: investment, operation and maintenance cost and electricity generation ratio.
Fig. 3 is a system structure diagram of an optimal system for improving an object of power utilization reliability of an intelligent power distribution network, which specifically includes: the data acquisition module is communicated with a background production management system and a marketing information management system of a power grid enterprise, acquires data of corresponding indexes of each intelligent power distribution network to be selected, and can also manually input part of index data by power supply network workers; the evaluation module evaluates the power utilization reliability improvement requirements of each to-be-selected intelligent power distribution network by using a comprehensive evaluation method based on an improved approximation idea and grey correlation analysis, and preferably selects the intelligent power distribution network which needs to be subjected to power utilization reliability improvement most; and the storage module stores the data and the evaluation result.
Examples
The following further description is made by combining examples, and 5 novel intelligent power distribution networks in a certain area are selected as research objects to perform implementation analysis of the invention.
Firstly, an evaluation index system for improving the power utilization reliability of the intelligent power distribution network is established from three aspects of power supply reliability of the power grid, power utilization experience of users and economy of transformation engineering. Wherein, the grid power supply reliability index includes 3 second grade indexes: island adequacy probability, load transfer efficiency and power supply reliability index completion rate; the user electricity utilization experience indexes comprise 4 secondary indexes: the electricity utilization reliability, the electricity utilization satisfaction, the user load grade and the voltage qualification rate; the economic indexes of the reconstruction project comprise 2 secondary indexes: investment, operation and maintenance cost and electricity generation ratio.
The statistical results of the original data of each index of the 5 novel intelligent power distribution networks are shown in table 2:
table 2 original index data of each distribution network
Normalizing and dimensionless the index values of all the objects to be selected to form a normalized index matrix as follows:
then, subjective weight values of the indexes are obtained by adopting an AHP method, objective weight values of the indexes are obtained by adopting an improved entropy weight method, comprehensive weight values of the indexes are obtained and are compared with a traditional entropy weight method, and the results are shown in a table 3:
TABLE 3 comparison between different empowerment methods
Secondly, the weighted normalized index matrix is obtained by calculation as follows:
determining a positive absolute ideal solution and a negative absolute ideal solution, calculating the gray correlation degree of each object to be selected, and then calculating the relative closeness degree R (i) of each object to be selected relative to the positive ideal solution. Meanwhile, in order to verify the effect of introducing the absolute ideal solution, the smart distribution network 5 is deleted, and the remaining 4 smart distribution networks are re-evaluated, with the result shown in fig. 3.
On the other hand, in order to verify the accuracy of the gray correlation degree used in the present invention, the embodiment compares the gray correlation analysis method with the proximity degree results calculated by the euclidean distance method and the projection method, as shown in fig. 4.
As can be seen from table 3, the weight of each index is determined by the conventional entropy weight method completely according to the distribution condition of the original data, and for the index with a small difference among 4 data, such as the completion rate of the reliable power supply index, the reliability rate of power utilization, the satisfaction degree of power utilization, the qualification rate of voltage and the like, the given weight is obviously smaller, while the weight occupied by the two indexes, such as the island adequacy and the user load, is too large, and deviates from the practical experience. Improving the entropy weight method can better compensate for this deficiency. The comprehensive weighting method provided by the invention combines the advantages of the AHP method and the improved entropy weight method, and gives higher weight to the indexes which can reflect the electricity utilization experience and the electricity utilization reliability of the user, such as the investment, operation and maintenance cost, the load grade of the user, the completion rate of the power supply reliability index, the island adequacy probability and the like, which are important factors in the actual engineering, so that the distribution condition of the original data is considered, and the actual operation experience of experts is also considered.
In the power distribution network reliability improvement project based on the power supply reliability rate, the preference sequence of reliability improvement of each power distribution network is ⑤ > ④ > ③ > ② > ①, however, similar to a power distribution network of ④, the user load level is not high, the power generation rate is low, the investment, operation and maintenance cost is high, and from the actual start of the power distribution network, it is obviously unreasonable to refer the priority level to a higher position.
From fig. 4, before the distribution network 5 is not deleted, the preferred sequence of the power utilization reliability improvement of each distribution network obtained based on the traditional TOPSIS method of the relatively ideal solution is ② > ⑤ > ① > ④ > ③, after the distribution network 5 is deleted, the preferred sequence is ② > ① > ③ > ④, before and after adjustment, the preferred sequence of the distribution network 3 and the distribution network 4 is reversed, the phenomenon of reverse sorting is generated, and the accuracy of the evaluation result is directly influenced.
As shown in fig. 5, in the case of introducing an absolute ideal solution, the closeness degree rank obtained by calculation by the euclidean distance method is ⑤ > ② > ① > ③ > ④, and the closeness degree ranks obtained by calculation by the gray correlation analysis method and the projection method are consistent, and are ② > ⑤ > ① > ③ > ④, which indicates that the gray correlation analysis method is more accurate in calculating the closeness degree than the euclidean distance method.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (6)
1. The utility model provides a preferred system of object is promoted to smart power distribution network power consumption reliability which characterized in that contains:
the data acquisition module is used for communicating with a background production management system and a marketing information management system of a power grid enterprise and acquiring data of corresponding indexes of each intelligent power distribution network to be selected or manually inputting part of index data by power supply network workers;
the evaluation module evaluates the power utilization reliability improvement requirements of each intelligent power distribution network to be selected by utilizing a comprehensive evaluation method based on an improved approximation ideal method (TOPSIS) and gray correlation analysis, and preferably selects the intelligent power distribution network which needs the most power utilization reliability improvement;
and the storage module stores the data and the evaluation result.
2. The method for optimizing the system for improving the electricity reliability of the intelligent power distribution network according to claim 1 is characterized by comprising the following steps:
s1, establishing an intelligent power distribution network power utilization reliability improvement demand evaluation index system from three aspects of power distribution network power supply reliability, user power utilization experience and engineering economy improvement;
s2, the data acquisition module acquires data of corresponding indexes through a power grid background production management system, a marketing information management system and a manual input mode, and stores the data in the storage module;
s3, the evaluation module calls the data of the corresponding indexes, and normalizes and dimensionless the index values of all the objects to be selected to form a normalized index matrix;
s4, the evaluation module obtains the subjective weight value of each index by adopting an Analytic Hierarchy Process (AHP), obtains the objective weight value of each index by adopting an improved entropy weight method, and obtains the comprehensive weight value of each index;
s5, the evaluation module calculates a weighted normalized index matrix, determines positive and negative absolute ideal solutions, and calculates the grey correlation degree of each object to be selected;
s6, the evaluation module calculates the relative closeness degree R (i) of each object to be selected relative to the positive ideal solution, selects the reliability improving object according to the size of R (i), and outputs the optimized result to the storage module.
3. The preferred method of claim 2, wherein: the evaluation index system for the power utilization reliability improvement demand of the intelligent power distribution network, which is mentioned in the step S1, comprises the following indexes:
the first-level indexes comprise a power grid power supply reliability index, a user power consumption experience index and a modification engineering economy index; the power supply reliability indexes of the power grid comprise 3 secondary indexes: island adequacy probability, load transfer efficiency and power supply reliability index completion rate; the user electricity utilization experience indexes comprise 4 secondary indexes: the electricity utilization reliability, the electricity utilization satisfaction, the user load grade and the voltage qualification rate; the economic indexes of the reconstruction project comprise 2 secondary indexes: investment, operation and maintenance cost and electricity generation ratio.
4. The preferred method of claim 2, wherein: the step S4 specifically includes:
s401: the subjective weight value of each index is obtained by adopting an AHP method,
and comparing the importance of each index pairwise by adopting three scales to establish a comparison matrix A:
wherein,
constructing judgment matrix C ═ C by range methodij)n×n：
In the formula, riIs the sum of the elements of each row of matrix A; c. CbIs a constant value determined by the relative importance of the range element pair under certain criteria, R-Rmax-rmin，rmax＝max(r1，r2，…，rn)，rmin＝min(r1，r2，…，rn)；
Obtaining the maximum characteristic value of the judgment matrix C, carrying out consistency check, introducing a compatibility index CI to check the consistency of the judgment matrix, wherein:
CI＝(λmax-n)/(n-1)
when CI is less than 0.1, the consistency performance of the judgment matrix is accepted; when the CI is larger than 0.1, the judgment matrix is modified again, and then the weight of the modified matrix is recalculated and consistency check is carried out;
when the maximum eigenvalue of the judgment matrix passes consistency check, calculating eigenvector omega 'corresponding to the maximum eigenvalue'A＝(ω’A1，ω’A2，…ω’An) The normalization processing is carried out to obtain the relative weight omega of each index of a certain level relative to the index of the previous levelA＝(ωA1，ωA2，…ωAn)；
S402: an improved entropy weight method is adopted to obtain objective weight values of all indexes,
m objects to be selected exist, n evaluation indexes exist, and for the index j, the characteristic proportion of the object to be selected i is as follows:
the entropy of index j is:
the objective weight of index j is then:
in the formula,is the average of all entropy values other than 1;
s403: the comprehensive weight value of each index is as follows:
ωj＝φωAj+(1-φ)ωEj，
comprehensively considering the characteristics of two weighting methods, and taking phi to be 0.5;
normalizing the weight vector omega, wherein
5. The preferred method of claim 2, wherein: step S5 includes the following steps:
s501: obtaining a weighted normalized matrix B ═ B through the normalized matrix and the standard comprehensive weight vectorij]m×nWherein:
s502: the positive and negative ideal solution data sequences of the ideal object are respectively defined as:
s503: calculating the grey correlation degree, wherein the calculation method of the grey correlation degree comprises the following steps:
in the formula,ρ is a resolution coefficient.
6. The preferred method of claim 2, wherein: the step S6 specifically includes:
s601: calculating relative closeness, and calculating the relative closeness R (i) of each object to be selected relative to a positive ideal solution based on gray correlation analysis:
the relative closeness reflects the closeness degree of the object to be selected and the positive ideal object or the negative ideal object on the situation change; and (e) sorting the objects to be selected according to the size of the R (i), preferably selecting the objects, and outputting the preferred result to a storage module.
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