CN105303194B - A kind of power grid index system method for building up, device and computing device - Google Patents
A kind of power grid index system method for building up, device and computing device Download PDFInfo
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Abstract
The invention discloses a kind of power grid index system method for building up, are executed in computing device, and method includes：Index system is created, index system includes key index data sequence；Sensitivity analysis is carried out to key index data sequence using Sensitivity Analysis, determines the first influence factor for influencing the first parameter；The quantitative relationship between the first influence factor and key index data sequence is established, determines the second influence factor for influencing key index data sequence；According to the second influence factor, classification processing is carried out to key index data sequence using clustering method；Using predetermined evaluation method, to classification, treated that key index data sequence is evaluated, and exports classification treated key index data sequence.Device is established the invention also discloses corresponding power grid index system and the computing device of device is established comprising the power grid index system.
Description
Technical field
The present invention relates to a kind of power grid index system method for building up, device and computing devices.
Background technology
With China's power industry and the rapid development of electric system, power grid scale constantly expands, and forms multi-layer
Power grid.For example, the provincial power network in nationwide integrated power grid next stage accepts extra-high voltage grid, numerous electricity power enterprises are connected, and directly
Towards mesolow electric energy products ＆ services are provided with network users.Monitoring for the power supply quality of power grids at different levels (such as provincial power network)
It is most important.To reinforce power supply quality supervision, since 2001, the committee of energy supervision mechanism of European Union came into effect power supply matter
Amount includes three dimensions to mark system to mark, European electrical power delivery quality, that is, power duration, quality of voltage, commerce services matter
Amount.U.S.Federal Energy Regulatory Commission tissue each ISO and RTO, Stake Holders and the common research and development evaluation of expert and measurement
The markets ISO and RTO are run and the evaluation method of performance, and Market Performance evaluation includes mainly reliability, Market Performance and tissue effect
Three aspects of rate.
However, domestic external power grid appraisement system, mostly with supervision examination for major function, biasing toward restriction operator need to reach
Minimum performance dimension, and take into account the social responsibility etc. of public utilities, provincial power network developing direction be directed toward there is no specific
Effect.Existing index system stresses international comparison and internal management of a company to mark level, fails to consider public's perceptibility,
Fail to carry out further investigation in terms of fusion degree, first-class level quantization science inside and outside index system.
Invention content
For this purpose, the present invention provides a kind of new power grid index system method for building up, device and computing device, to try hard to solve
Or at least alleviate above there are the problem of.
According to an aspect of the present invention, a kind of power grid index system method for building up is provided, is executed in computing device, side
Method includes：Index system is created, index system includes key index data sequence；Using Sensitivity Analysis to key index
Data sequence carries out sensitivity analysis, determines the first influence factor for influencing the first parameter；Establish the first influence factor and key
Quantitative relationship between achievement data sequence determines the second influence factor for influencing key index data sequence；According to the second shadow
The factor of sound, classification processing is carried out using clustering method to key index data sequence；Using predetermined evaluation method to classification
Treated, and key index data sequence is evaluated, and exports classification treated key index data sequence.
Optionally, in the method according to the invention, it establishes between the first influence factor and key index data sequence
The step of quantitative relationship includes：The mapping relations between the first influence factor and key index data sequence are established, using structure
Equation model is fitted.
Optionally, in the method according to the invention, predetermined evaluation method is the direct lateral comparison based on single index
Method.
Optionally, in the method according to the invention, predetermined evaluation method is the method given a mark based on weight.
According to an aspect of the present invention, a kind of power grid index system is provided and establishes device, is resided in computing device, it should
Device includes：Creating unit is suitable for creating index system, and index system includes key index data sequence；Achievement data is analyzed
Unit is suitable for carrying out sensitivity analysis to key index data sequence using Sensitivity Analysis, and determining influences the first parameter
The first influence factor, and the quantitative relationship that is adapted to set up between the first influence factor and key index data sequence determines
Influence the second influence factor of key index data sequence；Achievement data processing unit is suitable for, according to the second influence factor, using
Clustering method carries out classification processing to key index data sequence；Indication system judgment unit is suitable for using predetermined evaluation
Treated that index system is evaluated to classification for method, and exports classification treated key index data sequence.
Optionally, in a device in accordance with the invention, achievement data analytic unit is further adapted for：Establish the first influence factor with
Mapping relations between key index data sequence, are fitted using structural equation model.
Optionally, in a device in accordance with the invention, predetermined evaluation method is the direct lateral comparison based on single index
Method.
Optionally, in a device in accordance with the invention, predetermined evaluation method is the method given a mark based on weight.
According to an aspect of the present invention, a kind of computing device is provided, including power grid index system as above establishes device.
According to the technique and scheme of the present invention, index system is carried out the classification of key index data sequence processing
Differentiation, comprehensive evaluation.
Description of the drawings
To the accomplishment of the foregoing and related purposes, certain illustrative sides are described herein in conjunction with following description and drawings
Face, these aspects indicate the various modes that can put into practice principles disclosed herein, and all aspects and its equivalent aspect
It is intended to fall in the range of theme claimed.Read following detailed description in conjunction with the accompanying drawings, the disclosure it is above-mentioned
And other purposes, feature and advantage will be apparent.Throughout the disclosure, identical reference numeral generally refers to identical
Component or element.
Fig. 1 shows that power grid index system according to the present invention establishes the block diagram of the Example Computing Device 100 of device；
Fig. 2 shows the schematic diagrames according to the power grid index system method for building up 200 of an example of the present invention type embodiment；
Fig. 3 shows the equally distributed schematic diagram according to an example of the present invention type embodiment；
Fig. 4 shows the schematic diagram of the normal distribution according to an example of the present invention type embodiment；And
Fig. 5 shows that the power grid index system according to an example of the present invention type embodiment establishes the structure chart of device 500.
Specific implementation mode
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Completely it is communicated to those skilled in the art.
The power grid index system of the present invention is established device and is resided in computing device, and Fig. 1 is arranged as realizing according to the present invention
Power grid achievement data processing unit Example Computing Device 100 block diagram.In basic configuration 102,100 allusion quotation of computing device
Include type system storage 106 and one or more processor 104.Memory bus 108 can be used in processor 104
Communication between system storage 106.
Depending on desired configuration, processor 104 can be any kind of processing, including but not limited to：Microprocessor
((μ P), microcontroller (μ C), digital information processor (DSP) or any combination of them.Processor 104 may include all
Cache, processor core such as one or more rank of on-chip cache 110 and second level cache 112 etc
114 and register 116.Exemplary processor core 114 may include arithmetic and logical unit (ALU), floating-point unit (FPU),
Digital signal processing core (DSP core) or any combination of them.Exemplary Memory Controller 118 can be with processor
104 are used together, or in some implementations, and Memory Controller 118 can be an interior section of processor 104.
Depending on desired configuration, system storage 106 can be any type of memory, including but not limited to：Easily
The property lost memory (RAM), nonvolatile memory (ROM, flash memory etc.) or any combination of them.System stores
Device 106 may include operating system 120, one or more apply 122 and program data 124.Using 122 may include by
It is configured to power grid index system and establishes device 600.In some embodiments, it may be arranged on an operating system using 122
It is operated using program data 124.
Computing device 100 can also include contributing to from various interface equipments (for example, output equipment 142, Peripheral Interface
144 and communication equipment 146) to basic configuration 102 via the communication of bus/interface controller 130 interface bus 140.Example
Output equipment 142 include graphics processing unit 148 and audio treatment unit 150.They can be configured as contribute to via
One or more port A/V 152 is communicated with the various external equipments of such as display or loud speaker etc.Outside example
If interface 144 may include serial interface controller 154 and parallel interface controller 156, they, which can be configured as, contributes to
Via one or more port I/O 158 and such as input equipment (for example, keyboard, mouse, pen, voice-input device, touch
Input equipment) or the external equipment of other peripheral hardwares (such as printer, scanner etc.) etc communicated.Exemplary communication is set
Standby 146 may include network controller 160, can be arranged to convenient for via one or more communication port 164 and one
The communication that other a or multiple computing devices 162 pass through network communication link.
Network communication link can be an example of communication media.Communication media can be usually presented as in such as carrier wave
Or the computer-readable instruction in the modulated data signal of other transmission mechanisms etc, data structure, program module, and can
To include any information delivery media." modulated data signal " can such signal, one in its data set or more
It is a or it change can the mode of coding information in the signal carry out.As unrestricted example, communication media can be with
Include the wire medium of such as cable network or private line network etc, and such as sound, radio frequency (RF), microwave, infrared
(IR) the various wireless mediums or including other wireless mediums.Term computer-readable medium used herein may include depositing
Both storage media and communication media.
Computing device 100 can be implemented as a part for portable (or mobile) electronic equipment of small size, these electronics are set
It is standby can be such as cellular phone, personal digital assistant (PDA), it is personal media player device, wireless network browsing apparatus, a
People's helmet, application specific equipment or may include any of the above function mixing apparatus.Computing device 100 can be with
It is embodied as including desktop computer and the personal computer of notebook computer configuration.
Fig. 2 shows the schematic diagrames of power grid index system method for building up 200 according to an illustrative embodiment of the invention.
This method is resided in computing device 100 and is executed.
The method of the present invention is intended to establish the region that can adapt between Chinese different provinces, an economic development equal difference
It is different, it is capable of the index system of rational evaluation key index.
In step S210, index system is created, the index system includes key index data sequence, and above-mentioned key refers to
It includes multigroup achievement data to mark data sequence.
Specific key index data sequence can refer to table 1.
1 various countries' power grid key index of table
Then, in step S220, sensitivity analysis is carried out to key index data sequence using Sensitivity Analysis,
Determining influences the first influence factor of the first parameter.Wherein, the first parameter is the variation of Project Economy Benefit, the first influence factor
Including the level of economic development, energy consumption level, energy cleanliness levels and social development levels.
Then, in step S230, the quantitative relationship between the first influence factor and key index data sequence is established, really
Fixing rings the second influence factor of key index data sequence.Wherein, above-mentioned second influence factor is to influence key index data
The principal element of sequence, including the level of economic development and social development levels.
According to a kind of embodiment, the mapping relations between the first influence factor and key index data sequence are established, are adopted
It is fitted with structural equation model, establishes the quantitative relationship between the first influence factor and key index data sequence.
Then, in step S240, according to the second influence factor, using clustering method to key index data sequence
Carry out classification processing.Wherein, key index data sequence can be divided into flourishing, more flourishing, general, less-developed, backward etc. 5
Stage.
Then, in step s 250, index system is evaluated using predetermined evaluation method.And in step S260,
Output classification treated key index data sequence.The predetermined analysis method may include based on the direct of single index
Lateral comparison approach and the method given a mark based on weight.
Direct lateral comparison approach based on single index is by every group of achievement data row sequence in key index data sequence
It is compared, objectivity, specific aim are stronger, but globality is weaker.Method based on weight marking is by key index data
Every group of achievement data sequence assigns the weighted value of any in sequence, comprehensive relatively strong, but can be influenced by certain subjectivity.Specifically
Analysis method it is as follows：
Verified that the specific method is as follows to index system using the direct lateral comparison approach based on single index：
First, classify to key index data, statistically see, key index data as a kind of stochastic variable,
Its distribution pattern is divided into discrete distribution and continuous type distribution.Mainly be distributed, be uniformly distributed including 0-1, normal distribution, skewness point
Cloth and long-tail distribution (zipf) etc..
Fig. 3 and Fig. 4 respectively illustrates the schematic diagram according to an embodiment of the invention being uniformly distributed with normal distribution.
Each distribution pattern is specific as follows：
(1) 0-1 is distributed：If stochastic variable X is only possible to take 0 and 1 two value, its distribution law to be P { X=k }=pk(1-p
)1-k, k=0,1 (0<P ＜ 1), then X obey using p be referred to as parameter (0-1) distribution or Two-point distribution.
(2) it is uniformly distributed：If random variable of continuous type X has probability density f (x), then claim X to be obeyed on section (a, b)
It is uniformly distributed, as shown in Figure 3.Formula is as follows：
(3) normal distribution：Middle position, the frequency point at both ends are concentrated on as shown in figure 4, normal distribution refers to most frequencies
Cloth is substantially symmetric.If the probability density of random variable of continuous type X is f (x), wherein μ, σ (σ>0) it is constant, then X is claimed to obey ginseng
Number is normal distribution or the Gaussian Profile of μ, σ.Formula is as follows：
(4) partial velocities：Partial velocities refer to frequency disribution asymmetry, and concentrated position is amesiality, i.e. statistical data peak
Value and the unequal frequency distribution of average value.There are two features for partial velocities：First, left-right asymmetry (i.e. so-called skewness)；Second is that
When sample increases, mean tends to normal distribution.It can be divided into positive skewness distribution less than or greater than average value according to peak value and bear
Partial velocities two types, the degree deviateed can be portrayed with the degree of bias.
In theory, long-tail distribution meets Zipf laws.Zipf laws are the empirical laws of a statistical, can be described
For：In the corpus of natural language, frequency and its ranking in frequency meter that a word occurs are inversely proportional, frequency highest
The frequency that occurs of word be about 2 times of the deputy word of the frequency of occurrences, the deputy word of the frequency of occurrences is then to occur
2 times of the word that frequency is the 4th.This law is verified inside many distributions, such as the income of people, internet
Websites quantity and access ratio etc., the thought of 80/20 law in management is also similar.
According to parametric test, non-parametric test, P-P figures and Q-Q figures to the index distribution pattern of key index data sequence
It is divided.
(1) parametric test
Parametric test is the statistical check carried out to mean parameter, variance, and parametric test is the important set of inferential statistics
At part.Parametric test is first to calculate test statistics by the sample data measured, if the statistics magnitude calculated falls into agreement and shows
Write property horizontal a when region of rejection in, illustrate be detected parameter between it is statistically significant at the significance a arranged
Difference；If conversely, calculate statistics magnitude fall into agreement significance a when acceptance region in, illustrate be detected parameter between
It is same overall estimates of parameters statistically without significant difference.When parametric test is suitable for known to overall distribution, according to
Sample data infers the statistical parameter of overall distribution.At this point, overall distribution form is given or hypothesis, only
It is that some of parameters take value or range unknown, the main purpose of analysis is to estimate the value of parameter, or to it carry out certain
Statistical check.Such issues that often carry out statistical inference with parametric test.It not only can to overall characteristic parameter into
Row is inferred, additionally it is possible to realize that two or more overall parameters are compared.
(2) non-parametric test
Non-parametric test is the important component of statistical analysis technique, it collectively forms statistical inference with parametric test
Substance.Parametric test be known to overall distribution form, to parameter such as mean value, variance of overall distribution etc. into
The method that row is inferred.But in data analysis process, for various reasons, people can not often make overall distribution form
Simply it is assumed that the method for parametric test is just no longer applicable at this time.Non-parametric test be exactly it is a kind of in view of this consideration, in totality
Unknown Variance or know it is very few in the case of, overall distribution form etc. is inferred using sample data method.Due to non-
Parametric test method is not related to the parameter in relation to overall distribution during inferring, thus obtains entitled " nonparametric " and examine.Non- ginseng
It includes list sample K-S inspections, the Chi-square Test of overall distribution, bi-distribution inspection, variate-value randomized test etc. that number, which is examined,.
One-Sample K-S Test can utilize sample data extrapolated sample from totality whether obey a certain theoretical divide
Cloth is a kind of method of inspection of the goodness of fit, is suitable for exploring the distribution of random variable of continuous type.For example, Collection utilization is collected
Housing conditions investigation sample data, analysis family per capita living space whether Normal Distribution.What single sample K-S was examined
Null hypothesis is：Sample from totality with specified theoretical distribution without significant difference, be theoretically utilized in and examine normal distribution,
Even distribution, exponential distribution and Poisson distribution etc..
The Chi-square Test of overall distribution can infer overall distribution and desired distribution or a certain theory point according to sample data
Cloth whether there is significant difference, is a kind of identical property inspection, is typically suitable for the analysis of the overall distribution to there is multinomial classification value.Example
Such as, physician has found when the people that does some research on heart diseases dies suddenly number and the relationship on date：Among one week, Monday heart patient sudden death
Person is more, other dates are then substantially suitable.The ratio on the same day is approximately 2.8：1：1：1：1：1：1.It is dead to be now collected into heart patient
The sample data for dying the date, infers whether its overall distribution matches with above-mentioned theory distribution.Its null hypothesis is：Sample comes from
Overall distribution and desired distribution or a certain theoretical distribution indifference.
Bi-distribution examine be will by sample data test samples from totality whether obey specified probability as P
Bi-distribution.It is two-value to have very multidata value in life, for example, crowd is segmented into male and female, product can
Qualified and unqualified to be divided into, student is segmented into excellent student and non-excellent students, throws the result of coin experiment and can divide
At appearance front and there is reverse side etc..Usually such two-value is indicated with 1 or 0 respectively.If carrying out the identical experiment of n times,
The number for two classes (1 or 0) then occur can be described with discrete random variable X.If the probability that stochastic variable X is 1 is set as
P, then the probability Q that stochastic variable X values are 0 are just equal to 1-P, form bi-distribution.Its null hypothesis is：Sample from totality with
Specified bi-distribution is without significant difference.
Whether variate-value randomness test is realized random to overall variate-value appearance by the analysis to sample variable value
It tests.For example, when throwing coin, if indicating to occur that front with 1, indicates to occur that reverse side with 0, carry out
After inserting coins several times, it will obtain a variable value sequence with 1,0 composition.At this moment it may analyze that " positive and negative occurs in coin
Whether it is random " this problem.Variate-value randomness test is exactly an effective ways of such issues that solve.Its original
Assuming that being：Overall variate-value occurs being random.The important evidence of variable randomness test is the distance of swimming.The so-called distance of swimming is sample sequence
Continuously occurs the number of identical variate-value in row.Can directly understand, if the positive and negative of coin occur be it is random,
In data sequence, many 1 perhaps multiple 0 possibilities continuously occurred will be less big, meanwhile, 1 and 0 frequent cross occurrence
Possibility also can be smaller.Therefore, number of runs it is too big or too it is small all will indicate that variate-value exist not random phenomenon.Example：To examine
Whether certain pressure-resistant equipment works within certain time continues normally, to test and record setting in Each point in time in the period
The data of standby pressure resistance.Now this batch data is analyzed using runs test method.If the variation of pressure-resistant data is random,
It is believed that equipment work is normal always, otherwise it is assumed that the equipment has the phenomenon that cisco unity malfunction.
(3) P-P schemes
P-P figures are the figures that the relationship between accumulation ratio and the accumulation ratio of specified distribution according to variable is drawn.
Whether can meet specified distribution by P-P figures with inspection data.When the specified distribution of data fit, each point is approximate in P-P figures
In straight line.If each point is not linear in P-P figures, but has certain rule, variable data can be converted, make conversion
Data afterwards are distributed closer to specified.
(4) Q-Q schemes
Q-Q figures are a kind of scatter plots, can be used for the distribution of inspection data.Q-Q figures are point positions being distributed with variable data
Several relation curves between the quantile of specified distribution are tested.P-P figures are identical with the purposes of Q-Q figures,
Only the method for inspection has differences.By taking normal distribution as an example, it is horizontal seat that Q-Q figures, which are exactly by the quantile of standardized normal distribution,
Mark, sample value are the scatter plot of ordinate.Whether to be similar to normal distribution using QQ illustrated handbook other style notebook datas, need to only see that QQ schemes
On point whether approximatively near straight line, and the slope of the straight line is standard deviation, and intercept is mean value.May be used also with QQ figures
Obtain the coarse information of sample skewness and kurtosis.
The division of key index data sequence rank is directly influenced by pointer type.Therefore, there is different distributions type
Key index data sequence, different evaluation methods should be used.
(1) 0-1 is distributed：0-1 is distributed as discrete distribution, and it is possible that there are two types of the values of all key index data, because
The rank of this this kind of key index need to only be divided into two classes.If the type of key index data is positive type index, 1 is
A sections, 0 is B sections；If the type of key index data is negative sense type index, 0 is A sections, and 1 is B sections.It is this kind of for enterprise
The counter-measure of key index data should be to maintain or strive entering A sections, otherwise be affected to key index data rank.
(2) it is uniformly distributed：In being uniformly distributed, key index data in the probability of section (a, b) be it is impartial, it is this kind of
Key index data can be divided into five parts by key index data by finding quartile point.If the type of key index data
For positive index, then ((4b+a)/5, b) is A sections, and ((3b+2a)/5, (4b+a)/5) is B sections, ((2b+3a)/5, (3b+2a)/
5) it is C sections, ((b+4a)/5, (2b+3a)/5) is D sections, and (a, (b+4a)/5) is E sections；If the type of key index data is negative
To type index, then (a, (b+4a)/5) is A sections, and ((b+4a)/5, (2b+3a)/5) is B sections, ((2b+3a)/5, (3b+2a)/5)
It it is C sections, ((3b+2a)/5, (4b+a)/5) is D sections, and ((4a+b)/5, b) is E sections.For enterprise, this kind of key index number
According to counter-measure should be to maintain or strive enter A sections or B sections, be otherwise affected to key index data rank.
(3) normal distribution：In normal distribution, though the probability of key index data is not impartial, it is symmetrical, this
Index also can be divided into five parts by class index by finding four quantiles.For standardized normal distribution, four points are respectively μ-
+ 1 σ of 1.96 σ, μ -1 σ, μ.If the type of key index data is positive type index, (μ+1.96 σ, ∞) is A sections, (μ+1 σ, μ+
1.96 σ) it is B sections, (+1 σ of μ -1 σ, μ) is C sections, and (μ -1.96 σ, μ -1 σ) is D sections, and (- ∞, μ -1.96 σ) is E sections, and each section
Probability be respectively 2.5%, 13.37%, 68.27%, 13.37%, 2.5%；If the type of key index data is negative sense
Type index, then (- ∞, μ -1.96 σ) is A sections, and (μ -1.96 σ, μ -1 σ) is B sections, and (+1 σ of μ -1 σ, μ) is C sections, (μ+1 σ, μ+1.96
It is σ) D sections, (μ+1.96 σ, ∞) is E sections, and each section of probability is respectively 2.5%, 13.37%, 68.26%,
13.37%, 2.5%.For enterprise, the enterprise in C sections of key index data accounts for major part, if C sections of key index numbers
According to value be closer to B sections of critical values, then strive for enter next section should be paid close attention to if being closer to D sections of critical values,
It prevents from gliding；If being in B sections of key index data, the cost-effectiveness into A sections should be weighed, in economically viable condition
Under strive enter A sections；If should continue to keep in A sections；It in D sections or E sections, should redouble efforts to do it, at least ensure to enter C sections.
(4) partial velocities：In partial velocities, the distributions of key index data neither uniform, and be not it is symmetrical, this
When take clustering methodology to be segmented data.Clustering methodology is using index observation as foundation, according in statistical analysis
Hierarchical cluster or iteration clustering procedure, the larger sample of some similarity degrees is polymerized to a rank, while other are similar
More sample is polymerized to another rank, and the classification that can be obtained at this time according to clustering is segmented index.To enterprise
It for industry, should at least ensure the region for entering data aggregation, and strive for entering dominant area.
(5) long-tail is distributed：By being analyzed above it is found that long-tail distribution meets Zipf laws, Zipf laws are a statistics
The empirical law of type, rather than theoretical law, it is also similar with the thought of 80/20 law in management.Therefore, sectional evaluation
When, for preceding 80% data, it is equally divided into three parts, respectively A, B, C sections；20% data afterwards, are equally divided into two parts,
Respectively D, E sections.
Include analytic hierarchy process (AHP), moral using the method that the analysis by synthesis method given a mark based on weight verifies index system
Er Feifa and Information Entropy, it is specific as follows：
(1) analytic hierarchy process (AHP)
First, hierarchy Model is established.When being studied a question using analytic hierarchy process (AHP), it is related with problem it is various because
Then plain stratification constructs the hierarchy Model of a tree, referred to as hierarchical chart.
Top is destination layer (O)：The target or desired result of problem decision, only there are one elements.
Middle layer is rule layer (C)：Include each factor of intermediate link to realize involved by target, each factor is surely
Then, several sublayers can be divided into when criterion is more than 9.
Lowermost layer is solution layer (P)：Solution layer is to realize target and selective various measures, as decision scheme.
It is, in general, that each factor between each level, some is associated, and some is not necessarily associated；The factor of each level
Also not necessarily identical in practice, is mainly determined according to the classification of the property of problem and each correlative factor number.
Secondly, construction judges that matrix (in pairs relatively), construction comparator matrix are mainly by comparing each on same level
Influence of the factor to last layer correlative factor.Rather than all factors are put together and are compared, i.e., by same layer it is each because
Element is compared two-by-two.Relative scalar gauge is used when comparing, and is avoided as much as between factor of different nature mutually
The difficulty compared.Meanwhile it to be reduced as possible according to practical problem concrete condition since policymaker's subjective factor is caused by result
It influences.
If comparing n factor C1,C2,…,CnTo the influence degree of last layer (such as destination layer) O, i.e., to determine it in O
In shared proportion.To any two factor CiAnd Cj, use aijIndicate CiAnd CjTo the ratio between the influence degree of O, in 1~9 ratio
Scale measures aij(i, j=1,2 ..., n).Then, comparator matrix A=(a in pairs can be obtainedij)n×n, also known as judge
Matrix, it is clear that aij＞ 0, wherein
The determination of proportion quotiety：aijTake 9 grades of 1-9, ajiTake aijInverse, 1-9 scales determine it is as follows：
aij=1, element i are identical to the last layer time importance of factor as element j；
aij=3, element i is slightly more important than element j；
aij=5, element i is more important than element j；
aij=7, element i are than element j much more significants；
aij=9, element i is more of crucial importance than element j；
aij=2n, n=1,2, the importance of 3,4 ... element i and j is between aij=2n-1 and aijBetween=2n+1；
aij=1/n, n=1,2 ... 9 is and if only if aji=n.
As long as by the property of positive reciprocal matrix it is found that determining n (n-1)/2 element of upper (under the or) triangle of A.
In special circumstances, if it is determined that the element of matrix A has transitivity, that is, meet
aikakj=aij(i, j, k=1,2 ..., n)
Then A is referred to as consistency matrix, referred to as consistent battle array
Secondly, Mode of Level Simple Sequence and consistency check.Under normal conditions, it is not necessarily one by the judgment matrix actually obtained
It causes, i.e., not necessarily meets transitivity and consistency.In practice, it also absolutely sets up, but is required generally without requiring consistency
It is consistent, i.e., inconsistent degree should be in the range of allowing.Mainly examine or check following index：
Coincident indicator：
Consistency ratio index：
As CR ＜ 0.10, it is believed that the consistency of judgment matrix is acceptable, then λmaxCorresponding feature vector can be with
Weight vectors as sequence.At this time
Wherein, (AW)iIndicate i-th of component of AW.
Finally, combining weights and combination consistency check are calculated.Combining weights vector：If the upper n of -1 layer of kthk-1A element pair
The orderweight vector of general objective (top) is：
N on kth layerkA element is to the weight vectors of j-th of element on last layer (k-1 layers)
There is general formulae W to arbitrary k ＞ 2(k)=P(k)·P(k-1)·····P(3)·W(2)(k ＞ 2)
Wherein, W(2)Be on the second layer each element to total ordering vector of destination layer.
Work as CR(k)When ＜ 0.10, then it is assumed that the multilevel iudge matrix of entire level passes through consistency check.
(2) Delphi method
Delphi method is a kind of expert point rating method, it selectes several scoring items according to the specific requirement of evaluation object first
Mesh works up evaluation criterion further according to assessment item.The opinion that relevant expert is consulted by anonymous way carries out expert opinion
Statistics, processing are analyzed and are concluded, objectively comprehensive most expertises and subjective judgement, to being largely difficult to use technical method
The factor for carrying out quantitative analysis makes reasonable estimation, after excessively taking turns opinion and consulting, feed back and adjust, is worth and is worth to credits
The method that achievable degree is analyzed.Operating procedure is：
1. selecting expert；
2. determining the factor of credits value that influences, design value analyzes object consultation table；
3. providing credits background information to expert, expert opinion is consulted with anonymous way；
4. carrying out analysis summary to expert opinion, statistical result is fed back into expert；
5. expert corrects the opinion of oneself according to feedback result；
6. the excessive wheel anonymity of warp is consulted and suggestion feedback, final analytical conclusions are formed.
The computational methods of expert's score have：
1. addition evaluation type
The score value addition summation obtained by each index subjet will be evaluated, evaluation result is indicated by total score.This method is used for index
Between the simple person of relationship.
Wherein：W is evaluation object total score；Wi is i-th index score value；N is index item number.There are two types of sides for the method
Formula：Even add point system and a point meter addition evaluation assessment.
2. connecting long-pending evaluation type
The score value of each project is even multiplied, and performance effect is showed by its product size.This method sensitivity is very high,
The relationship being evaluated between each index of object is especially close, and the score of one of which is related to influence other every overall results, i.e.,
It is unqualified with a certain index, just to whole the characteristics of playing negative.
Wherein, W is evaluation object total score, and Wi is i project score values, and n is index item mesh number.
3. sum number multiplication evaluation type
The evaluation index of evaluation object is divided into several groups, the sum of each group score value is first calculated, then again comments each group
Score value even multiplies, and gained is total scoring.Person allows for degree difference in close relations and the side of influencing each other between each factor
Formula difference determines.
Wherein：WijFor i-th group of j index value in evaluation object, m is the group number of evaluation object, and n is the index contained in i groups
Item number
4. weighting evaluation type
Pair by the indices project in evaluation object according to the significance level of evaluation index, different weights is given, i.e.,
The significance level of each factor is treated with a certain discrimination.
Wherein, W is evaluation object total score, and Wi is the i index item scores of evaluation object, and Ai is the weights of i index item.
(3) Information Entropy
1. data matrix
Wherein, XijFor the numerical value of i-th of scheme, j-th of index.
2. the nonnegative numberization of data is handled
The ratio of same index value summation is accounted for using a certain index of each scheme since Information Entropy is calculated, is not deposited
It in the influence of dimension, need not be standardized, if there is negative in data, it is necessary to which non-negativeization processing is carried out to data.
In addition, when in order to avoid seeking entropy logarithm it is meaningless, need carry out data translation：
For the index being the bigger the better：
For the smaller the better index：
For convenience's sake, still remember non-negativeization treated that data are Xij
3. calculating the proportion that i-th of scheme under jth item index accounts for the index
4. calculating the entropy of jth item index
Wherein k ＞ 0, ln are natural logrithm, ej≥0.Constant k and sample number m has in formula
It closes,
K=1/lnm generally is enabled, then 0≤e≤1
5. calculating the coefficient of variation of jth item index
gj=1-ej
For jth item index, index value XijDifference it is bigger, to scheme evaluation effect it is bigger, entropy is with regard to smaller.
6. seeking flexible strategy
7. calculating the comprehensive score of each scheme
According to the technique and scheme of the present invention, index system is carried out the classification of key index data sequence processing
Differentiation, comprehensive evaluation.
Fig. 5 shows that the power grid index system according to an example of the present invention type embodiment establishes the structure chart of device 500.
The device resides in computing device, and described device 500 includes：Creating unit 510, achievement data analytic unit 520, index number
According to processing unit 530 and indication system judgment unit 540.
For creating unit 510 for creating index system, the index system includes key index data sequence.Achievement data
Analytic unit 520 carries out sensitivity analysis using Sensitivity Analysis to key index data sequence in creating unit 510, really
Fixing rings the first influence factor of the first parameter.The mapping established between the first influence factor and key index data sequence is closed
System, is fitted using structural equation model, establishes the quantitative relationship between the first influence factor and key index data sequence,
Determining influences the second influence factor of key index data sequence.Achievement data processing unit 530 is adopted according to the second influence factor
Classification processing is carried out to key index data sequence with clustering method.Indication system judgment unit 540 is using predetermined evaluation
Treated that index system is evaluated to classification for method, and exports classification treated key index data sequence.
According to a kind of embodiment, predetermined evaluation method is direct lateral comparison approach based on single index and is based on weight
The method of marking.The embodiment of the specific each algorithm please referred to above and corresponding each algorithm, does not do excessive solution herein
It releases.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice without these specific details.In some instances, well known method, knot is not been shown in detail
Structure and technology, so as not to obscure the understanding of this description.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of each inventive aspect,
Above in the description of exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：It is i.e. required to protect
Shield the present invention claims the feature more features than being expressly recited in each claim.More precisely, as following
As claims reflect, inventive aspect is all features less than single embodiment disclosed above.Therefore, it abides by
Thus the claims for following specific implementation mode are expressly incorporated in the specific implementation mode, wherein each claim itself
As a separate embodiment of the present invention.
Those skilled in the art should understand that the module of the equipment in example disclosed herein or unit or groups
Part can be arranged in equipment as depicted in this embodiment, or alternatively can be positioned at and the equipment in the example
In different one or more equipment.Module in aforementioned exemplary can be combined into a module or be segmented into addition multiple
Submodule.
Those skilled in the art, which are appreciated that, to carry out adaptively the module in the equipment in embodiment
Change and they are arranged in the one or more equipment different from the embodiment.It can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it may be used any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit requires, abstract and attached drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation
It replaces.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included certain features rather than other feature, but the combination of the feature of different embodiments means in of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
One of meaning mode can use in any combination.
In addition, be described as herein can be by the processor of computer system or by executing for some in the embodiment
The combination of method or method element that other devices of the function are implemented.Therefore, have for implementing the method or method
The processor of the necessary instruction of element forms the device for implementing this method or method element.In addition, device embodiment
Element described in this is the example of following device：The device is used to implement performed by the element by the purpose in order to implement the invention
Function.
As used in this, unless specifically stated, come using ordinal number " first ", " second ", " third " etc.
Description plain objects are merely representative of the different instances for being related to similar object, and are not intended to imply that the object being described in this way must
Must have the time it is upper, spatially, in terms of sequence or given sequence in any other manner.
Although the embodiment according to limited quantity describes the present invention, above description, the art are benefited from
It is interior it is clear for the skilled person that in the scope of the present invention thus described, it can be envisaged that other embodiments.Additionally, it should be noted that
The language that is used in this specification primarily to readable and introduction purpose and select, rather than in order to explain or limit
Determine subject of the present invention and selects.Therefore, without departing from the scope and spirit of the appended claims, for this
Many modifications and changes will be apparent from for the those of ordinary skill of technical field.For the scope of the present invention, to this
The done disclosure of invention is illustrative and not restrictive, and it is intended that the scope of the present invention be defined by the claims appended hereto.
Claims (9)
1. a kind of power grid index system method for building up, executes in computing device, the method includes:
Index system is created, the index system includes key index data sequence；
Sensitivity analysis is carried out to key index data sequence using Sensitivity Analysis, determining influences the first of the first parameter
Influence factor, wherein the first parameter is the variation of Project Economy Benefit；
The quantitative relationship between the first influence factor and key index data sequence is established, determining influences key index data sequence
The second influence factor；
According to the second influence factor, classification processing is carried out to key index data sequence using clustering method；
Using predetermined evaluation method, to classification, treated that key index data sequence is evaluated, and exports classification treated
Key index data sequence.
2. according to the method described in claim 1, wherein described establish between the first influence factor and key index data sequence
Quantitative relationship the step of include：
The mapping relations between the first influence factor and key index data sequence are established, are intended using structural equation model
It closes.
3. according to the method described in claim 1, the wherein described predetermined evaluation method is the direct lateral ratio based on single index
Compared with method.
4. according to the method described in claim 1, the wherein described predetermined evaluation method is the method given a mark based on weight.
5. a kind of power grid index system establishes device, reside in computing device, described device includes：
Creating unit is suitable for creating index system, and the index system includes key index data sequence；
Achievement data analytic unit is suitable for carrying out sensitivity analysis to key index data sequence using Sensitivity Analysis,
Determining influences the first influence factor of the first parameter, wherein and the first parameter is the variation of Project Economy Benefit, and
The quantitative relationship being adapted to set up between the first influence factor and key index data sequence, determining influences key index data
Second influence factor of sequence；
Achievement data processing unit is suitable for according to the second influence factor, using clustering method to key index data sequence
Carry out classification processing；
Indication system judgment unit, suitable for using predetermined evaluation method, to classification, treated that index system is evaluated, and it is defeated
Go out classification treated key index data sequence.
6. device according to claim 5, wherein the achievement data analytic unit is further adapted for：
The mapping relations between the first influence factor and key index data sequence are established, are intended using structural equation model
It closes.
7. device according to claim 5, wherein the predetermined evaluation method is the direct lateral ratio based on single index
Compared with method.
8. device according to claim 5, wherein the predetermined evaluation method is the method given a mark based on weight.
Include that power grid index system as described in any one of claim 5-8 establishes device 9. a kind of computing device.
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