CN115759765A - Method for evaluating running state of power optical transmission network - Google Patents
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Abstract
An evaluation method for the running state of an electric power optical transmission network relates to the field of electric power optical transmission networks, and aims to solve the problems that in the prior art, an evaluation index system evaluation method only aims at calculation of an evaluation result, and only designs a corresponding evaluation method according to evaluation index characteristics in an evaluation process, so that the evaluation method is single in evaluation aspect and poor in evaluation effect, the evaluation index for the running state of the electric power optical transmission network is determined, the combination weight of the state evaluation indexes is obtained through calculation, an electric power optical transmission network running state evaluation algorithm is designed, and the evaluation of the state evaluation indexes is completed. According to the method, evaluation results of three aspects of a fuzzy comprehensive evaluation method, a utility function method and an extension coupling evaluation method are designed according to three levels of index features under network scale, operation quality and management factors, the evaluation method is designed according to the index features to more accurately reflect the condition of each index, so that the evaluation results are closer to the actual condition, and the operation state evaluation results are calculated to facilitate operation and maintenance managers to integrally master the network condition.
Description
Technical Field
The invention relates to the field of power optical transmission networks, in particular to a method for evaluating the running state of a power optical transmission network.
Background
Along with the common application of the power optical transmission network, the transmitted information is more and more, the signal transmission rate is faster and faster, and once the power transmission network fails, the whole power communication system can be damaged greatly. In order to ensure the robustness, the operation safety and the reliability of the construction of the power optical transmission network, the condition of the network needs to be evaluated periodically, and potential high-level risk points existing in the power optical transmission network are analyzed, so that the management and the control of risk threats are facilitated. Therefore, scientific management and evaluation are necessary to ensure safe, normal and stable operation of the electric power optical transmission network.
Existing research has focused primarily on two directions. One class provides various electric power optical transmission network reliability evaluation indexes based on network operation; the other method is mainly based on the angles of equipment and optical cables, considers the capability of risk assessment network service operation safety of the power optical transmission network, and analyzes the network reliability from the angle of a service channel. The method is really important for evaluating the operation quality of the power optical transmission network, but the evaluation of the early network construction scale and the later management operation and maintenance should not be ignored.
For the optimization of an evaluation index system, a judgment weight method is mostly adopted in the existing research, a judgment matrix is constructed in an expert scoring mode, the weight occupied by each index is further calculated, and the index with the smaller weight is deleted. The expert scoring mode has strong subjectivity, the scoring difference of each expert can influence the accuracy of a calculation result, and the weight of the deleted index has no standard value. Therefore, the design of the overlapping information analysis method can accurately analyze the internal structure of the index and delete the redundant index.
In the current research, subjective methods such as an analytic hierarchy process and objective methods such as an entropy weight method are mostly adopted for determining the weight of the evaluation index. The analytic hierarchy process adopts an expert scoring mode to construct a judgment matrix so as to calculate the weight of each evaluation index, and the accuracy of weight calculation is influenced by differences of expert scoring; the entropy weight method utilizes index data to calculate information entropy so as to calculate the weight of each evaluation index, and the accuracy of weight calculation is also influenced due to different dimensions of the index data, so that the error of weight calculation can be greatly reduced by adopting subjective and objective combination weighting.
Most of the existing research of the evaluation method adopts a certain method of a fuzzy comprehensive evaluation method, a utility function method and the like to evaluate certain aspects of operation risk, reliability and the like, the evaluation method is not designed according to evaluation index characteristics, the evaluation aspect is single, and the comprehensive evaluation result is not calculated according to the evaluation result of each aspect. Therefore, the corresponding evaluation method is designed according to the index characteristics, and the comprehensive evaluation value is calculated, so that the running state of the power optical transmission network can be better reflected.
The current research only stays in the establishment of an evaluation index system and the calculation of an evaluation result, and a feasible improvement measure is not made by tracing the problem according to the evaluation result. And comparing according to the comprehensive evaluation result, wherein the better the comprehensive evaluation result is, the lower the urgency of investment is. There is a close correlation between the evaluation indexes of the electric power optical transmission network, such as: the resource utilization rate is too low, and the network operation efficiency is too low; the resource utilization rate is too high, and the network expansibility is too low, so that the method is difficult to be used for specifically guiding the planning, construction, operation, management and maintenance of the power communication network only by depending on the evaluation result.
Therefore, an index system is optimized, scientific and reasonable index weight is calculated, an evaluation method is designed according to index characteristics, and a comprehensive evaluation result is calculated according to the evaluation result of each aspect, so that the evaluation of the level of the power optical transmission network is facilitated.
Disclosure of Invention
The invention provides an operation state evaluation method of an electric power optical transmission network, aiming at solving the problems that in the prior art, the method for evaluating an index system only aims at calculation of an evaluation result, and only designs a corresponding evaluation method according to evaluation index characteristics in an evaluation process, so that the evaluation aspect is single, the evaluation effect is poor and the like.
The method for evaluating the running state of the electric power optical transmission network is realized by the following steps:
step one, determining an evaluation index of the running state of the power optical transmission network; the specific process is as follows:
analyzing the operation state evaluation requirement characteristics;
determining a hierarchical structure of an operation state evaluation index system;
step three, preliminarily selecting an operation state evaluation index;
step four, optimizing an operation state evaluation index;
step two, calculating to obtain the combined weight of the state evaluation indexes according to the determined operation state evaluation indexes of the power optical transmission network in the step one; the specific process is as follows:
step two, calculating subjective weight of an evaluation index by adopting an interval fuzzy analytic hierarchy process;
secondly, calculating the objective weight of the evaluation index by adopting an entropy weight method;
step three, calculating the combination weight of the state evaluation indexes by adopting a subjective and objective combination weight assignment method;
designing an operation state evaluation algorithm of the power optical transmission network to complete evaluation of the state evaluation index; the specific process is as follows:
thirdly, selecting an operation state evaluation method according to the index characteristics;
step two, determining a membership function, a utility function and an extension coupling function;
thirdly, calculating a comprehensive value of the network scale evaluation value;
and step three, determining a combined evaluation method according to the comprehensive value.
The invention has the beneficial effects that:
the overlapped information analysis rule designed by the invention adopts a mode of analyzing the formation of the index factors to construct the information overlapped matrix and further calculate the importance degree of each index in all three levels of indexes to realize the purpose of screening.
Firstly, improving a section fuzzy analytic hierarchy process, scoring by experts in a section number mode, constructing a section judgment matrix and further calculating the subjective weight of each index, wherein the section number mode greatly reduces the difference between scores of the experts; secondly, calculating objective weight of each index by adopting an entropy weight method; and finally, carrying out comprehensive calculation on the subjective weight and the objective weight by adopting a subjective and objective combination weighting method to obtain a combination weight, and making up the difference between an analytic hierarchy process and an entropy weight method by the method so that the evaluation weight can more accurately reflect objective facts.
The operation state evaluation algorithm provided by the invention is used for calculating evaluation results of a fuzzy comprehensive evaluation method, a utility function method and an extension coupling evaluation method according to three-level index characteristics under network scale, operation quality and management factors, and calculating the operation state evaluation result by adopting a combined evaluation method. The evaluation method can be designed according to the index characteristics, the condition of each index can be more accurately reflected, the evaluation result is closer to the actual condition, the operation state evaluation result is calculated, operation and maintenance managers can conveniently grasp the network condition integrally, and reference is provided for the next step of management and development.
The invention also traces problem indexes according to the evaluation result by a design problem tracing method so as to give a feasible suggestion. The algorithm can comprehensively reflect the condition of the electric power optical transmission network more comprehensively and provide reference for workers.
Drawings
Fig. 1 is a flowchart of the method for evaluating the operation state of the electric power optical transmission network according to the present invention.
Detailed Description
In the first embodiment, the method for evaluating the operation state of the power optical transmission network is described with reference to fig. 1, and the method is implemented by the following steps:
step 1: determining an evaluation index system of the running state of the power optical transmission network;
step 1-1: analyzing the running state evaluation demand characteristics;
step 1-2: determining a hierarchical structure of an operation state evaluation index system;
step 1-3: preliminarily selecting an operation state evaluation index;
in order to facilitate the calculation of the subsequent steps, the second-level index and the third-level index are respectively numbered in this embodiment: the second level index is B l (l =1,2,3) where the network size is B 1 Run mass of B 2 The management factor is B 3 (ii) a Has a three-level index of
(i l ＝1,2,...,n l ) Wherein, the three indexes of the total length of the optical cable under the network scale are respectively
The three-level indexes of the network connectivity under the operation quality are respectively
The three-level indexes of the defect elimination timeliness rate of the optical cable under the management factors are respectively
Step 1-4: optimizing an operation state evaluation index; the method comprises the following specific steps:
for each three-level index under the network scale, firstly, the three-level index under the network scale is determined
The analysis of the three-level indexes under the scale of the formed network
Fourth-order index of
Three-level index
The factor (c) is calculated as shown in formula (1):
in the formula:
represents the ith of the network scale 1 The third level index
A factor of (
Is the ith 1 The number of factors corresponding to each of the three levels of metrics),
is shown as
Factor i at network scale 1 All four levels of indexes of each index
The proportion of the active carbon in the water is as follows,
for three-level indexes only affecting the network scale
Of the specific factor (c).
Then, for the ith network scale 1 Individual three-level index
And k is 1 Individual three-level index
(k 1 ＝1,2,...,n 1 ) And (3) comparing to obtain element values in the information overlapping matrix to further construct the information overlapping matrix, wherein the comparison calculation among the three levels of indexes under the network scale is as shown in a formula (2):
in the formula: if present, is
Repeating the formula (ii), then the factor in the molecule
And factors in denominator
And simultaneously removing, wherein the ratio of the number of the residual factors in the numerator to the number of the residual factors in the denominator after the removal is the element value in the information overlapping matrix I of each three-level index under the network scale, and the information overlapping matrix I is as follows:
finally, calculating the eigenvalue vector of the information overlapping matrix I
Maximum eigenvalue is A max (max∈{1,2,...,n 1 }) according to the maximum eigenvalue a of the matrix max Corresponding feature vector
(
Is the ith on the scale of the network 1 The proportion of each three-level index in the three-level indexes under all network scales,
the larger the value of (d), the more important the index is represented), the feature vector W is extracted 1 In
Removing indexes smaller than 0.1 to obtain three-level indexes under the optimized network scale
(i′ 1 ＝1,2,...,n′ 1 )。
Repeating the above processes to obtain three-level index under optimized operation quality
(i' 2 ＝1,2,...,n' 2 ) And three-level index under management factors
(i′ 3 ＝1,2,...,n′ 3 )。
Step 2: calculating the combination weight of the operation state evaluation indexes of the power optical transmission network;
step 2-1: calculating subjective weight by adopting an interval fuzzy analytic hierarchy process; the method comprises the following specific steps:
first, a relative importance scale of the index is determined. Quantitatively describing three-level indexes under the network scale by adopting a method of 0.1-0.9 of the scale
With another three-level index
(k′ 1 ＝1,2,...,n′ 1 ) The relative importance scale of the indicators is shown in table 1:
TABLE 1
Wherein the content of the first and second substances,
is composed of
And
the resulting scale values are compared. By passing
Is calculated to obtain
And
scale value of comparison
Secondly, inviting experts to be three-level indexes under the optimized network scale respectively
And with
Comparing and scoring to obtain a scale value
Expressed in the form of interval number to obtain
Constructing interval judgment matrix
J 1 Is given as
J 1 Is given as
Judging the interval matrix J 1 In (1)
The interval number vector of (2) is recorded as
Wherein the content of the first and second substances,
O 1 is given as
O 1 Is given as
Then, the interval characteristic root method is used to respectively obtain
And
normalized eigenvector with positive component corresponding to the largest eigenvalue of (2)
And
and calculating the weight coefficient g of the three-level index under the network scale 1 And h 1 As shown in formulas (3) and (4):
finally, calculating a weight vector alpha in the form of interval number of three-level indexes under the optimized network scale 1 As shown in formula (5):
for the weight vector alpha 1 Taking median of each interval element in the network and normalizing to obtain three-level indexes of the correspondingly optimized network scale
Weight of (2)
Weight vector of all three-level indexes under optimized network scale
Repeating the above processes to obtain the subjective weight vectors of all three-level indexes under the optimized operation quality and management factors
And
for the determination of the subjective weight of the secondary index, firstly, the same quantitative scale as in Table 1, B, is adopted l (l =1,2,3) and B c (c =1,2,3) and expressed in terms of the number of intervals to obtain a scale value
Structural section determination matrix J' = (B) lc ) 3×3 The lower boundary matrix of J' is noted
The upper boundary matrix of J' is noted as
Judging B in the matrix J l The section number vector of (1) is represented by O '= (O' 1 ,o' 2 ,o' 3 ) Wherein, in the step (A),
the lower boundary matrix of O' is noted as
The upper boundary matrix of O' is noted as
Then, J 'was obtained by the interval feature root method' - And J' + Has a normalized feature vector x 'of positive component corresponding to the maximum feature value of (2)' - And x' + And calculating the weight coefficients g 'and h' of the secondary indexes as shown in formulas (6) and (7):
and finally, calculating a weight vector alpha' in the form of the interval number of the secondary indexes, as shown in the formula (8):
taking median of each interval element in alpha' and normalizing to obtain weight alpha corresponding to secondary index l Subjective weight vector α = (α) constituting all secondary indices 1 ,α 2 ,α 3 )。
Step 2-2: calculating objective weight by adopting an entropy weight method; the specific process is as follows:
firstly, establishing a three-level evaluation index matrix under an optimized network scale for an evaluation object of the power optical transmission network, wherein the three-level index under the optimized network scale
Is n' 1 The total of the evaluation sample data is gamma groups, each group has f index values, and a three-level index initial evaluation matrix Y under the network scale is formed 1 The following were used:
wherein the content of the first and second substances,
(i′ 1 ＝1,2,...,n′ 1 u =1,2.,. Gamma.) is the i ' in the u ' th set of assessment sample data ' 1 Three levels of index values at the scale of the network.
Secondly, each three-level index under the network scale
Are different in dimension and magnitude, they are normalized as shown in formula (9):
in the formula:
respectively represent a matrix Y 1 Of medium to' 1 The maximum and minimum values in all data are listed.
Three-level index under network scale
Obtaining an evaluation index matrix Y after carrying out dimensionalization processing 1 * The following were used:
calculating three-level index under network scale
Weight vector of
As shown in equation (10):
then, three-level indexes under the network scale are calculated
Information entropy of (2)
As shown in formula (11):
finally, calculating three-level index under network scale
Objective weight of
As shown in equation (12):
by the above calculationObtaining a three-level index objective weight vector under the optimized network scale
Repeating the above processes to obtain objective weight vectors of all three-level indexes under optimized operation quality and management factors
And
for determining the objective weight of the second-level index, a second-level evaluation index matrix is established first, wherein the second-level index B l The number of the evaluation sample data is 3, the evaluation sample data has gamma groups, each group has f index values, and a secondary index initial evaluation matrix Y is formed 2 The following were used:
wherein x is u1 ,x u2 ,x u3 Evaluating network scale, operation quality and management factor index data in the sample data for the u group respectively;
secondly, for the second level index B l The index value is normalized as shown in formula (13):
in the formula: max { x ul },min{x ul Represent matrices Y, respectively 2 Maximum and minimum values in all data in column I.
The second level index B l Obtaining an evaluation index matrix after carrying out dimensionalization processing
The following were used:
calculating a second level index B l Weight vector Z of l As shown in formula (14):
then, a secondary index B is calculated l Information entropy E of l As shown in formula (15):
finally, calculating a secondary index B l Objective weight ω of l As shown in formula (16):
obtaining objective weight vector omega = (omega) of all secondary indexes through the calculation 1 ,ω 2 ,ω 3 )。
Step 2-3: calculating the combination weight by adopting a subjective and objective combination weight assignment method;
subjective weight vector alpha 'of all three-level indexes under network scale after comprehensive optimization' 1 And objective weight vector ω' 1 And calculating the three-level index of the optimized network scale
Combining weights of
As shown in equation (17):
obtaining all three-level index combination weight vectors under the optimized network scale
Repeating the above processes to obtain all three-level index combination weight vectors under optimized operation quality and management factors
And
for the determination of the combination weight of the secondary indexes, the subjective weight vector alpha and the objective weight vector omega of the secondary indexes are synthesized, and the secondary indexes B are calculated l Combining weight λ of l As shown in formula (18):
obtaining a combined weight vector lambda = (lambda) of the secondary index 1 ,λ 2 ,λ 3 )。
And step 3: designing an evaluation algorithm of the running state of the power optical transmission network;
step 3-1: selecting an operation state evaluation method according to the index characteristics;
step 3-2: determining a membership function, a utility function and an extension coupling function;
step 3-3: calculating a comprehensive value of the network scale evaluation value; the method specifically comprises the following steps: the fuzzy value linear sequence method based on the Vague set comprises the following specific steps of:
as the fuzzy comprehensive evaluation is to calculate four grade values of excellence, good, medium and poor by the membership function, the fuzzy comprehensive evaluation is used for carrying out data synthesis on the network scale evaluation value by adopting a fuzzy value linear sequence method based on a figure in order to facilitate the calculation of the evaluation value of the operation state of the electric power optical transmission network by a combination evaluation method.
The first step is as follows: according to the fuzzy value obtained by calculating the membership function of the three-level index under the network scale, constructing a fuzzy value matrix eta as follows:
wherein the content of the first and second substances,
respectively represent three-level indexes according to the network scale
The membership function of the system is used for calculating four grades of fuzzy values of excellence, good, medium and difference.
The second step is that: for a certain row element value in the fuzzy value matrix eta
Corresponding index
Fuzzy value of
i′ 1 ＝1,2,...,n′ 1 ；q 1 Column 1, q, representing the matrix eta 2 Column 2, q, representing the matrix eta 3 Column 3, q, representing the matrix eta 4 Representing column 4 of the matrix η. Sorting according to the following scheme:
(1) If it is
(
And d is 2 ≠d 1 ) Then scheme D 1 Arranged in scheme D 2 In the front. (
And
respectively, the d-th in the fuzzy value matrix eta 1 Line q 1 Column and d 2 Line q 1 Value of the element of the column, D 1 Denotes the d-th 1 Line q 1 Index of column, D 2 Denotes the d-th 2 Line q 1 Index of column)
(2) If it is
Then if
Then scheme D 1 Arranged in scheme D 2 In the front.
(3) If it is
And is
Then if
Then scheme D 1 Arranged in scheme D 2 In the front.
Sequencing each pattern according to the numerical value of each row in a fuzzy value matrix eta to form 4 fuzzy value linear sequences which are respectively marked as L 1 ,L 2 ,L 3 ,L 4 。
The third step: constructing a fuzzy priority matrix Q in the scheme set X, as shown in formula (19):
the fourth step: scheme sorting is carried out, tau =0.5 is taken to cut the fuzzy priority matrix Q, and a cut matrix Q is obtained τ As shown in formula (20):
calculating a fuzzy comprehensive value as shown in formula (21):
in the formula: xi 1 ,ξ 2 ,ξ 3 ,ξ 4 Representing excellent, good, medium, and poor weights.
Step 3-4: determining a combined evaluation method;
adopting the Pierman grade as a judgment basis of the method consistency, carrying out prior consistency test on a fuzzy comprehensive evaluation method, a utility function method and an extension coupling evaluation method, and expressing by rho as shown in a formula (22):
in the formula: delta r is a sample grade difference value under different methods; rho belongs to [0,1], when rho → 1, the consistency among the methods is better, which indicates that the relevance among the methods is larger, otherwise, the relevance is smaller; if the calculation result exceeds the interval range, the selection method should be considered again.
And then, obtaining the optimal evaluation value according to the condition that the sum of the deviations between the sample combination evaluation value and the epsilon single evaluation methods is minimum as an optimization target. For epsilon single evaluation methods V = (V) 1 ,V 2 ,...,V ε ) E objects to be evaluated P = (P) 1 ,P 2 ,...,P e ) And the evaluation vector obtained under the t evaluation method is shown as the formula (23):
the combined evaluation value vector is as shown in equation (24):
the deviation of the combined evaluation value vector and the j-th evaluation value vector is as shown in equation (25):
constructing an optimization model with the minimum deviation square sum, and calculating to obtain a comprehensive evaluation value F of the operation state of the power optical transmission network, as shown in formula (26):
step 3-5: designing an index problem tracing method and analyzing, summarizing and evaluating suggestions; the problem tracing method comprises the following steps:
firstly, summarizing and classifying related indexes according to the definition and the calculation formula of each index;
secondly, determining a reference suggested value standard of each index;
thirdly, judging whether to perform problem tracing according to the second-level index evaluation result, if so, judging the evaluation result of the third-level index, and finding a problem index;
then, calculating the difference between the real data of the problem index and the index suggested value;
and finally, giving out operability suggestions according to the calculated difference.
And 4, step 4: calculating an operation state evaluation value of the power optical transmission network, tracing problems and giving suggestions;
step 4-1: calculating an index value according to each index formula and recording the index value;
step 4-2: respectively calculating corresponding membership function values, utility function values and extension coupling function values and grading;
step 4-3: calculating a comprehensive value of the network scale evaluation value;
step 4-4: calculating a combined evaluation value;
and 4-5: and tracing the problems according to the evaluation result and giving an evaluation suggestion.
The second embodiment is an example of the method for evaluating the operation state of the power optical transmission network according to the first embodiment: the method comprises the following specific steps:
step 1, determining an evaluation index system of the running state of the power optical transmission network; the method specifically comprises the following steps:
step 1-1: analyzing the running state evaluation requirement characteristics;
the operation state of the electric power optical transmission network should take the robustness of construction, the safety and the reliability of operation as basic criteria. On one hand, the existing electric power optical transmission network lacks long-term unified planning, the stability and the safety of an early-stage cake-type extensive network architecture are poor, the structure of a logic system is complex, the management difficulty is high, once a core node fails, the processing is relatively difficult, and the construction of a robust and stable electric power optical transmission network is a precondition for ensuring the safe operation of the electric power optical transmission network. Once the existing electric power optical transmission network fails, the existing electric power optical transmission network faces the operation risk of losing monitoring and being incapable of scheduling, and the safe and effective operation and maintenance management is the follow-up guarantee of the safe operation of the existing electric power optical transmission network.
Step 1-2: determining a hierarchical structure of an operation state evaluation index system;
according to an index system of an analytic hierarchy process, a model is established, firstly, an electric power optical transmission network is divided into a plurality of sides or dimensions, then, the sides or the dimensions are continuously subdivided, and a hierarchical structure is established. The invention adopts a four-layer structure of an analytic hierarchy process to initially construct an electric power optical transmission network evaluation index system, a target layer is electric power optical transmission network operation state evaluation, a criterion layer is network scale evaluation, operation quality evaluation and management factor evaluation, an index layer is each index under each criterion, and a factor layer is four-layer indexes forming each three-level index.
Step 1-3: preliminarily selecting an operation state evaluation index;
the network scale evaluation selects seven indexes of total length of the optical cable, annual increase rate of the optical cable, coverage rate of the optical fiber, total number of optical transmission equipment, increase rate of the optical transmission equipment, total number of service channels and total number of service nodes from three aspects of optical fiber scale, equipment scale and service scale. The operation quality evaluation selects twenty-four indexes of network connectivity, network topology ring formation rate, important service double channel rate, spare part integrity rate, network disaster-resistant emergency capacity, optical transmission link performance, optical fiber circuit availability, service channel protection rate, equipment automatic monitoring rate, equipment utilization shared protection ring rate, machine room environment, spare part stock rate, instrument and meter stock rate, bandwidth reservation, optical cable resource average utilization rate, fiber core resource average utilization rate, optical transmission equipment resource utilization rate, link resource utilization rate, node capacity utilization rate, load balance degree, optical cable fault condition, optical transmission equipment fault condition, service fault condition and power equipment monthly fault duration from four aspects of network structure, safe operation level, resource utilization condition and network fault condition. Management factor evaluation selects eight indexes of optical cable defect elimination timeliness, optical cable section fault average repair efficiency, equipment defect elimination timeliness, equipment fault average repair efficiency, maintenance plan accuracy, special work on-time completion rate, communication technology research level and standard specification item number from two aspects of communication operation and maintenance management and communication professional management.
Step 1-4: optimizing an operation state evaluation index;
first, the definition of each three-level index is clarified, and the four-level indexes constituting each three-level index are analyzed, and the four-level indexes of the three-level indexes at the network scale are shown in table 2:
TABLE 2
Secondly, a three-level index X is obtained according to the formula (1) 12 ～X 15 Factor composition, total length of optical cable X 11 Optical cable growth rate X 12 Optical fiber coverage ratio X 13 Total number of optical transmission devices X 14 Optical transmission apparatus growth rate X 15 Cause of indexThe sub-components are as follows:
X 11 ＝0.6×C 111 +0.2×C 112 +0.2×C 113
X 12 ＝0.6×C 121 +0.6×C 122 +0.2×C 123 +0.2×C 124 +0.2×C 125 +0.2×C 126
X 13 ＝0.1×C 131 +0.1×C 132 +0.2×C 133 +0.2×C 134 +0.1×C 135 +0.1×C 136 +0.1×C 137 +0.1×C 138 +0.2×C 139 +0.2×C 1310 +0.1×C 1311 +0.1×C 1312 +0.1×C 1313 +0.1×C 1314 +0.1×C 1315 +0.1×C 1316
X 14 ＝0.5×C 141 +0.3×C 142 +0.2×C 143
X 15 ＝0.5×C 151 +0.5×C 152 +0.3×C 153 +0.3×C 154 +0.2×C 155 +0.2×C 156
according to X 11 ～X 15 Factor formation formula and formula (2), X 11 ～X 15 Every two indexes are compared to obtain the element values of the information overlapping matrix so as to construct the information overlapping matrix, as shown in table 3:
TABLE 3
By calculating and normalizing the eigenvector of the maximum eigenvalue of the overlapped information matrix, the weight vector of each three-level index to the evaluation target under the network scale can be obtained as follows: w 1 ＝(0.0008,0.1478,0.1474,0.1141,0.1577,0.16,0.1733)。
And the weight of the total length of the index optical cable is less than 0.1, and the deletion is further optimized.
By utilizing the above-mentioned overlapping information analysis method, the weight of three-level indexes such as the completeness rate of spare parts, the performance of an optical transmission link, the average utilization rate of optical cable resources and the like is less than 0.1, and the deletion is further optimized.
Step 2, calculating the combination weight of the evaluation indexes of the running state of the power optical transmission network, which specifically comprises the following steps:
step 2-1: calculating subjective weight by adopting an interval fuzzy analytic hierarchy process;
inviting experts to adopt the form of interval number as a secondary index B according to the scale determined by interval fuzzy analytic hierarchy process l And constructing a judgment matrix. Network scale B as a secondary index in an evaluation index system of the running state of an electric power optical transmission network 1 Run quality B 2 Management factor B 3 The interval determination matrix J' of (c) is as follows:
lower boundary matrix J 'of interval judgment matrix J' - The following were used:
upper boundary matrix J 'of interval judgment matrix J' + The following were used:
j 'is obtained by the segment feature root method' - And J' + Has a normalized feature vector x 'of positive component corresponding to the maximum feature value of (2)' - And x' + Respectively as follows:
x' - ＝(0.3253,0.2384,0.4363)，x' + ＝(0.3203,0.2669,0.4129)
from the expressions (6) and (7), the values of the weight coefficients g 'and h' of the secondary index are calculated as g '=1.5347, h' =1.3694.
The weight vector α' = ([ 0.4455,0.4916], [0.3264,0.4096], [0.5975,0.6336 ]) in the number of intervals of all the secondary indices is calculated from equation (8).
And (4) taking the median and normalizing to obtain the subjective weight vector alpha = (0.3227,0.2543,0.4239) of all secondary indexes.
Step 2-2: calculating objective weight by adopting an entropy weight method;
processing the real data of the electric power optical transmission network in the region to obtain a second-level index B l As shown in table 4:
TABLE 4
According to the formula (13) to obtain a second-level index B l The data is standardized to obtain B l Dimensionalized evaluation index matrix
The following were used:
calculation of B from equation (14) l Weight vector Z of 1 ＝(0,0,0.4040)，Z 2 ＝(0.7745,0.5143,0.5960)，Z 3 ＝(0.2255,0.4857,0)。
Calculation of B from equation (15) l Information entropy E of 1 ＝0.4858，E 2 ＝0.6305,E 3 ＝0.6140。
Calculation of B from equation (16) l Objective weight ω of 1 ＝0.4050，ω 2 ＝0.2910，ω 3 ＝0.3040。
The objective weight vector ω = (0) of the secondary index . 4050,0 . 2910,0 . 3040)。
Step 2-3: calculating the combination weight by adopting a subjective and objective combination weight assignment method;
the secondary index B can be calculated from the formula (18) 1 、B 2 、B 3 The combining weight λ = (0.3890,0.2202,0.3836).
According to the subjective weight value, the objective weight value and the combined weight value obtained by the calculation, the following results are obtained by analysis: b is 1 In the subjective and objective weight solution, the difference between the first and second positions is more than 0.1, B 2 The difference between the first position and the second position in the subjective weight solution and the objective weight solution is more than 0.1, and B is reduced by the subjective and objective combined solution 1 And B 2 The weight result is more scientific and reasonable due to the difference.
The weights of the indexes of the three levels are calculated by repeating the above processes and are shown in table 5:
TABLE 5
The step 3 specifically comprises the following steps:
step 3-1: selecting an operation state evaluation method according to the index characteristics;
the assessment of the network scale of the power optical transmission network has the characteristic of ambiguity, and is an evaluation problem with multiple comments and multiple factors. And the fuzzy comprehensive evaluation method based on the fuzzy set theory and the maximum membership principle can process the evaluation problem of designing the fuzzy concept and can well process the fuzzy boundary. The membership degree in the fuzzy comprehensive evaluation is determined by membership functions, and the fuzzy distribution of the membership functions is various, such as rectangular distribution, trapezoidal distribution, K-parabolic distribution, gaussian distribution, cauchy distribution and the like. The determination of the membership function should determine the type of the primary value interval, whether the two sides of the membership function are symmetrical or not and the form of the transition zone of the membership function.
The numerical difference between the operation quality indexes is large, a utility function method is used for evaluation, each evaluation index can be quantized according to a certain method to become a quantized value of the operation quality measurement, namely a utility function value, and then the total evaluation value of the operation quality is obtained according to certain weighting of a synthesis model.
The extendibility evaluation method evaluates the research object from the angle of satisfaction degree, and carries out quantitative calculation through the correlation function. The method can evaluate each scheme to obtain the priority level of each scheme. The extension set and the variable set have strong compatibility in the aspect of processing the uncertainty problem, and main tools of the extension set and the variable set, namely an association function and an opposite difference function, have more consistent characteristics and structures although the value intervals of the association function and the opposite difference function are different. The correlation function and the opposition difference function can be converted into each other by a simple mapping. Therefore, in the problem of having a completely clear boundary, the opposition difference function can be evaluated and optimized as a special correlation function.
Step 3-2: determining a membership function, a utility function and an extension coupling function;
the present invention selects ridge distribution function to describe the membership function of the total optical cable length, annual optical cable growth rate and other indexes. According to statistical data, data of the same index are sorted in an increasing order, the first 10% of the sorted data is defined as 'excellent' of the evaluation factor, the sorting positions are 10% -40% as 'good', the sorting positions are 40% -80% as 'medium', and the last 20% is 'poor'.
And properly adjusting the main value interval generated by the statistical data sorting. The main value interval after the adjustment of the optical cable growth rate is as follows: excellent [ ∞,0.14], good [0.09,0.11], medium [0.055,0.08], poor [0,0.045]. Similarly, the principal value interval is adjusted for the "growth rate of the optical transmission device" and the "coverage rate of the optical fiber", and a membership function of the ridge-shaped function is obtained. As shown in table 6, table 6 is a membership function of the three-level index at the network scale.
TABLE 6
And calculating excellent, good, medium and poor fuzzy values of the corresponding indexes according to the membership function of each index under the network scale, wherein the grade with the maximum fuzzy value is the evaluation grade of the corresponding index. The matrix η of blur values can thus be obtained as follows:
wherein the content of the first and second substances,
respectively represent three-level indexes according to the network scale
The membership function of the system is used for calculating four grades of fuzzy values of excellence, good, medium and difference.
Combined weight lambda 'according to three-level indexes under network scale' 1 Calculating an evaluation value vector of the network scale as shown in equation (27):
F′ 1 ＝λ′ 1 η (27)
in the formula: f' 1 The evaluation value vector of the network scale is composed of fuzzy values of four grades of excellent, good, medium and poor, and the grade with the largest fuzzy value is the evaluation grade of the network scale.
The network connectivity and bandwidth reservation are moderate indexes, and the optimal value intervals of the indexes are respectively [2.5,3.5 ]]、[90,160]First, adopt
Performing a steering-type inverse transformation on the class index, C 1 To constrain the total, usually take C 1 =1, and then the result of the inverse steering transformation is substituted into a utility function to calculate the evaluation value of the two indexes. The utility function of the two indexes is shown as equation (28) and equation (29):
in the formula: c 2 A constant value, which is determined by evaluating the expected value, and the above index is good at 55% and 75%, C is taken 2 ＝4。
The network load balance degree is an inverse index, and the larger the value of the index is, the more unbalanced the network load is represented, and the more unreasonable the service arrangement is. The utility function is shown in formula (30):
y 23 ＝1-x 23 (30)
the index value of the power supply equipment month mean time of failure is large, so that the index value is not beneficial to being directly used for evaluation, a non-linear logarithmic utility function is adopted for the index to obtain a quantifiable index utility function value, and the non-linear logarithmic utility function is shown as a formula (31):
in the formula: x is the number of 23 Is an index actual value, x On the upper part And x Lower part The upper and lower limits of the index are respectively, a and b are constants, and a =0.4 and b =0.6 are generally taken.
Then the utility function of the mean time between month fault duration of the power supply equipment is shown as the following formula (32):
in this embodiment, the operation quality assessment value is calculated by an arithmetic mean method, as shown in formula (33):
in the formula: f ″) 2 As an evaluation value of the running quality,
is a three-level index
The combined weight of (a) is determined,
the utility function value of each index is obtained. According to F ″) 2 The result obtained is evaluated, F ″ 2 The higher the value, the better the operation quality of the electric power optical transmission network. For the extended coupling function, the first step: determining a set of solutions u j (j =1,2,. Multidot., m) and an evaluation index
(i' 3 ＝1,2,...,n' 3 ) Using matrices
And (4) showing. In order to eliminate different dimensions of each index, the matrix X is subjected to standardization treatment, and according to the data characteristics of three-level indexes under the management factors, the mapping interval of each index is set to be [0.002,0.096 ]]。
For the calculation of the index of the type "greater and better", as shown in formula (34):
for the calculation of the smaller and better type index, the formula (35) shows:
the normalized matrix C for X is obtained as follows:
wherein the content of the first and second substances,
is a scheme u j Index of (2)
Is measured.
The second step is that: according to the evaluation scheme and the index condition, constructing an object element matrix R to be evaluated j And the classical domain R of the object element oh (h is the evaluation rating, h =1,2. To-be-evaluated object element matrix R j And the classical domain R of the object element oh The following were used:
the third step: calculating a gradient quality point matrix K, as shown in formula (36):
the fourth step: matter element N to be evaluated j Of i' 3 An index
With the classical domain R of the matter element oh The relative membership matrix of (c) is as follows:
the fifth step: and calculating the relevance of each scheme to different evaluation standards by using an opposite difference function in a variable set. Scheme u j Of 3i' 3 The degree of opposition difference of each index with respect to the level h > h +1 (h =1,2.., p-1) is
Wherein h > h +1 indicates that the h-th order is higher than the h + 1-th order.
And a sixth step: according to the determined evaluation index weight, the scheme u can be obtained j The overall degree of contrast for the h > h +1 (h =1,2., p-1) level is
The seventh step: according to the variable quantity and quality principle, the priority level of each scheme is determined
When, that is, if a qualitative change occurs from the h-th order to the h + 1-th order, the evaluation grade is determined according to the degree of opposition difference between excellent, good, medium, and bad.
Step 3-3: calculating a comprehensive value of the network scale evaluation value;
step 3-4: calculating a combined evaluation value;
step 3-5: indexes related to induction classification according to definition and calculation formula of each index
Taking the total length of the optical cable, the annual growth rate of the optical cable and the coverage rate of the optical fiber in the network scale evaluation index as examples, the total length of the optical cable is composed of the total length of the ADSS optical cable, the total length of the OPGW optical cable and the total length of the OPPC optical cable, and then the calculation formula is y = x 1 *w 1 +x 2 *w 2 +x 3 *w 3 (ii) a The optical cable annual growth rate consists of ADSS optical cable annual growth rate, OPGW annual growth rate and OPPC annual growth rate, and the calculation formula is the same as the formula; the optical fiber coverage rate is the proportion of the number of the communication coverage stations in the power optical fiber network in the counting period to the number of the coverage stations. The three indexes are all related to the optical cable, and the higher the annual increase rate of the optical cable is, the higher the total length of the optical cable is, the higher the optical fiber coverage rate is, but the resource waste is also caused; the lower the annual increase rate of the optical cable, the lower the total length of the optical cable and the lower the coverage rate of the optical fiber, the lower the network connectivity of the electric power optical transmission network, and therefore the three indexes belong to moderate indexes.
Step 3-6: designing an index problem tracing method and analyzing, summarizing and evaluating suggestions;
taking the total length of the optical cable, the annual growth rate of the optical cable and the optical fiber coverage rate in the network scale evaluation index as an example, if the evaluation result is medium or poor, sequentially traversing the sites and the line data, and judging whether a double optical cable is connected between every two sites. If no optical cable is connected between the two stations, an optical cable with the thickness of XXKm is recommended to be added between the two stations; if there is no dual cable connection between the two stations, it is recommended to add an XXKm cable between the two stations to achieve dual cable access.
Step 4, calculating an operation state evaluation value of the electric power optical transmission network, tracing problems and giving suggestions; the method specifically comprises the following steps:
step 4-1: calculating an index value according to each index formula and recording the index value;
the statistical data of evaluation indexes such as the optical cable growth rate in the area are shown in table 7:
TABLE 7
Step 4-2: respectively calculating corresponding membership function values, utility function values and extension coupling function values and grading;
and calculating the membership function value of each index in the network scale by adopting the determined membership function according to the data of the network scale index: eta 1 ＝{0.7176,0.2824,0,0}，η 2 ＝{0,0.6847,0.3126,0},η 3 ＝{1,0,0,0},η 4 ＝{1,0,0,0},η 5 ＝{0.4867,0.1979,0,0},η 6 ＝{0.4867,0.1979,0,0},η 7 ＝{0,07129,0.2871,0}。
Normalizing the membership function values to obtain a comprehensive fuzzy matrix eta as follows:
according to the obtained weight vector lambda' 1 ＝(0.3016,0.2562,0.4422,0.3405,0.4731,0.1883,0.4800,0.5200) and the comprehensive fuzzy matrix eta to obtain a fuzzy comprehensive evaluation result vector F' 1 = (0.5833,0.3188,0.0856,0). According to the principle of maximum membership degree, the network scale evaluation result of the electric power optical transmission network in the region is excellent.
According to the data of the operation quality indexes, the determined utility function is adopted to calculate the utility function value of each index in the operation quality, as shown in table 8: table 8 shows the transmission network operating quality indicator data.
TABLE 8
And (3) according to the obtained weight vector, obtaining an evaluation value of the running quality by using an arithmetic mean method: f ″) 2 =0.9126, the result of the evaluation of the operation quality of the power optical transmission network in the area is excellent.
According to the specific requirements and actual data conditions of the regional power optical transmission network, the grade is divided into 4 grades, and the evaluation standard matrix is shown in table 9:
TABLE 9
Calculating a gradual change type quality change point matrix K and a relative membership matrix of the management factor indexes of the electric power optical transmission network in the area, as shown in a table 10:
watch 10
The contradictory difference of the management factor indexes of the electric power optical transmission network in the area to different evaluation standards is shown in table 11:
TABLE 11
The obtained index weights are combined to obtain the degree of opposition difference of the electric power optical transmission network in the area with respect to the evaluation level, as shown in table 12:
TABLE 12
As can be seen from table 12: the excellent value is higher than the good value, the good value is lower than the medium value, and the medium value is higher than the difference value, so the evaluation grade of the management factors of the power optical transmission network in the area is medium.
Step 4-3: calculating a comprehensive value of the network scale evaluation value;
before calculating the combined evaluation value, the integrated value of the fuzzy integrated evaluation value is first calculated, and a fuzzy value linear sequence is constructed as follows:
L 1 (Excellent) ＝{x 1 ,x 3 ,x 5 ,x 6 ,x 4 ,x 2 ,x 7 }，
L 2 (good) ＝{x 4 ,x 7 ,x 2 ,x 5 ,x 6 ,x 1 ,x 3 }，
L 3 (middle) ＝{x 2 ,x 7 ,x 1 ,x 3 ,x 4 ,x 5 ,x 6 }，
L 4 (poor) ＝{x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ,x 7 }。
Then, the fuzzy priority matrix eta is constructed according to the formula (19) and the intercept matrix Q is obtained according to the formula (20) τ The following were used:
according to the formula (21), a fuzzy comprehensive value is F' 1 =0.85. The evaluation grade was excellent, consistent with the results of the fuzzy comprehensive evaluation method.
Step 4-4: calculating a combined evaluation value; after the fuzzy comprehensive evaluation value is obtained, the combined evaluation can be carried out, firstly, the prior examination is carried out, and the fuzzy comprehensive evaluation value can be obtained according to the formula (30):
therefore, rho → 1, the consistency between the methods is better, and the combined evaluation can be carried out.
Three evaluation vectors for the regional power optical transmission network are Z 1 = (0.8500,0.9126,0.0750), combined evaluation value Z 0 。
Combining the deviation (Z) of the evaluation value vector and the t-th evaluation value vector 0 -0.8500,Z 0 -0.9126,Z 0 -0.0750)。
And constructing an optimization model with the minimum deviation square sum and obtaining a combined evaluation value of F =0.6289, so that the evaluation level of the operation state of the power optical transmission network in the region is medium.
And 4-5: and tracing the problems according to the evaluation result and giving an evaluation suggestion.
According to the evaluation result obtained from the network scale index evaluation data of the utility power optical transmission network, the value of the optical cable coverage is 0.9256, and the standard value cannot be reached to 0.9655, so that the number of the optical cables should be increased. According to the national grid company guide and the actual situation of the city, 20 optical cables are added to achieve the optical cable coverage rate of 0.9665.
According to the evaluation result obtained by the operation quality index evaluation data of the mains power optical transmission network, the average utilization rate value of the optical cable resources is 0.7436, which cannot reach the standard 0.9999, and further causes the excessively low utilization rate of the fiber core resources. The bandwidth of the fiber optic cable utilizing 2654M should be increased according to national grid codes and the actual conditions of the city. The value of the shared protection ring of the equipment is 0.6578, the standard value cannot reach 0.9999, and the shared protection ring is added to 269 equipment according to the national grid guide rule and the actual situation of the city.
According to the evaluation result obtained from the management factor index evaluation data of the mains power optical transmission network, the fault repair efficiency of the optical cable and the equipment is low, so that the maintenance plan accuracy and the on-time completion rate of special work are low and do not reach 100%. And according to the national grid guide rule and the actual condition of the city, the problems of optical cable and equipment faults and equipment defects are repaired in time.
Therefore, the method of the embodiment can effectively obtain the quantitative result of the operation state evaluation of the electric power optical transmission network, can find the problem index through the evaluation result and give the suggestion with operability, and is favorable for specifying and improving the operation state of the electric power optical transmission network.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (8)
1. The method for evaluating the running state of the power optical transmission network is characterized by comprising the following steps: the method is realized by the following steps:
step one, determining an evaluation index of the running state of the power optical transmission network; the specific process is as follows:
analyzing the operation state evaluation requirement characteristics;
determining a hierarchical structure of an operation state evaluation index system;
step three, preliminarily selecting an operation state evaluation index;
step four, optimizing an operation state evaluation index;
step two, calculating to obtain the combined weight of the state evaluation indexes according to the determined operation state evaluation indexes of the power optical transmission network in the step one; the specific process is as follows:
step two, calculating subjective weight of an evaluation index by adopting an interval fuzzy analytic hierarchy process;
secondly, calculating the objective weight of the evaluation index by adopting an entropy weight method;
step three, calculating the combination weight of the state evaluation indexes by adopting a subjective and objective combination weight assignment method;
designing an operation state evaluation algorithm of the power optical transmission network to complete evaluation of the state evaluation index; the specific process is as follows:
thirdly, selecting an operation state evaluation method according to the index characteristics;
step two, determining a membership function, a utility function and an extension coupling function;
thirdly, calculating a comprehensive value of the network scale evaluation value;
and step three, determining a combined evaluation method according to the comprehensive value.
2. The method according to claim 1, wherein the method comprises: in the first step, the preliminarily selected running state evaluation indexes are as follows:
and respectively numbering the second-level indexes and the third-level indexes: the second level index is B l (l =1,2,3) where the network size is B 1 Run mass of B 2 The management factor is B 3 (ii) a Has a three-level index of
Wherein, the three-level indexes under the network scale are respectively
The three-level indexes under the operation quality are respectively
The three-level indexes under the management factors are respectively
3. The method according to claim 2, wherein the method comprises: screening indexes by adopting an overlapped information analysis method; finally determining an evaluation index of the running state of the power optical transmission network; the specific process is as follows:
firstly, the ith network size is defined 1 Individual three-level index
Fourth-order index of
Then, for the ith network scale 1 Individual three-level index
And k is 1 Individual three-level index
Comparing to obtain an information overlapping matrix I; the comparison between the three levels of indexes under the network scale is represented by the following formula:
in the formula:
is the ith on the scale of the network 1 The third level index
The number of the factors is determined by the number of the factors,
is as follows
Factor i at network scale 1 All four levels of indexes of each index
The proportion of the active carbon in the water is as follows,
for three-level indexes only affecting the network scale
The specific factor of (a);
finally, calculating the eigenvalue vector of the information overlapping matrix I
The maximum eigenvalue of the matrix is A max (max∈{1,2,...,n 1 }) according to the maximum eigenvalue a of the matrix max Corresponding feature vector
Is the ith on the scale of the network 1 The proportion of the three-level indexes in the three-level indexes under all network scales is calculated, and the characteristic vector W is used 1 In
Removing indexes smaller than 0.1 to obtain three-level indexes under the optimized network scale
Repeating the above processes to obtain three-level index under optimized operation quality
And three-level index under management factors
4. The method according to claim 3, wherein the method comprises: the specific process of the first step is as follows:
firstly, determining the index relative importance scale, and quantitatively describing the optimized three-level index under the network scale by adopting a method of 0.1-0.9 of the scale
With another three-level index
The relative degree of importance;
secondly, the three-level indexes under the optimized network scale are respectively
And
comparing and scoring to obtain scale values
Expressed in the form of interval number to obtain
The interval scale value that is scored for the expert,
is the lower limit of the interval scale value,
representing the upper limit of interval scale value, and constructing interval judgment matrix
Then, a lower boundary matrix of the interval judgment matrix is respectively obtained by using an interval characteristic root method
And an upper boundary matrix
Is most characteristic ofNormalized feature vector with positive component corresponding to value
And
and calculating the weight coefficient g of the three-level index under the network scale 1 And h 1 ；
Finally, calculating a weight vector alpha 1 in the form of interval number of the three-level indexes under the optimized network scale, and aiming at the weight vector alpha 1 Taking the median of each interval element in the network, and normalizing to obtain the three-level index of the correspondingly optimized network scale
Weight of (2)
Constructing weight vectors of all three-level indexes under optimized network scale
Repeating the above processes to obtain the subjective weight vectors of all three-level indexes under the optimized operation quality and management factors
And
5. the method according to claim 4, wherein the method comprises: in the second step, for the determination of the subjective weight of the secondary index, firstly, the same quantity scale is adopted, B l (l =1,2,3) and B c (c =1,2,3) and the scale values are obtained by comparison, and the scale values are expressed in the form of interval numbers to obtain
Structural section determination matrix J' = (B) lc ) 3×3 ；
Then, a lower boundary matrix J 'of the section judgment matrix is obtained by a section feature root method' - And an upper boundary matrix J' + Has a normalized feature vector x 'of positive component corresponding to the maximum feature value of (2)' - And x' + And weight coefficients g 'and h' are calculated.
Finally, calculating a weight vector alpha 'of the secondary index in the form of interval number, taking a median from each interval element in the weight vector alpha', and normalizing to obtain the weight alpha corresponding to the secondary index l Subjective weight vector α = (α) constituting all secondary indices 1 ,α 2 ,α 3 )。
6. The method according to claim 5, wherein the method comprises: in the third step, an improved fuzzy value linear sequence method based on a figure set is adopted to carry out data synthesis on the network scale evaluation value, and the concrete process is as follows:
the first step is as follows: according to the fuzzy value obtained by calculating the membership function of the three-level index under the network scale, constructing a fuzzy value matrix eta as follows:
wherein the content of the first and second substances,
respectively represent three levels of indexes according to the network scale
The membership function of the fuzzy logic algorithm is calculated to obtain fuzzy values of four grades of excellence, good, medium and difference;
the second step is that: for a certain row element value in the fuzzy value matrix eta
Corresponding index
Fuzzy value of
q 1 Column 1, q, representing the matrix eta 2 Column 2, q, representing the matrix eta 3 Column 3, q, representing the matrix η 4 Column 4, representing the matrix η, ordered as follows:
if it is
Then scheme D 1 Arranged in scheme D 2 Front face, D 1 Is d at 1 Line q 1 Index of column, D 2 Is d at 2 Line q 1 An index of the column;
if it is
Then if it is
Then scheme D 1 Arranged in scheme D 2 A front face;
if it is
And is
Then if
Then scheme D 1 Arranged in scheme D 2 A front face;
sequencing each scheme according to the numerical value of each row in the fuzzy value matrix eta to form four fuzzy value linear sequences which are respectively marked as L 1 ,L 2 ,L 3 ,L 4 ；
The third step: constructing a fuzzy priority matrix Q in the scheme set X, which is expressed as follows:
the fourth step: scheme sorting is carried out, tau =0.5 is taken to cut the fuzzy priority matrix Q, and a cut matrix Q is obtained τ Expressed by the following formula:
in the formula: d represents the d-th row of the matrix Q, and Q represents the Q-th column of the matrix Q;
calculating a fuzzy integral value represented by the following formula:
in the formula: xi 1 ,ξ 2 ,ξ 3 ,ξ 4 Excellent, good, medium, and poor weights, respectively.
7. The electric power optical transmission network operation state evaluation method according to claim 6, further characterized by: the concrete process of the third step and the fourth step is as follows:
adopting the Pierman grade as a judgment basis of the method consistency, carrying out prior consistency test on a fuzzy comprehensive evaluation method, a utility function method and an extension coupling evaluation method, and expressing the results by rho and the following formula:
in the formula: delta r is a sample grade difference value under different methods; rho is belonged to [0,1];
then, obtaining the optimal evaluation value for the optimization target according to the minimum sum of the deviation between the sample combination evaluation value and the epsilon single evaluation methods; for epsilon single evaluation methods V = (V) 1 ,V 2 ,...,V ε ) E objects to be evaluated P = (P) 1 ,P 2 ,...,P e ) The estimation vector obtained under the t-th estimation method is represented by the following formula:
combining evaluation value vectors
The deviation of the combined evaluation value vector and the t-th evaluation value vector is:
constructing an optimization model with the minimum deviation square sum, and calculating to obtain a comprehensive evaluation value F of the operation state of the power optical transmission network, wherein the comprehensive evaluation value F is expressed by the following formula:
8. the electric power optical transmission network operation state evaluation method according to claim 7, further characterized by: designing an index problem tracing method and analyzing, summarizing and evaluating suggestions; the specific process is as follows:
firstly, determining a reference suggested value standard of each index;
secondly, judging whether to trace the problem according to the evaluation result of the second-level index, if so, judging the evaluation result of the third-level index, and finding the problem index;
then, calculating the difference between the real data of the problem index and the index suggested value;
and finally, giving an operability suggestion according to the calculated difference.
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