CN105844396A - Enterprise ecosystem theory-based electric power transaction information value added service evaluation method - Google Patents

Enterprise ecosystem theory-based electric power transaction information value added service evaluation method Download PDF

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CN105844396A
CN105844396A CN201610163586.2A CN201610163586A CN105844396A CN 105844396 A CN105844396 A CN 105844396A CN 201610163586 A CN201610163586 A CN 201610163586A CN 105844396 A CN105844396 A CN 105844396A
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transaction information
layer
added service
electricity transaction
basal layer
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何洋
尚金成
文福栓
代勇
高春成
郑瑞晨
倪琳娜
陶力
史述红
方印
吴雨健
袁明珠
王蕾
李守保
王清波
刘杰
赵显�
谭翔
汪涛
袁晓鹏
王春艳
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Zhejiang University ZJU
State Grid Corp of China SGCC
Beijing Kedong Electric Power Control System Co Ltd
State Grid Henan Electric Power Co Ltd
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Zhejiang University ZJU
State Grid Corp of China SGCC
Beijing Kedong Electric Power Control System Co Ltd
State Grid Henan Electric Power Co Ltd
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Abstract

The invention belongs to the electric power system analysis field and relates to an enterprise ecosystem theory-based electric power transaction information value added service evaluation method. The method includes the following steps that: step 1, an electric power transaction information value added service evaluation index system composed of a target layer, a criterion layer, a sub criterion layer and a base layer is formed; step 2, index values of the base layer in the evaluation index system are obtained; step 3, the index values of the base layer obtained in the step 2 are processed, and layer-by-layer normalization is carried out, so that the index values of the criterion layer can be obtained; step 4, the normalized index values of the criterion layer which are obtained in the step 3 are utilized to construct row vectors, the row vectors are adopted as a sample for characterizing electric power transaction information value added service performance, clustering analysis is performed on the sample through adopting a K-means clustering algorithm; and step 5, the service performance of the electric power transaction information value added services is analyzed according to a calculation result obtained in the step 4. With the method of the invention adopted, the implementation of the electric power transaction information value added services can be guided more scientifically and reasonably.

Description

A kind of electricity transaction Information value-added service evaluation methodology theoretical based on enterprise ecosystem
Technical field
The invention belongs to Power System Analysis field, be specifically related to a kind of electricity transaction letter theoretical based on enterprise ecosystem Breath value-added service evaluation methodology.
Background technology
Along with Power Market Construction ground constantly advances, all kinds of market members play an active part in all kinds of power market transaction, to electric power Market operation rule is the most familiar, thus the most more pays close attention to market overall operation situation, it would be desirable to by reliable information Obtain channel, it is achieved collection and the analysis to all kinds of market informations, draw the key factor affecting self and managing, reduce certainly Body operating cost and business risk, improve the competitiveness of self, promotes transaction maximizing the benefits.Electricity transaction business relates to And various information of a great variety, data volume is huge, is distributed the most relatively decentralized.Storage is collected for single market member The workload of these data is very big, needs to put into special, huge manpower and time.Meanwhile, each market member couple So analysis ability of mass data is limited, how to collect and to obtain magnanimity information, how to get from magnanimity information with Self manages relevant effective information, to help market member to pinpoint the problems, optimizing management, increases the benefit, is electric power city The problem needing solution badly that field member is faced.Therefore based on above-mentioned situation, study a kind of being easy to and determine scientific and reasonable electricity The Performance evaluation of power Transaction Information service mode is particularly important.
Summary of the invention
For above-mentioned deficiency of the prior art, it is an object of the invention to provide one can believe electricity transaction scientifically and rationally Breath value-added service performance is estimated, and the raw based on enterprise of offer foundation is formulated in the exploitation for electricity transaction Information value-added service State systemtheoretical electricity transaction Information value-added service evaluation methodology.
For realizing above-mentioned technical purpose, the technology used in the present invention means are as follows:
A kind of electricity transaction Information value-added service evaluation methodology theoretical based on enterprise ecosystem, step is as follows:
Step 1, theoretical according to enterprise ecosystem, the external environment condition commenced business from company, profitability and to society Contributing three general orientation, the most each aspect sets corresponding evaluation index, thus defines one by destination layer, standard The electricity transaction Information value-added service assessment indicator system that then layer, sub-rule layer, basal layer are constituted.
Step 2, obtains the indices value of basal layer in assessment indicator system.
Step 3, processes the basal layer desired value in step 2, successively normalization, obtains rule layer desired value.
Step 4, the normalized rule layer desired value obtained by step 3 builds row vector, and using row vector as sign The sample of electricity transaction Information value-added service performance, uses K means clustering algorithm to carry out cluster analysis in sample.
Step 5, according to the result of calculation of step 4, is analyzed the service performance of electricity transaction Information value-added service.
In step 1, the electricity transaction Information value-added service theoretical based on enterprise ecosystem as shown in Figure 1 is with outside Assessment indicator system is set up, as shown in Figure 2 on the basis of environmental interaction mechanism.Assessment indicator system comprise destination layer, Rule layer, sub-rule layer, four levels of basal layer.The performance of destination layer reflection electricity transaction Information value-added service.Criterion Layer includes external environment condition, company's profitability and contribution to society, constitutes one-level subsystem.Sub-rule layer is two grades of subsystems, It it is the extension of one-level subsystem.The sub-rule layer of external environment condition includes market prospect and macro policy；Company's profitability Sub-rule layer includes project input, project output and technical support；The sub-rule layer of contribution to society includes socioeconomic Impact, the impact on ecological environment and the impact on society.Three grades of subsystems are indicator layer, are that knot is evaluated in impact The really key point of accuracy.The basal layer of market prospect include market share, the market coverage, product competitiveness, Market development degree and user's rate of increase；The basal layer of macro policy includes that policy support and policy limit；Project puts into Basal layer includes that human resources puts into, goods and materials put into and fund input；The basal layer of project output include investment yield and Static payback time；The basal layer of technical support includes technology maturity and innovation ability；Base is affected on socioeconomic Plinth layer includes promoting industrial economy development, promoting Regional Economic Development, technological progress benefit and employment benefit；To ecological ring The basal layer of border impact includes saving the energy, social resources are shared and reduce environmental pollution；Basis on society impact Layer includes property easy to use and its usage economy.
In step 3, specifically comprise the following steps that
3.1, data prediction.
Min-max method is used to be normalized basal layer desired value Vij under each sub-rule layer index, formula As follows:
V i j ′ = V i j - V i j m i n V i j m a x - V i j m i n - - - ( 1 ) ;
Wherein, i=1,2 ..., 5, j=1,2 ..., k, k represent that the basal layer under i-th sub-rule layer index refers to Mark number, Vijmin, Vijmax represent maximum and the minimum of electricity transaction information service basal layer desired value Vij respectively Value.
3.2, calculate coefficient of variation Bij of the basal layer desired value after normalization.
Coefficient of variation Bij computing formula is as follows:
B i j = σ i j V i j ′ ‾ - - - ( 2 ) ;
Wherein, Bij represents the basal layer desired value Vij coefficient of variation after normalization, σ ijRepresent basal layer desired value Vij standard deviation after normalization,Represent basal layer desired value Vij meansigma methods after normalization.
3.3, calculate weight wi of basal layer desired value Vijj。
Weight wijComputing formula as follows:
w i j = B i j Σ j = 1 k B i j - - - ( 3 ) .
3.4, calculate normalized sub-rule layer desired value Vi.
Normalized sub-rule layer desired value Vi target computing formula is as follows:
V i = Σ j = 1 k V i j ′ * w i j - - - ( 4 ) .
3.5, the method using 3.1～3.4, sub-rule layer desired value obtain normalized rule layer desired value.
In step 4, described row vector is xn=[Vn1 Vn2 Vn3], n represents the sample of electricity transaction Information value-added service This number.Specifically comprising the following steps that of cluster analysis
4.1, n sample of electricity transaction Information value-added service randomly selects 3 samples as in initial cluster The heart.
4.2, calculate n-3 the sample of residue distance dij to each cluster centre, by Samples Estimates to closest apoplexy due to endogenous wind. Distance dij computing formula is:
(dij)2=| | xi-xj||A=(xi-xj)TA(xi-xj) (5)；
Wherein, A takes unit matrix.
4.3, recalculate the cluster centre of each new class, until criterion function E convergence；The cluster centre of new class is new The meansigma methods of class.
4.4, obtain final Cluster Evaluation result and cluster centre Matrix C according to step 4.3；Cluster centre Matrix C represents Electricity transaction Information value-added service performance.
It is divided into 3 classes through cluster analysis sample the most at last, uses C respectivelym(m=1 ..., 3) represent three after Cluster Evaluation Class sample set, cluster centre parameter takes all kinds of sample parameter meansigma methodss, obtains cluster centre Matrix C:
C = I 11 L I 1 i M M I m 1 L I m i
Wherein:Represent the i-th rule layer index parameter of m-th cluster centre, VniRepresent certain sample I-th rule layer index parameter.
In steps of 5, carry out according to the evaluation index corresponding to electricity transaction Information value-added service and cluster centre Matrix C Analyze, hold the weak link that electricity transaction information service exists, find out solution and the reply service strategy of its correspondence, Formulate corresponding electricity transaction Information Service Mode accordingly, the more scientific electricity transaction Information value-added service that reasonably instructs Carry out.
The beneficial effects of the present invention is:
The performance of electricity transaction Information value-added service is divided into destination layer, criterion based on enterprise ecosystem theory by the present invention Layer, sub-rule layer, four levels of basal layer.The performance of destination layer reflection electricity transaction Information value-added service；Sub-rule layer It is two grades of subsystems, is the extension of one-level subsystem；Three grades of subsystems are indicator layer, are to affect evaluation result accuracy Key point.By using Min-max method to carry out preliminary normalization basal layer desired value, VC Method is utilized to obtain Go out the basal layer index weights after normalization, and then obtain normalized sub-rule layer desired value, in like manner can obtain rule layer Desired value.Utilize the rule layer desired value row vector after normalization, increase using row vector as building sign electricity transaction information The sample of value service performance, carries out cluster analysis to sample, draws final Cluster Evaluation result and cluster centre matrix.Root It is analyzed according to the evaluation index corresponding to electricity transaction Information value-added service and cluster centre Matrix C, holds electricity transaction The weak link that information service exists, finds out solution and the reply service strategy of its correspondence, formulates corresponding accordingly Electricity transaction Information Service Mode, more scientific reasonably instructs carrying out of electricity transaction Information value-added service.
Accompanying drawing explanation
Fig. 1 is that the present invention interacts with external environment condition based on the electricity transaction Information value-added service that enterprise ecosystem is theoretical Mechanism figure.
Fig. 2 is the assessment indicator system of the present invention.
Fig. 3 is the basic framework of the present invention.
Detailed description of the invention
Embodiment: a kind of electricity transaction Information value-added service evaluation methodology theoretical based on enterprise ecosystem, step is such as Under:
Step 1, theoretical according to enterprise ecosystem, the external environment condition commenced business from company, profitability and to society Contributing three general orientation, the most each aspect sets corresponding evaluation index, thus defines one by destination layer, standard The electricity transaction Information value-added service assessment indicator system that then layer, sub-rule layer, basal layer are constituted.
In the electricity transaction Information value-added service theoretical based on enterprise ecosystem as shown in Figure 1 and external environment condition phase interaction Assessment indicator system is set up, as shown in Figure 2 on the basis of mechanism.Assessment indicator system comprise destination layer, rule layer, Sub-rule layer, four levels of basal layer.The performance of destination layer reflection electricity transaction Information value-added service.Outside rule layer includes Portion's environment, company's profitability and contribution to society, constitute one-level subsystem.Sub-rule layer is two grades of subsystems, is one-level The extension of subsystem.The sub-rule layer of external environment condition includes market prospect and macro policy；The sub-criterion of company's profitability Layer includes project input, project output and technical support；The sub-rule layer of contribution to society include on socioeconomic impact, Impact on ecological environment and the impact on society.Three grades of subsystems are indicator layer, are that to affect evaluation result accurate The key point of property.The basal layer of market prospect includes that market share, the market coverage, product competitiveness, market are opened Send out degree and user's rate of increase；The basal layer of macro policy includes that policy support and policy limit；The basal layer that project puts into Put into including human resources, goods and materials put into and fund input；The basal layer of project output includes investment yield and static throwing Provide payoff period；The basal layer of technical support includes technology maturity and innovation ability；Basal layer bag is affected on socioeconomic Include promotion industrial economy development, promote Regional Economic Development, technological progress benefit and employment benefit；To eco-environmental impact Basal layer include save the energy, social resources share and reduce environmental pollution；The basal layer of society impact is included Property easy to use and its usage economy.
Step 2, obtains the indices value of basal layer in assessment indicator system.
Step 3, processes the basal layer desired value in step 2, successively normalization, obtains rule layer desired value.
Normalized specifically comprise the following steps that
3.1, data prediction.
Min-max method is used to be normalized basal layer desired value Vij under each sub-rule layer index, formula As follows:
V i j ′ = V i j - V i j m i n V i j m a x - V i j m i n - - - ( 1 ) ;
Wherein, i=1,2 ..., 5, j=1,2 ..., k, k represent that the basal layer under i-th sub-rule layer index refers to Mark number, Vijmin, Vijmax represent maximum and the minimum of electricity transaction information service basal layer desired value Vij respectively Value.
3.2, calculate coefficient of variation Bij of the basal layer desired value after normalization.
Coefficient of variation Bij computing formula is as follows:
B i j = σ i j V i j ′ ‾ - - - ( 2 ) ;
Wherein, Bij represents the basal layer desired value Vij coefficient of variation after normalization, σijRepresent basal layer desired value Vij standard deviation after normalization,Represent basal layer desired value Vij meansigma methods after normalization.
3.3, calculate weight w of basal layer desired value Vijij。
Weight wijComputing formula as follows:
w i j = B i j Σ j = 1 k B i j - - - ( 3 ) .
3.4, calculate normalized sub-rule layer desired value Vi.
Normalized sub-rule layer desired value Vi target computing formula is as follows:
V i = Σ j = 1 k V i j ′ * w i j - - - ( 4 ) .
3.5, the method using 3.1～3.4, sub-rule layer desired value obtain normalized rule layer desired value.
Step 4, the normalized rule layer desired value obtained by step 3 builds row vector, and using row vector as sign The sample of electricity transaction Information value-added service performance, uses K means clustering algorithm to carry out cluster analysis in sample.
Described row vector is xn=[Vn1 Vn2 Vn3], n represents the sample number of electricity transaction Information value-added service.
Specifically comprising the following steps that of cluster analysis
4.1, n sample of electricity transaction Information value-added service randomly selects 3 samples as in initial cluster The heart.
4.2, calculate n-3 the sample of residue distance dij to each cluster centre, by Samples Estimates to closest apoplexy due to endogenous wind. Distance dij computing formula is:
(dij)2=| | xi-xj||A=(xi-xj)TA(xi-xj) (5)；
Wherein, A takes unit matrix.
4.3, recalculate the cluster centre of each new class, until criterion function E convergence；The cluster centre of new class is new The meansigma methods of class.
4.4, obtain final Cluster Evaluation result and cluster centre Matrix C according to step 4.3；Cluster centre Matrix C represents Electricity transaction Information value-added service performance.
It is divided into 3 classes through cluster analysis sample the most at last, uses C respectivelym(m=1 ..., 3) represent three after Cluster Evaluation Class sample set, cluster centre parameter takes all kinds of sample parameter meansigma methodss, obtains cluster centre Matrix C:
C = I 11 L I 1 i M M I m 1 L I m i
Wherein:Represent the i-th rule layer index parameter of m-th cluster centre, VniRepresent certain sample I-th rule layer index parameter.
Step 5, according to the result of calculation of step 4, is analyzed the service performance of electricity transaction Information value-added service.
It is analyzed according to the evaluation index corresponding to electricity transaction Information value-added service and cluster centre Matrix C, holds electricity The weak link that power Transaction Information service exists, finds out solution and the reply service strategy of its correspondence, formulates accordingly Corresponding electricity transaction Information Service Mode, more scientific reasonably instructs carrying out of electricity transaction Information value-added service.
Evaluation index corresponding to electricity transaction Information value-added service each performance level and reply service strategy are as shown in table 1.
Table 1 electricity transaction Information value-added service evaluation index and coping strategy

Claims (5)

1. an electricity transaction Information value-added service evaluation methodology based on enterprise ecosystem theory, it is characterised in that Described method step is as follows:
Step 1: theoretical according to enterprise ecosystem, the external environment condition commenced business from company, profitability and to society Contributing three general orientation, the most each aspect sets corresponding evaluation index, thus defines one by destination layer, standard The electricity transaction Information value-added service assessment indicator system that then layer, sub-rule layer, basal layer are constituted；
Step 2: obtain the indices value of basal layer in assessment indicator system；
Step 3: the basal layer desired value in step 2 processed, successively normalization, obtains rule layer desired value；
Step 4: the normalized rule layer desired value obtained by step 3 builds row vector, and using row vector as sign The sample of electricity transaction Information value-added service performance, uses K means clustering algorithm to carry out cluster analysis in sample；
Step 5: according to the result of calculation of step 4, the service performance of electricity transaction Information value-added service is analyzed.
The electricity transaction Information value-added service evaluation side theoretical based on enterprise ecosystem the most according to claim 1 Method, it is characterised in that:
In described step 1, the performance of destination layer reflection electricity transaction Information value-added service；Rule layer include external environment condition, Company's profitability and contribution to society, constitute one-level subsystem；Sub-rule layer is two grades of subsystems, is one-level subsystem Extension；The sub-rule layer of external environment condition includes market prospect and macro policy；The sub-rule layer of company's profitability includes item Mesh input, project output and technical support；The sub-rule layer of contribution to society includes on socioeconomic impact, to ecological ring The impact in border and the impact on society；Three grades of subsystems are indicator layer, are the keys affecting evaluation result accuracy Place；The basal layer of market prospect include market share, the market coverage, product competitiveness, market development degree and User's rate of increase；The basal layer of macro policy includes that policy support and policy limit；The basal layer that project puts into includes manpower Resource input, goods and materials put into and fund input；The basal layer of project output includes investment yield and static payback time； The basal layer of technical support includes technology maturity and innovation ability；The socioeconomic basal layer that affects is included promotion industry Economic development, promotion Regional Economic Development, technological progress benefit and employment benefit；Basal layer bag to eco-environmental impact Include the saving energy, social resources are shared and reduce environmental pollution；The basal layer of society impact is included property easy to use And its usage economy.
The electricity transaction Information value-added service evaluation side theoretical based on enterprise ecosystem the most according to claim 1 Method, it is characterised in that in described step 3, specifically comprise the following steps that
3.1, data prediction；
Min-max method is used to be normalized basal layer desired value Vij under each sub-rule layer index, formula As follows:
V i j ′ = V i j - V i j m i n V i j m a x - V i j min - - - ( 1 ) ;
Wherein, i=1,2 ..., 5, j=1,2 ..., k, k represent that the basal layer under i-th sub-rule layer index refers to Mark number, Vijmin, Vijmax represent maximum and the minimum of electricity transaction information service basal layer desired value Vij respectively Value；
3.2, calculate coefficient of variation Bij of the basal layer desired value after normalization；
Coefficient of variation Bij computing formula is as follows:
B i j = σ i j V i j ′ ‾ - - - ( 2 ) ;
Wherein, Bij represents the basal layer desired value Vij coefficient of variation after normalization, σijRepresent basal layer desired value Vij standard deviation after normalization,Represent basal layer desired value Vij meansigma methods after normalization；
3.3, calculate weight w of basal layer desired value Vijij；
Weight wijComputing formula as follows:
w i j = B i j Σ j = 1 k B i j - - - ( 3 ) ;
3.4, calculate normalized sub-rule layer desired value Vi；
Normalized sub-rule layer desired value Vi target computing formula is as follows:
V i = Σ j = 1 k V i j ′ * w i j - - - ( 4 ) ;
3.5, the method using 3.1～3.4, sub-rule layer desired value obtain normalized rule layer desired value.
The electricity transaction Information value-added service evaluation side theoretical based on enterprise ecosystem the most according to claim 1 Method, it is characterised in that in described step 4, described row vector is xn=[Vn1 Vn2 Vn3], n represents that electricity transaction is believed The sample number of breath value-added service；Specifically comprising the following steps that of cluster analysis
4.1, n sample of electricity transaction Information value-added service randomly selects 3 samples as in initial cluster The heart；
4.2, calculate n-3 the sample of residue distance dij to each cluster centre, by Samples Estimates to closest apoplexy due to endogenous wind； Distance dij computing formula is:
(dij)2=| | xi-xj||Α=(xi-xj)TA(xi-xj) (5)；
Wherein, A takes unit matrix；
4.3, recalculate the cluster centre of each new class, until criterion function E convergence；The cluster centre of new class is new The meansigma methods of class；
4.4, obtain final Cluster Evaluation result and cluster centre Matrix C according to step 4.3；Cluster centre Matrix C represents Electricity transaction Information value-added service performance；
It is divided into 3 classes through cluster analysis sample the most at last, uses C respectivelym(m=1 ..., 3) represent three after Cluster Evaluation Class sample set, cluster centre parameter takes all kinds of sample parameter meansigma methodss, obtains cluster centre Matrix C:
C = I 11 L I 1 i M M I m 1 L I m i
Wherein:Represent the i-th rule layer index parameter of m-th cluster centre, VniRepresent certain sample I-th rule layer index parameter.
The electricity transaction Information value-added service evaluation side theoretical based on enterprise ecosystem the most according to claim 1 Method, it is characterised in that:
In described step 5, enter according to the evaluation index corresponding to electricity transaction Information value-added service and cluster centre Matrix C Row is analyzed, and holds the weak link that electricity transaction information service exists, and finds out solution and the reply service plan of its correspondence Slightly, formulate corresponding electricity transaction Information Service Mode accordingly, more scientific reasonably instruct electricity transaction information increment to take Carrying out of business.
CN201610163586.2A 2016-03-22 2016-03-22 Enterprise ecosystem theory-based electric power transaction information value added service evaluation method Pending CN105844396A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110880082A (en) * 2019-11-29 2020-03-13 中国工商银行股份有限公司 Service evaluation method, device, system, electronic equipment and readable storage medium
CN111612330A (en) * 2020-05-19 2020-09-01 浙江大学 Service mode quantitative evaluation method for cross-boundary service

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110880082A (en) * 2019-11-29 2020-03-13 中国工商银行股份有限公司 Service evaluation method, device, system, electronic equipment and readable storage medium
CN111612330A (en) * 2020-05-19 2020-09-01 浙江大学 Service mode quantitative evaluation method for cross-boundary service
CN111612330B (en) * 2020-05-19 2023-06-16 浙江大学 Service mode quantitative evaluation method for cross-border service

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