CN107395393A - A kind of power communication backbone network node evaluation method of automatic weight coefficient - Google Patents
A kind of power communication backbone network node evaluation method of automatic weight coefficient Download PDFInfo
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Abstract
The invention provides a kind of power communication backbone network node evaluation method of automatic weight coefficient, the limitation that can not be set automatically for the evaluation method weight coefficient of power communication backbone network node, take into full account the data characteristic of electric power backbone network communication node, on meter and the automatic weight coefficient Adjustable calculation analysis foundation of Principal Component Analysis Method, according to the weighting parameters of the pitch point importance adjust automatically evaluation of different indexs.The Node evaluation statistics of electric power backbone communications is obtained first, then the Node evaluation component value of electric power backbone communications is calculated, secondly the weight coefficient sequence for carrying out electric power backbone communications Node evaluation calculates, the sequence evaluation of electric power backbone communications node importance is finally realized, realizes the node availability evaluation of power communication backbone network.
Description
Technical field
The invention belongs to intelligent grid technical field of information communication, more particularly to a kind of power communication of automatic weight coefficient
Backbone network node evaluation method.
Background technology
With the construction of intelligent grid, power business species is on the increase with quantity, and power communication web frame is also further multiple
Miscellaneous, fail-safe analysis and risk management to network turn into a urgent problem to be solved.Research shows, Node Contraction in Complex Networks
Significance level significant difference be present, will cause serious network paralysis when key node is under attack.It is therefore desirable to pass through
Evaluation node significance level, the communication node being had a great influence to operation of power networks is effectively found, and it is reasonably protected, with
Prevent from causing large effect to power network due to the node failure.
The evaluation method of existing power communication backbone network node is the multiple joint behavior indexs of selection, passes through weighting scheme meter
The importance value of operator node, sorted from big to small according to calculating importance value and realize Node evaluation, typically step analysis adds
Quan Fa.The limitation that can not be set automatically for the weight coefficient of existing method, therefore the present invention passes through meter and principal component analysis
The automatic weight coefficient of method calculates, and obtains the optimal weighting coefficientses of the communication node of power communication backbone network, realizes power communication
The effect of the efficiency evaluation of the communication node of backbone network.
The content of the invention
It is an object of the invention to provide a kind of power communication backbone network node evaluation method of automatic weight coefficient, the party
Method comprises the following steps.
S1. the Node evaluation statistics of electric power backbone communications is obtained.
To the electric power backbone communications being made up of N number of node, each node k (k=1,2 ... N), statistics acquisition is every successively
Individual node includes the M node one including node connectivity, bearer service number, business shortest path number, reliable redundancy etc.
Level evaluating item, M is integer, and value is 4 ~ 8；Each one-level evaluating item i (i=1 ..., M) is directed to simultaneously, and statistics obtains
Include the node of M node two-level appraisement subitem including data packetloss rate, data delay rate, Jitter ratio, throughput etc.
Statistics, two-level appraisement subitem sequence number are designated as j (j=1 ..., M)；Thus electric power backbone communications node k M*M dimensions are built
Node evaluation data matrix Sk, wherein matrix element Sk(i, j) represents node k No.i one-level evaluating item, No.j individual two
The data value of level evaluation subitem, value are arithmetic number, and scope is 0 ~ 1.
S2. the Node evaluation component value of electric power backbone communications is calculated.
To each node k evaluating data matrix SkPrincipal Component Analysis Method operation is carried out, obtains Principal Component Analysis Method first
M characteristic value L of the correlation matrix after Orthogonal Decompositionm（m=1,2..,.M）, and its characteristic value LmCorresponding characteristic vector am；So
Matrix S is built afterwardskCorresponding M*M dimensional feature vectors matrix Ak= [a1,…,am,…,aM]T, wherein amIt is a character pair value
1*M characteristic vector；Then node k importance evaluation component value Matrix CkAccording to formula Ck= Sk•AkCalculate and obtain, wherein Ck
It is that node k No.i evaluating item, the importance of No.j two-level appraisement parameter evaluates component value corresponding to (i, j).
S3. the weight coefficient sequence of electric power backbone communications Node evaluation calculates.
If regulation coefficient is a, wherein a values are arithmetic number, and scope is 0 ~ 1；To each node k evaluating data matrix Sk
Principal Component Analysis Method operation is carried out, obtains M characteristic value L of the correlation matrix after Principal Component Analysis Method Orthogonal Decomposition firstm（m=
1,2..,.M）；Then by characteristic parameter LmAccording to descending sequence is worth, L can be obtained1>…>Lindex>…>LMIf characteristic value LmIt is right
The serial number index answered, then node k importance weight coefficient WmAccording to formula Wm=a* (1-a) ^ (index-1), which is calculated, to be obtained；
Then node k optimum weighting coefficient optWk,mAccording to formula optWk,m = Wm/(W1+…+ Wm…+ WM) calculate acquisition.
S4. electric power backbone communications node importance sequence evaluation.
If its corresponding electric power backbone communications Node evaluation importance degree Ik, it is important to each node k, its Node evaluation
Property degree IkAccording to formula Ik =(optWk1*Ck(1,1)+ optWk2*Ck(2,2) …optWkm*Ck(m, m)) calculate；Then by Ik
According to the descending sequence I of the size of value1>…>Lk>…>LN, that is, realize that electric power backbone communications node importance is evaluated, wherein
IkValue is bigger, then illustrates that corresponding node k importance is higher.
Compared with general technology, the power communication backbone network node evaluation method of the automatic weight coefficient of the present invention, for electricity
The limitation that the evaluation method weight coefficient of power communication backbone node can not be set automatically, take into full account electric power backbone's Network Communication
The data characteristic of node, calculated in meter and the automatic weight coefficient of Principal Component Analysis Method on analysis foundation, according to different indexs
Importance adjust automatically evaluation weighting parameters, realize power communication backbone network node availability evaluation.
Brief description of the drawings
Fig. 1 is the inventive method overall flow figure.
Fig. 2 is representative power backbone communication network topology schematic diagram.
Fig. 3 is weight coefficient of the present invention sequence calculating process schematic diagram.
Embodiment
It is as shown in Figure 1 flow chart of the method for the present invention, traveling one is entered to the method for the present invention below in conjunction with instantiation
Step explanation, the power communication backbone network node evaluation method of automatic weight coefficient provided by the invention, comprises the following steps.
S1. the Node evaluation statistics of electric power backbone communications is obtained.
To the electric power backbone communications being made up of N number of node, each node k (k=1,2 ... N), statistics acquisition is every successively
Individual node includes the M node one including node connectivity, bearer service number, business shortest path number, reliable redundancy etc.
Level evaluating item, M is integer, and value is 4 ~ 8；Each one-level evaluating item i (i=1 ..., M) is directed to simultaneously, and statistics obtains
Include the node of M node two-level appraisement subitem including data packetloss rate, data delay rate, Jitter ratio, throughput etc.
Statistics, two-level appraisement subitem sequence number are designated as j (j=1 ..., M)；Thus electric power backbone communications node k M*M dimensions are built
Node evaluation data matrix Sk, wherein matrix element Sk(i, j) represents node k No.i one-level evaluating item, No.j individual two
The data value of level evaluation subitem, value are arithmetic number, and scope is 0 ~ 1.
One representative power backbone communication network topology is as shown in Fig. 2 electric power bone in example to being made up of N=6 node
Dry communication network, each node k (k=1,2 ... N), count each node of acquisition successively includes node connectivity, bearer service
Count, business shortest path number, the M=4 node one-level evaluating item including reliable redundancy；Commented simultaneously for each one-level
Valency parameter item i (i=1 ..., M), statistics obtain and include data packetloss rate, data delay rate, Jitter ratio, including throughput
The node statistics of M node two-level appraisement subitem, two-level appraisement subitem sequence number are designated as j (j=1 ..., M)；Thus structure electricity
Power backbone communications node k M*M dimension Node evaluation data matrix Ssk, wherein matrix element Sk(i, j) represents node k No.i
The data value of individual one-level evaluating item, No.j two-level appraisement parameter, as shown in Figure 3.
S2. the Node evaluation component value of electric power backbone communications is calculated.
To each node k evaluating data matrix SkPrincipal Component Analysis Method operation is carried out, obtains Principal Component Analysis Method first
M characteristic value L of the correlation matrix after Orthogonal Decompositionm（m=1,2..,.M）, and its characteristic value LmCorresponding characteristic vector am；So
Matrix S is built afterwardskCorresponding M*M dimensional feature vectors matrix Ak= [a1,…,am,…,aM]T, wherein amIt is a character pair value
1*M characteristic vector；Then node k importance evaluation component value Matrix CkAccording to formula Ck= Sk•AkCalculate and obtain, wherein Ck
It is that node k No.i evaluating item, the importance of No.j two-level appraisement parameter evaluates component value corresponding to (i, j).
To each node k evaluating data matrix S in examplekPrincipal Component Analysis Method operation is carried out, obtains main composition first
M=4 characteristic value the L of correlation matrix after analytic approach Orthogonal Decompositionm（m=1,2..,.M）, and its characteristic value LmCorresponding feature to
Measure am；Then matrix S is builtkCorresponding M*M dimensional feature vectors matrix Ak= [a1,…,am,…,aM]T, wherein amIt is a correspondence
Characteristic value obtains 1*M characteristic vector；Then node k importance evaluation component value Matrix CkAccording to formula Ck= Sk•AkCalculating obtains
, wherein CkIt is that node k No.i evaluating item, the importance of No.j two-level appraisement parameter is evaluated corresponding to (i, j)
Component value.
S3. the weight coefficient sequence of electric power backbone communications Node evaluation calculates.
If regulation coefficient is a, wherein a values are arithmetic number, and scope is 0 ~ 1；To each node k evaluating data matrix Sk
Principal Component Analysis Method operation is carried out, obtains M characteristic value L of the correlation matrix after Principal Component Analysis Method Orthogonal Decomposition firstm（m=
1,2..,.M）；Then by characteristic parameter LmAccording to descending sequence is worth, L can be obtained1>…>Lindex>…>LMIf characteristic value LmIt is right
The serial number index answered, then node k importance weight coefficient WmAccording to formula Wm=a* (1-a) ^ (index-1), which is calculated, to be obtained；
Then node k optimum weighting coefficient optWk,mAccording to formula optWk,m = Wm/(W1+…+ Wm…+ WM) calculate acquisition.
Characteristic parameter { L is set in examplem}={ 0.34,0.56,0.32,0.12 }, regulation coefficient is a=0.6；To each
Node k evaluating data matrix SkPrincipal Component Analysis Method operation is carried out, obtains the phase after Principal Component Analysis Method Orthogonal Decomposition first
Close M characteristic value L of matrixm（m=1,2..,.M）；Then by characteristic parameter LmAccording to descending sequence is worth, L can be obtained1>…>
Lindex>…>LMIf characteristic value LmCorresponding serial number index, then node k importance weight coefficient WmAccording to formula Wm=a*
(1-a) ^ (index-1), which is calculated, to be obtained, and can calculate { Wm}={0.24,0.6,0.096,0.0384}；Then node k optimum weighting
Coefficient optWk,mAccording to formula optWk,m = Wm/(W1+…+ Wm…+ WM) acquisition is calculated, { optW can be calculatedm}=[0.2463,
0.6158,0.0985,0.0394], as shown in Figure 3.
S4. electric power backbone communications node importance sequence evaluation.
If its corresponding electric power backbone communications Node evaluation importance degree Ik, it is important to each node k, its Node evaluation
Property degree IkAccording to formula Ik =(optWk1*Ck(1,1)+ optWk2*Ck(2,2) …optWkm*Ck(m, m)) calculate；Then will
IkAccording to the descending sequence I of the size of value1>…>Lk>…>LN, that is, realize that electric power backbone communications node importance is evaluated, its
Middle IkValue is bigger, then illustrates that corresponding node k importance is higher.
The foregoing is only a preferred embodiment of the present invention, but protection scope of the present invention be not limited thereto,
Any one skilled in the art the invention discloses technical scope in, the change or replacement that can readily occur in,
It should all be included within the scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims
It is defined.
Claims (3)
1. a kind of power communication backbone network node evaluation method of automatic weight coefficient, comprises the following steps：
S1. the Node evaluation statistics of electric power backbone communications is obtained；
S2. the Node evaluation component value of electric power backbone communications is calculated；
S3. the weight coefficient sequence of electric power backbone communications Node evaluation calculates；
S4. electric power backbone communications node importance sequence evaluation.
2. the power communication backbone network node evaluation method of automatic weight coefficient according to claim 1, it is characterised in that
In the step S1, to the electric power backbone communications being made up of N number of node, each node k (k=1,2 ... N), containing M node
One-level evaluating item, M are integer, and value is 4 ~ 8；Each one-level evaluating item i (i=1 ..., M) is directed to simultaneously, containing M
Node two-level appraisement subitem, two-level appraisement importance parameter sequence number are designated as j (j=1 ..., M)；SkIt is node k M*M dimension nodes
Evaluating data matrix, wherein matrix element Sk(i, j) represents node k No.i one-level scoring item, No.j two-level appraisement
The data value of item, value are arithmetic number, and scope is 0 ~ 1.
3. electric power backbone network communication node evaluation method according to claim 1, it is characterised in that in the step S3,
If regulation coefficient is a, wherein a values are arithmetic number, and scope is 0 ~ 1；To each node k evaluating data matrix SkCarry out it is main into
Part analytic approach operation, M characteristic value L of the correlation matrix after Principal Component Analysis Method Orthogonal Decomposition is obtained firstm（m=1,2..,
.M）；Then by characteristic parameter LmAccording to descending sequence is worth, L can be obtained1>…>Lindex>…>LMIf characteristic value LmCorresponding sequence
Number be index, then node k importance weight coefficient WmAccording to formula Wm=a* (1-a) ^ (index-1), which is calculated, to be obtained；Then node
K optimum weighting coefficient optWk,mAccording to formula optWk,m = Wm/(W1+…+ Wm…+ WM) calculate acquisition.
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