CN106952047A - A kind of data processing method and device for electric grid investment benefit - Google Patents

A kind of data processing method and device for electric grid investment benefit Download PDF

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Publication number
CN106952047A
CN106952047A CN201710184290.3A CN201710184290A CN106952047A CN 106952047 A CN106952047 A CN 106952047A CN 201710184290 A CN201710184290 A CN 201710184290A CN 106952047 A CN106952047 A CN 106952047A
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index
value
level index
level
result
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刘道新
张凯
郭利杰
贾善杰
张启立
梁富兴
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State Grid Corp of China SGCC
State Grid Information and Telecommunication Co Ltd
Beijing Guodiantong Network Technology Co Ltd
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Information and Telecommunication Co Ltd
Beijing China Power Information Technology Co Ltd
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Priority to CN201710184290.3A priority Critical patent/CN106952047A/en
Publication of CN106952047A publication Critical patent/CN106952047A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention provides a kind of data processing method and device for electric grid investment benefit, pre-establish the electric grid investment benefit assessment indicator system for including three level indexs, and to each index assignment respective weights value, it is weighted by the way that the respective scoring fraction of multiple three-level indexs will be corresponded to, multiple two-level index results comprising these three-level indexs can be obtained, two-level index result is weighted afterwards, multiple first class index results can be obtained, and then first class index result is weighted again, to obtain electric grid investment benefit evaluation result, it can be seen that, input-occupancy-output analysis is associated with output index and merged, and the size of electric grid investment benefit influence power is meticulously referred in different levels index according to the index after association, comprehensively the indices in power network actual motion can be brought into electric grid investment benefit assessment indicator system, the accuracy of evaluation result is lifted based on the relationship between superior and subordinate and computation sequence of different index levels simultaneously.

Description

A kind of data processing method and device for electric grid investment benefit
Technical field
In particular, it is related at a kind of data for electric grid investment benefit the present invention relates to technical field of electric power Manage method and device.
Background technology
Power grid enterprises have irreplaceable important position as the infrastructural industries of China in the development of national economy Put.With the fast development of power grid construction, power grid enterprises increase year by year for the investment of power grid construction, therefore to power grid construction Mesh carries out analysis of Investment Benefit and evaluated increasingly to be paid close attention to by power grid enterprises.
At present, it is mainly for the method that electric grid investment benefit is evaluated and pre-establishes electric grid investment benefit evaluation index Evaluation index in system, the system includes input-occupancy-output analysis and the class evaluation index of output index two, to contained every in the system One evaluation index carries out weight assignment, and each evaluation index data are collected afterwards and are handled, then by the input-occupancy-output analysis number after processing Each be weighted according to output achievement data, obtain respectively the system output score and input score, and then by this Two scores are divided by, to calculate the evaluation result of the electric grid investment benefit assessment indicator system.However, existing evaluation method Although have chosen multinomial evaluation index, the entirety point of evaluation index from dimensions such as power grid security, service efficiency, economic benefits Class is still not careful enough, comprehensive, and then makes the evaluation result of acquisition be difficult to more truly reflect electric grid investment benefit, is unfavorable for Power grid enterprises' depth excavates the irrationality of electric grid investment.
The content of the invention
In view of this, the invention provides a kind of data processing method and device for electric grid investment benefit, Neng Gouquan The indices for reflecting asset utilization ratio in power grid enterprises' actual motion from different perspectives are included to face into electric grid investment benefit to comment In valency index system, make evaluation result more accurate, and then more reasonable, sections are provided for power grid enterprises' raising own operations ability Decision-making.
To achieve the above object, the present invention provides following technical scheme：
A kind of data processing method for electric grid investment benefit, pre-establishes electric grid investment benefit assessment indicator system, The electric grid investment benefit assessment indicator system includes multiple first class index, and each described first class index refers to comprising multiple two grades Mark, each described two-level index includes multiple three-level indexs, and each described first class index, two-level index and three-level index are equal Corresponding determination weighted value is assigned, including：
N power network sample data of target grid is obtained, each described power network sample data is thrown corresponding to the power network A three-level index in benefit appraisal index system is provided, wherein n is positive integer；
The n power network sample datas are subjected to nondimensionalization processing, n three-level index initial value is obtained；
The n three-level index initial values are substituted into the score function with respective corresponding three-level index respectively, n is obtained Individual scoring fraction, the score function is pre-established；
The n scoring fractions are weighted, m two-level index result is obtained, wherein m is less than n, and m is just whole Number；
The m two-level index results are weighted, k first class index result is obtained, wherein, k is less than m, and k is Positive integer；
The k first class index results are weighted, electric grid investment benefit evaluation result is obtained.
Preferably, to first class index each described, two-level index and the corresponding weighted value that determines of three-level index assignment Process includes：
Weight assignment is carried out to weight index undetermined using Delphi method, the first weighted value is obtained, the weight undetermined refers to It is designated as any first class index, two-level index or three-level index；
Weight assignment is carried out to the weight index undetermined using analytic hierarchy process (AHP), the second weighted value is obtained；
Weight assignment is carried out to the weight index undetermined using entropy assessment, the 3rd weighted value is obtained；
First weighted value, the second weighted value and the 3rd weighted value are multiplied, the weight index undetermined is obtained Corresponding determination weighted value.
Preferably, pre-establishing the process of the score function of each three-level index includes：
The first score value corresponding to the maximum of acquisition target three-level index, the second score value corresponding to standard value, And the 3rd score value corresponding to minimum value, the target three-level index is any three-level index；
Calculate maximum, standard value and the minimum value of the target three-level index；
First score value and the maximum are subjected to combinatorial coordinates, the first coordinate value is obtained；
Second score value and the standard value are subjected to combinatorial coordinates, the second coordinate value is obtained；
3rd score value and the minimum value are subjected to combinatorial coordinates, the 3rd coordinate value is obtained；
First coordinate value, the second coordinate value and the 3rd coordinate value are substituted into quadratic function, the target three-level is obtained The score function of index, wherein, the quadratic function is y=ax2+ bx+c, x are the maximum of the target three-level index, mark Quasi- value or minimum value, y are the score value of the target three-level index, and a is secondary term coefficient, and b is Monomial coefficient, and c is random Error term.
Preferably, maximum, standard value and the minimum value for calculating the target three-level index, including：
First object matrix is set up, the first object matrix presets the power network sample in the time limit comprising the objective area Target three-level index initial value corresponding to data；
Irrational target three-level index initial value in the first object matrix is deleted, the second objective matrix is obtained；
Size according to the target three-level index initial value in second objective matrix is grouped, and obtains L packet Scope, L is positive integer；
Calculate frequency of the target three-level index initial value in second objective matrix in each packet scope；
Maximum, standard value and the minimum value of the target three-level index are calculated using the frequency.
Preferably, after the acquisition electric grid investment benefit evaluation result, also include：
Obtain maximum first class index result or minimum first class index result in the k first class index results；
When getting the maximum first class index result, two grades of the maximum of the correspondence maximum first class index result is obtained Index result；
Obtain the scoring fraction of the maximum three-level index of the correspondence maximum two-level index result；
Show the scoring fraction and the three-level index of the maximum three-level index；
When getting the minimum first class index result, the most young waiter in a wineshop or an inn level of the correspondence minimum first class index result is obtained Index result；
Obtain the scoring fraction of the minimum three-level index of the correspondence minimum two-level index result；
Show the scoring fraction and the three-level index of the minimum three-level index.
A kind of data processing equipment for electric grid investment benefit, including：
First sets up module, and for setting up electric grid investment benefit assessment indicator system, the electric grid investment benefit evaluation refers to Mark system includes multiple first class index, and each described first class index includes multiple two-level index, each described two-level index Include multiple three-level indexs；
First weight assignment module, for first class index, two-level index and the equal assignment pair of three-level index each described The determination weighted value answered；
First acquisition module, the n power network sample data for obtaining target grid, each described power network sample data Corresponding to a three-level index in the electric grid investment benefit assessment indicator system, wherein n is positive integer；
Nondimensionalization processing module, for the n power network sample datas to be carried out into nondimensionalization processing, obtains n three Level index initial value；
Score fraction computing module, for n three-level index initial values to be substituted into and each corresponding three respectively The score function of level index, obtains n scoring fraction, and the score function is pre-established；
First weighting block, for the n scoring fractions to be weighted, obtains m two-level index result, its Middle m is less than n, and m is positive integer；
Second weighting block, for the m two-level index results to be weighted, obtains k first class index knot Really, wherein, k be less than m, k is positive integer；
3rd weighting block, for the k first class index results to be weighted, obtains electric grid investment benefit and comments Valency result.
Preferably, the first weight assignment module includes：
Second weight assignment module, for carrying out weight assignment to weight index undetermined using Delphi method, obtains first Weighted value, the weight index undetermined is any first class index, two-level index or three-level index；
3rd weight assignment module, for carrying out weight assignment to the weight index undetermined using analytic hierarchy process (AHP), is obtained Obtain the second weighted value；
4th weight assignment module, for carrying out weight assignment to the weight index undetermined using entropy assessment, obtains the Three weighted values；
Weight computation module, for first weighted value, the second weighted value and the 3rd weighted value to be multiplied, is obtained The corresponding determination weighted value of the weight index undetermined.
Preferably, described device also includes：
Score acquisition module, for obtaining the first score value corresponding to the maximum of target three-level index, standard value institute Corresponding second score value, and the 3rd score value corresponding to minimum value, the target three-level index are any three-level Index；
Index value computing module, maximum, standard value and minimum value for calculating the target three-level index；
First coordinate combination module, for first score value and the maximum to be carried out into combinatorial coordinates, obtains the One coordinate value；
Second coordinate combination module, for second score value and the standard value to be carried out into combinatorial coordinates, obtains the Two coordinate values；
3rd coordinate combination module, for the 3rd score value and the minimum value to be carried out into combinatorial coordinates, obtains the Three coordinate values；
Score function computing module, it is secondary for first coordinate value, the second coordinate value and the 3rd coordinate value to be substituted into Function, obtains the score function of the target three-level index, wherein, the quadratic function is y=ax2+ bx+c, x are the mesh Maximum, standard value or the minimum value of three-level index are marked, y is the score value of the target three-level index, and a is secondary term coefficient, b For Monomial coefficient, c is stochastic error.
Preferably, the index value computing module includes：
Second sets up module, and for setting up first object matrix, the first object matrix is pre- comprising the objective area If the target three-level index initial value corresponding to the power network sample data in the time limit；
Removing module, for deleting irrational target three-level index initial value in the first object matrix, obtains the Two objective matrixs；
Grouping module, is divided for the size according to the target three-level index initial value in second objective matrix Group, obtains L packet scope, L is positive integer；
Frequency computing module, for calculating the target three-level index initial value in second objective matrix in each institute State the frequency of packet scope；
Index value calculating sub module, maximum, mark for calculating the target three-level index using the frequency Quasi- value and minimum value.
Preferably, in the 3rd weighting block, obtain after electric grid investment benefit evaluation result, also include：
Second acquisition module, for obtaining maximum first class index result or minimum one-level in the k first class index results Index result；
3rd acquisition module, for when second acquisition module gets the maximum first class index result, obtaining The maximum two-level index result of the correspondence maximum first class index result；
4th acquisition module, the scoring point of the maximum three-level index for obtaining the correspondence maximum two-level index result Number；
First display module, scoring fraction and the three-level index for showing the maximum three-level index；
5th acquisition module, for when second acquisition module gets the minimum first class index result, obtaining The minimum two-level index result of the correspondence minimum first class index result；
6th acquisition module, the scoring point of the minimum three-level index for obtaining the correspondence minimum two-level index result Number；
Second display module, scoring fraction and the three-level index for showing the minimum three-level index.
Understood via above-mentioned technical scheme, compared with prior art, the invention provides one kind for electric grid investment effect The data processing method and device of benefit, pre-establish electric grid investment benefit assessment indicator system, the electric grid investment benefit evaluation refers to Mark system includes multiple first class index, and each first class index includes multiple two-level index, and each two-level index is comprising multiple Three-level index, each first class index, two-level index and three-level index are assigned corresponding determination weighted value, by that will correspond to It is weighted in multiple scoring fractions of multiple three-level indexs, multiple two grades comprising these three-level indexs can be obtained and referred to Result is marked, multiple two-level index results are weighted afterwards, multiple one-levels comprising these two-level index can be obtained Index result, and then multiple first class index results are weighted again, can to obtain electric grid investment benefit evaluation result See, input-occupancy-output analysis is associated with output index and merged, and according to the index after association for electric grid investment benefit influence power Size be meticulously referred in first class index, two-level index and three-level index, can be comprehensively actual by power grid enterprises Operating indices bring electric grid investment benefit assessment indicator system into, meanwhile, based on upper between different index levels Inferior relation and the sequencing of calculating, can make evaluation result more reasonable, science, more truly reflect electric grid investment Benefit.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this The embodiment of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis The accompanying drawing of offer obtains other accompanying drawings.
Fig. 1 is a kind of method flow of data processing method for electric grid investment benefit provided in an embodiment of the present invention Figure；
Fig. 2 is the method flow of another data processing method for electric grid investment benefit provided in an embodiment of the present invention Figure；
Fig. 3 is provided in an embodiment of the present invention a kind of to index assignment weight contained by electric grid investment benefit assessment indicator system The method flow diagram of method；
Fig. 4 is a kind of method flow diagram of the method for building up of the score function of three-level index provided in an embodiment of the present invention；
Fig. 5 is a kind of structural representation of data processing equipment for electric grid investment benefit provided in an embodiment of the present invention Figure；
Fig. 6 is the structural representation of another data processing equipment for electric grid investment benefit provided in an embodiment of the present invention Figure；
Fig. 7 is provided in an embodiment of the present invention a kind of to index assignment weight contained by electric grid investment benefit assessment indicator system The structural representation of device；
Fig. 8 is the structural representation of another data processing equipment for electric grid investment benefit provided in an embodiment of the present invention Figure.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of creative work is not made Embodiment, belongs to the scope of protection of the invention.
The present invention, can be with a variety of moneys of comprehensive monitoring power grid enterprises by pre-establishing electric grid investment benefit assessment indicator system The distribution situation of source input, and then know the evaluation result of electric grid investment benefit, it is each power grid enterprises' enhancing input and output consciousness Technical support is provided with benefit consciousness.
During the foundation of electric grid investment benefit assessment indicator system, with electric grid investment management system and development strategy mesh Guiding principle is designated as, and the crucial input-occupancy-output analysis and output index of influence power grid enterprises returns of investment are determined by component voltage grade, and Using critical success factor decomposition method, set up from qualitative and quantitative angle between both input-occupancy-output analysis and output index Incidence relation, and then the input-output evaluation of urban index set up out in electric grid investment benefit assessment indicator system, i.e. first class index, two Level index and three-level index, specific manifestation form is as shown in table 1 below, wherein, first class index, two-level index and three-level index this three According to the size to electric grid investment benefit influence power between person, and above and below setting out hierarchical relationship and calculate when priority it is suitable Sequence；
Afterwards, then according to size of the index contained by each level to electric grid investment benefit influence power, respectively to the electric grid investment The corresponding determination weight of each first class index, two-level index and three-level index assignment that benefit appraisal index system is included Value so that when obtaining evaluation result using electric grid investment benefit assessment indicator system, can be by the different brackets that constructs Index carry out the accuracy and reasonability final to lift evaluation result of computing layer by layer.
Voltage class involved by the embodiment of the present invention can include：750KV and the above, 220KV, 110KV, 35KV, 10KV and following.
The crucial input-occupancy-output analysis of influence power grid enterprises returns of investment can include：Line maintenance is invested, substation equipment maintenance and repair investment, Circuit investment in technological upgrading, power transformation investment in technological upgrading solves equipment heavy duty, overload investment, meets newly-increased load power reguirements investment, eliminates Equipment safety hidden danger is invested, and strengthens grid structure investment, is met supporting send out of transformer station and is invested, meets plant-grid connection investment, intelligence Energyization investment etc..
The crucial output index of influence power grid enterprises returns of investment can include：Supplying power allocation ability, power supply quality, power network Efficiency, comprehensive benefit, electric network composition, equipment, intelligent level etc..
Table 1：Electric grid investment benefit assessment indicator system
The embodiment of the invention discloses a kind of data processing method for electric grid investment benefit, accompanying drawing 1 is referred to, it is described Method specifically includes following steps：
S101：N power network sample data of target grid is obtained, each described power network sample data corresponds to the electricity A three-level index in net Evaluation of Investment-Benefit index system, wherein n is positive integer；
Specifically, after electric grid investment benefit assessment indicator system is established, can directly utilize the power network established Evaluation of Investment-Benefit index system carries out the evaluation of returns of investment to multiple regional power grid enterprises, to know that corresponding power network is thrown Provide benefit evaluation result.Therefore, after the power grid enterprises of Evaluation of Investment-Benefit are carried out needed for determining this, first by the power network Enterprise, according to the three-level index in electric grid investment benefit assessment indicator system, obtains these corresponding indexs as target grid Power network sample data, wherein it is possible to according to it is required progress Evaluation of Investment-Benefit mainly for certain resource input situation, to select The power network sample data of some three-level indexs of correspondence is obtained to selecting property, with the data instance in table 1, as this is needed to target electricity When the power supply quality of net carries out Evaluation of Investment-Benefit, then only obtaining the target grid, " specific investment cost is used corresponding to three-level index Family annual reduction power off time ", " specific investment cost user annual reduces frequency of power cut ", " specific investment cost solution feeder ear electricity Press out-of-limit problem number " related power network sample data to " specific investment cost reduce receiving end " low-voltage " number of users ".
S102：The n power network sample datas are subjected to nondimensionalization processing, n three-level index initial value is obtained；
Specifically, because each three-level index may have respective unit and the order of magnitude, it is therefore desirable to getting Multiple power network sample datas corresponding to three-level index after, make these power network sample datas according to nondimensionalization processing formula enter Row nondimensionalization processing, nondimensionalization handles formula and is：
Wherein, xijFor power network sample data,For three-level index initial value, Mj=max { xij},mj=min { xij}。
S103：The n three-level index initial values are substituted into the score function with respective corresponding three-level index respectively, N scoring fraction is obtained, the score function is pre-established；
Specifically, by the three-level index initial value obtained has a dynamic random, therefore score function can be introduced, Three-level index initial value is converted to corresponding scoring fraction to realize quantitative analysis.
S104：The n scoring fractions are weighted, m two-level index result are obtained, wherein m is less than n, and m is Positive integer；
Specifically, making the weight for being multiplied by its corresponding three-level index respectively corresponding to each scoring fraction of three-level index Value, according still further to the three-level index contained by two-level index, optionally carries out sum operation, to obtain by result resulting after multiplication Obtain corresponding two-level index result.Still with the data instance in table 1, the scoring fraction of the three-level index such as obtained includes：" unit 70 points of power off time of investment user annual reduction ", " specific investment cost user annual reduces 60 points of frequency of power cut ", " unit throwing Money, which increases, supplies 100 points of load ", " specific investment cost increase 88 points of electricity sales amount ", because " specific investment cost user annual is reduced in scoring fraction 70 points of power off time " and " specific investment cost user annual reduces 60 points of frequency of power cut " correspond to same two-level index " unit Investment power supply ", and the fraction that scores " specific investment cost, which increases, supplies 100 points of load ", " specific investment cost increases 88 points of electricity sales amount " correspond to two grades Index " lifting of specific investment cost benefit ", therefore will " specific investment cost user annual reduces 70 points of power off time ", " specific investment cost use Family annual reduces 60 points of frequency of power cut " addition after the weight of its each self-corresponding three-level index is multiplied by, to obtain two-level index The two-level index result of " specific investment cost is powered ", will " specific investment cost, which increases, supplies 100 points of load ", " specific investment cost increases 88 points of electricity sales amount " It is multiplied by after the weight of its each self-corresponding three-level index and is added, is referred to two grades that obtain two-level index " lifting of specific investment cost benefit " Mark result.
S105：The m two-level index results are weighted, k first class index result is obtained, wherein, k is less than M, k are positive integer；
Specifically, making each two-level index result corresponding to two-level index be multiplied by its corresponding two-level index respectively Result resulting after multiplication is optionally carried out addition fortune by weighted value, the two-level index included according still further to first class index Calculate, to obtain corresponding first class index result.
S106：The k first class index results are weighted, electric grid investment benefit evaluation result is obtained；
Specifically, making each first class index result corresponding to first class index be multiplied by its corresponding first class index respectively Weighted value, the final numerical value of gained carries out the evaluation result of electric grid investment benefit assessment for this.
In the disclosed data processing method for electric grid investment benefit of the embodiment of the present invention, electric grid investment effect is pre-established Beneficial assessment indicator system, and determine to weigh to each index assignment contained by the electric grid investment benefit assessment indicator system is corresponding Weight values, by the way that multiple scoring fractions corresponding to multiple three-level indexs are weighted, can be obtained comprising these three-levels Multiple two-level index results of index, multiple two-level index results are weighted afterwards, can obtain comprising these two Multiple first class index results of level index, and then multiple first class index results are weighted again, to obtain electric grid investment Benefit evaluation result, so as to based on by input-occupancy-output analysis and output index association merge after constructed by first class index, two grades Index and three-level index, are realized the indices in power grid enterprises' actual motion according to it to electric grid investment benefit influence power Include to size reasonable in electric grid investment benefit assessment indicator system, meanwhile, closed using the superior and the subordinate between different index levels System and the sequencing calculated, can improve the reasonability and science of evaluation result, more can truly reflect Electric grid investment benefit.
The embodiment of the invention discloses another data processing method for electric grid investment benefit, accompanying drawing 2, institute are referred to The method of stating specifically includes following steps：
S201：N power network sample data of target grid is obtained, each described power network sample data corresponds to the electricity A three-level index in net Evaluation of Investment-Benefit index system, wherein n is positive integer.
S202：The n power network sample datas are subjected to nondimensionalization processing, n three-level index initial value is obtained.
S203：The n three-level index initial values are substituted into the score function with respective corresponding three-level index respectively, N scoring fraction is obtained, the score function is pre-established.
S204：The n scoring fractions are weighted, m two-level index result are obtained, wherein m is less than n, and m is Positive integer.
S205：The m two-level index results are weighted, k first class index result is obtained, wherein, k is less than M, k are positive integer.
S206：The k first class index results are weighted, electric grid investment benefit evaluation result is obtained.
S207：Maximum first class index result or minimum first class index result in the k first class index results are obtained, if obtaining What is taken is maximum first class index result, then performs S208, if what is obtained is minimum first class index result, performs S209；
Specifically, after calculating obtains final electric grid investment benefit evaluation result, we can be by the electric grid investment The size of benefit evaluation result value weighs power grid enterprises efficiency input in terms of some or all evaluation index, effect Fruit and benefit, but can not but be learnt for the specific targets for causing the electric grid investment benefit evaluation result numerical value big or small, And then investment decision can not be formulated for the specific targets, distribute ability rationally with preferably lift power network resources.Therefore, need The first class index result for causing the electric grid investment benefit evaluation result numerical value big or small is further determined that out, meanwhile, may be used also So that the maximum first class index result got or minimum first class index result to be shown.
S208：The maximum two-level index result of the correspondence maximum first class index result is obtained, and performs S2010；
Specifically, when getting maximum first class index result, can be according to the layer between first class index and two-level index Level relation and operation relation, is being weighted during computing draws multiple two-level index results of maximum first class index result, it is determined that Go out the maximum two-level index result of numerical value, i.e., maximum two-level index result, so as to obtain what is more refined compared with first class index Two-level index, at the same time it can also which the maximum two-level index result got is shown.
S209：The minimum two-level index result of the correspondence minimum first class index result is obtained, and performs S2011；
Specifically, when getting minimum first class index result, can be according to the layer between first class index and two-level index Level relation and operation relation, is being weighted during computing draws multiple two-level index results of minimum first class index result, it is determined that Go out the minimum two-level index result of numerical value, i.e., minimum two-level index result, so as to obtain what is more refined compared with first class index Two-level index, at the same time it can also which the minimum two-level index result got is shown.
S2010：The scoring fraction of the maximum three-level index of the correspondence maximum two-level index result is obtained, and is performed S2012；
Specifically, multiple two-level index results can be calculated because the scoring fraction of three-level index is weighted computing, Therefore, it is possible to according to the maximum two-level index result determined, further determine that out the multiple of the composition maximum two-level index result The scoring fraction of a maximum three-level index in the scoring fraction of three-level index.
S2011：The scoring fraction of the minimum three-level index of the correspondence minimum two-level index result is obtained, and is performed S2013；
Specifically, multiple two-level index results can be calculated because the scoring fraction of three-level index is weighted computing, Therefore, it is possible to according to the minimum two-level index result determined, further determine that out the multiple of the composition minimum two-level index result The scoring fraction of a minimum three-level index in the scoring fraction of three-level index.
S2012：Show the scoring fraction and the three-level index of the maximum three-level index；
S2013：Show the scoring fraction and the three-level index of the minimum three-level index.
In the disclosed data processing method for electric grid investment benefit of the embodiment of the present invention, by getting power network throwing Provide after benefit evaluation result, be weighted computing and draw multiple first class index results of the electric grid investment benefit evaluation result In, maximum first class index result or minimum first class index result are determined, and according to hierarchical relationship and operation relation, obtain successively To the scoring fraction of maximum two-level index result corresponding with maximum first class index result and maximum three-level index, or with minimum The scoring fraction of the corresponding minimum two-level index result of first class index result and minimum three-level index, afterwards by maximum three-level index Scoring fraction and three-level index shown, or the scoring fraction and three-level index of minimum three-level index are shown, And then can quickly know the specific evaluation index for causing electric grid investment benefit evaluation result larger or smaller, so as to for The evaluation index formulates corresponding modification strategy, it is to avoid the irrationality of electric grid investment.
The each of being included to electric grid investment benefit assessment indicator system of being provided in accompanying drawing 3, above-described embodiment is provided Individual first class index, two-level index and the corresponding process that implements for determining weighted value of three-level index assignment comprise the following steps：
S301：Weight assignment is carried out to weight index undetermined using Delphi method, the first weighted value, the power undetermined is obtained Weight index is any first class index, two-level index or three-level index；
Specifically, because the determination of index weightses directly influences the size of evaluation result, it is therefore desirable to which selection is suitable Tax power method.The embodiment of the present invention is utilizing Delphi method to any one in first class index, two-level index or three-level index , can be first according to the different experts got to multiple power given by the weight index undetermined when individual index carries out weight assignment Weight values are calculated, and obtain the average and standard deviation of the weight index undetermined, and in the multiple weighted values for judging to be obtained and its When the deviation of the average calculated is less than or equal to standard value set in advance, the average currently calculated is regard as the weight undetermined First weighted value of index, otherwise just obtains multiple weights that different experts provide again to the weight index undetermined again Value.
S302：Weight assignment is carried out to the weight index undetermined using analytic hierarchy process (AHP), the second weighted value is obtained；
Specifically, utilizing analytic hierarchy process (AHP) to any one index in first class index, two-level index or three-level index When carrying out weight assignment, a Primary Judgement Matrix A=(a can be first set upij)n×n, then according to Primary Judgement Matrix foundation Antisymmetric matrix B=(bij)n×n, bij=lgaij(i, j=1,2 ..., n), the optimal transmission of antisymmetric matrix is calculated afterwards MatrixAt this point it is possible to by the element a in Primary Judgement MatrixijReplace with a*ij, And then obtain judgment matrixProduct root method is finally recycled to calculate one The second weighted value corresponding to any one index in level index, two-level index or three-level index, wherein, the second weighted value Computing formula is：
Wherein
S303：Weight assignment is carried out to the weight index undetermined using entropy assessment, the 3rd weighted value is obtained；
Specifically, being carried out using entropy assessment to any one index in first class index, two-level index or three-level index During weight assignment, an original index data matrix X=(x can be first set upij)n×m, xijIt is target grid i on index j's Initial value, m is index number, and n is power network number, then selects from original index data matrix the optimal of each index ValueWherein, if j is direct index,For maximum, if j is inverse indicators,For minimum value, degree of approach meter is utilized afterwards Calculate formula and calculate xijWithThe degree of approach, wherein proximity computation formula is：
Obtain degree of approach matrix D=(Dij)n×m。
Then, using formula is normalized to degree of approach matrix the first normalized of progress, first, which normalizes formula, is：
Obtain matrix d=(dij)n×m, wherein, 0≤dij≤ 1,
Utilize matrix d=(dij)n×mParameter j conditional entropy Ej, computing formula is：
Wherein,
Afterwards, E is utilizedmaxTo conditional entropy EjThe second normalized is carried out, the second normalized formula is：
Obtain index j importance entropy e (dj), wherein, EmaxFor conditional entropy EjMaximum.
Finally, by importance entropy e (dj) substitute into weight computing formula：
Obtain index j weights λj, i.e. the 3rd weighted value, wherein,And λjMeet：0≤λj≤ 1,
S304：First weighted value, the second weighted value and the 3rd weighted value are multiplied, the weight undetermined is obtained The corresponding determination weighted value of index.
In the embodiment of the present invention, any one first class index, two-level index or three-level index are selected as a power undetermined Weight index, the power corresponding to the weight index undetermined is calculated by using Delphi method, analytic hierarchy process (AHP) and entropy assessment respectively Weight values, then three weighted values of gained are subjected to multiplication operation, and then result in the combining weights of the weight index undetermined, i.e., Final determination weighted value, so that Delphi method, the advantage of three kinds of weight assignment methods of analytic hierarchy process (AHP) and entropy assessment be carried out Comprehensive integration, effectively prevent the deviation because caused by subjectivity is too strong, improve the accurate of electric grid investment benefit evaluation result Property.
In the embodiment of the present invention, the weight undetermined is calculated using Delphi method, analytic hierarchy process (AHP) and entropy assessment respectively and referred to The sequencing performed is not present between the calculating process of the corresponding weighted value of mark, can concurrently and independently be calculated.
In embodiment corresponding to above-mentioned Fig. 1, the score function of each three-level index is to pre-establish, therefore this hair Bright embodiment discloses a kind of method for building up of the score function of three-level index, refers to accompanying drawing 4, methods described specifically include with Lower step：
S401：The first score value corresponding to the maximum of target three-level index is obtained, second corresponding to standard value comments Score value, and the 3rd score value corresponding to minimum value, the target three-level index are any three-level index；
For example, the first score value corresponding to the maximum of target three-level index can be set as 100 points, standard value The second corresponding score value can be set as 70 points, and the 3rd score value corresponding to minimum value can be set as 0 point.
S402：Calculate maximum, standard value and the minimum value of the target three-level index；
Specifically, can using matrix can analytic approach calculate the maximum of the target three-level index, standard value and most Small value, specific steps include：
1) objective matrix X is set up
Wherein, xijInitial value for target grid i in jth year on three-level index x, m is three-level index number, and n is electricity Net number.
2) irrational target three-level index initial value in the first object matrix is deleted, the second objective matrix is obtained；
3) it is grouped according to the size of the target three-level index initial value in second objective matrix, obtains L points Group scope, L is positive integer；
4) frequency of the target three-level index initial value in second objective matrix in each packet scope is calculated Number；
5) maximum, standard value and the minimum value of the target three-level index are calculated using the frequency.
S403：First score value and the maximum are subjected to combinatorial coordinates, the first coordinate value is obtained.
S404：Second score value and the standard value are subjected to combinatorial coordinates, the second coordinate value is obtained.
S405：3rd score value and the minimum value are subjected to combinatorial coordinates, the 3rd coordinate value is obtained.
S406：First coordinate value, the second coordinate value and the 3rd coordinate value are substituted into quadratic function, the target is obtained The score function of three-level index, wherein, the quadratic function is y=ax2+ bx+c, x are the maximum of the target three-level index Value, standard value or minimum value, y are the score value of the target three-level index, and a is secondary term coefficient, and b is Monomial coefficient, and c is Stochastic error.
Specifically, after three coordinate values are obtained respectively, by the way that these three coordinate values are substituted into quadratic function y respectively =ax2In+bx+c, secondary term coefficient a, Monomial coefficient b and stochastic error c concrete numerical value can be obtained, now, will The quadratic function y=ax that a, b, c concrete numerical value are obtained after substituting into2+ bx+c is the score function of target three-level index.
In the method for building up of the score function of three-level index disclosed in the embodiment of the present invention, pass through the target three-level determined Maximum, standard value and the minimum value of index, and it distinguishes corresponding score value, can obtain three coordinate values, and then After these three coordinate values are updated in quadratic function, the coefficient and stochastic error of the quadratic function are capable of determining that, with The score function of the target three-level index is obtained, so as to combine the actual conditions of power network returns of investment over the years, determines and comments The relation function divided between fraction and evaluation index, improves the degree of accuracy in electric grid investment benefit evaluation result calculating process.
The embodiment of the invention discloses a kind of data processing equipment for electric grid investment benefit, accompanying drawing 5 is referred to, it is described Device is specifically included：
First sets up module 501, for setting up electric grid investment benefit assessment indicator system, the electric grid investment benefit evaluation Index system includes multiple first class index, and each described first class index includes multiple two-level index, and each described two grades refer to Mark includes multiple three-level indexs；
First weight assignment module 502, for first class index, two-level index and the equal assignment of three-level index each described Corresponding determination weighted value；
First acquisition module 503, the n power network sample data for obtaining target grid, each described power network sample The three-level index that data correspond in the electric grid investment benefit assessment indicator system, wherein n is positive integer；
Nondimensionalization processing module 504, for the n power network sample datas to be carried out into nondimensionalization processing, obtains n Three-level index initial value；
Score fraction computing module 505, for by the n three-level index initial values substitute into respectively with it is each corresponding The score function of three-level index, obtains n scoring fraction, and the score function is pre-established；
First weighting block 506, for the n scoring fractions to be weighted, obtains m two-level index knot Really, wherein m is less than n, and m is positive integer；
Second weighting block 507, for the m two-level index results to be weighted, obtains k first class index As a result, wherein, k be less than m, k is positive integer；
3rd weighting block 508, for the k first class index results to be weighted, obtains electric grid investment effect Beneficial evaluation result.
In the disclosed data processing equipment for electric grid investment benefit of the embodiment of the present invention, module is set up by first in advance 501 set up electric grid investment benefit assessment indicator system, the first 502 pairs of weight assignment module electric grid investment benefit evaluation index body The contained corresponding determination weighted value of each index assignment of system, will be referred to by the first weighting block 506 corresponding to multiple three-levels The multiple scoring fractions of target are weighted, and can obtain multiple two-level index results comprising these three-level indexs, afterwards Multiple two-level index results are weighted by the second weighting block 507, can be obtained comprising many of these two-level index Individual first class index result, and then multiple first class index results are weighted the 3rd weighting block 508, to obtain power network throwing Benefit evaluation result is provided, so as to based on first class index constructed after merging input-occupancy-output analysis and the association of output index, two Level index and three-level index, are realized the indices in power grid enterprises' actual motion according to it to electric grid investment benefit influence power Size reasonable include in electric grid investment benefit assessment indicator system, meanwhile, utilize the superior and the subordinate between different index levels Relation and the sequencing of calculating, can improve the reasonability and science of evaluation result, more can truly reflect Go out electric grid investment benefit.
The course of work of modules provided in an embodiment of the present invention, refer to the method flow diagram corresponding to accompanying drawing 1, tool Body running process is repeated no more.
The embodiment of the invention discloses another data processing equipment for electric grid investment benefit, accompanying drawing 6, institute are referred to Device is stated to specifically include：
First sets up module 501, the first weight assignment module 502, the first acquisition module 503, nondimensionalization processing module 504, scoring fraction computing module 505, the first weighting block 506, the second weighting block 507, the 3rd weighting block 508, second Acquisition module 509, the 3rd acquisition module 5010, the 4th acquisition module 5011, the first display module 5012, the 5th acquisition module 5013, the 6th acquisition module 5014 and the second display module 5015；
Wherein, second acquisition module 509, for obtaining maximum first class index knot in the k first class index results Fruit or minimum first class index result；
3rd acquisition module 5010, for getting the maximum first class index result in second acquisition module 509 When, obtain the maximum two-level index result of the correspondence maximum first class index result；
4th acquisition module 5011, the scoring of the maximum three-level index for obtaining the correspondence maximum two-level index result Fraction；
First display module 5012, scoring fraction and the three-level index for showing the maximum three-level index；
5th acquisition module 5013, for getting the minimum first class index result in second acquisition module 509 When, obtain the minimum two-level index result of the correspondence minimum first class index result；
6th acquisition module 5014, the scoring of the minimum three-level index for obtaining the correspondence minimum two-level index result Fraction；
Second display module 5015, scoring fraction and the three-level index for showing the minimum three-level index.
In the disclosed data processing method for electric grid investment benefit of the embodiment of the present invention, by getting power network throwing Provide after benefit evaluation result, the electric grid investment benefit evaluation result is drawn being weighted computing by the second acquisition module 509 In multiple first class index results, determine maximum first class index result or minimum first class index result, and according to hierarchical relationship and Operation relation, is got corresponding with maximum first class index result successively by the 3rd acquisition module 5010, the 4th acquisition module 5011 Maximum two-level index result and maximum three-level index scoring fraction, or obtained by the 5th acquisition module the 5013, the 6th Module 5014 gets the scoring point of corresponding with minimum first class index result minimum two-level index result and minimum three-level index Number, is afterwards shown the scoring fraction and three-level index of maximum three-level index using the first display module 5012, or be The scoring fraction and three-level index of minimum three-level index are shown by the second display module 5015, and then can be by quick The specific evaluation index for causing electric grid investment benefit evaluation result larger or smaller is known, so as to for the evaluation index system Fixed corresponding modification strategy, it is to avoid the irrationality of electric grid investment.
The course of work of modules provided in an embodiment of the present invention, refer to the method flow diagram corresponding to accompanying drawing 2, tool Body running process is repeated no more.
Accompanying drawing 7 is referred to, in embodiment corresponding to above-mentioned Fig. 5, the first weight assignment module 502 is specifically included：
Second weight assignment module 5021, for carrying out weight assignment to weight index undetermined using Delphi method, is obtained First weighted value, the weight index undetermined is any first class index, two-level index or three-level index；
3rd weight assignment module 5022, for carrying out weight tax to the weight index undetermined using analytic hierarchy process (AHP) Value, obtains the second weighted value；
4th weight assignment module 5023, for carrying out weight assignment to the weight index undetermined using entropy assessment, is obtained Obtain the 3rd weighted value；
Weight computation module 5024, for first weighted value, the second weighted value and the 3rd weighted value to be multiplied, Obtain the corresponding determination weighted value of the weight index undetermined.
In the embodiment of the present invention, any one first class index, two-level index or three-level index are regard as a weight undetermined Index, is adopted successively by the second weight assignment module 5021, the 3rd weight assignment module 5022, the 4th weight assignment module 5023 Weighted value corresponding to calculating the weight index undetermined respectively with Delphi method, analytic hierarchy process (AHP) and entropy assessment, then by weight Three weighted values of gained are carried out multiplication operation by computing module 5024, and then result in the combined weights of the weight index undetermined Weight, i.e., final determination weighted value, so that by Delphi method, the advantage of three kinds of weight assignment methods of analytic hierarchy process (AHP) and entropy assessment Comprehensive integration is carried out, the deviation because caused by subjectivity is too strong is effectively prevent, improves electric grid investment benefit evaluation result Accuracy.
The course of work of modules provided in an embodiment of the present invention, refer to the method flow diagram corresponding to accompanying drawing 3, tool Body running process is repeated no more.
Accompanying drawing 8 is referred to, on the basis of embodiment corresponding to above-mentioned Fig. 5, the embodiment of the invention discloses another pin To the data processing equipment of electric grid investment benefit, described device is specifically included：
First sets up module 501, the first weight assignment module 502, the first acquisition module 503, nondimensionalization processing module 504, scoring fraction computing module 505, the first weighting block 506, the second weighting block 507, the 3rd weighting block 508, scoring Acquisition module 5016, index value computing module 5017, the first coordinate combination module 5018, the second coordinate combination module 5019, 3rd coordinate combination module 5020, score function computing module 5021；
Wherein, the scoring acquisition module 5016, is commented for obtaining first corresponding to the maximum of target three-level index Score value, the second score value corresponding to standard value, and the 3rd score value corresponding to minimum value, the target three-level index is Any three-level index；
The index value computing module 5017, for calculating the maximum of the target three-level index, standard value and most Small value；
First coordinate combination module 5018, for first score value and the maximum to be carried out into set of coordinates Close, obtain the first coordinate value；
Second coordinate combination module 5019, for second score value and the standard value to be carried out into set of coordinates Close, obtain the second coordinate value；
3rd coordinate combination module 5020, for the 3rd score value and the minimum value to be carried out into set of coordinates Close, obtain the 3rd coordinate value；
The score function computing module 5021, for by first coordinate value, the second coordinate value and the 3rd coordinate value Quadratic function is substituted into, the score function of the target three-level index is obtained, wherein, the quadratic function is y=ax2+ bx+c, x For maximum, standard value or the minimum value of the target three-level index, y is the score value of the target three-level index, and a is secondary Term coefficient, b is Monomial coefficient, and c is stochastic error.
The index value computing module 5017 is specifically included：
Second sets up module 50171, and for setting up first object matrix, the first object matrix is with including the target Preset the target three-level index initial value corresponding to the power network sample data in the time limit in area；
Removing module 50172, for deleting irrational target three-level index initial value in the first object matrix, is obtained Obtain the second objective matrix；
Grouping module 50173, enters for the size according to the target three-level index initial value in second objective matrix Row packet, obtains L packet scope, L is positive integer；
Frequency computing module 50174, for calculating the target three-level index initial value in second objective matrix every The frequency of one packet scope；
Index value calculating sub module 50175, the maximum for calculating the target three-level index using the frequency Value, standard value and minimum value.
In the method for building up of the score function of three-level index disclosed in the embodiment of the present invention, pass through index value computing module 5017th, scoring acquisition module 5016 determines the maximum, standard value and minimum value of target three-level index respectively, and it divides Not corresponding score value, then by the first coordinate combination module 5018, the second coordinate combination module 5019, the 3rd coordinate combination module 5020 obtain three coordinate values respectively, and then these three coordinate values are updated into quadratic function in score function computing module 5021 In after, be capable of determining that the coefficient and stochastic error of the quadratic function, to obtain the score function of the target three-level index, from And the actual conditions of power network returns of investment over the years can be combined, the relation function between scoring fraction and evaluation index is determined, Improve the degree of accuracy in evaluation result calculating process.
The course of work of modules provided in an embodiment of the present invention, refer to the method flow diagram corresponding to accompanying drawing 4, tool Body running process is repeated no more.
The foregoing description of the disclosed embodiments, enables professional and technical personnel in the field to realize or using the present invention. A variety of modifications to these embodiments will be apparent for those skilled in the art, as defined herein General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention The embodiments shown herein is not intended to be limited to, and is to fit to and principles disclosed herein and features of novelty phase one The most wide scope caused.

Claims (10)

1. a kind of data processing method for electric grid investment benefit, it is characterised in that pre-establish electric grid investment benefit evaluation Index system, the electric grid investment benefit assessment indicator system includes multiple first class index, and each described first class index is included Multiple two-level index, each described two-level index include multiple three-level indexs, each described first class index, two-level index and Three-level index is assigned corresponding determination weighted value, including：
N power network sample data of target grid is obtained, each described power network sample data is imitated corresponding to the electric grid investment A three-level index in beneficial assessment indicator system, wherein n is positive integer；
The n power network sample datas are subjected to nondimensionalization processing, n three-level index initial value is obtained；
The n three-level index initial values are substituted into the score function with respective corresponding three-level index respectively, n is obtained and comments Divide fraction, the score function is pre-established；
The n scoring fractions are weighted, m two-level index result are obtained, wherein m is less than n, and m is positive integer；
The m two-level index results are weighted, k first class index result is obtained, wherein, k is less than m, and k is just whole Number；
The k first class index results are weighted, electric grid investment benefit evaluation result is obtained.
2. according to the method described in claim 1, it is characterised in that to first class index, two-level index and three-level each described The corresponding process for determining weighted value of index assignment includes：
Weight assignment is carried out to weight index undetermined using Delphi method, the first weighted value is obtained, the weight index undetermined is Any first class index, two-level index or three-level index；
Weight assignment is carried out to the weight index undetermined using analytic hierarchy process (AHP), the second weighted value is obtained；
Weight assignment is carried out to the weight index undetermined using entropy assessment, the 3rd weighted value is obtained；
First weighted value, the second weighted value and the 3rd weighted value are multiplied, the weight index correspondence undetermined is obtained Determination weighted value.
3. according to the method described in claim 1, it is characterised in that pre-establish the score function of each three-level index Process include：
The first score value corresponding to the maximum of acquisition target three-level index, the second score value corresponding to standard value, and The 3rd score value corresponding to minimum value, the target three-level index is any three-level index；
Calculate maximum, standard value and the minimum value of the target three-level index；
First score value and the maximum are subjected to combinatorial coordinates, the first coordinate value is obtained；
Second score value and the standard value are subjected to combinatorial coordinates, the second coordinate value is obtained；
3rd score value and the minimum value are subjected to combinatorial coordinates, the 3rd coordinate value is obtained；
First coordinate value, the second coordinate value and the 3rd coordinate value are substituted into quadratic function, the target three-level index is obtained Score function, wherein, the quadratic function be y=ax2+ bx+c, x are the maximum of the target three-level index, standard value Or minimum value, y is the score value of the target three-level index, and a is secondary term coefficient, and b is Monomial coefficient, and c is random error .
4. method according to claim 3, it is characterised in that the maximum of the calculating target three-level index, mark Quasi- value and minimum value, including：
First object matrix is set up, the first object matrix presets the power network sample data in the time limit comprising the objective area Corresponding target three-level index initial value；
Irrational target three-level index initial value in the first object matrix is deleted, the second objective matrix is obtained；
Size according to the target three-level index initial value in second objective matrix is grouped, and obtains L packet scope, L is positive integer；
Calculate frequency of the target three-level index initial value in second objective matrix in each packet scope；
Maximum, standard value and the minimum value of the target three-level index are calculated using the frequency.
5. according to the method described in claim 1, it is characterised in that after the acquisition electric grid investment benefit evaluation result, Also include：
Obtain maximum first class index result or minimum first class index result in the k first class index results；
When getting the maximum first class index result, the maximum two-level index of the correspondence maximum first class index result is obtained As a result；
Obtain the scoring fraction of the maximum three-level index of the correspondence maximum two-level index result；
Show the scoring fraction and the three-level index of the maximum three-level index；
When getting the minimum first class index result, the minimum two-level index of the correspondence minimum first class index result is obtained As a result；
Obtain the scoring fraction of the minimum three-level index of the correspondence minimum two-level index result；
Show the scoring fraction and the three-level index of the minimum three-level index.
6. a kind of data processing equipment for electric grid investment benefit, it is characterised in that including：
First sets up module, for setting up electric grid investment benefit assessment indicator system, the electric grid investment benefit evaluation index body System includes multiple first class index, and each described first class index includes multiple two-level index, and each described two-level index is included Multiple three-level indexs；
First weight assignment module, for corresponding to first class index, two-level index and the equal assignment of three-level index each described Determine weighted value；
First acquisition module, the n power network sample data for obtaining target grid, each described power network sample data correspondence A three-level index in the electric grid investment benefit assessment indicator system, wherein n is positive integer；
Nondimensionalization processing module, for the n power network sample datas to be carried out into nondimensionalization processing, obtains n three-level and refers to Mark initial value；
Score fraction computing module, refers to for the n three-level index initial values to be substituted into respectively with respective corresponding three-level Target score function, obtains n scoring fraction, and the score function is pre-established；
First weighting block, for the n scoring fractions to be weighted, obtains m two-level index result, wherein m Less than n, m is positive integer；
Second weighting block, for the m two-level index results to be weighted, obtains k first class index result, its In, k is less than m, and k is positive integer；
3rd weighting block, for the k first class index results to be weighted, obtains electric grid investment benefit evaluation knot Really.
7. device according to claim 6, it is characterised in that the first weight assignment module includes：
Second weight assignment module, for carrying out weight assignment to weight index undetermined using Delphi method, obtains the first weight Value, the weight index undetermined is any first class index, two-level index or three-level index；
3rd weight assignment module, for carrying out weight assignment to the weight index undetermined using analytic hierarchy process (AHP), obtains the Two weighted values；
4th weight assignment module, for carrying out weight assignment to the weight index undetermined using entropy assessment, obtains the 3rd and weighs Weight values；
Weight computation module, for first weighted value, the second weighted value and the 3rd weighted value to be multiplied, obtains described The corresponding determination weighted value of weight index undetermined.
8. device according to claim 6, it is characterised in that also include：
Score acquisition module, for obtaining the first score value corresponding to the maximum of target three-level index, corresponding to standard value The second score value, and the 3rd score value corresponding to minimum value, the target three-level index is any three-level index；
Index value computing module, maximum, standard value and minimum value for calculating the target three-level index；
First coordinate combination module, for first score value and the maximum to be carried out into combinatorial coordinates, obtains first and sits Scale value；
Second coordinate combination module, for second score value and the standard value to be carried out into combinatorial coordinates, obtains second and sits Scale value；
3rd coordinate combination module, for the 3rd score value and the minimum value to be carried out into combinatorial coordinates, obtains the 3rd and sits Scale value；
Score function computing module, for first coordinate value, the second coordinate value and the 3rd coordinate value to be substituted into quadratic function, The score function of the target three-level index is obtained, wherein, the quadratic function is y=ax2+ bx+c, x are the target three-level Maximum, standard value or the minimum value of index, y are the score value of the target three-level index, and a is secondary term coefficient, and b is once Term coefficient, c is stochastic error.
9. device according to claim 8, it is characterised in that the index value computing module includes：
Second sets up module, and for setting up first object matrix, the first object matrix presets year comprising the objective area Target three-level index initial value corresponding to power network sample data in limit；
Removing module, for deleting irrational target three-level index initial value in the first object matrix, obtains the second mesh Mark matrix；
Grouping module, is grouped for the size according to the target three-level index initial value in second objective matrix, is obtained L packet scope is obtained, L is positive integer；
Frequency computing module, for calculating the target three-level index initial value in second objective matrix at each described point The frequency of group scope；
Index value calculating sub module, maximum, standard value for calculating the target three-level index using the frequency And minimum value.
10. device according to claim 6, it is characterised in that in the 3rd weighting block, obtains electric grid investment benefit After evaluation result, also include：
Second acquisition module, for obtaining maximum first class index result or minimum first class index in the k first class index results As a result；
3rd acquisition module, for when second acquisition module gets the maximum first class index result, obtaining correspondence The maximum two-level index result of the maximum first class index result；
4th acquisition module, the scoring fraction of the maximum three-level index for obtaining the correspondence maximum two-level index result；
First display module, scoring fraction and the three-level index for showing the maximum three-level index；
5th acquisition module, for when second acquisition module gets the minimum first class index result, obtaining correspondence The minimum two-level index result of the minimum first class index result；
6th acquisition module, the scoring fraction of the minimum three-level index for obtaining the correspondence minimum two-level index result；
Second display module, scoring fraction and the three-level index for showing the minimum three-level index.
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CN111460260A (en) * 2020-03-31 2020-07-28 上海智芝全智能科技有限公司 Data processing system, method and medium for multi-type data
CN111724057A (en) * 2020-06-16 2020-09-29 国网河北省电力有限公司电力科学研究院 Post-investment evaluation method for distribution network area
CN117056326A (en) * 2023-08-11 2023-11-14 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) GNSS data quality evaluation method and system based on gray correlation self-adaptive determination

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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
CN108921425A (en) * 2018-06-28 2018-11-30 广东电网有限责任公司 A kind of method, system and the server of asset item classifcation of investment
CN109559012A (en) * 2018-10-18 2019-04-02 国网山东省电力公司经济技术研究院 Power network planning scheme integrated evaluating method under the conditions of a kind of wind-electricity integration
CN109615245A (en) * 2018-12-14 2019-04-12 国网山东省电力公司经济技术研究院 A kind of method of pair of Electric Power Network Planning total evaluation
CN109615246A (en) * 2018-12-14 2019-04-12 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 A kind of active distribution network economical operation state determines method
CN111369083A (en) * 2018-12-25 2020-07-03 中国电力科学研究院有限公司 Evaluation method and system for investment benefits of power grid project
CN111460260A (en) * 2020-03-31 2020-07-28 上海智芝全智能科技有限公司 Data processing system, method and medium for multi-type data
CN111724057A (en) * 2020-06-16 2020-09-29 国网河北省电力有限公司电力科学研究院 Post-investment evaluation method for distribution network area
CN117056326A (en) * 2023-08-11 2023-11-14 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) GNSS data quality evaluation method and system based on gray correlation self-adaptive determination
CN117056326B (en) * 2023-08-11 2024-05-03 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) GNSS data quality evaluation method and system based on gray correlation self-adaptive determination

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